diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
new file mode 100644
index 0000000000000000000000000000000000000000..240770aeba2c52b329ad13042b227251248abc9f
--- /dev/null
+++ b/.devcontainer/devcontainer.json
@@ -0,0 +1,33 @@
+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "app/streamlitapp.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run app/streamlitapp.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}
\ No newline at end of file
diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 0000000000000000000000000000000000000000..6238c764d5be7840024023411766815f43257242
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,7 @@
+.git
+.gitignore
+__pycache__/
+*.pyc
+.DS_Store
+Notebooks/
+Kaggle Output/
diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..a73a3e57ef81a4d67f859311649287c248861705 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,1012 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Kaggle[[:space:]]Output/V1.0.50/model-checkpoint/checkpoint.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+Kaggle[[:space:]]Output/V1.50.96/Kaggle[[:space:]]error[[:space:]]while[[:space:]]saving[[:space:]]this[[:space:]]notebook.png filter=lfs diff=lfs merge=lfs -text
+Kaggle[[:space:]]Output/V1.50.96/model-checkpoint/checkpoint.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+app/animation.gif filter=lfs diff=lfs merge=lfs -text
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diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 0000000000000000000000000000000000000000..dd84ea7824f11be1eeda22377549cbc1aec7f980
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,38 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior:
+1. Go to '...'
+2. Click on '....'
+3. Scroll down to '....'
+4. See error
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Screenshots**
+If applicable, add screenshots to help explain your problem.
+
+**Desktop (please complete the following information):**
+ - OS: [e.g. iOS]
+ - Browser [e.g. chrome, safari]
+ - Version [e.g. 22]
+
+**Smartphone (please complete the following information):**
+ - Device: [e.g. iPhone6]
+ - OS: [e.g. iOS8.1]
+ - Browser [e.g. stock browser, safari]
+ - Version [e.g. 22]
+
+**Additional context**
+Add any other context about the problem here.
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 0000000000000000000000000000000000000000..bbcbbe7d61558adde3cbfd0c7a63a67c27ed6d30
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000000000000000000000000000000000000..18c91471812cb6f4c4e8d0fc407f70c4612e1648
--- /dev/null
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,128 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, religion, or sexual identity
+and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the
+  overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or
+  advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email
+  address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+.
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series
+of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or
+permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior,  harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within
+the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.0, available at
+https://www.contributor-covenant.org/version/2/0/code_of_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct
+enforcement ladder](https://github.com/mozilla/diversity).
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000000000000000000000000000000000000..9c558e357c41674e39880abb6c3209e539de42e2
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1 @@
+.
diff --git a/Dockerfile b/Dockerfile
index 5f51ead59f36f13043e036290df9440e25fe8cbe..41b1150998c9a6775105eeceeee625a4cdf27bfd 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,20 +1,19 @@
-FROM python:3.13.5-slim
+FROM python:3.10-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1
 
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends ffmpeg \
     && rm -rf /var/lib/apt/lists/*
 
-COPY requirements.txt ./
-COPY src/ ./src/
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
 
-RUN pip3 install -r requirements.txt
+COPY . .
 
 EXPOSE 8501
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
\ No newline at end of file
+CMD ["streamlit", "run", "app/streamlitapp.py", "--server.address=0.0.0.0", "--server.port=8501"]
diff --git a/Kaggle Output/V1.0.50/lipnet_v_1.0.50.ipynb b/Kaggle Output/V1.0.50/lipnet_v_1.0.50.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c63fa6207b782f2744e73c595212c9c2ced90ac6
--- /dev/null
+++ b/Kaggle Output/V1.0.50/lipnet_v_1.0.50.ipynb	
@@ -0,0 +1 @@
+{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":5,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 0. Install and Import Dependencies","metadata":{"tags":[]}},{"cell_type":"code","source":"!pip list","metadata":{"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:02.690213Z","iopub.execute_input":"2023-10-22T10:10:02.690550Z","iopub.status.idle":"2023-10-22T10:10:06.359399Z","shell.execute_reply.started":"2023-10-22T10:10:02.690523Z","shell.execute_reply":"2023-10-22T10:10:06.358349Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Package                                  Version               Editable project location\n---------------------------------------- --------------------- -------------------------\nabsl-py                                  1.4.0\naccelerate                               0.22.0\naccess                                   1.1.9\naffine                                   2.4.0\naiobotocore                              2.5.4\naiofiles                                 22.1.0\naiohttp                                  3.8.4\naiohttp-cors                             0.7.0\naioitertools                             0.11.0\naiorwlock                                1.3.0\naiosignal                                1.3.1\naiosqlite                                0.19.0\nalbumentations                           1.3.1\nalembic                                  1.12.0\naltair                                   5.1.1\nannotated-types                          0.5.0\nannoy                                    1.17.3\nansiwrap                                 0.8.4\nanyio                                    3.7.0\napache-beam                              2.46.0\naplus                                    0.11.0\nappdirs                                  1.4.4\nargon2-cffi                              21.3.0\nargon2-cffi-bindings                     21.2.0\narray-record                             0.4.0\narrow                                    1.2.3\narviz                                    0.12.1\nastroid                                  2.15.6\nastropy                                  5.3.2\nasttokens                                2.2.1\nastunparse                               1.6.3\nasync-timeout                            4.0.2\natpublic                                 4.0\nattrs                                    23.1.0\naudioread                                3.0.0\nautopep8                                 2.0.4\nBabel                                    2.12.1\nbackcall                                 0.2.0\nbackoff                                  2.2.1\nbackports.functools-lru-cache            1.6.4\nbayesian-optimization                    1.4.3\nbayespy                                  0.5.26\nbeatrix-jupyterlab                       2023.621.222118\nbeautifulsoup4                           4.12.2\nbidict                                   0.22.1\nbiopython                                1.81\nblake3                                   0.2.1\nbleach                                   6.0.0\nblessed                                  1.20.0\nblinker                                  1.6.2\nblis                                     0.7.10\nblosc2                                   2.0.0\nbokeh                                    3.2.2\nboltons                                  23.0.0\nBoruta                                   0.3\nboto3                                    1.26.100\nbotocore                                 1.31.17\nbq-helper                                0.4.1                 /root/src/BigQuery_Helper\nbqplot                                   0.12.40\nbranca                                   0.6.0\nbrewer2mpl                               1.4.1\nbrotlipy                                 0.7.0\ncachetools                               4.2.4\nCartopy                                  0.22.0\ncatalogue                                2.0.9\ncatalyst                                 22.4\ncatboost                                 1.2.1\ncategory-encoders                        2.6.2\ncertifi                                  2023.7.22\ncesium                                   0.12.1\ncffi                                     1.15.1\ncftime                                   1.6.2\ncharset-normalizer                       3.1.0\nchex                                     0.1.82\ncleverhans                               4.0.0\nclick                                    8.1.7\nclick-plugins                            1.1.1\ncligj                                    0.7.2\ncloud-tpu-client                         0.10\ncloud-tpu-profiler                       2.4.0\ncloudpickle                              2.2.1\ncmaes                                    0.10.0\ncmdstanpy                                1.1.0\ncmudict                                  1.0.13\ncolorama                                 0.4.6\ncolorcet                                 3.0.1\ncolorful                                 0.5.5\ncolorlog                                 6.7.0\ncolorlover                               0.3.0\ncomm                                     0.1.3\nconda                                    23.7.3\nconda-content-trust                      0+unknown\nconda-package-handling                   2.0.2\nconda_package_streaming                  0.8.0\nconfection                               0.1.1\ncontextily                               1.3.0\ncontourpy                                1.1.0\nconvertdate                              2.4.0\ncrcmod                                   1.7\ncryptography                             38.0.4\ncubinlinker                              0.3.0\ncubinlinker-cu11                         0.3.0.post1\ncuda-python                              11.8.2\ncudf                                     23.8.0\ncufflinks                                0.17.3\ncuml                                     23.8.0\ncupy                                     12.2.0\nCVXcanon                                 0.1.2\ncycler                                   0.11.0\ncymem                                    2.0.7\ncysignals                                1.11.2\nCython                                   0.29.35\ncytoolz                                  0.12.2\ndaal                                     2023.2.1\ndaal4py                                  2023.2.1\ndacite                                   1.8.1\ndask                                     2023.9.0\ndask-cuda                                23.8.0\ndask-cudf                                23.8.0\ndataclasses-json                         0.6.0\ndatasets                                 2.1.0\ndatashader                               0.15.2\ndatashape                                0.5.2\ndatatile                                 1.0.3\ndb-dtypes                                1.1.1\ndeap                                     1.4.1\ndebugpy                                  1.6.7\ndecorator                                5.1.1\ndefusedxml                               0.7.1\nDelorean                                 1.0.0\ndeprecat                                 2.1.1\nDeprecated                               1.2.14\ndeprecation                              2.1.0\ndescartes                                1.1.0\ndill                                     0.3.7\ndipy                                     1.7.0\ndistlib                                  0.3.6\ndistributed                              2023.7.1\ndm-tree                                  0.1.8\ndocker                                   6.1.3\ndocker-pycreds                           0.4.0\ndocopt                                   0.6.2\ndocstring-parser                         0.15\ndocstring-to-markdown                    0.12\ndocutils                                 0.20.1\nearthengine-api                          0.1.367\neasydict                                 1.10\neasyocr                                  1.7.1\necos                                     2.0.12\neli5                                     0.13.0\nemoji                                    2.8.0\nen-core-web-lg                           3.6.0\nen-core-web-sm                           3.6.0\nentrypoints                              0.4\nephem                                    4.1.4\nesda                                     2.5.0\nessentia                                 2.1b6.dev1034\net-xmlfile                               1.1.0\netils                                    1.3.0\nexceptiongroup                           1.1.1\nexecuting                                1.2.0\nexplainable-ai-sdk                       1.3.3\nfastai                                   2.7.12\nfastapi                                  0.98.0\nfastavro                                 1.7.4\nfastcore                                 1.5.29\nfastdownload                             0.0.7\nfasteners                                0.18\nfastjsonschema                           2.17.1\nfastprogress                             1.0.3\nfastrlock                                0.8.2\nfasttext                                 0.9.2\nfbpca                                    1.0\nfeather-format                           0.4.1\nfeaturetools                             1.27.0\nfilelock                                 3.12.2\nFiona                                    1.9.4.post1\nfitter                                   1.6.0\nflake8                                   6.0.0\nflashtext                                2.7\nFlask                                    2.3.3\nflatbuffers                              23.5.26\nflax                                     0.7.2\nflit_core                                3.9.0\nfolium                                   0.14.0\nfonttools                                4.40.0\nfqdn                                     1.5.1\nfrozendict                               2.3.8\nfrozenlist                               1.3.3\nfsspec                                   2023.9.0\nfuncy                                    2.0\nfury                                     0.9.0\nfuture                                   0.18.3\nfuzzywuzzy                               0.18.0\ngast                                     0.4.0\ngatspy                                   0.3\ngcsfs                                    2023.6.0\ngensim                                   4.3.2\ngeographiclib                            2.0\nGeohash                                  1.0\ngeojson                                  3.0.1\ngeopandas                                0.13.2\ngeoplot                                  0.5.1\ngeopy                                    2.4.0\ngeoviews                                 1.10.1\nggplot                                   0.11.5\ngiddy                                    2.3.4\ngitdb                                    4.0.10\nGitPython                                3.1.31\ngoogle-api-core                          2.11.1\ngoogle-api-python-client                 2.97.0\ngoogle-apitools                          0.5.31\ngoogle-auth                              2.20.0\ngoogle-auth-httplib2                     0.1.0\ngoogle-auth-oauthlib                     1.0.0\ngoogle-cloud-aiplatform                  0.6.0a1\ngoogle-cloud-artifact-registry           1.8.1\ngoogle-cloud-automl                      1.0.1\ngoogle-cloud-bigquery                    2.34.4\ngoogle-cloud-bigtable                    1.7.3\ngoogle-cloud-core                        2.3.2\ngoogle-cloud-datastore                   2.17.0\ngoogle-cloud-dlp                         3.12.1\ngoogle-cloud-language                    2.11.0\ngoogle-cloud-monitoring                  2.15.0\ngoogle-cloud-pubsub                      2.17.1\ngoogle-cloud-pubsublite                  1.8.2\ngoogle-cloud-recommendations-ai          0.7.1\ngoogle-cloud-resource-manager            1.10.1\ngoogle-cloud-spanner                     3.36.0\ngoogle-cloud-storage                     1.44.0\ngoogle-cloud-translate                   3.12.0\ngoogle-cloud-videointelligence           2.11.3\ngoogle-cloud-vision                      2.8.0\ngoogle-crc32c                            1.5.0\ngoogle-pasta                             0.2.0\ngoogle-resumable-media                   2.5.0\ngoogleapis-common-protos                 1.59.1\ngplearn                                  0.4.2\ngpustat                                  1.0.0\ngpxpy                                    1.5.0\ngraphviz                                 0.20.1\ngreenlet                                 2.0.2\ngrpc-google-iam-v1                       0.12.6\ngrpcio                                   1.51.1\ngrpcio-status                            1.46.3\ngviz-api                                 1.10.0\ngym                                      0.26.2\ngym-notices                              0.0.8\nGymnasium                                0.26.3\ngymnasium-notices                        0.0.1\nh11                                      0.14.0\nh2o                                      3.42.0.3\nh5py                                     3.9.0\nhaversine                                2.8.0\nhdfs                                     2.7.0\nhep-ml                                   0.7.2\nhijri-converter                          2.3.1\nhmmlearn                                 0.3.0\nholidays                                 0.24\nholoviews                                1.17.1\nhpsklearn                                0.1.0\nhtml5lib                                 1.1\nhtmlmin                                  0.1.12\nhttplib2                                 0.21.0\nhttptools                                0.6.0\nhuggingface-hub                          0.16.4\nhumanize                                 4.8.0\nhunspell                                 0.5.5\nhusl                                     4.0.3\nhydra-slayer                             0.4.1\nhyperopt                                 0.2.7\nhypertools                               0.8.0\nibis-framework                           6.2.0\nidna                                     3.4\nigraph                                   0.10.6\nimagecodecs                              2023.9.4\nImageHash                                4.3.1\nimageio                                  2.31.1\nimbalanced-learn                         0.11.0\nimgaug                                   0.4.0\nimportlib-metadata                       6.7.0\nimportlib-resources                      5.12.0\ninequality                               1.0.0\niniconfig                                2.0.0\nipydatawidgets                           4.3.5\nipykernel                                6.23.3\nipyleaflet                               0.17.3\nipympl                                   0.7.0\nipython                                  8.14.0\nipython-genutils                         0.2.0\nipython-sql                              0.5.0\nipyvolume                                0.6.3\nipyvue                                   1.10.0\nipyvuetify                               1.8.10\nipywebrtc                                0.6.0\nipywidgets                               7.7.1\nisoduration                              20.11.0\nisort                                    5.12.0\nisoweek                                  1.3.3\nitsdangerous                             2.1.2\nJanome                                   0.5.0\njaraco.classes                           3.2.3\njax                                      0.4.13\njaxlib                                   0.4.13+cuda11.cudnn86\njedi                                     0.18.2\njeepney                                  0.8.0\njieba                                    0.42.1\nJinja2                                   3.1.2\njmespath                                 1.0.1\njoblib                                   1.3.2\njson5                                    0.9.14\njsonpatch                                1.32\njsonpointer                              2.0\njsonschema                               4.17.3\njupyter_client                           7.4.9\njupyter-console                          6.6.3\njupyter_core                             5.3.1\njupyter-events                           0.6.3\njupyter-http-over-ws                     0.0.8\njupyter-lsp                              1.5.1\njupyter_server                           2.6.0\njupyter_server_fileid                    0.9.0\njupyter-server-mathjax                   0.2.6\njupyter_server_proxy                     4.0.0\njupyter_server_terminals                 0.4.4\njupyter_server_ydoc                      0.8.0\njupyter-ydoc                             0.2.5\njupyterlab                               3.6.5\njupyterlab-git                           0.41.0\njupyterlab-lsp                           4.2.0\njupyterlab-pygments                      0.2.2\njupyterlab_server                        2.23.0\njupyterlab-widgets                       3.0.7\njupytext                                 1.14.6\nkaggle                                   1.5.16\nkaggle-environments                      1.12.0\nkeras                                    2.12.0\nkeras-core                               0.1.5\nkeras-cv                                 0.6.1\nkeras-nlp                                0.6.1\nkeras-tuner                              1.3.5\nkeyring                                  24.2.0\nkeyrings.google-artifactregistry-auth    1.1.2\nkfp                                      2.0.1\nkfp-pipeline-spec                        0.2.2\nkfp-server-api                           2.0.0\nkiwisolver                               1.4.4\nkmapper                                  2.0.1\nkmodes                                   0.12.2\nkorean-lunar-calendar                    0.3.1\nkornia                                   0.7.0\nkt-legacy                                1.0.5\nkubernetes                               26.1.0\nlangcodes                                3.3.0\nlangid                                   1.1.6\nlazy_loader                              0.2\nlazy-object-proxy                        1.9.0\nlearntools                               0.3.4\nleven                                    1.0.4\nLevenshtein                              0.21.1\nlibclang                                 16.0.0\nlibmambapy                               1.4.9\nlibpysal                                 4.7.0\nlibrosa                                  0.10.1\nlightgbm                                 3.3.2\nlightning-utilities                      0.9.0\nlime                                     0.2.0.1\nline-profiler                            4.1.1\nlinkify-it-py                            2.0.2\nllvmlite                                 0.40.1\nlml                                      0.1.0\nlocket                                   1.0.0\nLunarCalendar                            0.0.9\nlxml                                     4.9.3\nlz4                                      4.3.2\nMako                                     1.2.4\nmamba                                    1.4.9\nmapclassify                              2.6.0\nmarisa-trie                              1.0.0\nMarkdown                                 3.4.3\nmarkdown-it-py                           2.2.0\nmarkovify                                0.9.4\nMarkupSafe                               2.1.3\nmarshmallow                              3.20.1\nmatplotlib                               3.7.2\nmatplotlib-inline                        0.1.6\nmatplotlib-venn                          0.11.9\nmccabe                                   0.7.0\nmdit-py-plugins                          0.4.0\nmdurl                                    0.1.0\nmemory-profiler                          0.61.0\nmercantile                               1.2.1\nmgwr                                     2.1.2\nmissingno                                0.5.2\nmistune                                  0.8.4\nmizani                                   0.10.0\nml-dtypes                                0.2.0\nmlcrate                                  0.2.0\nmlens                                    0.2.3\nmlxtend                                  0.22.0\nmmh3                                     4.0.1\nmne                                      1.5.0\nmnist                                    0.2.2\nmock                                     5.1.0\nmomepy                                   0.6.0\nmore-itertools                           9.1.0\nmpld3                                    0.5.9\nmpmath                                   1.3.0\nmsgpack                                  1.0.5\nmsgpack-numpy                            0.4.8\nmultidict                                6.0.4\nmultimethod                              1.9.1\nmultipledispatch                         1.0.0\nmultiprocess                             0.70.15\nmunkres                                  1.1.4\nmurmurhash                               1.0.9\nmypy-extensions                          1.0.0\nnamex                                    0.0.7\nnb-conda                                 2.2.1\nnb-conda-kernels                         2.3.1\nnbclassic                                1.0.0\nnbclient                                 0.5.13\nnbconvert                                6.4.5\nnbdime                                   3.2.0\nnbformat                                 5.9.0\nnest-asyncio                             1.5.6\nnetCDF4                                  1.6.4\nnetworkx                                 3.1\nnibabel                                  5.1.0\nnilearn                                  0.10.1\nninja                                    1.11.1\nnltk                                     3.2.4\nnose                                     1.3.7\nnotebook                                 6.5.4\nnotebook-executor                        0.2\nnotebook_shim                            0.2.3\nnumba                                    0.57.1\nnumexpr                                  2.8.5\nnumpy                                    1.23.5\nnvidia-ml-py                             11.495.46\nnvtx                                     0.2.5\noauth2client                             4.1.3\noauthlib                                 3.2.2\nobjsize                                  0.6.1\nodfpy                                    1.4.1\nolefile                                  0.46\nonnx                                     1.14.1\nopencensus                               0.11.2\nopencensus-context                       0.1.3\nopencv-contrib-python                    4.8.0.76\nopencv-python                            4.8.0.76\nopencv-python-headless                   4.8.0.76\nopenpyxl                                 3.1.2\nopenslide-python                         1.3.0\nopentelemetry-api                        1.18.0\nopentelemetry-exporter-otlp              1.18.0\nopentelemetry-exporter-otlp-proto-common 1.18.0\nopentelemetry-exporter-otlp-proto-grpc   1.18.0\nopentelemetry-exporter-otlp-proto-http   1.18.0\nopentelemetry-proto                      1.18.0\nopentelemetry-sdk                        1.18.0\nopentelemetry-semantic-conventions       0.39b0\nopt-einsum                               3.3.0\noptax                                    0.1.7\noptuna                                   3.3.0\norbax-checkpoint                         0.3.5\norderedmultidict                         1.0.1\norjson                                   3.9.1\nortools                                  9.4.1874\nosmnx                                    1.1.1\noverrides                                6.5.0\npackaging                                21.3\npandas                                   2.0.2\npandas-datareader                        0.10.0\npandas-profiling                         3.6.6\npandas-summary                           0.2.0\npandasql                                 0.7.3\npandocfilters                            1.5.0\npanel                                    1.2.2\npapermill                                2.4.0\nparam                                    1.13.0\nparso                                    0.8.3\nparsy                                    2.1\npartd                                    1.4.0\npath                                     16.7.1\npath.py                                  12.5.0\npathos                                   0.3.1\npathtools                                0.1.2\npathy                                    0.10.1\npatsy                                    0.5.3\npdf2image                                1.16.3\npexpect                                  4.8.0\nphik                                     0.12.3\npickleshare                              0.7.5\nPillow                                   9.5.0\npip                                      23.1.2\npkgutil_resolve_name                     1.3.10\nplatformdirs                             3.10.0\nplotly                                   5.15.0\nplotly-express                           0.4.1\nplotnine                                 0.10.1\npluggy                                   1.0.0\npointpats                                2.3.0\npolars                                   0.19.2\npolyglot                                 16.7.4\npooch                                    1.7.0\npox                                      0.3.3\nppca                                     0.0.4\nppft                                     1.7.6.7\npreprocessing                            0.1.13\npreshed                                  3.0.8\nprettytable                              3.8.0\nprogressbar2                             4.2.0\nprometheus-client                        0.17.0\npromise                                  2.3\nprompt-toolkit                           3.0.38\npronouncing                              0.2.0\nprophet                                  1.1.1\nproto-plus                               1.22.3\nprotobuf                                 3.20.3\npsutil                                   5.9.3\nptxcompiler                              0.8.1\nptxcompiler-cu11                         0.7.0.post1\nptyprocess                               0.7.0\npudb                                     2022.1.3\nPuLP                                     2.7.0\npure-eval                                0.2.2\npy-cpuinfo                               9.0.0\npy-lz4framed                             0.14.0\npy-spy                                   0.3.14\npy4j                                     0.10.9.7\npyaml                                    23.9.1\nPyArabic                                 0.6.15\npyarrow                                  11.0.0\npyasn1                                   0.4.8\npyasn1-modules                           0.2.7\nPyAstronomy                              0.19.0\npybind11                                 2.11.1\npyclipper                                1.3.0.post4\npycodestyle                              2.10.0\npycolmap                                 0.4.0\npycosat                                  0.6.4\npycparser                                2.21\npycryptodome                             3.18.0\npyct                                     0.5.0\npycuda                                   2022.2.2\npydantic                                 1.10.9\npydantic_core                            2.6.3\npydegensac                               0.1.2\npydicom                                  2.4.3\npydocstyle                               6.3.0\npydot                                    1.4.2\npydub                                    0.25.1\npyemd                                    1.0.0\npyerfa                                   2.0.0.3\npyexcel-io                               0.6.6\npyexcel-ods                              0.6.0\npyfasttext                               0.4.6\npyflakes                                 3.0.1\npygltflib                                1.16.0\nPygments                                 2.15.1\nPyJWT                                    2.7.0\npykalman                                 0.9.5\npyLDAvis                                 3.2.2\npylibraft                                23.8.0\npylint                                   2.17.5\npymc3                                    3.11.5\nPyMeeus                                  0.5.12\npymongo                                  3.13.0\nPympler                                  1.0.1\npynndescent                              0.5.10\npynvml                                   11.4.1\npynvrtc                                  9.2\npyocr                                    0.8.3\npyOpenSSL                                23.2.0\npyparsing                                3.0.9\npypdf                                    3.15.5\npyproj                                   3.6.0\npyrsistent                               0.19.3\npysal                                    23.7\npyshp                                    2.3.1\nPySocks                                  1.7.1\npytesseract                              0.3.10\npytest                                   7.4.1\npython-bidi                              0.4.2\npython-dateutil                          2.8.2\npython-dotenv                            1.0.0\npython-json-logger                       2.0.7\npython-Levenshtein                       0.21.1\npython-louvain                           0.16\npython-lsp-jsonrpc                       1.0.0\npython-lsp-server                        1.7.4\npython-slugify                           8.0.1\npython-utils                             3.7.0\npythreejs                                2.4.2\npytoolconfig                             1.2.5\npytools                                  2023.1.1\npytorch-ignite                           0.4.12\npytorch-lightning                        2.0.8\npytz                                     2023.3\npyu2f                                    0.1.5\nPyUpSet                                  0.1.1.post7\npyviz_comms                              3.0.0\nPyWavelets                               1.4.1\nPyYAML                                   6.0\npyzmq                                    25.1.0\nqgrid                                    1.3.1\nqtconsole                                5.4.4\nQtPy                                     2.4.0\nquantecon                                0.7.1\nquantities                               0.14.1\nqudida                                   0.0.4\nraft-dask                                23.8.0\nrandomgen                                1.23.1\nrapidfuzz                                3.2.0\nrasterio                                 1.3.8\nrasterstats                              0.19.0\nray                                      2.5.1\nray-cpp                                  2.5.1\nregex                                    2023.6.3\nrequests                                 2.31.0\nrequests-oauthlib                        1.3.1\nrequests-toolbelt                        0.10.1\nresponses                                0.18.0\nretrying                                 1.3.3\nrfc3339-validator                        0.1.4\nrfc3986-validator                        0.1.1\nrgf-python                               3.12.0\nrich                                     13.4.2\nrmm                                      23.8.0\nrope                                     1.9.0\nrsa                                      4.9\nRtree                                    1.0.1\nruamel.yaml                              0.17.32\nruamel.yaml.clib                         0.2.7\nruamel-yaml-conda                        0.15.100\ns2sphere                                 0.2.5\ns3fs                                     2023.9.0\ns3transfer                               0.6.2\nsafetensors                              0.3.3\nscattertext                              0.1.19\nscikit-image                             0.21.0\nscikit-learn                             1.2.2\nscikit-learn-intelex                     2023.2.1\nscikit-multilearn                        0.2.0\nscikit-optimize                          0.9.0\nscikit-plot                              0.3.7\nscikit-surprise                          1.1.3\nscipy                                    1.11.2\nseaborn                                  0.12.2\nSecretStorage                            3.3.3\nsegment-anything                         1.0\nsegregation                              2.5\nsemver                                   3.0.1\nSend2Trash                               1.8.2\nsentencepiece                            0.1.99\nsentry-sdk                               1.30.0\nsetproctitle                             1.3.2\nsetuptools                               68.0.0\nsetuptools-git                           1.2\nsetuptools-scm                           7.1.0\nshap                                     0.42.1\nShapely                                  1.8.5.post1\nshellingham                              1.5.3\nsimpervisor                              1.0.0\nSimpleITK                                2.2.1\nsimplejson                               3.19.1\nsix                                      1.16.0\nsklearn-pandas                           2.2.0\nslicer                                   0.0.7\nsmart-open                               6.3.0\nsmhasher                                 0.150.1\nsmmap                                    5.0.0\nsniffio                                  1.3.0\nsnowballstemmer                          2.2.0\nsnuggs                                   1.4.7\nsortedcontainers                         2.4.0\nsoundfile                                0.12.1\nsoupsieve                                2.3.2.post1\nsoxr                                     0.3.6\nspacy                                    3.6.1\nspacy-legacy                             3.0.12\nspacy-loggers                            1.0.4\nspaghetti                                1.7.4\nspectral                                 0.23.1\nspglm                                    1.0.8\nsphinx-rtd-theme                         0.2.4\nspint                                    1.0.7\nsplot                                    1.1.5.post1\nspopt                                    0.5.0\nspreg                                    1.3.2\nspvcm                                    0.3.0\nSQLAlchemy                               2.0.17\nsqlglot                                  17.16.2\nsqlparse                                 0.4.4\nsquarify                                 0.4.3\nsrsly                                    2.4.7\nstack-data                               0.6.2\nstarlette                                0.27.0\nstatsmodels                              0.14.0\nstemming                                 1.0.1\nstop-words                               2018.7.23\nstopit                                   1.1.2\nstumpy                                   1.12.0\nsympy                                    1.12\ntables                                   3.8.0\ntabulate                                 0.9.0\ntangled-up-in-unicode                    0.2.0\ntbb                                      2021.10.0\ntblib                                    1.7.0\ntenacity                                 8.2.2\ntensorboard                              2.12.3\ntensorboard-data-server                  0.7.1\ntensorboard-plugin-profile               2.13.0\ntensorboardX                             2.6\ntensorflow                               2.12.0\ntensorflow-addons                        0.21.0\ntensorflow-cloud                         0.1.16\ntensorflow-datasets                      4.9.2\ntensorflow-decision-forests              1.4.0\ntensorflow-estimator                     2.12.0\ntensorflow-hub                           0.12.0\ntensorflow-io                            0.32.0\ntensorflow-io-gcs-filesystem             0.32.0\ntensorflow-metadata                      0.14.0\ntensorflow-probability                   0.20.1\ntensorflow-serving-api                   2.12.1\ntensorflow-text                          2.12.1\ntensorflow-transform                     0.14.0\ntensorflowjs                             3.15.0\ntensorpack                               0.11\ntensorstore                              0.1.41\ntermcolor                                2.3.0\nterminado                                0.17.1\ntestpath                                 0.6.0\ntext-unidecode                           1.3\ntextblob                                 0.17.1\ntexttable                                1.6.7\ntextwrap3                                0.9.2\nTheano                                   1.0.5\nTheano-PyMC                              1.1.2\nthinc                                    8.1.12\nthreadpoolctl                            3.1.0\ntifffile                                 2023.4.12\ntimm                                     0.9.7\ntinycss2                                 1.2.1\ntobler                                   0.11.1\ntokenizers                               0.13.3\ntoml                                     0.10.2\ntomli                                    2.0.1\ntomlkit                                  0.12.1\ntoolz                                    0.12.0\ntorch                                    2.0.0\ntorchaudio                               2.0.1\ntorchdata                                0.6.0\ntorchinfo                                1.8.0\ntorchmetrics                             1.1.1\ntorchtext                                0.15.1\ntorchvision                              0.15.1\ntornado                                  6.3.2\nTPOT                                     0.12.1\ntqdm                                     4.66.1\ntraceml                                  1.0.8\ntraitlets                                5.9.0\ntraittypes                               0.2.1\ntransformers                             4.33.0\ntreelite                                 3.2.0\ntreelite-runtime                         3.2.0\ntrueskill                                0.4.5\ntsfresh                                  0.20.1\ntypeguard                                2.13.3\ntyper                                    0.9.0\ntyping_extensions                        4.6.3\ntyping-inspect                           0.9.0\ntyping-utils                             0.1.0\ntzdata                                   2023.3\ntzlocal                                  5.0.1\nuc-micro-py                              1.0.2\nucx-py                                   0.33.0\nujson                                    5.8.0\numap-learn                               0.5.3\nunicodedata2                             15.0.0\nUnidecode                                1.3.6\nupdate-checker                           0.18.0\nuri-template                             1.3.0\nuritemplate                              3.0.1\nurllib3                                  1.26.15\nurwid                                    2.1.2\nurwid-readline                           0.13\nuvicorn                                  0.22.0\nuvloop                                   0.17.0\nvaex                                     4.17.0\nvaex-astro                               0.9.3\nvaex-core                                4.17.1\nvaex-hdf5                                0.14.1\nvaex-jupyter                             0.8.2\nvaex-ml                                  0.18.3\nvaex-server                              0.9.0\nvaex-viz                                 0.5.4\nvecstack                                 0.4.0\nvirtualenv                               20.21.0\nvisions                                  0.7.5\nvowpalwabbit                             9.9.0\nvtk                                      9.2.6\nWand                                     0.6.11\nwandb                                    0.15.9\nwasabi                                   1.1.2\nwatchfiles                               0.20.0\nwavio                                    0.0.7\nwcwidth                                  0.2.6\nwebcolors                                1.13\nwebencodings                             0.5.1\nwebsocket-client                         1.6.0\nwebsockets                               11.0.3\nWerkzeug                                 2.3.7\nwfdb                                     4.1.2\nwhatthepatch                             1.0.5\nwheel                                    0.40.0\nwidgetsnbextension                       3.6.5\nwitwidget                                1.8.1\nwoodwork                                 0.26.0\nWordbatch                                1.4.9\nwordcloud                                1.9.2\nwordsegment                              1.3.1\nwrapt                                    1.14.1\nwurlitzer                                3.0.3\nxarray                                   2023.8.0\nxarray-einstats                          0.6.0\nxgboost                                  1.7.6\nxvfbwrapper                              0.2.9\nxxhash                                   3.3.0\nxyzservices                              2023.7.0\ny-py                                     0.6.0\nyapf                                     0.40.1\nyarl                                     1.9.2\nydata-profiling                          4.3.1\nyellowbrick                              1.5\nypy-websocket                            0.8.4\nzict                                     3.0.0\nzipp                                     3.15.0\nzstandard                                0.19.0\n","output_type":"stream"}]},{"cell_type":"code","source":"%pip install opencv-python matplotlib imageio gdown tensorflow","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:06.361751Z","iopub.execute_input":"2023-10-22T10:10:06.362137Z","iopub.status.idle":"2023-10-22T10:10:18.711959Z","shell.execute_reply.started":"2023-10-22T10:10:06.362103Z","shell.execute_reply":"2023-10-22T10:10:18.710724Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Requirement already satisfied: opencv-python in /opt/conda/lib/python3.10/site-packages (4.8.0.76)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (3.7.2)\nRequirement already satisfied: imageio in /opt/conda/lib/python3.10/site-packages (2.31.1)\nCollecting gdown\n  Downloading gdown-4.7.1-py3-none-any.whl (15 kB)\nRequirement already satisfied: tensorflow in /opt/conda/lib/python3.10/site-packages (2.12.0)\nRequirement already satisfied: numpy>=1.21.2 in /opt/conda/lib/python3.10/site-packages (from opencv-python) (1.23.5)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.1.0)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (0.11.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (4.40.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.4.4)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (21.3)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (9.5.0)\nRequirement already satisfied: pyparsing<3.1,>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (2.8.2)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages 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in /opt/conda/lib/python3.10/site-packages (from tensorflow) (68.0.0)\nRequirement already satisfied: tensorboard<2.13,>=2.12 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.12.3)\nRequirement already satisfied: tensorflow-estimator<2.13,>=2.12.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.12.0)\nRequirement already satisfied: termcolor>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.3.0)\nRequirement already satisfied: typing-extensions>=3.6.6 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (4.6.3)\nRequirement already satisfied: wrapt<1.15,>=1.11.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.14.1)\nRequirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.32.0)\nRequirement already satisfied: wheel<1.0,>=0.23.0 in /opt/conda/lib/python3.10/site-packages (from astunparse>=1.6.0->tensorflow) (0.40.0)\nRequirement already satisfied: ml-dtypes>=0.1.0 in /opt/conda/lib/python3.10/site-packages (from jax>=0.3.15->tensorflow) (0.2.0)\nRequirement already satisfied: scipy>=1.7 in /opt/conda/lib/python3.10/site-packages (from jax>=0.3.15->tensorflow) (1.11.2)\nRequirement already satisfied: google-auth<3,>=1.6.3 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.20.0)\nRequirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (1.0.0)\nRequirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (3.4.3)\nRequirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (0.7.1)\nRequirement already satisfied: werkzeug>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.3.7)\nRequirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.10/site-packages (from beautifulsoup4->gdown) (2.3.2.post1)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (3.1.0)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (3.4)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (1.26.15)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (2023.7.22)\nRequirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (1.7.1)\nRequirement already satisfied: cachetools<6.0,>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (4.2.4)\nRequirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.2.7)\nRequirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (4.9)\nRequirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (1.3.1)\nRequirement already satisfied: MarkupSafe>=2.1.1 in /opt/conda/lib/python3.10/site-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow) (2.1.3)\nRequirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.4.8)\nRequirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.10/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (3.2.2)\nInstalling collected packages: gdown\nSuccessfully installed gdown-4.7.1\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"import os\nimport cv2\nimport tensorflow as tf\nimport numpy as np\nfrom typing import List\nfrom matplotlib import pyplot as plt\n","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:18.713932Z","iopub.execute_input":"2023-10-22T10:10:18.714350Z","iopub.status.idle":"2023-10-22T10:10:27.330650Z","shell.execute_reply.started":"2023-10-22T10:10:18.714312Z","shell.execute_reply":"2023-10-22T10:10:27.329646Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"code","source":"import imageio","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:27.332981Z","iopub.execute_input":"2023-10-22T10:10:27.333546Z","iopub.status.idle":"2023-10-22T10:10:27.403598Z","shell.execute_reply.started":"2023-10-22T10:10:27.333518Z","shell.execute_reply":"2023-10-22T10:10:27.402816Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"tf.config.list_physical_devices('GPU')","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:27.404867Z","iopub.execute_input":"2023-10-22T10:10:27.405229Z","iopub.status.idle":"2023-10-22T10:10:27.854627Z","shell.execute_reply.started":"2023-10-22T10:10:27.405197Z","shell.execute_reply":"2023-10-22T10:10:27.853576Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"},"metadata":{}}]},{"cell_type":"code","source":"physical_devices = tf.config.list_physical_devices('GPU')\ntry:\n    tf.config.experimental.set_memory_growth(physical_devices[0], True)\nexcept:\n    pass","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:27.856143Z","iopub.execute_input":"2023-10-22T10:10:27.856517Z","iopub.status.idle":"2023-10-22T10:10:27.866651Z","shell.execute_reply.started":"2023-10-22T10:10:27.856483Z","shell.execute_reply":"2023-10-22T10:10:27.865676Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"# 1. Build Data Loading Functions","metadata":{"tags":[]}},{"cell_type":"code","source":"import gdown","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:27.867878Z","iopub.execute_input":"2023-10-22T10:10:27.868669Z","iopub.status.idle":"2023-10-22T10:10:28.067791Z","shell.execute_reply.started":"2023-10-22T10:10:27.868625Z","shell.execute_reply":"2023-10-22T10:10:28.066773Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\noutput = 'data.zip'\ngdown.download(url, output, quiet=False)\ngdown.extractall('data.zip')","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:28.069009Z","iopub.execute_input":"2023-10-22T10:10:28.069568Z","iopub.status.idle":"2023-10-22T10:10:37.122536Z","shell.execute_reply.started":"2023-10-22T10:10:28.069541Z","shell.execute_reply":"2023-10-22T10:10:37.121346Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"Downloading...\nFrom (uriginal): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL\nFrom (redirected): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL&confirm=t&uuid=bc7524fb-eed0-4e74-b949-992a3779613a\nTo: /kaggle/working/data.zip\n100%|██████████| 423M/423M [00:05<00:00, 84.7MB/s] \n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"['data/',\n 'data/alignments/',\n 'data/alignments/s1/',\n 'data/alignments/s1/bbaf2n.align',\n 'data/alignments/s1/bbaf3s.align',\n 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[]\n    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))): \n        ret, frame = cap.read()\n        frame = tf.image.rgb_to_grayscale(frame)\n        frames.append(frame[190:236,80:220,:])\n    cap.release()\n    \n    mean = tf.math.reduce_mean(frames)\n    std = tf.math.reduce_std(tf.cast(frames, tf.float32))\n    return tf.cast((frames - mean), tf.float32) / std","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:37.123763Z","iopub.execute_input":"2023-10-22T10:10:37.124085Z","iopub.status.idle":"2023-10-22T10:10:37.130961Z","shell.execute_reply.started":"2023-10-22T10:10:37.124039Z","shell.execute_reply":"2023-10-22T10:10:37.129964Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"vocab = [x for x in \"abcdefghijklmnopqrstuvwxyz'?!123456789 \"]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:37.134623Z","iopub.execute_input":"2023-10-22T10:10:37.134942Z","iopub.status.idle":"2023-10-22T10:10:37.145439Z","shell.execute_reply.started":"2023-10-22T10:10:37.134917Z","shell.execute_reply":"2023-10-22T10:10:37.144512Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\nnum_to_char = tf.keras.layers.StringLookup(\n    vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n)\n\nprint(\n    f\"The vocabulary is: {char_to_num.get_vocabulary()} \"\n    f\"(size ={char_to_num.vocabulary_size()})\"\n)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:37.146606Z","iopub.execute_input":"2023-10-22T10:10:37.146953Z","iopub.status.idle":"2023-10-22T10:10:39.602817Z","shell.execute_reply.started":"2023-10-22T10:10:37.146918Z","shell.execute_reply":"2023-10-22T10:10:39.601869Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', '1', '2', '3', '4', '5', '6', '7', '8', '9', ' '] (size =40)\n","output_type":"stream"}]},{"cell_type":"code","source":"char_to_num.get_vocabulary()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:39.603975Z","iopub.execute_input":"2023-10-22T10:10:39.604262Z","iopub.status.idle":"2023-10-22T10:10:39.612158Z","shell.execute_reply.started":"2023-10-22T10:10:39.604238Z","shell.execute_reply":"2023-10-22T10:10:39.611251Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"['',\n 'a',\n 'b',\n 'c',\n 'd',\n 'e',\n 'f',\n 'g',\n 'h',\n 'i',\n 'j',\n 'k',\n 'l',\n 'm',\n 'n',\n 'o',\n 'p',\n 'q',\n 'r',\n 's',\n 't',\n 'u',\n 'v',\n 'w',\n 'x',\n 'y',\n 'z',\n \"'\",\n '?',\n '!',\n '1',\n '2',\n '3',\n '4',\n '5',\n '6',\n '7',\n '8',\n '9',\n ' ']"},"metadata":{}}]},{"cell_type":"code","source":"char_to_num(['n','i','c','k'])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:39.613287Z","iopub.execute_input":"2023-10-22T10:10:39.613569Z","iopub.status.idle":"2023-10-22T10:10:40.222281Z","shell.execute_reply.started":"2023-10-22T10:10:39.613545Z","shell.execute_reply":"2023-10-22T10:10:40.220980Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(4,), dtype=int64, numpy=array([14,  9,  3, 11])>"},"metadata":{}}]},{"cell_type":"code","source":"num_to_char([14,  9,  3, 11])","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:40.223763Z","iopub.execute_input":"2023-10-22T10:10:40.224193Z","iopub.status.idle":"2023-10-22T10:10:40.372812Z","shell.execute_reply.started":"2023-10-22T10:10:40.224156Z","shell.execute_reply":"2023-10-22T10:10:40.371827Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(4,), dtype=string, numpy=array([b'n', b'i', b'c', b'k'], dtype=object)>"},"metadata":{}}]},{"cell_type":"code","source":"def load_alignments(path:str) -> List[str]: \n    with open(path, 'r') as f: \n        lines = f.readlines() \n    tokens = []\n    for line in lines:\n        line = line.split()\n        if line[2] != 'sil': \n            tokens = [*tokens,' ',line[2]]\n    return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:40.373985Z","iopub.execute_input":"2023-10-22T10:10:40.374374Z","iopub.status.idle":"2023-10-22T10:10:40.382120Z","shell.execute_reply.started":"2023-10-22T10:10:40.374339Z","shell.execute_reply":"2023-10-22T10:10:40.381140Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"def load_data(path: str): \n#     path = bytes.decode(path.numpy())\n#     print(path)\n    file_name = path.numpy().decode('utf-8').split('/')[-1].split('.')[0]\n    # File name splitting for windows\n#     file_name = path.split('\\\\')[-1].split('.')[0]\n    video_path = os.path.join('data','s1',f'{file_name}.mpg')\n    alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')\n    frames = load_video(video_path) \n    alignments = load_alignments(alignment_path)\n    \n    return frames, alignments\n#     return file_name","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:40.383283Z","iopub.execute_input":"2023-10-22T10:10:40.383560Z","iopub.status.idle":"2023-10-22T10:10:40.392914Z","shell.execute_reply.started":"2023-10-22T10:10:40.383536Z","shell.execute_reply":"2023-10-22T10:10:40.392101Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"test_path = './data/s1/bbal6n.mpg'","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:40.394349Z","iopub.execute_input":"2023-10-22T10:10:40.395038Z","iopub.status.idle":"2023-10-22T10:10:40.407892Z","shell.execute_reply.started":"2023-10-22T10:10:40.395005Z","shell.execute_reply":"2023-10-22T10:10:40.407056Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"# test_path = bytes.decode(test_path.numpy())","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:40.408958Z","iopub.execute_input":"2023-10-22T10:10:40.409258Z","iopub.status.idle":"2023-10-22T10:10:40.419885Z","shell.execute_reply.started":"2023-10-22T10:10:40.409234Z","shell.execute_reply":"2023-10-22T10:10:40.418938Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"# hi = tf.convert_to_tensor(test_path).numpy().decode('utf-8')\n# hi","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:40.421159Z","iopub.execute_input":"2023-10-22T10:10:40.421830Z","iopub.status.idle":"2023-10-22T10:10:40.431815Z","shell.execute_reply.started":"2023-10-22T10:10:40.421794Z","shell.execute_reply":"2023-10-22T10:10:40.430911Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('/')[-1].split('.')[0]","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:40.432840Z","iopub.execute_input":"2023-10-22T10:10:40.433115Z","iopub.status.idle":"2023-10-22T10:10:40.446122Z","shell.execute_reply.started":"2023-10-22T10:10:40.433077Z","shell.execute_reply":"2023-10-22T10:10:40.445010Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"'bbal6n'"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"frames, alignments = load_data(tf.convert_to_tensor(test_path))","metadata":{"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:40.447186Z","iopub.execute_input":"2023-10-22T10:10:40.447458Z","iopub.status.idle":"2023-10-22T10:10:43.484392Z","shell.execute_reply.started":"2023-10-22T10:10:40.447425Z","shell.execute_reply":"2023-10-22T10:10:43.483525Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"alignments","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:43.485467Z","iopub.execute_input":"2023-10-22T10:10:43.485741Z","iopub.status.idle":"2023-10-22T10:10:43.492399Z","shell.execute_reply.started":"2023-10-22T10:10:43.485717Z","shell.execute_reply":"2023-10-22T10:10:43.491378Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(21,), dtype=int64, numpy=\narray([ 2,  9, 14, 39,  2, 12, 21,  5, 39,  1, 20, 39, 12, 39, 19,  9, 24,\n       39, 14, 15, 23])>"},"metadata":{}}]},{"cell_type":"code","source":"plt.imshow(frames[40])","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:43.493616Z","iopub.execute_input":"2023-10-22T10:10:43.494000Z","iopub.status.idle":"2023-10-22T10:10:43.795286Z","shell.execute_reply.started":"2023-10-22T10:10:43.493973Z","shell.execute_reply":"2023-10-22T10:10:43.794338Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7fb04077ee90>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"alignments","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:43.796677Z","iopub.execute_input":"2023-10-22T10:10:43.797336Z","iopub.status.idle":"2023-10-22T10:10:43.806703Z","shell.execute_reply.started":"2023-10-22T10:10:43.797298Z","shell.execute_reply":"2023-10-22T10:10:43.804964Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(21,), dtype=int64, numpy=\narray([ 2,  9, 14, 39,  2, 12, 21,  5, 39,  1, 20, 39, 12, 39, 19,  9, 24,\n       39, 14, 15, 23])>"},"metadata":{}}]},{"cell_type":"code","source":"tf.strings.reduce_join([bytes.decode(x) for x in num_to_char(alignments.numpy()).numpy()])","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:43.808475Z","iopub.execute_input":"2023-10-22T10:10:43.808843Z","iopub.status.idle":"2023-10-22T10:10:43.826526Z","shell.execute_reply.started":"2023-10-22T10:10:43.808810Z","shell.execute_reply":"2023-10-22T10:10:43.825116Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'bin blue at l six now'>"},"metadata":{}}]},{"cell_type":"code","source":"def mappable_function(path:str) ->List[str]:\n    result = tf.py_function(load_data, [path], (tf.float32, tf.int64))\n    return result","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:43.828275Z","iopub.execute_input":"2023-10-22T10:10:43.828794Z","iopub.status.idle":"2023-10-22T10:10:43.836270Z","shell.execute_reply.started":"2023-10-22T10:10:43.828757Z","shell.execute_reply":"2023-10-22T10:10:43.834371Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"markdown","source":"# 2. Create Data Pipeline","metadata":{"tags":[]}},{"cell_type":"code","source":"from matplotlib import pyplot as plt","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:43.837753Z","iopub.execute_input":"2023-10-22T10:10:43.838244Z","iopub.status.idle":"2023-10-22T10:10:43.849851Z","shell.execute_reply.started":"2023-10-22T10:10:43.838201Z","shell.execute_reply":"2023-10-22T10:10:43.848603Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"data = tf.data.Dataset.list_files('./data/s1/*.mpg')\ndata = data.shuffle(500, reshuffle_each_iteration=False)\ndata = data.map(mappable_function)\ndata = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\ndata = data.prefetch(tf.data.AUTOTUNE)\n# Added for split \ntrain = data.take(450)\ntest = data.skip(450)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:43.860601Z","iopub.execute_input":"2023-10-22T10:10:43.860976Z","iopub.status.idle":"2023-10-22T10:10:43.995518Z","shell.execute_reply.started":"2023-10-22T10:10:43.860943Z","shell.execute_reply":"2023-10-22T10:10:43.994448Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"len(train)","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:43.996639Z","iopub.execute_input":"2023-10-22T10:10:43.997350Z","iopub.status.idle":"2023-10-22T10:10:44.006855Z","shell.execute_reply.started":"2023-10-22T10:10:43.997310Z","shell.execute_reply":"2023-10-22T10:10:44.005798Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"450"},"metadata":{}}]},{"cell_type":"code","source":"frames, alignments = data.as_numpy_iterator().next()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:44.008123Z","iopub.execute_input":"2023-10-22T10:10:44.008460Z","iopub.status.idle":"2023-10-22T10:10:44.785749Z","shell.execute_reply.started":"2023-10-22T10:10:44.008429Z","shell.execute_reply":"2023-10-22T10:10:44.784813Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"len(frames)","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:44.786946Z","iopub.execute_input":"2023-10-22T10:10:44.787257Z","iopub.status.idle":"2023-10-22T10:10:44.792829Z","shell.execute_reply.started":"2023-10-22T10:10:44.787230Z","shell.execute_reply":"2023-10-22T10:10:44.791987Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"2"},"metadata":{}}]},{"cell_type":"code","source":"sample = data.as_numpy_iterator()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:44.793856Z","iopub.execute_input":"2023-10-22T10:10:44.794131Z","iopub.status.idle":"2023-10-22T10:10:45.241587Z","shell.execute_reply.started":"2023-10-22T10:10:44.794105Z","shell.execute_reply":"2023-10-22T10:10:45.240615Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"val = sample.next(); val[0]","metadata":{"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:45.242881Z","iopub.execute_input":"2023-10-22T10:10:45.243220Z","iopub.status.idle":"2023-10-22T10:10:45.710607Z","shell.execute_reply.started":"2023-10-22T10:10:45.243193Z","shell.execute_reply":"2023-10-22T10:10:45.709263Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"array([[[[[1.340357  ],\n          [1.340357  ],\n          [1.340357  ],\n          ...,\n          [0.31745297],\n          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[[0.96165156],\n          [0.96165156],\n          [0.96165156],\n          ...,\n          [9.693448  ],\n          [9.693448  ],\n          [9.57805   ]],\n\n         [[0.96165156],\n          [0.96165156],\n          [0.96165156],\n          ...,\n          [9.616515  ],\n          [9.57805   ],\n          [9.57805   ]]]]], dtype=float32)"},"metadata":{}}]},{"cell_type":"code","source":"# imageio.mimsave('./animation.gif', val[0][0], fps=10)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:45.712300Z","iopub.execute_input":"2023-10-22T10:10:45.712607Z","iopub.status.idle":"2023-10-22T10:10:45.718521Z","shell.execute_reply.started":"2023-10-22T10:10:45.712582Z","shell.execute_reply":"2023-10-22T10:10:45.716916Z"},"trusted":true},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"# 0:videos, 0: 1st video out of the batch,  0: return the first frame in the video \nplt.imshow(val[0][0][35])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:45.719888Z","iopub.execute_input":"2023-10-22T10:10:45.720307Z","iopub.status.idle":"2023-10-22T10:10:46.014736Z","shell.execute_reply.started":"2023-10-22T10:10:45.720277Z","shell.execute_reply":"2023-10-22T10:10:46.013074Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7fb040358550>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:46.017488Z","iopub.execute_input":"2023-10-22T10:10:46.017807Z","iopub.status.idle":"2023-10-22T10:10:46.091253Z","shell.execute_reply.started":"2023-10-22T10:10:46.017781Z","shell.execute_reply":"2023-10-22T10:10:46.089596Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'bin white by t nine again'>"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3. Design the Deep Neural Network","metadata":{"tags":[]}},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential \nfrom tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:46.094219Z","iopub.execute_input":"2023-10-22T10:10:46.094514Z","iopub.status.idle":"2023-10-22T10:10:46.108106Z","shell.execute_reply.started":"2023-10-22T10:10:46.094488Z","shell.execute_reply":"2023-10-22T10:10:46.105666Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"data.as_numpy_iterator().next()[0][0].shape","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:46.110468Z","iopub.execute_input":"2023-10-22T10:10:46.110798Z","iopub.status.idle":"2023-10-22T10:10:47.062736Z","shell.execute_reply.started":"2023-10-22T10:10:46.110773Z","shell.execute_reply":"2023-10-22T10:10:47.061778Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"(75, 46, 140, 1)"},"metadata":{}}]},{"cell_type":"code","source":"model = Sequential()\nmodel.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPool3D((1,2,2)))\n\nmodel.add(Conv3D(256, 3, padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPool3D((1,2,2)))\n\nmodel.add(Conv3D(75, 3, padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPool3D((1,2,2)))\n\nmodel.add(TimeDistributed(Flatten()))\n\nmodel.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\nmodel.add(Dropout(.5))\n\nmodel.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\nmodel.add(Dropout(.5))\n\nmodel.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:47.063807Z","iopub.execute_input":"2023-10-22T10:10:47.064125Z","iopub.status.idle":"2023-10-22T10:10:49.303629Z","shell.execute_reply.started":"2023-10-22T10:10:47.064095Z","shell.execute_reply":"2023-10-22T10:10:49.302785Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:49.304892Z","iopub.execute_input":"2023-10-22T10:10:49.305226Z","iopub.status.idle":"2023-10-22T10:10:49.352570Z","shell.execute_reply.started":"2023-10-22T10:10:49.305198Z","shell.execute_reply":"2023-10-22T10:10:49.351686Z"},"trusted":true},"execution_count":40,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n conv3d (Conv3D)             (None, 75, 46, 140, 128)  3584      \n                                                                 \n activation (Activation)     (None, 75, 46, 140, 128)  0         \n                                                                 \n max_pooling3d (MaxPooling3D  (None, 75, 23, 70, 128)  0         \n )                                                               \n                                                                 \n conv3d_1 (Conv3D)           (None, 75, 23, 70, 256)   884992    \n                                                                 \n activation_1 (Activation)   (None, 75, 23, 70, 256)   0         \n                                                                 \n max_pooling3d_1 (MaxPooling  (None, 75, 11, 35, 256)  0         \n 3D)                                                             \n                                                                 \n conv3d_2 (Conv3D)           (None, 75, 11, 35, 75)    518475    \n                                                                 \n activation_2 (Activation)   (None, 75, 11, 35, 75)    0         \n                                                                 \n max_pooling3d_2 (MaxPooling  (None, 75, 5, 17, 75)    0         \n 3D)                                                             \n                                                                 \n time_distributed (TimeDistr  (None, 75, 6375)         0         \n ibuted)                                                         \n                                                                 \n bidirectional (Bidirectiona  (None, 75, 256)          6660096   \n l)                                                              \n                                                                 \n dropout (Dropout)           (None, 75, 256)           0         \n                                                                 \n bidirectional_1 (Bidirectio  (None, 75, 256)          394240    \n nal)                                                            \n                                                                 \n dropout_1 (Dropout)         (None, 75, 256)           0         \n                                                                 \n dense (Dense)               (None, 75, 41)            10537     \n                                                                 \n=================================================================\nTotal params: 8,471,924\nTrainable params: 8,471,924\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"5*17*75","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:49.353716Z","iopub.execute_input":"2023-10-22T10:10:49.353997Z","iopub.status.idle":"2023-10-22T10:10:49.360454Z","shell.execute_reply.started":"2023-10-22T10:10:49.353972Z","shell.execute_reply":"2023-10-22T10:10:49.359420Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"6375"},"metadata":{}}]},{"cell_type":"code","source":"yhat = model.predict(val[0])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:49.361724Z","iopub.execute_input":"2023-10-22T10:10:49.362008Z","iopub.status.idle":"2023-10-22T10:10:55.123953Z","shell.execute_reply.started":"2023-10-22T10:10:49.361983Z","shell.execute_reply":"2023-10-22T10:10:55.123017Z"},"trusted":true},"execution_count":42,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 6s 6s/step\n","output_type":"stream"}]},{"cell_type":"code","source":"tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)])","metadata":{"execution":{"iopub.status.busy":"2023-10-22T10:10:55.125423Z","iopub.execute_input":"2023-10-22T10:10:55.125914Z","iopub.status.idle":"2023-10-22T10:10:55.225395Z","shell.execute_reply.started":"2023-10-22T10:10:55.125877Z","shell.execute_reply":"2023-10-22T10:10:55.224505Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'uuuuuyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy'>"},"metadata":{}}]},{"cell_type":"code","source":"tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:55.226768Z","iopub.execute_input":"2023-10-22T10:10:55.227078Z","iopub.status.idle":"2023-10-22T10:10:55.309360Z","shell.execute_reply.started":"2023-10-22T10:10:55.227038Z","shell.execute_reply":"2023-10-22T10:10:55.308375Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'uuuuuyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy'>"},"metadata":{}}]},{"cell_type":"code","source":"model.input_shape","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:55.310584Z","iopub.execute_input":"2023-10-22T10:10:55.310930Z","iopub.status.idle":"2023-10-22T10:10:55.316825Z","shell.execute_reply.started":"2023-10-22T10:10:55.310904Z","shell.execute_reply":"2023-10-22T10:10:55.315909Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"(None, 75, 46, 140, 1)"},"metadata":{}}]},{"cell_type":"code","source":"model.output_shape","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:55.318254Z","iopub.execute_input":"2023-10-22T10:10:55.318626Z","iopub.status.idle":"2023-10-22T10:10:55.331850Z","shell.execute_reply.started":"2023-10-22T10:10:55.318592Z","shell.execute_reply":"2023-10-22T10:10:55.330866Z"},"trusted":true},"execution_count":46,"outputs":[{"execution_count":46,"output_type":"execute_result","data":{"text/plain":"(None, 75, 41)"},"metadata":{}}]},{"cell_type":"markdown","source":"# 4. Setup Training Options and Train","metadata":{"tags":[]}},{"cell_type":"code","source":"def scheduler(epoch, lr):\n    if epoch < 30:\n        return lr\n    else:\n        return lr * tf.math.exp(-0.1)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:55.333176Z","iopub.execute_input":"2023-10-22T10:10:55.333566Z","iopub.status.idle":"2023-10-22T10:10:55.341889Z","shell.execute_reply.started":"2023-10-22T10:10:55.333531Z","shell.execute_reply":"2023-10-22T10:10:55.340989Z"},"trusted":true},"execution_count":47,"outputs":[]},{"cell_type":"code","source":"def CTCLoss(y_true, y_pred):\n    batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n    input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n    label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n\n    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n\n    loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n    return loss","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:55.343046Z","iopub.execute_input":"2023-10-22T10:10:55.343426Z","iopub.status.idle":"2023-10-22T10:10:55.353837Z","shell.execute_reply.started":"2023-10-22T10:10:55.343393Z","shell.execute_reply":"2023-10-22T10:10:55.353036Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"code","source":"class ProduceExample(tf.keras.callbacks.Callback): \n    def __init__(self, dataset) -> None: \n        self.dataset = dataset.as_numpy_iterator()\n    \n    def on_epoch_end(self, epoch, logs=None) -> None:\n        data = self.dataset.next()\n        yhat = self.model.predict(data[0])\n        decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n        for x in range(len(yhat)):           \n            print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n            print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n            print('~'*100)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:55.355015Z","iopub.execute_input":"2023-10-22T10:10:55.355353Z","iopub.status.idle":"2023-10-22T10:10:55.365770Z","shell.execute_reply.started":"2023-10-22T10:10:55.355328Z","shell.execute_reply":"2023-10-22T10:10:55.364874Z"},"trusted":true},"execution_count":49,"outputs":[]},{"cell_type":"code","source":"model.compile(optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.0001), loss=CTCLoss)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:55.366764Z","iopub.execute_input":"2023-10-22T10:10:55.367047Z","iopub.status.idle":"2023-10-22T10:10:55.394916Z","shell.execute_reply.started":"2023-10-22T10:10:55.367021Z","shell.execute_reply":"2023-10-22T10:10:55.393984Z"},"trusted":true},"execution_count":50,"outputs":[]},{"cell_type":"code","source":"checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True) ","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:55.396282Z","iopub.execute_input":"2023-10-22T10:10:55.396652Z","iopub.status.idle":"2023-10-22T10:10:55.401488Z","shell.execute_reply.started":"2023-10-22T10:10:55.396606Z","shell.execute_reply":"2023-10-22T10:10:55.400447Z"},"trusted":true},"execution_count":51,"outputs":[]},{"cell_type":"code","source":"schedule_callback = LearningRateScheduler(scheduler)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:55.402734Z","iopub.execute_input":"2023-10-22T10:10:55.403016Z","iopub.status.idle":"2023-10-22T10:10:55.412863Z","shell.execute_reply.started":"2023-10-22T10:10:55.402991Z","shell.execute_reply":"2023-10-22T10:10:55.412000Z"},"trusted":true},"execution_count":52,"outputs":[]},{"cell_type":"code","source":"example_callback = ProduceExample(test)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:55.414051Z","iopub.execute_input":"2023-10-22T10:10:55.414446Z","iopub.status.idle":"2023-10-22T10:10:55.434915Z","shell.execute_reply.started":"2023-10-22T10:10:55.414419Z","shell.execute_reply":"2023-10-22T10:10:55.434193Z"},"trusted":true},"execution_count":53,"outputs":[]},{"cell_type":"code","source":"model.fit(train, validation_data=test, epochs=96, callbacks=[checkpoint_callback, schedule_callback, example_callback])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T10:10:55.436011Z","iopub.execute_input":"2023-10-22T10:10:55.436303Z","iopub.status.idle":"2023-10-22T17:54:08.372889Z","shell.execute_reply.started":"2023-10-22T10:10:55.436278Z","shell.execute_reply":"2023-10-22T17:54:08.371497Z"},"trusted":true},"execution_count":54,"outputs":[{"name":"stdout","text":"Epoch 1/96\n 86/450 [====>.........................] - ETA: 4:18 - loss: 112.1342","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x55e2445e4f40] ac-tex damaged at 22 17\n[mpeg1video @ 0x55e2445e4f40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 85.1955","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafb006d5c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafb006d5c0] Warning MVs not available\n[mpeg1video @ 0x7fb10c078180] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb10c078180] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 2s 2s/step\nOriginal: place white in p six now\nPrediction: le e e e e o\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay blue sp with e six please\nPrediction: le e e e o\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 760s 2s/step - loss: 85.1955 - val_loss: 71.5942 - lr: 1.0000e-04\nEpoch 2/96\n173/450 [==========>...................] - ETA: 3:18 - loss: 72.6446","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf880fd280] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf880fd280] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 71.0840","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb0581d77c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb0581d77c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\nOriginal: lay red with r nine soon\nPrediction: la e e e e o\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place blue at p one again\nPrediction: la e e e e o\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 548s 1s/step - loss: 71.0840 - val_loss: 64.8817 - lr: 1.0000e-04\nEpoch 3/96\n 91/450 [=====>........................] - ETA: 4:15 - loss: 67.9789","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf68003200] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf68003200] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 67.2240","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafa809df40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafa809df40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\nOriginal: bin red with n three again\nPrediction: la e e t e on\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay white in r four please\nPrediction: la e e t e on\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 544s 1s/step - loss: 67.2240 - val_loss: 63.2823 - lr: 1.0000e-04\nEpoch 4/96\n202/450 [============>.................] - ETA: 2:56 - loss: 64.9651","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb1300f0900] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb1300f0900] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 64.2933","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf7006bd00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf7006bd00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 124ms/step\nOriginal: lay blue by k seven again\nPrediction: la e e t e oan\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place blue with j five again\nPrediction: la e e t e an\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 545s 1s/step - loss: 64.2933 - val_loss: 60.4680 - lr: 1.0000e-04\nEpoch 5/96\n306/450 [===================>..........] - ETA: 1:42 - loss: 62.4382","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb10c1bd940] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb10c1bd940] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 61.9440","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf54024140] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf54024140] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: lay blue by r one again\nPrediction: la re t e e an\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay white in r five again\nPrediction: la re t e an\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 542s 1s/step - loss: 61.9440 - val_loss: 57.6276 - lr: 1.0000e-04\nEpoch 6/96\n200/450 [============>.................] - ETA: 2:56 - loss: 60.1163","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafb007d1c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafb007d1c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 59.8636","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb125a2ddc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb125a2ddc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 118ms/step\nOriginal: set red in h one soon\nPrediction: la re i e an\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay green with t one soon\nPrediction: la re i e an\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 543s 1s/step - loss: 59.8636 - val_loss: 57.7013 - lr: 1.0000e-04\nEpoch 7/96\n422/450 [===========================>..] - ETA: 19s - loss: 58.2182","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf702af140] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf702af140] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 58.1452","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafbc026d80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafbc026d80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 124ms/step\nOriginal: lay red sp with l five soon\nPrediction: la re i e asn\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay green in r nine soon\nPrediction: la re t e on\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 545s 1s/step - loss: 58.1452 - val_loss: 53.6593 - lr: 1.0000e-04\nEpoch 8/96\n408/450 [==========================>...] - ETA: 29s - loss: 56.4018","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf5c0915c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf5c0915c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 56.3587","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafa81088c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafa81088c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 118ms/step\nOriginal: bin white by a five soon\nPrediction: la re i e on\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin blue in f zero please\nPrediction: la re i e ae\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 542s 1s/step - loss: 56.3587 - val_loss: 52.2985 - lr: 1.0000e-04\nEpoch 9/96\n298/450 [==================>...........] - ETA: 1:46 - loss: 55.0418","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb128046040] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb128046040] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 54.5534","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fadb3b15c00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fadb3b15c00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 125ms/step\nOriginal: bin white sp with n six now\nPrediction: set re t e aon\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay white by s two please\nPrediction: sen re t e ae\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 54.5534 - val_loss: 47.9571 - lr: 1.0000e-04\nEpoch 10/96\n 22/450 [>.............................] - ETA: 4:59 - loss: 50.7712","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafbc04a400] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafbc04a400] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 50.4764","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf500c5740] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf500c5740] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: place green at q six now\nPrediction: la re t n o\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place green at q eight please\nPrediction: la re t e eae\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 538s 1s/step - loss: 50.4764 - val_loss: 45.7617 - lr: 1.0000e-04\nEpoch 11/96\n 14/450 [..............................] - ETA: 5:05 - loss: 50.4029","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf80043500] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf80043500] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 48.4628","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb10c1a7bc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb10c1a7bc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 122ms/step\nOriginal: lay green at f four now\nPrediction: set re n o o\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin green at h zero now\nPrediction: la re it on o\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 536s 1s/step - loss: 48.4628 - val_loss: 44.7726 - lr: 1.0000e-04\nEpoch 12/96\n 85/450 [====>.........................] - ETA: 4:16 - loss: 47.3887","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf7027fdc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf7027fdc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 46.6186","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafb003b040] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafb003b040] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: lay white with m one soon\nPrediction: let re it o o\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin green with o three soon\nPrediction: la re it on o\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 537s 1s/step - loss: 46.6186 - val_loss: 43.5727 - lr: 1.0000e-04\nEpoch 13/96\n 44/450 [=>............................] - ETA: 4:44 - loss: 45.7983","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf4c01f0c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf4c01f0c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 44.3584","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf7aac7e00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf7aac7e00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\nOriginal: place white in x two please\nPrediction: la re t re plae\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place blue with c eight now\nPrediction: plac ble it ie on\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 44.3584 - val_loss: 40.2542 - lr: 1.0000e-04\nEpoch 14/96\n410/450 [==========================>...] - ETA: 28s - loss: 42.3026","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb10c1bd940] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb10c1bd940] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 42.1926","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf64019c40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf64019c40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 122ms/step\nOriginal: lay red at e two now\nPrediction: set re it o ow\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay red at y six please\nPrediction: set re t e an\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 536s 1s/step - loss: 42.1926 - val_loss: 37.9253 - lr: 1.0000e-04\nEpoch 15/96\n186/450 [===========>..................] - ETA: 3:04 - loss: 41.1123","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafb4053400] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafb4053400] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 39.9234","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb130cce640] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb130cce640] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\nOriginal: set white by i six please\nPrediction: set re it oe pleae\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay green with t two please\nPrediction: set re it or pleae\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 534s 1s/step - loss: 39.9234 - val_loss: 33.8579 - lr: 1.0000e-04\nEpoch 16/96\n 39/450 [=>............................] - ETA: 4:49 - loss: 38.7671","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf7ab59780] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf7ab59780] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 37.6335","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb058058880] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb058058880] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\nOriginal: bin red with g six now\nPrediction: len re it in ow\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin blue by m zero now\nPrediction: bit blue by oe ow\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 536s 1s/step - loss: 37.6335 - val_loss: 32.2507 - lr: 1.0000e-04\nEpoch 17/96\n325/450 [====================>.........] - ETA: 1:28 - loss: 35.2139","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf5c035680] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf5c035680] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 35.3310","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafb006a4c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafb006a4c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 122ms/step\nOriginal: bin white at g five soon\nPrediction: len re it eie son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place red with q three soon\nPrediction: plac re it ie son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 539s 1s/step - loss: 35.3310 - val_loss: 29.4248 - lr: 1.0000e-04\nEpoch 18/96\n234/450 [==============>...............] - ETA: 2:31 - loss: 33.5974","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb1280519c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb1280519c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 33.1856","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faea7422bc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faea7422bc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 118ms/step\nOriginal: set green with x three soon\nPrediction: set re it re son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place blue with p six now\nPrediction: place blue by io ow\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 535s 1s/step - loss: 33.1856 - val_loss: 26.9494 - lr: 1.0000e-04\nEpoch 19/96\n143/450 [========>.....................] - ETA: 3:33 - loss: 31.9843","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb0441eea00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb0441eea00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 31.3043","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf54097980] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf54097980] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 118ms/step\nOriginal: place red by x three soon\nPrediction: place re by e on\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay red in e zero please\nPrediction: sen re it ir please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 531s 1s/step - loss: 31.3043 - val_loss: 25.3467 - lr: 1.0000e-04\nEpoch 20/96\n288/450 [==================>...........] - ETA: 1:52 - loss: 28.7436","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafac002b00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafac002b00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 28.9096","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb1101f4100] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb1101f4100] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\nOriginal: lay red with s zero please\nPrediction: lay re it o please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place white with r zero please\nPrediction: place gre it or please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 531s 1s/step - loss: 28.9096 - val_loss: 22.7949 - lr: 1.0000e-04\nEpoch 21/96\n304/450 [===================>..........] - ETA: 1:41 - loss: 27.0307","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf7016af40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf7016af40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 27.0639","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafbc078980] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafbc078980] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 120ms/step\nOriginal: bin white with h three soon\nPrediction: bin white with tre son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay white with s seven again\nPrediction: lay white wit eie agin\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 538s 1s/step - loss: 27.0639 - val_loss: 22.0067 - lr: 1.0000e-04\nEpoch 22/96\n364/450 [=======================>......] - ETA: 1:00 - loss: 25.0067","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf54001340] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf54001340] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 24.8791","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf84015500] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf84015500] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 122ms/step\nOriginal: place green with l one soon\nPrediction: place red it ene son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place white with q nine soon\nPrediction: place white ith ie son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 537s 1s/step - loss: 24.8791 - val_loss: 19.1597 - lr: 1.0000e-04\nEpoch 23/96\n438/450 [============================>.] - ETA: 8s - loss: 22.9350","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb1200babc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb1200babc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 22.8940","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf5c02df00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf5c02df00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\nOriginal: set blue by h two now\nPrediction: set blue by o now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set blue with h eight please\nPrediction: set blue it it please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 535s 1s/step - loss: 22.8940 - val_loss: 18.0147 - lr: 1.0000e-04\nEpoch 24/96\n 37/450 [=>............................] - ETA: 4:48 - loss: 21.9018","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafbc0425c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafbc0425c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 21.0697","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf50116340] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf50116340] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: place white with e one soon\nPrediction: place white with oe son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place white in j three soon\nPrediction: place white with thre son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 21.0697 - val_loss: 16.4850 - lr: 1.0000e-04\nEpoch 25/96\n392/450 [=========================>....] - ETA: 40s - loss: 19.5278","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafac007d00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafac007d00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 19.4981","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb0580a0f80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb0580a0f80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: place red in p one soon\nPrediction: place red in ne son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place white with e two please\nPrediction: place white wit to please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 539s 1s/step - loss: 19.4981 - val_loss: 15.4640 - lr: 1.0000e-04\nEpoch 26/96\n 77/450 [====>.........................] - ETA: 4:21 - loss: 18.2201","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fadb3b5db80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fadb3b5db80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 17.9257","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafc0023940] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafc0023940] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: lay blue at k three again\nPrediction: lay blue at tre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin green with i one again\nPrediction: bin gren with one again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 17.9257 - val_loss: 13.1815 - lr: 1.0000e-04\nEpoch 27/96\n337/450 [=====================>........] - ETA: 1:19 - loss: 16.7400","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x55e23df34980] ac-tex damaged at 22 17\n[mpeg1video @ 0x55e23df34980] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 16.7814","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf84083380] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf84083380] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: place white in j five again\nPrediction: place white it five again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin green in a four please\nPrediction: bin gren in for please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 537s 1s/step - loss: 16.7814 - val_loss: 12.1376 - lr: 1.0000e-04\nEpoch 28/96\n206/450 [============>.................] - ETA: 2:51 - loss: 15.5050","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb1101a1540] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb1101a1540] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 15.6964","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf4c06fac0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf4c06fac0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: bin red at s six now\nPrediction: bin red at six now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set blue by h three soon\nPrediction: set blue by tre son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 538s 1s/step - loss: 15.6964 - val_loss: 11.5575 - lr: 1.0000e-04\nEpoch 29/96\n293/450 [==================>...........] - ETA: 1:50 - loss: 14.6915","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafa803af00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafa803af00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 14.3988","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb1207d2640] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb1207d2640] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\nOriginal: place red with x six now\nPrediction: place red with six now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay red in y one soon\nPrediction: lay red in one son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 539s 1s/step - loss: 14.3988 - val_loss: 10.0799 - lr: 1.0000e-04\nEpoch 30/96\n352/450 [======================>.......] - ETA: 1:09 - loss: 13.6119","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faea6f669c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faea6f669c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 13.4218","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb05808ad80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb05808ad80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 123ms/step\nOriginal: set red by i one again\nPrediction: set red by one again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set red with i two now\nPrediction: set red with wo now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 13.4218 - val_loss: 11.4866 - lr: 1.0000e-04\nEpoch 31/96\n234/450 [==============>...............] - ETA: 2:32 - loss: 12.9117","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf54067dc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf54067dc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 12.5811","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafa8039f40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafa8039f40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\nOriginal: lay red at r zero now\nPrediction: lay red at zero now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place red in p three again\nPrediction: place red in hre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 12.5811 - val_loss: 9.1893 - lr: 9.0484e-05\nEpoch 32/96\n114/450 [======>.......................] - ETA: 3:56 - loss: 11.6906","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb12002af00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb12002af00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 11.5668","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf6401dac0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf6401dac0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 130ms/step\nOriginal: bin white with n nine again\nPrediction: bin white with nine again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place blue in b nine again\nPrediction: place blue in nine again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 11.5668 - val_loss: 7.8694 - lr: 8.1873e-05\nEpoch 33/96\n 62/450 [===>..........................] - ETA: 4:34 - loss: 10.6531","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafc401a6c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafc401a6c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 10.8546","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb1300e5c40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb1300e5c40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 127ms/step\nOriginal: lay blue with k nine soon\nPrediction: lay blue with nine son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay red with f three again\nPrediction: lay red with thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 10.8546 - val_loss: 7.0539 - lr: 7.4082e-05\nEpoch 34/96\n260/450 [================>.............] - ETA: 2:14 - loss: 10.0236","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf80008a00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf80008a00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 10.1667","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb11c087fc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb11c087fc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\nOriginal: place white with y three soon\nPrediction: place white with thre son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin green with h eight now\nPrediction: bin gren with eight now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 10.1667 - val_loss: 7.2296 - lr: 6.7032e-05\nEpoch 35/96\n344/450 [=====================>........] - ETA: 1:14 - loss: 9.5860","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf68043400] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf68043400] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 9.4515","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafb003fd80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafb003fd80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\nOriginal: bin red in l nine soon\nPrediction: bin red in nine son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place blue in i three again\nPrediction: place blue in thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 9.4515 - val_loss: 6.4875 - lr: 6.0653e-05\nEpoch 36/96\n160/450 [=========>....................] - ETA: 3:24 - loss: 8.9734","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf4c581180] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf4c581180] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 9.0505","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faea6f9acc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faea6f9acc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: lay green in s one again\nPrediction: lay gren in one again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay blue by q eight now\nPrediction: lay blue by eight now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 9.0505 - val_loss: 5.7669 - lr: 5.4881e-05\nEpoch 37/96\n266/450 [================>.............] - ETA: 2:10 - loss: 8.4695","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb1180149c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb1180149c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 8.4782","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf5c016c40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf5c016c40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\nOriginal: set red in a six now\nPrediction: set red in six now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin green with i zero please\nPrediction: bin gren with zero please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 542s 1s/step - loss: 8.4782 - val_loss: 5.5085 - lr: 4.9659e-05\nEpoch 38/96\n274/450 [=================>............] - ETA: 2:04 - loss: 8.2205","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafb4044680] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafb4044680] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 8.2210","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb125db8bc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb125db8bc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: lay white by z seven again\nPrediction: lay white by seven again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place blue in i one soon\nPrediction: place blue in one son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 541s 1s/step - loss: 8.2210 - val_loss: 4.8387 - lr: 4.4933e-05\nEpoch 39/96\n116/450 [======>.......................] - ETA: 3:57 - loss: 8.0864","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf800a7140] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf800a7140] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 7.7534","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb10c07d800] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb10c07d800] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 129ms/step\nOriginal: bin blue at f five again\nPrediction: bin blue at f five again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin blue at s three again\nPrediction: bin blue at thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 542s 1s/step - loss: 7.7534 - val_loss: 4.8322 - lr: 4.0657e-05\nEpoch 40/96\n447/450 [============================>.] - ETA: 2s - loss: 7.3993","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf6001c640] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf6001c640] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 7.3972","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafb0163200] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafb0163200] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 127ms/step\nOriginal: lay white at f zero please\nPrediction: lay white at zero please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set white at i three again\nPrediction: set white at thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 543s 1s/step - loss: 7.3972 - val_loss: 4.3613 - lr: 3.6788e-05\nEpoch 41/96\n200/450 [============>.................] - ETA: 2:56 - loss: 7.2302","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb1200c6e80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb1200c6e80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 7.1828","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faea6f97bc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faea6f97bc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: set blue in g seven again\nPrediction: set blue in seven again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin white at a one soon\nPrediction: bin white at a one son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 543s 1s/step - loss: 7.1828 - val_loss: 4.3238 - lr: 3.3287e-05\nEpoch 42/96\n125/450 [=======>......................] - ETA: 3:50 - loss: 7.2686","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb044024880] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb044024880] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 6.8739","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf54098640] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf54098640] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 131ms/step\nOriginal: place green in d seven again\nPrediction: place gren in seven again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay green in z five again\nPrediction: lay gren in five again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 6.8739 - val_loss: 4.1965 - lr: 3.0119e-05\nEpoch 43/96\n285/450 [==================>...........] - ETA: 1:56 - loss: 6.4387","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafb40a24c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafb40a24c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 6.5179","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb125dd2a00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb125dd2a00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 118ms/step\nOriginal: place white at q zero now\nPrediction: place white at zero now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set green in o seven soon\nPrediction: set gren in o seven son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 541s 1s/step - loss: 6.5179 - val_loss: 4.1021 - lr: 2.7253e-05\nEpoch 44/96\n408/450 [==========================>...] - ETA: 29s - loss: 6.3226","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf70061780] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf70061780] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 6.3005","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafc440f700] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafc440f700] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 131ms/step\nOriginal: place red at c six now\nPrediction: place red at c six now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set green in v three again\nPrediction: set gren in thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 544s 1s/step - loss: 6.3005 - val_loss: 3.4295 - lr: 2.4660e-05\nEpoch 45/96\n178/450 [==========>...................] - ETA: 3:12 - loss: 6.1629","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf4c058bc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf4c058bc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 6.1288","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf8405c500] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf8405c500] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: bin red in f five soon\nPrediction: bin red in f five son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place green with l three again\nPrediction: place gren with l thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 542s 1s/step - loss: 6.1288 - val_loss: 3.5842 - lr: 2.2313e-05\nEpoch 46/96\n 53/450 [==>...........................] - ETA: 4:40 - loss: 5.8659","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb120075400] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb120075400] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.9979","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fadb3a28cc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fadb3a28cc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 118ms/step\nOriginal: lay green with t three again\nPrediction: lay gren with thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay blue in d three soon\nPrediction: lay blue in thre son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 541s 1s/step - loss: 5.9979 - val_loss: 3.1175 - lr: 2.0190e-05\nEpoch 47/96\n334/450 [=====================>........] - ETA: 1:22 - loss: 5.7999","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafc0032000] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafc0032000] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.7116","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb13009c0c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb13009c0c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: place red at c eight please\nPrediction: place red at c eight please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set green with q zero please\nPrediction: set gren with zero please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 543s 1s/step - loss: 5.7116 - val_loss: 3.7012 - lr: 1.8268e-05\nEpoch 48/96\n174/450 [==========>...................] - ETA: 3:15 - loss: 5.6131","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafac0634c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafac0634c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.5815","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb10c1be180] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb10c1be180] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 113ms/step\nOriginal: place red with k one again\nPrediction: place red with one again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set green in c one again\nPrediction: set gren in one again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 542s 1s/step - loss: 5.5815 - val_loss: 2.8747 - lr: 1.6530e-05\nEpoch 49/96\n140/450 [========>.....................] - ETA: 3:39 - loss: 5.7162","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fadb3a283c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fadb3a283c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.6103","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fafc0044d40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fafc0044d40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 114ms/step\nOriginal: lay white in e five soon\nPrediction: lay white in five son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay red at r two please\nPrediction: lay red at two please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 542s 1s/step - loss: 5.6103 - val_loss: 2.9940 - lr: 1.4957e-05\nEpoch 50/96\n262/450 [================>.............] - ETA: 2:12 - loss: 5.2824","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb13007e800] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb13007e800] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.3198","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf7a98db40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf7a98db40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 113ms/step\nOriginal: lay red at e four please\nPrediction: lay red at four please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin white by g nine soon\nPrediction: bin white by g nine son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 540s 1s/step - loss: 5.3198 - val_loss: 2.8131 - lr: 1.3534e-05\nEpoch 51/96\n308/450 [===================>..........] - ETA: 1:40 - loss: 5.2943","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fb11c03f6c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fb11c03f6c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.2782","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7faf54144e00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faf54144e00] Warning MVs not available\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)","Cell \u001b[0;32mIn[54], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m96\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschedule_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexample_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n","Cell \u001b[0;32mIn[49], line 6\u001b[0m, in \u001b[0;36mProduceExample.on_epoch_end\u001b[0;34m(self, epoch, logs)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mon_epoch_end\u001b[39m(\u001b[38;5;28mself\u001b[39m, epoch, logs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m----> 6\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m     yhat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mpredict(data[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m      8\u001b[0m     decoded \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mbackend\u001b[38;5;241m.\u001b[39mctc_decode(yhat, [\u001b[38;5;241m75\u001b[39m,\u001b[38;5;241m75\u001b[39m], greedy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mnumpy()\n","\u001b[0;31mStopIteration\u001b[0m: "],"ename":"StopIteration","evalue":"","output_type":"error"}]},{"cell_type":"markdown","source":"# 5. Make a Prediction ","metadata":{"tags":[]}},{"cell_type":"code","source":"# url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n# output = 'checkpoints.zip'\n# gdown.download(url, output, quiet=False)\n# gdown.extractall('checkpoints.zip', 'models')","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T17:54:08.373999Z","iopub.status.idle":"2023-10-22T17:54:08.374491Z","shell.execute_reply.started":"2023-10-22T17:54:08.374252Z","shell.execute_reply":"2023-10-22T17:54:08.374276Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# model.load_weights('./models/checkpoint')","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T17:54:08.375782Z","iopub.status.idle":"2023-10-22T17:54:08.376272Z","shell.execute_reply.started":"2023-10-22T17:54:08.376003Z","shell.execute_reply":"2023-10-22T17:54:08.376026Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_data = test.as_numpy_iterator()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T17:56:00.282397Z","iopub.execute_input":"2023-10-22T17:56:00.283603Z","iopub.status.idle":"2023-10-22T17:56:00.294261Z","shell.execute_reply.started":"2023-10-22T17:56:00.283569Z","shell.execute_reply":"2023-10-22T17:56:00.293287Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"sample = test_data.next()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T17:56:02.712926Z","iopub.execute_input":"2023-10-22T17:56:02.713286Z","iopub.status.idle":"2023-10-22T17:59:18.977666Z","shell.execute_reply.started":"2023-10-22T17:56:02.713259Z","shell.execute_reply":"2023-10-22T17:59:18.975855Z"},"trusted":true},"execution_count":56,"outputs":[{"name":"stderr","text":"[mpeg1video @ 0x7faea7433c80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7faea7433c80] Warning MVs not available\n","output_type":"stream"}]},{"cell_type":"code","source":"sample[0].shape","metadata":{"execution":{"iopub.status.busy":"2023-10-22T18:02:25.818961Z","iopub.execute_input":"2023-10-22T18:02:25.819363Z","iopub.status.idle":"2023-10-22T18:02:25.826322Z","shell.execute_reply.started":"2023-10-22T18:02:25.819334Z","shell.execute_reply":"2023-10-22T18:02:25.825137Z"},"trusted":true},"execution_count":72,"outputs":[{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"TensorShape([75, 46, 140, 1])"},"metadata":{}}]},{"cell_type":"code","source":"sample[1].shape","metadata":{"execution":{"iopub.status.busy":"2023-10-22T18:02:37.818461Z","iopub.execute_input":"2023-10-22T18:02:37.818827Z","iopub.status.idle":"2023-10-22T18:02:37.824992Z","shell.execute_reply.started":"2023-10-22T18:02:37.818798Z","shell.execute_reply":"2023-10-22T18:02:37.824078Z"},"trusted":true},"execution_count":73,"outputs":[{"execution_count":73,"output_type":"execute_result","data":{"text/plain":"TensorShape([23])"},"metadata":{}}]},{"cell_type":"code","source":"sample[1]","metadata":{"execution":{"iopub.status.busy":"2023-10-22T18:02:48.420305Z","iopub.execute_input":"2023-10-22T18:02:48.420671Z","iopub.status.idle":"2023-10-22T18:02:48.427519Z","shell.execute_reply.started":"2023-10-22T18:02:48.420642Z","shell.execute_reply":"2023-10-22T18:02:48.426619Z"},"trusted":true},"execution_count":74,"outputs":[{"execution_count":74,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(23,), dtype=int64, numpy=\narray([ 2,  9, 14, 39, 18,  5,  4, 39,  1, 20, 39, 19, 39, 14,  9, 14,  5,\n       39,  1,  7,  1,  9, 14])>"},"metadata":{}}]},{"cell_type":"code","source":"yhat = model.predict(sample[0])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T18:00:21.634286Z","iopub.execute_input":"2023-10-22T18:00:21.634717Z","iopub.status.idle":"2023-10-22T18:00:21.806541Z","shell.execute_reply.started":"2023-10-22T18:00:21.634686Z","shell.execute_reply":"2023-10-22T18:00:21.805454Z"},"trusted":true},"execution_count":57,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 0s 122ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"print('~'*100, 'REAL TEXT')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T18:00:24.577237Z","iopub.execute_input":"2023-10-22T18:00:24.577619Z","iopub.status.idle":"2023-10-22T18:00:24.660844Z","shell.execute_reply.started":"2023-10-22T18:00:24.577576Z","shell.execute_reply":"2023-10-22T18:00:24.659942Z"},"trusted":true},"execution_count":58,"outputs":[{"name":"stdout","text":"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REAL TEXT\n","output_type":"stream"},{"execution_count":58,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'place red with k one again'>,\n <tf.Tensor: shape=(), dtype=string, numpy=b'place green at y zero now'>]"},"metadata":{}}]},{"cell_type":"code","source":"decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T18:00:30.989169Z","iopub.execute_input":"2023-10-22T18:00:30.989998Z","iopub.status.idle":"2023-10-22T18:00:31.001990Z","shell.execute_reply.started":"2023-10-22T18:00:30.989965Z","shell.execute_reply":"2023-10-22T18:00:31.001032Z"},"trusted":true},"execution_count":59,"outputs":[]},{"cell_type":"code","source":"print('~'*100, 'PREDICTIONS')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-22T18:00:36.169139Z","iopub.execute_input":"2023-10-22T18:00:36.170009Z","iopub.status.idle":"2023-10-22T18:00:36.305222Z","shell.execute_reply.started":"2023-10-22T18:00:36.169976Z","shell.execute_reply":"2023-10-22T18:00:36.304320Z"},"trusted":true},"execution_count":60,"outputs":[{"name":"stdout","text":"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PREDICTIONS\n","output_type":"stream"},{"execution_count":60,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'place red with one again'>,\n <tf.Tensor: shape=(), dtype=string, numpy=b'place green at y zero now'>]"},"metadata":{}}]},{"cell_type":"markdown","source":"# Test on a Video","metadata":{}},{"cell_type":"code","source":"sample = load_data(tf.convert_to_tensor('./data/s1/bras9a.mpg'))","metadata":{"execution":{"iopub.status.busy":"2023-10-22T18:00:48.002893Z","iopub.execute_input":"2023-10-22T18:00:48.003616Z","iopub.status.idle":"2023-10-22T18:00:48.242859Z","shell.execute_reply.started":"2023-10-22T18:00:48.003584Z","shell.execute_reply":"2023-10-22T18:00:48.242074Z"},"trusted":true},"execution_count":61,"outputs":[]},{"cell_type":"code","source":"print('~'*100, 'REAL TEXT')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in [sample[1]]]","metadata":{"execution":{"iopub.status.busy":"2023-10-22T18:00:50.378029Z","iopub.execute_input":"2023-10-22T18:00:50.378441Z","iopub.status.idle":"2023-10-22T18:00:50.414269Z","shell.execute_reply.started":"2023-10-22T18:00:50.378411Z","shell.execute_reply":"2023-10-22T18:00:50.413350Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REAL TEXT\n","output_type":"stream"},{"execution_count":62,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'bin red at s nine again'>]"},"metadata":{}}]},{"cell_type":"code","source":"yhat = model.predict(tf.expand_dims(sample[0], axis=0))","metadata":{"execution":{"iopub.status.busy":"2023-10-22T18:00:53.344884Z","iopub.execute_input":"2023-10-22T18:00:53.345264Z","iopub.status.idle":"2023-10-22T18:00:53.911982Z","shell.execute_reply.started":"2023-10-22T18:00:53.345233Z","shell.execute_reply":"2023-10-22T18:00:53.911086Z"},"trusted":true},"execution_count":63,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 1s 521ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()","metadata":{"execution":{"iopub.status.busy":"2023-10-22T18:00:55.085211Z","iopub.execute_input":"2023-10-22T18:00:55.085576Z","iopub.status.idle":"2023-10-22T18:00:55.095829Z","shell.execute_reply.started":"2023-10-22T18:00:55.085547Z","shell.execute_reply":"2023-10-22T18:00:55.095036Z"},"trusted":true},"execution_count":64,"outputs":[]},{"cell_type":"code","source":"print('~'*100, 'PREDICTIONS')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]","metadata":{"execution":{"iopub.status.busy":"2023-10-22T18:00:57.237162Z","iopub.execute_input":"2023-10-22T18:00:57.237930Z","iopub.status.idle":"2023-10-22T18:00:57.311037Z","shell.execute_reply.started":"2023-10-22T18:00:57.237898Z","shell.execute_reply":"2023-10-22T18:00:57.310134Z"},"trusted":true},"execution_count":65,"outputs":[{"name":"stdout","text":"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PREDICTIONS\n","output_type":"stream"},{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'bin red at s nine again'>]"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
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+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 0. Install and Import Dependencies","metadata":{"tags":[]}},{"cell_type":"code","source":"!pip list","metadata":{"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:50:44.319384Z","iopub.execute_input":"2023-10-23T03:50:44.319721Z","iopub.status.idle":"2023-10-23T03:50:47.901733Z","shell.execute_reply.started":"2023-10-23T03:50:44.319692Z","shell.execute_reply":"2023-10-23T03:50:47.900060Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Package                                  Version               Editable project location\n---------------------------------------- --------------------- -------------------------\nabsl-py                                  1.4.0\naccelerate                               0.22.0\naccess                                   1.1.9\naffine                                   2.4.0\naiobotocore                              2.5.4\naiofiles                                 22.1.0\naiohttp                                  3.8.4\naiohttp-cors                             0.7.0\naioitertools                             0.11.0\naiorwlock                                1.3.0\naiosignal                                1.3.1\naiosqlite                                0.19.0\nalbumentations                           1.3.1\nalembic                                  1.12.0\naltair                                   5.1.1\nannotated-types                          0.5.0\nannoy                                    1.17.3\nansiwrap                                 0.8.4\nanyio                                    3.7.0\napache-beam                              2.46.0\naplus                                    0.11.0\nappdirs                                  1.4.4\nargon2-cffi                              21.3.0\nargon2-cffi-bindings                     21.2.0\narray-record                             0.4.0\narrow                                    1.2.3\narviz                                    0.12.1\nastroid                                  2.15.6\nastropy                                  5.3.2\nasttokens                                2.2.1\nastunparse                               1.6.3\nasync-timeout                            4.0.2\natpublic                                 4.0\nattrs                                    23.1.0\naudioread                                3.0.0\nautopep8                                 2.0.4\nBabel                                    2.12.1\nbackcall                                 0.2.0\nbackoff                                  2.2.1\nbackports.functools-lru-cache            1.6.4\nbayesian-optimization                    1.4.3\nbayespy                                  0.5.26\nbeatrix-jupyterlab                       2023.621.222118\nbeautifulsoup4                           4.12.2\nbidict                                   0.22.1\nbiopython                                1.81\nblake3                                   0.2.1\nbleach                                   6.0.0\nblessed                                  1.20.0\nblinker                                  1.6.2\nblis                                     0.7.10\nblosc2                                   2.0.0\nbokeh                                    3.2.2\nboltons                                  23.0.0\nBoruta                                   0.3\nboto3                                    1.26.100\nbotocore                                 1.31.17\nbq-helper                                0.4.1                 /root/src/BigQuery_Helper\nbqplot                                   0.12.40\nbranca                                   0.6.0\nbrewer2mpl                               1.4.1\nbrotlipy                                 0.7.0\ncachetools                               4.2.4\nCartopy                                  0.22.0\ncatalogue                                2.0.9\ncatalyst                                 22.4\ncatboost                                 1.2.1\ncategory-encoders                        2.6.2\ncertifi                                  2023.7.22\ncesium                                   0.12.1\ncffi                                     1.15.1\ncftime                                   1.6.2\ncharset-normalizer                       3.1.0\nchex                                     0.1.82\ncleverhans                               4.0.0\nclick                                    8.1.7\nclick-plugins                            1.1.1\ncligj                                    0.7.2\ncloud-tpu-client                         0.10\ncloud-tpu-profiler                       2.4.0\ncloudpickle                              2.2.1\ncmaes                                    0.10.0\ncmdstanpy                                1.1.0\ncmudict                                  1.0.13\ncolorama                                 0.4.6\ncolorcet                                 3.0.1\ncolorful                                 0.5.5\ncolorlog                                 6.7.0\ncolorlover                               0.3.0\ncomm                                     0.1.3\nconda                                    23.7.3\nconda-content-trust                      0+unknown\nconda-package-handling                   2.0.2\nconda_package_streaming                  0.8.0\nconfection                               0.1.1\ncontextily                               1.3.0\ncontourpy                                1.1.0\nconvertdate                              2.4.0\ncrcmod                                   1.7\ncryptography                             38.0.4\ncubinlinker                              0.3.0\ncubinlinker-cu11                         0.3.0.post1\ncuda-python                              11.8.2\ncudf                                     23.8.0\ncufflinks                                0.17.3\ncuml                                     23.8.0\ncupy                                     12.2.0\nCVXcanon                                 0.1.2\ncycler                                   0.11.0\ncymem                                    2.0.7\ncysignals                                1.11.2\nCython                                   0.29.35\ncytoolz                                  0.12.2\ndaal                                     2023.2.1\ndaal4py                                  2023.2.1\ndacite                                   1.8.1\ndask                                     2023.9.0\ndask-cuda                                23.8.0\ndask-cudf                                23.8.0\ndataclasses-json                         0.6.0\ndatasets                                 2.1.0\ndatashader                               0.15.2\ndatashape                                0.5.2\ndatatile                                 1.0.3\ndb-dtypes                                1.1.1\ndeap                                     1.4.1\ndebugpy                                  1.6.7\ndecorator                                5.1.1\ndefusedxml                               0.7.1\nDelorean                                 1.0.0\ndeprecat                                 2.1.1\nDeprecated                               1.2.14\ndeprecation                              2.1.0\ndescartes                                1.1.0\ndill                                     0.3.7\ndipy                                     1.7.0\ndistlib                                  0.3.6\ndistributed                              2023.7.1\ndm-tree                                  0.1.8\ndocker                                   6.1.3\ndocker-pycreds                           0.4.0\ndocopt                                   0.6.2\ndocstring-parser                         0.15\ndocstring-to-markdown                    0.12\ndocutils                                 0.20.1\nearthengine-api                          0.1.367\neasydict                                 1.10\neasyocr                                  1.7.1\necos                                     2.0.12\neli5                                     0.13.0\nemoji                                    2.8.0\nen-core-web-lg                           3.6.0\nen-core-web-sm                           3.6.0\nentrypoints                              0.4\nephem                                    4.1.4\nesda                                     2.5.0\nessentia                                 2.1b6.dev1034\net-xmlfile                               1.1.0\netils                                    1.3.0\nexceptiongroup                           1.1.1\nexecuting                                1.2.0\nexplainable-ai-sdk                       1.3.3\nfastai                                   2.7.12\nfastapi                                  0.98.0\nfastavro                                 1.7.4\nfastcore                                 1.5.29\nfastdownload                             0.0.7\nfasteners                                0.18\nfastjsonschema                           2.17.1\nfastprogress                             1.0.3\nfastrlock                                0.8.2\nfasttext                                 0.9.2\nfbpca                                    1.0\nfeather-format                           0.4.1\nfeaturetools                             1.27.0\nfilelock                                 3.12.2\nFiona                                    1.9.4.post1\nfitter                                   1.6.0\nflake8                                   6.0.0\nflashtext                                2.7\nFlask                                    2.3.3\nflatbuffers                              23.5.26\nflax                                     0.7.2\nflit_core                                3.9.0\nfolium                                   0.14.0\nfonttools                                4.40.0\nfqdn                                     1.5.1\nfrozendict                               2.3.8\nfrozenlist                               1.3.3\nfsspec                                   2023.9.0\nfuncy                                    2.0\nfury                                     0.9.0\nfuture                                   0.18.3\nfuzzywuzzy                               0.18.0\ngast                                     0.4.0\ngatspy                                   0.3\ngcsfs                                    2023.6.0\ngensim                                   4.3.2\ngeographiclib                            2.0\nGeohash                                  1.0\ngeojson                                  3.0.1\ngeopandas                                0.13.2\ngeoplot                                  0.5.1\ngeopy                                    2.4.0\ngeoviews                                 1.10.1\nggplot                                   0.11.5\ngiddy                                    2.3.4\ngitdb                                    4.0.10\nGitPython                                3.1.31\ngoogle-api-core                          2.11.1\ngoogle-api-python-client                 2.97.0\ngoogle-apitools                          0.5.31\ngoogle-auth                              2.20.0\ngoogle-auth-httplib2                     0.1.0\ngoogle-auth-oauthlib                     1.0.0\ngoogle-cloud-aiplatform                  0.6.0a1\ngoogle-cloud-artifact-registry           1.8.1\ngoogle-cloud-automl                      1.0.1\ngoogle-cloud-bigquery                    2.34.4\ngoogle-cloud-bigtable                    1.7.3\ngoogle-cloud-core                        2.3.2\ngoogle-cloud-datastore                   2.17.0\ngoogle-cloud-dlp                         3.12.1\ngoogle-cloud-language                    2.11.0\ngoogle-cloud-monitoring                  2.15.0\ngoogle-cloud-pubsub                      2.17.1\ngoogle-cloud-pubsublite                  1.8.2\ngoogle-cloud-recommendations-ai          0.7.1\ngoogle-cloud-resource-manager            1.10.1\ngoogle-cloud-spanner                     3.36.0\ngoogle-cloud-storage                     1.44.0\ngoogle-cloud-translate                   3.12.0\ngoogle-cloud-videointelligence           2.11.3\ngoogle-cloud-vision                      2.8.0\ngoogle-crc32c                            1.5.0\ngoogle-pasta                             0.2.0\ngoogle-resumable-media                   2.5.0\ngoogleapis-common-protos                 1.59.1\ngplearn                                  0.4.2\ngpustat                                  1.0.0\ngpxpy                                    1.5.0\ngraphviz                                 0.20.1\ngreenlet                                 2.0.2\ngrpc-google-iam-v1                       0.12.6\ngrpcio                                   1.51.1\ngrpcio-status                            1.46.3\ngviz-api                                 1.10.0\ngym                                      0.26.2\ngym-notices                              0.0.8\nGymnasium                                0.26.3\ngymnasium-notices                        0.0.1\nh11                                      0.14.0\nh2o                                      3.42.0.3\nh5py                                     3.9.0\nhaversine                                2.8.0\nhdfs                                     2.7.0\nhep-ml                                   0.7.2\nhijri-converter                          2.3.1\nhmmlearn                                 0.3.0\nholidays                                 0.24\nholoviews                                1.17.1\nhpsklearn                                0.1.0\nhtml5lib                                 1.1\nhtmlmin                                  0.1.12\nhttplib2                                 0.21.0\nhttptools                                0.6.0\nhuggingface-hub                          0.16.4\nhumanize                                 4.8.0\nhunspell                                 0.5.5\nhusl                                     4.0.3\nhydra-slayer                             0.4.1\nhyperopt                                 0.2.7\nhypertools                               0.8.0\nibis-framework                           6.2.0\nidna                                     3.4\nigraph                                   0.10.6\nimagecodecs                              2023.9.4\nImageHash                                4.3.1\nimageio                                  2.31.1\nimbalanced-learn                         0.11.0\nimgaug                                   0.4.0\nimportlib-metadata                       6.7.0\nimportlib-resources                      5.12.0\ninequality                               1.0.0\niniconfig                                2.0.0\nipydatawidgets                           4.3.5\nipykernel                                6.23.3\nipyleaflet                               0.17.3\nipympl                                   0.7.0\nipython                                  8.14.0\nipython-genutils                         0.2.0\nipython-sql                              0.5.0\nipyvolume                                0.6.3\nipyvue                                   1.10.0\nipyvuetify                               1.8.10\nipywebrtc                                0.6.0\nipywidgets                               7.7.1\nisoduration                              20.11.0\nisort                                    5.12.0\nisoweek                                  1.3.3\nitsdangerous                             2.1.2\nJanome                                   0.5.0\njaraco.classes                           3.2.3\njax                                      0.4.13\njaxlib                                   0.4.13+cuda11.cudnn86\njedi                                     0.18.2\njeepney                                  0.8.0\njieba                                    0.42.1\nJinja2                                   3.1.2\njmespath                                 1.0.1\njoblib                                   1.3.2\njson5                                    0.9.14\njsonpatch                                1.32\njsonpointer                              2.0\njsonschema                               4.17.3\njupyter_client                           7.4.9\njupyter-console                          6.6.3\njupyter_core                             5.3.1\njupyter-events                           0.6.3\njupyter-http-over-ws                     0.0.8\njupyter-lsp                              1.5.1\njupyter_server                           2.6.0\njupyter_server_fileid                    0.9.0\njupyter-server-mathjax                   0.2.6\njupyter_server_proxy                     4.0.0\njupyter_server_terminals                 0.4.4\njupyter_server_ydoc                      0.8.0\njupyter-ydoc                             0.2.5\njupyterlab                               3.6.5\njupyterlab-git                           0.41.0\njupyterlab-lsp                           4.2.0\njupyterlab-pygments                      0.2.2\njupyterlab_server                        2.23.0\njupyterlab-widgets                       3.0.7\njupytext                                 1.14.6\nkaggle                                   1.5.16\nkaggle-environments                      1.12.0\nkeras                                    2.12.0\nkeras-core                               0.1.5\nkeras-cv                                 0.6.1\nkeras-nlp                                0.6.1\nkeras-tuner                              1.3.5\nkeyring                                  24.2.0\nkeyrings.google-artifactregistry-auth    1.1.2\nkfp                                      2.0.1\nkfp-pipeline-spec                        0.2.2\nkfp-server-api                           2.0.0\nkiwisolver                               1.4.4\nkmapper                                  2.0.1\nkmodes                                   0.12.2\nkorean-lunar-calendar                    0.3.1\nkornia                                   0.7.0\nkt-legacy                                1.0.5\nkubernetes                               26.1.0\nlangcodes                                3.3.0\nlangid                                   1.1.6\nlazy_loader                              0.2\nlazy-object-proxy                        1.9.0\nlearntools                               0.3.4\nleven                                    1.0.4\nLevenshtein                              0.21.1\nlibclang                                 16.0.0\nlibmambapy                               1.4.9\nlibpysal                                 4.7.0\nlibrosa                                  0.10.1\nlightgbm                                 3.3.2\nlightning-utilities                      0.9.0\nlime                                     0.2.0.1\nline-profiler                            4.1.1\nlinkify-it-py                            2.0.2\nllvmlite                                 0.40.1\nlml                                      0.1.0\nlocket                                   1.0.0\nLunarCalendar                            0.0.9\nlxml                                     4.9.3\nlz4                                      4.3.2\nMako                                     1.2.4\nmamba                                    1.4.9\nmapclassify                              2.6.0\nmarisa-trie                              1.0.0\nMarkdown                                 3.4.3\nmarkdown-it-py                           2.2.0\nmarkovify                                0.9.4\nMarkupSafe                               2.1.3\nmarshmallow                              3.20.1\nmatplotlib                               3.7.2\nmatplotlib-inline                        0.1.6\nmatplotlib-venn                          0.11.9\nmccabe                                   0.7.0\nmdit-py-plugins                          0.4.0\nmdurl                                    0.1.0\nmemory-profiler                          0.61.0\nmercantile                               1.2.1\nmgwr                                     2.1.2\nmissingno                                0.5.2\nmistune                                  0.8.4\nmizani                                   0.10.0\nml-dtypes                                0.2.0\nmlcrate                                  0.2.0\nmlens                                    0.2.3\nmlxtend                                  0.22.0\nmmh3                                     4.0.1\nmne                                      1.5.0\nmnist                                    0.2.2\nmock                                     5.1.0\nmomepy                                   0.6.0\nmore-itertools                           9.1.0\nmpld3                                    0.5.9\nmpmath                                   1.3.0\nmsgpack                                  1.0.5\nmsgpack-numpy                            0.4.8\nmultidict                                6.0.4\nmultimethod                              1.9.1\nmultipledispatch                         1.0.0\nmultiprocess                             0.70.15\nmunkres                                  1.1.4\nmurmurhash                               1.0.9\nmypy-extensions                          1.0.0\nnamex                                    0.0.7\nnb-conda                                 2.2.1\nnb-conda-kernels                         2.3.1\nnbclassic                                1.0.0\nnbclient                                 0.5.13\nnbconvert                                6.4.5\nnbdime                                   3.2.0\nnbformat                                 5.9.0\nnest-asyncio                             1.5.6\nnetCDF4                                  1.6.4\nnetworkx                                 3.1\nnibabel                                  5.1.0\nnilearn                                  0.10.1\nninja                                    1.11.1\nnltk                                     3.2.4\nnose                                     1.3.7\nnotebook                                 6.5.4\nnotebook-executor                        0.2\nnotebook_shim                            0.2.3\nnumba                                    0.57.1\nnumexpr                                  2.8.5\nnumpy                                    1.23.5\nnvidia-ml-py                             11.495.46\nnvtx                                     0.2.5\noauth2client                             4.1.3\noauthlib                                 3.2.2\nobjsize                                  0.6.1\nodfpy                                    1.4.1\nolefile                                  0.46\nonnx                                     1.14.1\nopencensus                               0.11.2\nopencensus-context                       0.1.3\nopencv-contrib-python                    4.8.0.76\nopencv-python                            4.8.0.76\nopencv-python-headless                   4.8.0.76\nopenpyxl                                 3.1.2\nopenslide-python                         1.3.0\nopentelemetry-api                        1.18.0\nopentelemetry-exporter-otlp              1.18.0\nopentelemetry-exporter-otlp-proto-common 1.18.0\nopentelemetry-exporter-otlp-proto-grpc   1.18.0\nopentelemetry-exporter-otlp-proto-http   1.18.0\nopentelemetry-proto                      1.18.0\nopentelemetry-sdk                        1.18.0\nopentelemetry-semantic-conventions       0.39b0\nopt-einsum                               3.3.0\noptax                                    0.1.7\noptuna                                   3.3.0\norbax-checkpoint                         0.3.5\norderedmultidict                         1.0.1\norjson                                   3.9.1\nortools                                  9.4.1874\nosmnx                                    1.1.1\noverrides                                6.5.0\npackaging                                21.3\npandas                                   2.0.2\npandas-datareader                        0.10.0\npandas-profiling                         3.6.6\npandas-summary                           0.2.0\npandasql                                 0.7.3\npandocfilters                            1.5.0\npanel                                    1.2.2\npapermill                                2.4.0\nparam                                    1.13.0\nparso                                    0.8.3\nparsy                                    2.1\npartd                                    1.4.0\npath                                     16.7.1\npath.py                                  12.5.0\npathos                                   0.3.1\npathtools                                0.1.2\npathy                                    0.10.1\npatsy                                    0.5.3\npdf2image                                1.16.3\npexpect                                  4.8.0\nphik                                     0.12.3\npickleshare                              0.7.5\nPillow                                   9.5.0\npip                                      23.1.2\npkgutil_resolve_name                     1.3.10\nplatformdirs                             3.10.0\nplotly                                   5.15.0\nplotly-express                           0.4.1\nplotnine                                 0.10.1\npluggy                                   1.0.0\npointpats                                2.3.0\npolars                                   0.19.2\npolyglot                                 16.7.4\npooch                                    1.7.0\npox                                      0.3.3\nppca                                     0.0.4\nppft                                     1.7.6.7\npreprocessing                            0.1.13\npreshed                                  3.0.8\nprettytable                              3.8.0\nprogressbar2                             4.2.0\nprometheus-client                        0.17.0\npromise                                  2.3\nprompt-toolkit                           3.0.38\npronouncing                              0.2.0\nprophet                                  1.1.1\nproto-plus                               1.22.3\nprotobuf                                 3.20.3\npsutil                                   5.9.3\nptxcompiler                              0.8.1\nptxcompiler-cu11                         0.7.0.post1\nptyprocess                               0.7.0\npudb                                     2022.1.3\nPuLP                                     2.7.0\npure-eval                                0.2.2\npy-cpuinfo                               9.0.0\npy-lz4framed                             0.14.0\npy-spy                                   0.3.14\npy4j                                     0.10.9.7\npyaml                                    23.9.1\nPyArabic                                 0.6.15\npyarrow                                  11.0.0\npyasn1                                   0.4.8\npyasn1-modules                           0.2.7\nPyAstronomy                              0.19.0\npybind11                                 2.11.1\npyclipper                                1.3.0.post4\npycodestyle                              2.10.0\npycolmap                                 0.4.0\npycosat                                  0.6.4\npycparser                                2.21\npycryptodome                             3.18.0\npyct                                     0.5.0\npycuda                                   2022.2.2\npydantic                                 1.10.9\npydantic_core                            2.6.3\npydegensac                               0.1.2\npydicom                                  2.4.3\npydocstyle                               6.3.0\npydot                                    1.4.2\npydub                                    0.25.1\npyemd                                    1.0.0\npyerfa                                   2.0.0.3\npyexcel-io                               0.6.6\npyexcel-ods                              0.6.0\npyfasttext                               0.4.6\npyflakes                                 3.0.1\npygltflib                                1.16.0\nPygments                                 2.15.1\nPyJWT                                    2.7.0\npykalman                                 0.9.5\npyLDAvis                                 3.2.2\npylibraft                                23.8.0\npylint                                   2.17.5\npymc3                                    3.11.5\nPyMeeus                                  0.5.12\npymongo                                  3.13.0\nPympler                                  1.0.1\npynndescent                              0.5.10\npynvml                                   11.4.1\npynvrtc                                  9.2\npyocr                                    0.8.3\npyOpenSSL                                23.2.0\npyparsing                                3.0.9\npypdf                                    3.15.5\npyproj                                   3.6.0\npyrsistent                               0.19.3\npysal                                    23.7\npyshp                                    2.3.1\nPySocks                                  1.7.1\npytesseract                              0.3.10\npytest                                   7.4.1\npython-bidi                              0.4.2\npython-dateutil                          2.8.2\npython-dotenv                            1.0.0\npython-json-logger                       2.0.7\npython-Levenshtein                       0.21.1\npython-louvain                           0.16\npython-lsp-jsonrpc                       1.0.0\npython-lsp-server                        1.7.4\npython-slugify                           8.0.1\npython-utils                             3.7.0\npythreejs                                2.4.2\npytoolconfig                             1.2.5\npytools                                  2023.1.1\npytorch-ignite                           0.4.12\npytorch-lightning                        2.0.8\npytz                                     2023.3\npyu2f                                    0.1.5\nPyUpSet                                  0.1.1.post7\npyviz_comms                              3.0.0\nPyWavelets                               1.4.1\nPyYAML                                   6.0\npyzmq                                    25.1.0\nqgrid                                    1.3.1\nqtconsole                                5.4.4\nQtPy                                     2.4.0\nquantecon                                0.7.1\nquantities                               0.14.1\nqudida                                   0.0.4\nraft-dask                                23.8.0\nrandomgen                                1.23.1\nrapidfuzz                                3.2.0\nrasterio                                 1.3.8\nrasterstats                              0.19.0\nray                                      2.5.1\nray-cpp                                  2.5.1\nregex                                    2023.6.3\nrequests                                 2.31.0\nrequests-oauthlib                        1.3.1\nrequests-toolbelt                        0.10.1\nresponses                                0.18.0\nretrying                                 1.3.3\nrfc3339-validator                        0.1.4\nrfc3986-validator                        0.1.1\nrgf-python                               3.12.0\nrich                                     13.4.2\nrmm                                      23.8.0\nrope                                     1.9.0\nrsa                                      4.9\nRtree                                    1.0.1\nruamel.yaml                              0.17.32\nruamel.yaml.clib                         0.2.7\nruamel-yaml-conda                        0.15.100\ns2sphere                                 0.2.5\ns3fs                                     2023.9.0\ns3transfer                               0.6.2\nsafetensors                              0.3.3\nscattertext                              0.1.19\nscikit-image                             0.21.0\nscikit-learn                             1.2.2\nscikit-learn-intelex                     2023.2.1\nscikit-multilearn                        0.2.0\nscikit-optimize                          0.9.0\nscikit-plot                              0.3.7\nscikit-surprise                          1.1.3\nscipy                                    1.11.2\nseaborn                                  0.12.2\nSecretStorage                            3.3.3\nsegment-anything                         1.0\nsegregation                              2.5\nsemver                                   3.0.1\nSend2Trash                               1.8.2\nsentencepiece                            0.1.99\nsentry-sdk                               1.30.0\nsetproctitle                             1.3.2\nsetuptools                               68.0.0\nsetuptools-git                           1.2\nsetuptools-scm                           7.1.0\nshap                                     0.42.1\nShapely                                  1.8.5.post1\nshellingham                              1.5.3\nsimpervisor                              1.0.0\nSimpleITK                                2.2.1\nsimplejson                               3.19.1\nsix                                      1.16.0\nsklearn-pandas                           2.2.0\nslicer                                   0.0.7\nsmart-open                               6.3.0\nsmhasher                                 0.150.1\nsmmap                                    5.0.0\nsniffio                                  1.3.0\nsnowballstemmer                          2.2.0\nsnuggs                                   1.4.7\nsortedcontainers                         2.4.0\nsoundfile                                0.12.1\nsoupsieve                                2.3.2.post1\nsoxr                                     0.3.6\nspacy                                    3.6.1\nspacy-legacy                             3.0.12\nspacy-loggers                            1.0.4\nspaghetti                                1.7.4\nspectral                                 0.23.1\nspglm                                    1.0.8\nsphinx-rtd-theme                         0.2.4\nspint                                    1.0.7\nsplot                                    1.1.5.post1\nspopt                                    0.5.0\nspreg                                    1.3.2\nspvcm                                    0.3.0\nSQLAlchemy                               2.0.17\nsqlglot                                  17.16.2\nsqlparse                                 0.4.4\nsquarify                                 0.4.3\nsrsly                                    2.4.7\nstack-data                               0.6.2\nstarlette                                0.27.0\nstatsmodels                              0.14.0\nstemming                                 1.0.1\nstop-words                               2018.7.23\nstopit                                   1.1.2\nstumpy                                   1.12.0\nsympy                                    1.12\ntables                                   3.8.0\ntabulate                                 0.9.0\ntangled-up-in-unicode                    0.2.0\ntbb                                      2021.10.0\ntblib                                    1.7.0\ntenacity                                 8.2.2\ntensorboard                              2.12.3\ntensorboard-data-server                  0.7.1\ntensorboard-plugin-profile               2.13.0\ntensorboardX                             2.6\ntensorflow                               2.12.0\ntensorflow-addons                        0.21.0\ntensorflow-cloud                         0.1.16\ntensorflow-datasets                      4.9.2\ntensorflow-decision-forests              1.4.0\ntensorflow-estimator                     2.12.0\ntensorflow-hub                           0.12.0\ntensorflow-io                            0.32.0\ntensorflow-io-gcs-filesystem             0.32.0\ntensorflow-metadata                      0.14.0\ntensorflow-probability                   0.20.1\ntensorflow-serving-api                   2.12.1\ntensorflow-text                          2.12.1\ntensorflow-transform                     0.14.0\ntensorflowjs                             3.15.0\ntensorpack                               0.11\ntensorstore                              0.1.41\ntermcolor                                2.3.0\nterminado                                0.17.1\ntestpath                                 0.6.0\ntext-unidecode                           1.3\ntextblob                                 0.17.1\ntexttable                                1.6.7\ntextwrap3                                0.9.2\nTheano                                   1.0.5\nTheano-PyMC                              1.1.2\nthinc                                    8.1.12\nthreadpoolctl                            3.1.0\ntifffile                                 2023.4.12\ntimm                                     0.9.7\ntinycss2                                 1.2.1\ntobler                                   0.11.1\ntokenizers                               0.13.3\ntoml                                     0.10.2\ntomli                                    2.0.1\ntomlkit                                  0.12.1\ntoolz                                    0.12.0\ntorch                                    2.0.0\ntorchaudio                               2.0.1\ntorchdata                                0.6.0\ntorchinfo                                1.8.0\ntorchmetrics                             1.1.1\ntorchtext                                0.15.1\ntorchvision                              0.15.1\ntornado                                  6.3.2\nTPOT                                     0.12.1\ntqdm                                     4.66.1\ntraceml                                  1.0.8\ntraitlets                                5.9.0\ntraittypes                               0.2.1\ntransformers                             4.33.0\ntreelite                                 3.2.0\ntreelite-runtime                         3.2.0\ntrueskill                                0.4.5\ntsfresh                                  0.20.1\ntypeguard                                2.13.3\ntyper                                    0.9.0\ntyping_extensions                        4.6.3\ntyping-inspect                           0.9.0\ntyping-utils                             0.1.0\ntzdata                                   2023.3\ntzlocal                                  5.0.1\nuc-micro-py                              1.0.2\nucx-py                                   0.33.0\nujson                                    5.8.0\numap-learn                               0.5.3\nunicodedata2                             15.0.0\nUnidecode                                1.3.6\nupdate-checker                           0.18.0\nuri-template                             1.3.0\nuritemplate                              3.0.1\nurllib3                                  1.26.15\nurwid                                    2.1.2\nurwid-readline                           0.13\nuvicorn                                  0.22.0\nuvloop                                   0.17.0\nvaex                                     4.17.0\nvaex-astro                               0.9.3\nvaex-core                                4.17.1\nvaex-hdf5                                0.14.1\nvaex-jupyter                             0.8.2\nvaex-ml                                  0.18.3\nvaex-server                              0.9.0\nvaex-viz                                 0.5.4\nvecstack                                 0.4.0\nvirtualenv                               20.21.0\nvisions                                  0.7.5\nvowpalwabbit                             9.9.0\nvtk                                      9.2.6\nWand                                     0.6.11\nwandb                                    0.15.9\nwasabi                                   1.1.2\nwatchfiles                               0.20.0\nwavio                                    0.0.7\nwcwidth                                  0.2.6\nwebcolors                                1.13\nwebencodings                             0.5.1\nwebsocket-client                         1.6.0\nwebsockets                               11.0.3\nWerkzeug                                 2.3.7\nwfdb                                     4.1.2\nwhatthepatch                             1.0.5\nwheel                                    0.40.0\nwidgetsnbextension                       3.6.5\nwitwidget                                1.8.1\nwoodwork                                 0.26.0\nWordbatch                                1.4.9\nwordcloud                                1.9.2\nwordsegment                              1.3.1\nwrapt                                    1.14.1\nwurlitzer                                3.0.3\nxarray                                   2023.8.0\nxarray-einstats                          0.6.0\nxgboost                                  1.7.6\nxvfbwrapper                              0.2.9\nxxhash                                   3.3.0\nxyzservices                              2023.7.0\ny-py                                     0.6.0\nyapf                                     0.40.1\nyarl                                     1.9.2\nydata-profiling                          4.3.1\nyellowbrick                              1.5\nypy-websocket                            0.8.4\nzict                                     3.0.0\nzipp                                     3.15.0\nzstandard                                0.19.0\n","output_type":"stream"}]},{"cell_type":"code","source":"%pip install opencv-python matplotlib imageio gdown tensorflow","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:50:47.909564Z","iopub.execute_input":"2023-10-23T03:50:47.912778Z","iopub.status.idle":"2023-10-23T03:50:59.856899Z","shell.execute_reply.started":"2023-10-23T03:50:47.912726Z","shell.execute_reply":"2023-10-23T03:50:59.855780Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Requirement already satisfied: opencv-python in /opt/conda/lib/python3.10/site-packages (4.8.0.76)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (3.7.2)\nRequirement already satisfied: imageio in /opt/conda/lib/python3.10/site-packages (2.31.1)\nCollecting gdown\n  Downloading gdown-4.7.1-py3-none-any.whl (15 kB)\nRequirement already satisfied: tensorflow in /opt/conda/lib/python3.10/site-packages (2.12.0)\nRequirement already satisfied: numpy>=1.21.2 in /opt/conda/lib/python3.10/site-packages (from opencv-python) (1.23.5)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.1.0)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (0.11.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (4.40.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.4.4)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (21.3)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (9.5.0)\nRequirement already satisfied: pyparsing<3.1,>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (2.8.2)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from gdown) (3.12.2)\nRequirement already satisfied: requests[socks] in /opt/conda/lib/python3.10/site-packages (from gdown) (2.31.0)\nRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from gdown) (1.16.0)\nRequirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from gdown) (4.66.1)\nRequirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.10/site-packages (from gdown) (4.12.2)\nRequirement already satisfied: absl-py>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.4.0)\nRequirement already satisfied: astunparse>=1.6.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.6.3)\nRequirement already satisfied: flatbuffers>=2.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (23.5.26)\nRequirement already satisfied: gast<=0.4.0,>=0.2.1 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.4.0)\nRequirement already satisfied: google-pasta>=0.1.1 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.2.0)\nRequirement already satisfied: grpcio<2.0,>=1.24.3 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.51.1)\nRequirement already satisfied: h5py>=2.9.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (3.9.0)\nRequirement already satisfied: jax>=0.3.15 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.4.13)\nRequirement already satisfied: keras<2.13,>=2.12.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.12.0)\nRequirement already satisfied: libclang>=13.0.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (16.0.0)\nRequirement already satisfied: opt-einsum>=2.3.2 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (3.3.0)\nRequirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (3.20.3)\nRequirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from tensorflow) (68.0.0)\nRequirement already satisfied: tensorboard<2.13,>=2.12 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.12.3)\nRequirement already satisfied: tensorflow-estimator<2.13,>=2.12.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.12.0)\nRequirement already satisfied: termcolor>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.3.0)\nRequirement already satisfied: typing-extensions>=3.6.6 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (4.6.3)\nRequirement already satisfied: wrapt<1.15,>=1.11.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.14.1)\nRequirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.32.0)\nRequirement already satisfied: wheel<1.0,>=0.23.0 in /opt/conda/lib/python3.10/site-packages (from astunparse>=1.6.0->tensorflow) (0.40.0)\nRequirement already satisfied: ml-dtypes>=0.1.0 in /opt/conda/lib/python3.10/site-packages (from jax>=0.3.15->tensorflow) (0.2.0)\nRequirement already satisfied: scipy>=1.7 in /opt/conda/lib/python3.10/site-packages (from jax>=0.3.15->tensorflow) (1.11.2)\nRequirement already satisfied: google-auth<3,>=1.6.3 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.20.0)\nRequirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (1.0.0)\nRequirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (3.4.3)\nRequirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (0.7.1)\nRequirement already satisfied: werkzeug>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.3.7)\nRequirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.10/site-packages (from beautifulsoup4->gdown) (2.3.2.post1)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (3.1.0)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (3.4)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (1.26.15)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (2023.7.22)\nRequirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (1.7.1)\nRequirement already satisfied: cachetools<6.0,>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (4.2.4)\nRequirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.2.7)\nRequirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (4.9)\nRequirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (1.3.1)\nRequirement already satisfied: MarkupSafe>=2.1.1 in /opt/conda/lib/python3.10/site-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow) (2.1.3)\nRequirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.4.8)\nRequirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.10/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (3.2.2)\nInstalling collected packages: gdown\nSuccessfully installed gdown-4.7.1\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"import os\nimport cv2\nimport tensorflow as tf\nimport numpy as np\nfrom typing import List\nfrom matplotlib import pyplot as plt\n","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:50:59.858394Z","iopub.execute_input":"2023-10-23T03:50:59.858707Z","iopub.status.idle":"2023-10-23T03:51:07.406716Z","shell.execute_reply.started":"2023-10-23T03:50:59.858680Z","shell.execute_reply":"2023-10-23T03:51:07.405911Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"code","source":"import imageio","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:07.408684Z","iopub.execute_input":"2023-10-23T03:51:07.409244Z","iopub.status.idle":"2023-10-23T03:51:07.474440Z","shell.execute_reply.started":"2023-10-23T03:51:07.409215Z","shell.execute_reply":"2023-10-23T03:51:07.473730Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"tf.config.list_physical_devices('GPU')","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:07.475482Z","iopub.execute_input":"2023-10-23T03:51:07.475812Z","iopub.status.idle":"2023-10-23T03:51:07.816515Z","shell.execute_reply.started":"2023-10-23T03:51:07.475780Z","shell.execute_reply":"2023-10-23T03:51:07.815634Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"},"metadata":{}}]},{"cell_type":"code","source":"physical_devices = tf.config.list_physical_devices('GPU')\ntry:\n    tf.config.experimental.set_memory_growth(physical_devices[0], True)\nexcept:\n    pass","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:07.817683Z","iopub.execute_input":"2023-10-23T03:51:07.818016Z","iopub.status.idle":"2023-10-23T03:51:07.824801Z","shell.execute_reply.started":"2023-10-23T03:51:07.817990Z","shell.execute_reply":"2023-10-23T03:51:07.824036Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"# 1. Build Data Loading Functions","metadata":{"tags":[]}},{"cell_type":"code","source":"import gdown","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:07.826082Z","iopub.execute_input":"2023-10-23T03:51:07.826631Z","iopub.status.idle":"2023-10-23T03:51:07.996140Z","shell.execute_reply.started":"2023-10-23T03:51:07.826606Z","shell.execute_reply":"2023-10-23T03:51:07.995171Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\noutput = 'data.zip'\ngdown.download(url, output, quiet=False)\ngdown.extractall('data.zip')","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:07.997198Z","iopub.execute_input":"2023-10-23T03:51:07.997721Z","iopub.status.idle":"2023-10-23T03:51:28.099564Z","shell.execute_reply.started":"2023-10-23T03:51:07.997693Z","shell.execute_reply":"2023-10-23T03:51:28.098657Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"Downloading...\nFrom (uriginal): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL\nFrom (redirected): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL&confirm=t&uuid=846f9093-30ec-4c2c-833f-60f5e0b92730\nTo: /kaggle/working/data.zip\n100%|██████████| 423M/423M [00:14<00:00, 29.6MB/s] \n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"['data/',\n 'data/alignments/',\n 'data/alignments/s1/',\n 'data/alignments/s1/bbaf2n.align',\n 'data/alignments/s1/bbaf3s.align',\n 'data/alignments/s1/bbaf4p.align',\n 'data/alignments/s1/bbaf5a.align',\n 'data/alignments/s1/bbal6n.align',\n 'data/alignments/s1/bbal7s.align',\n 'data/alignments/s1/bbal8p.align',\n 'data/alignments/s1/bbal9a.align',\n 'data/alignments/s1/bbas1s.align',\n 'data/alignments/s1/bbas2p.align',\n 'data/alignments/s1/bbas3a.align',\n 'data/alignments/s1/bbaszn.align',\n 'data/alignments/s1/bbaz4n.align',\n 'data/alignments/s1/bbaz5s.align',\n 'data/alignments/s1/bbaz6p.align',\n 'data/alignments/s1/bbaz7a.align',\n 'data/alignments/s1/bbbf6n.align',\n 'data/alignments/s1/bbbf7s.align',\n 'data/alignments/s1/bbbf8p.align',\n 'data/alignments/s1/bbbf9a.align',\n 'data/alignments/s1/bbbm1s.align',\n 'data/alignments/s1/bbbm2p.align',\n 'data/alignments/s1/bbbm3a.align',\n 'data/alignments/s1/bbbmzn.align',\n 'data/alignments/s1/bbbs4n.align',\n 'data/alignments/s1/bbbs5s.align',\n 'data/alignments/s1/bbbs6p.align',\n 'data/alignments/s1/bbbs7a.align',\n 'data/alignments/s1/bbbz8n.align',\n 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'data/alignments/s1/sgbxzp.align',\n 'data/alignments/s1/sgib8n.align',\n 'data/alignments/s1/sgib9s.align',\n 'data/alignments/s1/sgic1a.align',\n 'data/alignments/s1/sgiczp.align',\n 'data/alignments/s1/sgii2n.align',\n 'data/alignments/s1/sgii3s.align',\n 'data/alignments/s1/sgii4p.align',\n 'data/alignments/s1/sgii5a.align',\n 'data/alignments/s1/sgio6n.align',\n 'data/alignments/s1/sgio7s.align',\n 'data/alignments/s1/sgio8p.align',\n 'data/alignments/s1/sgio9a.align',\n 'data/alignments/s1/sgiv1s.align',\n 'data/alignments/s1/sgiv2p.align',\n 'data/alignments/s1/sgiv3a.align',\n 'data/alignments/s1/sgivzn.align',\n 'data/alignments/s1/sgwd1s.align',\n 'data/alignments/s1/sgwd2p.align',\n 'data/alignments/s1/sgwd3a.align',\n 'data/alignments/s1/sgwdzn.align',\n 'data/alignments/s1/sgwj4n.align',\n 'data/alignments/s1/sgwj5s.align',\n 'data/alignments/s1/sgwj6p.align',\n 'data/alignments/s1/sgwj7a.align',\n 'data/alignments/s1/sgwp8n.align',\n 'data/alignments/s1/sgwp9s.align',\n 'data/alignments/s1/sgwq1a.align',\n 'data/alignments/s1/sgwqzp.align',\n 'data/alignments/s1/sgwx2n.align',\n 'data/alignments/s1/sgwx3s.align',\n 'data/alignments/s1/sgwx4p.align',\n 'data/alignments/s1/sgwx5a.align',\n 'data/alignments/s1/srab1s.align',\n 'data/alignments/s1/srab2p.align',\n 'data/alignments/s1/srab3a.align',\n 'data/alignments/s1/srabzn.align',\n 'data/alignments/s1/srah4n.align',\n 'data/alignments/s1/srah5s.align',\n 'data/alignments/s1/srah6p.align',\n 'data/alignments/s1/srah7a.align',\n 'data/alignments/s1/sran8n.align',\n 'data/alignments/s1/sran9s.align',\n 'data/alignments/s1/srao1a.align',\n 'data/alignments/s1/sraozp.align',\n 'data/alignments/s1/srau2n.align',\n 'data/alignments/s1/srau3s.align',\n 'data/alignments/s1/srau4p.align',\n 'data/alignments/s1/srau5a.align',\n 'data/alignments/s1/srbb4n.align',\n 'data/alignments/s1/srbb5s.align',\n 'data/alignments/s1/srbb6p.align',\n 'data/alignments/s1/srbb7a.align',\n 'data/alignments/s1/srbh8n.align',\n 'data/alignments/s1/srbh9s.align',\n 'data/alignments/s1/srbi1a.align',\n 'data/alignments/s1/srbizp.align',\n 'data/alignments/s1/srbo2n.align',\n 'data/alignments/s1/srbo3s.align',\n 'data/alignments/s1/srbo4p.align',\n 'data/alignments/s1/srbo5a.align',\n 'data/alignments/s1/srbu6n.align',\n 'data/alignments/s1/srbu7s.align',\n 'data/alignments/s1/srbu8p.align',\n 'data/alignments/s1/srbu9a.align',\n 'data/alignments/s1/sria6n.align',\n 'data/alignments/s1/sria7s.align',\n 'data/alignments/s1/sria8p.align',\n 'data/alignments/s1/sria9a.align',\n 'data/alignments/s1/srih1s.align',\n 'data/alignments/s1/srih2p.align',\n 'data/alignments/s1/srih3a.align',\n 'data/alignments/s1/srihzn.align',\n 'data/alignments/s1/srin4n.align',\n 'data/alignments/s1/srin5s.align',\n 'data/alignments/s1/srin6p.align',\n 'data/alignments/s1/srin7a.align',\n 'data/alignments/s1/srit8n.align',\n 'data/alignments/s1/srit9s.align',\n 'data/alignments/s1/sriu1a.align',\n 'data/alignments/s1/sriuzp.align',\n 'data/alignments/s1/srwb8n.align',\n 'data/alignments/s1/srwb9s.align',\n 'data/alignments/s1/srwc1a.align',\n 'data/alignments/s1/srwczp.align',\n 'data/alignments/s1/srwi2n.align',\n 'data/alignments/s1/srwi3s.align',\n 'data/alignments/s1/srwi4p.align',\n 'data/alignments/s1/srwi5a.align',\n 'data/alignments/s1/srwo6n.align',\n 'data/alignments/s1/srwo7s.align',\n 'data/alignments/s1/srwo8p.align',\n 'data/alignments/s1/srwo9a.align',\n 'data/alignments/s1/srwv1s.align',\n 'data/alignments/s1/srwv2p.align',\n 'data/alignments/s1/srwv3a.align',\n 'data/alignments/s1/srwvzn.align',\n 'data/alignments/s1/swab6n.align',\n 'data/alignments/s1/swab7s.align',\n 'data/alignments/s1/swab8p.align',\n 'data/alignments/s1/swab9a.align',\n 'data/alignments/s1/swai1s.align',\n 'data/alignments/s1/swai2p.align',\n 'data/alignments/s1/swai3a.align',\n 'data/alignments/s1/swaizn.align',\n 'data/alignments/s1/swao4n.align',\n 'data/alignments/s1/swao5s.align',\n 'data/alignments/s1/swao6p.align',\n 'data/alignments/s1/swao7a.align',\n 'data/alignments/s1/swau8n.align',\n 'data/alignments/s1/swau9s.align',\n 'data/alignments/s1/swav1a.align',\n 'data/alignments/s1/swavzp.align',\n 'data/alignments/s1/swbc1s.align',\n 'data/alignments/s1/swbc2p.align',\n 'data/alignments/s1/swbc3a.align',\n 'data/alignments/s1/swbczn.align',\n 'data/alignments/s1/swbi4n.align',\n 'data/alignments/s1/swbi5s.align',\n 'data/alignments/s1/swbi6p.align',\n 'data/alignments/s1/swbi7a.align',\n 'data/alignments/s1/swbo8n.align',\n 'data/alignments/s1/swbo9s.align',\n 'data/alignments/s1/swbp1a.align',\n 'data/alignments/s1/swbpzp.align',\n 'data/alignments/s1/swbv2n.align',\n 'data/alignments/s1/swbv3s.align',\n 'data/alignments/s1/swbv4p.align',\n 'data/alignments/s1/swbv5a.align',\n 'data/alignments/s1/swib2n.align',\n 'data/alignments/s1/swib3s.align',\n 'data/alignments/s1/swib4p.align',\n 'data/alignments/s1/swib5a.align',\n 'data/alignments/s1/swih6n.align',\n 'data/alignments/s1/swih7s.align',\n 'data/alignments/s1/swih8p.align',\n 'data/alignments/s1/swih9a.align',\n 'data/alignments/s1/swio1s.align',\n 'data/alignments/s1/swio2p.align',\n 'data/alignments/s1/swio3a.align',\n 'data/alignments/s1/swiozn.align',\n 'data/alignments/s1/swiu4n.align',\n 'data/alignments/s1/swiu5s.align',\n 'data/alignments/s1/swiu6p.align',\n 'data/alignments/s1/swiu7a.align',\n 'data/alignments/s1/swwc4n.align',\n 'data/alignments/s1/swwc5s.align',\n 'data/alignments/s1/swwc6p.align',\n 'data/alignments/s1/swwc7a.align',\n 'data/alignments/s1/swwi8n.align',\n 'data/alignments/s1/swwi9s.align',\n 'data/alignments/s1/swwj1a.align',\n 'data/alignments/s1/swwjzp.align',\n 'data/alignments/s1/swwp2n.align',\n 'data/alignments/s1/swwp3s.align',\n 'data/alignments/s1/swwp4p.align',\n 'data/alignments/s1/swwp5a.align',\n 'data/alignments/s1/swwv6n.align',\n ...]"},"metadata":{}}]},{"cell_type":"code","source":"def load_video(path:str) -> List[float]: \n\n    cap = cv2.VideoCapture(path)\n    frames = []\n    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))): \n        ret, frame = cap.read()\n        frame = tf.image.rgb_to_grayscale(frame)\n        frames.append(frame[190:236,80:220,:])\n    cap.release()\n    \n    mean = tf.math.reduce_mean(frames)\n    std = tf.math.reduce_std(tf.cast(frames, tf.float32))\n    return tf.cast((frames - mean), tf.float32) / std","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:28.101152Z","iopub.execute_input":"2023-10-23T03:51:28.101508Z","iopub.status.idle":"2023-10-23T03:51:28.108462Z","shell.execute_reply.started":"2023-10-23T03:51:28.101476Z","shell.execute_reply":"2023-10-23T03:51:28.107497Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"vocab = [x for x in \"abcdefghijklmnopqrstuvwxyz'?!123456789 \"]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:28.111985Z","iopub.execute_input":"2023-10-23T03:51:28.112352Z","iopub.status.idle":"2023-10-23T03:51:28.117737Z","shell.execute_reply.started":"2023-10-23T03:51:28.112328Z","shell.execute_reply":"2023-10-23T03:51:28.117017Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\nnum_to_char = tf.keras.layers.StringLookup(\n    vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n)\n\nprint(\n    f\"The vocabulary is: {char_to_num.get_vocabulary()} \"\n    f\"(size ={char_to_num.vocabulary_size()})\"\n)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:28.118757Z","iopub.execute_input":"2023-10-23T03:51:28.119102Z","iopub.status.idle":"2023-10-23T03:51:30.218303Z","shell.execute_reply.started":"2023-10-23T03:51:28.119071Z","shell.execute_reply":"2023-10-23T03:51:30.217389Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', '1', '2', '3', '4', '5', '6', '7', '8', '9', ' '] (size =40)\n","output_type":"stream"}]},{"cell_type":"code","source":"char_to_num.get_vocabulary()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:30.219458Z","iopub.execute_input":"2023-10-23T03:51:30.219750Z","iopub.status.idle":"2023-10-23T03:51:30.227325Z","shell.execute_reply.started":"2023-10-23T03:51:30.219725Z","shell.execute_reply":"2023-10-23T03:51:30.226457Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"['',\n 'a',\n 'b',\n 'c',\n 'd',\n 'e',\n 'f',\n 'g',\n 'h',\n 'i',\n 'j',\n 'k',\n 'l',\n 'm',\n 'n',\n 'o',\n 'p',\n 'q',\n 'r',\n 's',\n 't',\n 'u',\n 'v',\n 'w',\n 'x',\n 'y',\n 'z',\n \"'\",\n '?',\n '!',\n '1',\n '2',\n '3',\n '4',\n '5',\n '6',\n '7',\n '8',\n '9',\n ' ']"},"metadata":{}}]},{"cell_type":"code","source":"char_to_num(['n','i','c','k'])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:30.228394Z","iopub.execute_input":"2023-10-23T03:51:30.228742Z","iopub.status.idle":"2023-10-23T03:51:30.240875Z","shell.execute_reply.started":"2023-10-23T03:51:30.228718Z","shell.execute_reply":"2023-10-23T03:51:30.239929Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(4,), dtype=int64, numpy=array([14,  9,  3, 11])>"},"metadata":{}}]},{"cell_type":"code","source":"num_to_char([14,  9,  3, 11])","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:30.242215Z","iopub.execute_input":"2023-10-23T03:51:30.242791Z","iopub.status.idle":"2023-10-23T03:51:30.256665Z","shell.execute_reply.started":"2023-10-23T03:51:30.242760Z","shell.execute_reply":"2023-10-23T03:51:30.255901Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(4,), dtype=string, numpy=array([b'n', b'i', b'c', b'k'], dtype=object)>"},"metadata":{}}]},{"cell_type":"code","source":"def load_alignments(path:str) -> List[str]: \n    with open(path, 'r') as f: \n        lines = f.readlines() \n    tokens = []\n    for line in lines:\n        line = line.split()\n        if line[2] != 'sil': \n            tokens = [*tokens,' ',line[2]]\n    return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:30.257594Z","iopub.execute_input":"2023-10-23T03:51:30.257823Z","iopub.status.idle":"2023-10-23T03:51:30.263525Z","shell.execute_reply.started":"2023-10-23T03:51:30.257803Z","shell.execute_reply":"2023-10-23T03:51:30.262649Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"def load_data(path: str): \n#     path = bytes.decode(path.numpy())\n#     print(path)\n    file_name = path.numpy().decode('utf-8').split('/')[-1].split('.')[0]\n    # File name splitting for windows\n#     file_name = path.split('\\\\')[-1].split('.')[0]\n    video_path = os.path.join('data','s1',f'{file_name}.mpg')\n    alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')\n    frames = load_video(video_path) \n    alignments = load_alignments(alignment_path)\n    \n    return frames, alignments\n#     return file_name","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:30.264535Z","iopub.execute_input":"2023-10-23T03:51:30.264775Z","iopub.status.idle":"2023-10-23T03:51:30.272213Z","shell.execute_reply.started":"2023-10-23T03:51:30.264754Z","shell.execute_reply":"2023-10-23T03:51:30.271486Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"test_path = './data/s1/bbal6n.mpg'","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:30.273138Z","iopub.execute_input":"2023-10-23T03:51:30.273379Z","iopub.status.idle":"2023-10-23T03:51:30.283640Z","shell.execute_reply.started":"2023-10-23T03:51:30.273357Z","shell.execute_reply":"2023-10-23T03:51:30.282908Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"# test_path = bytes.decode(test_path.numpy())","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:30.284553Z","iopub.execute_input":"2023-10-23T03:51:30.284805Z","iopub.status.idle":"2023-10-23T03:51:30.292141Z","shell.execute_reply.started":"2023-10-23T03:51:30.284784Z","shell.execute_reply":"2023-10-23T03:51:30.291350Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"# hi = tf.convert_to_tensor(test_path).numpy().decode('utf-8')\n# hi","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:30.293091Z","iopub.execute_input":"2023-10-23T03:51:30.293347Z","iopub.status.idle":"2023-10-23T03:51:30.301391Z","shell.execute_reply.started":"2023-10-23T03:51:30.293324Z","shell.execute_reply":"2023-10-23T03:51:30.300616Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('/')[-1].split('.')[0]","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:30.302568Z","iopub.execute_input":"2023-10-23T03:51:30.302958Z","iopub.status.idle":"2023-10-23T03:51:30.311738Z","shell.execute_reply.started":"2023-10-23T03:51:30.302928Z","shell.execute_reply":"2023-10-23T03:51:30.310915Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"'bbal6n'"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"frames, alignments = load_data(tf.convert_to_tensor(test_path))","metadata":{"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:30.312790Z","iopub.execute_input":"2023-10-23T03:51:30.313076Z","iopub.status.idle":"2023-10-23T03:51:32.974224Z","shell.execute_reply.started":"2023-10-23T03:51:30.313053Z","shell.execute_reply":"2023-10-23T03:51:32.973448Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"alignments","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:32.975321Z","iopub.execute_input":"2023-10-23T03:51:32.975599Z","iopub.status.idle":"2023-10-23T03:51:32.982230Z","shell.execute_reply.started":"2023-10-23T03:51:32.975575Z","shell.execute_reply":"2023-10-23T03:51:32.981426Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(21,), dtype=int64, numpy=\narray([ 2,  9, 14, 39,  2, 12, 21,  5, 39,  1, 20, 39, 12, 39, 19,  9, 24,\n       39, 14, 15, 23])>"},"metadata":{}}]},{"cell_type":"code","source":"plt.imshow(frames[40])","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:32.983410Z","iopub.execute_input":"2023-10-23T03:51:32.983678Z","iopub.status.idle":"2023-10-23T03:51:33.257238Z","shell.execute_reply.started":"2023-10-23T03:51:32.983655Z","shell.execute_reply":"2023-10-23T03:51:33.256282Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7fbe9dbc38e0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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7Xsy6zn6NgN8PBBd+VAURVEUpabox4eiKIqiKDVl2ppdvDAk77dLcJWaU6oxH4znuImEm1rQtOA8b5ztuZbtXOYb19hnIHMtv6a8XKIOkzzj6xhmLm4VcK2SVnO/XfOEZ6N1ZfHFc6uJlMrbd52HmlURRRSzwVowiiVmrhVL6BVGTX2/nvg6celdwJbaQ+j3QChlmlzuiplc0SzBEQmOoeetzFzTBOYSUUdSnodmnv6ENQOFYD7gEl7sNzfnYOZaLuedx6TE7/fbXm8RzCW8dXyE6uHeDLPbjdFvMxVamTHabZ+xJoJ6Dw028aBpxUWWjWPKwzL7i2rad74XqglPwKuMmG7t3eGyW2w/2YFzyJa1+PKaqskwfSKiKx+KoiiKotQU/fhQFEVRFKWm6MeHoiiKoig1Zdr6fJAx1lY2Xr+O6Sav5fC+laZARuWSnLG+ea6+RSTCLCx7/6AoShTnxlbjlUB6WmE4ZJRi+9wO67j3Efku74sji+77BzjCqzO5Y2TcHGHpXRk5uUzvHegbl8HW++iPIfvNpbY5V0hth38A1snltSgD5vbqsSTBWeaTcWpCzptWf6S8PQ8yt7b4LGQ/zIUkcxYagQysx0JbT0dC9nsoHIZjbRu9oczUfLhoJcLo11HPfEc6wK+D+0DMheew3rNtfGrhclFWzTvr3w/tLG/z6yWS2VJRPj3AjkUfn2Exh+SYNojw/egPIXfDiIjVwmcYZlHm4f3R38hwmT+EMPf4/lg+Ho5ng8t0w9IwFLK+OtJ8oK8IlyXPS8iySObeGUbVKx9PPvkkXXHFFbRgwQLyPI8eeOABUW6MoW984xt08sknU11dHa1evZpeeeWVieqvoiiKoignOFV/fAwNDdGHP/xh2rx586jlf/u3f0vf/e536a677qLdu3dTQ0MDrVmzhnK53KjHK4qiKIoyu6ja7HLppZfSpZdeOmqZMYbuuOMO+su//Eu66qqriIjoX/7lX6i9vZ0eeOAB+uxnP3t8vX2/kfGVHc+x0wmxfF9FZFSXnBflnezYSFZbhhc45G6wfOmVWITNNNQJyUETw3YJO8QMtK5ooBwcG4cpyaRTo26/fzBk7mUaR68YvywayZzL++NYPjdwLwJn1lFbzzDM55aI9NYfdZtISiqLsGTOF7dbfGl2WLPg3PL2fYf+S5QdY/LhgVCOKWZrnZcYKG+fnpTmi4xn58qnF31MlPFxxEyiRwNr6uiFIcyxTLUoA/Vhv55JfYueNDWcmj5a3m4AswuP1InRR+t920YPzOc23/6T9uDhvaLs6lP/d3nbS0u5sinIvv2fpR+3ZSXZNz5WoZHmg7fZ/BsO5HPa5GMcWQs3sw2Y+Gy/REQNbEyzIFHmcxrNg/XM1IIyVBwPjsgIG8j767nei/juK9p+e0k5p4XZp6FBlhXix43LzotgyopGDY4H3xuV4rG/GeOtY7xMqMPpa6+9Rt3d3bR69ery71paWmjVqlW0c+dOx5mKoiiKoswWJtThtLu7m4iI2tvbxe/b29vLZUg+n6d83jpn9ff3T2SXFEVRFEWZZky51HbTpk3U0tJS/lm8ePFUd0lRFEVRlElkQlc+Ojo6iIiop6eHTj755PLve3p66Nxzzx31nI0bN9KGDRvK+/39/bPjA8QVxtsVipv7DmA4b99RJ9rz+Lnog+Dyq+Dn4XHcV6Qo5X2JQbu6FTSAfRYjirskhZWGQ3ZllXXZeVEii74bvNpqbKS83yCv9JifiQdh8BPMPyMBEkVuvsYMpC4wbDa3raPUloe47gtHRNm/v7mLtS/71sbmaXtCVpoBO/8Vp3SVt9F2H+Z4iHF53g8OPlXe/g10nPuZ9IMPwrGgsbydA3+UNpD68oy0eOwcJqGtBxnwMZad14cswsPsxr0bSP+AbtYehnPf9NKO8nYL+F/MS8hX+e//r4ts+42yjUs/aH1HHn55hygbYj4gCWgjx7LsYqbaIRZqvmhkX+ohZD0/15XxlmBME2KeSv8Iw1bPKSXnkOc7JKuurNkgURZ+NfAe8pgfk8vHA33huM9RBkLG542jngmi1n4enAld+Vi6dCl1dHTQ9u3by7/r7++n3bt3U1dX16jnZDIZam5uFj+KoiiKosxcql75GBwcpF//+tfl/ddee432799PbW1t1NnZSTfffDN961vfotNPP52WLl1KX//612nBggV09dVXT2S/FUVRFEU5Qan64+Ppp5+m3/3d3y3v/9Zksm7dOrrnnnvoK1/5Cg0NDdEXv/hF6u3tpY997GP06KOPUjabjatSURRFUZRZhGfM9Ap40d/fTy0tLXTxBzdQMuHWi5/QuPwaivFpxMdNhSHLiUjaQR3p3z1HP82xXrnfuaC8XZoj40XwGCBERH7e1htmIFQyi62RGJJ2X29I+iTIDsTHRxGxPVzh1ImkL4nDj8YZXj0nbeCmaG27ZkQG4wuHrQ3+R29KufqbbPjrwY+iDfxKXOHVuWV5KJTX3+DH+//wFOeNYK/+9KKV5W0f//FIxceH+dmBX4j93xStD8YQ+BIcKrXaslC+K7h/xgCERS8y34W+Un1sX4ikz0cKxpjvo19HPoy/Rn4s7wsRUZbFwEA/mtYEhPRmdCT7xP7iZG95exH8i/nZ036X4nj4NzZey3vg43OMXSLGbuG+KwkYCww9z8cN46zwfYwr0sT8LFIwNs2+nWOXn7JSlInw6o65R0RE7FmMvCeYP5YH74WQ+Xl46JfG/Lb8BjkX/++z/1HeXpmRfRsO7fvtmiUfdXZ7Qnw3HDGFKqVkivSEeYD6+vrGdKGYcrWLoiiKoiizC/34UBRFURSlpkzfrLYzHW6ywKV9l5yWgxazakwrHJf0FOFtONpDa57PTCJek1wiDzNy6Vko86AeHl6dhzonguSZ48xqO2bY/cAhUXbB68WMwiXHXKiwjRxIFjFbKV/ARjltgZ2bBzNAmi3FpqArGbac/OnFq0SZX8+Wl2EO//tL28V+T2CXl58YkUu1B4tLyttoPnGZTN5jZWjaSLDQ3G8XGkVZKZTHNiRt3zJ+vJkRM7XmWWjypI/yaXsDMJw7N8n4HkiUWftZMEm8XWoS+y97NtQBmmu+9dKT5e3TUihDtc9miy/NZQmyD2aKClBm+/p6aY4oG4L8Cc0shDzPWkwkrxnnm+stVWIybDRBiGoCd6ZY/t7yHHL9iEKYPScGTJd+1s4FDEGAc3M2oSsfiqIoiqLUFP34UBRFURSlpujHh6IoiqIoNUV9PqYj3D8Aw7C7fEVcIdMRl5w2ri9V4GFqetZvPyftnoWTZDjk5BDrOzYv/GEc9luUnvHrHa+vBp6LkjqeOh3P4/3B9pkPiIeh19k+hlfng4M+BxiYmUto8Vie/h19ELikEkN68/48/ObTooyHhn4VXCUeHpaJJ48UT7L9Bht4d77F1gnyzpHA7r9XkP4gnNOb3hb7XOrZWfdebBkR0WBg/RWOFWSY8qHAzlsct/6i9ZdIg69INmH30R+kEDJfEZCslpijQV1C3gs81sULqYXl7SXZd0TZhzKHy9unpWSSzzbfXm8rXNMAl3LDfSpU4dfAQ7HnDD7fto0sPApFfuxxSEaFhBZ82niTxvHO9FIQHoC9Cw3I7FFqzKn307FlMwFd+VAURVEUpabox4eiKIqiKDVl+ppdgpCIjj/iWkVUIzWdKFxL/66stnHHjVXmylyLZfzcatrgQIZdk7fSvMR7A7Jr7VIyya0XXsR8UeGcwPO4hA7G1LgyDEdk0Lwv8W1E7psog+VkLuGDfnPZIGau5RlBUZaJctoi6zhmEkWTAYdH2SyCuaaXZf0sGrkMf6Bo5Za/HOkUZUcLUhY6Etrl5SJIXV8fbIvtW75kX1+9I1IW2lpn5ZwXz31ZlC3LHGH9lq9AH945PAPugcTJooybrxampfmGMxjIvnHTEo5FT95KjVNgkkkxOelAUcpXUSKcZqYdnBt9zCT0Vq5FlL2enVveXt7wuig7M/NWeTvroXku3kQwDNFn08y0FcL/v0PG1pMmGbU1JaLNyrno83o8x/s8kgm8imNdVGieNiX5nGAUW86aheeVt70ZqMjVlQ9FURRFUWqKfnwoiqIoilJT9ONDURRFUZSaMn19PmpJpf4XozFen4xq5J5xVYDPgUHpZ6U4/CPGew0RqS33cwC5WeQ6UtbAideUHGHZI9F3otJ+I66MkPh5Hsa3EclkG4NBWzK3A0f8QZh9HNoT9mK4hBxJI3GO+RnkjLw3RUcZb+MwyyL7/nn29fFuIMOUv56zvgMHR6TfRgl8TgqBbf9YTspZ3xu2EtpSEP+/UjYtpad1Sbt/tChDts9LWglpCH3BsOV8POoTct62s0yyF9a9Lsoy7NYcCaTPQ3fJ+lmclGwVZQGbcI3QHpcBHwikXLl7SF4jnw7ZpPQz4GNTAIluj2d9UPaapbH9rvdleHVOXyBlzzjGnFaCTL08AgD4QzTwrLbg15Hx2J8ykOCbkuO9iNJ2xzvNJa8VRGT+to2fvrFHFO3N22Mv6VwBDU5CdvNphK58KIqiKIpSU/TjQ1EURVGUmqJml9EYZ1TPqpgAs0ukSodJwIw34+0EwZcsTS4nypKDEI+TjQ1mrhXj5jJzoPmCX//xyNa4GajkkNPiHOL9CXBZ2C6vmoJczn7k8DPl7XdhOdcVHRGXrLncsTeMzwbbG8iyAbaEfmC4Qx5btGXDJWmuGWByzuEiRLwsydfOUM7KKxeeJOWV85sGy9tHB6Rph1/9+fMPi7KGpDVZPNe3QJT9ZmheeXtuZlCU8X4TEQ2U7LgNl6Sc9AONMjpoHCjn5aBJ6Ln3bF8xaik3l7w9Is1Tb3WfJPZNaO9/MiuX75sabSbZejBXcQlvz7Ds20u+vf/ZBMbQtczJSFMKvxdEREVmVg3g/985NMjK4t+RGU/OKZ7Vlj8zREQhmykp0KyiqcNgxmleVmLXDGafhw/vK29fcYrM8MzNMG8FcmxSHptTUKeXnNl/nnXlQ1EURVGUmqIfH4qiKIqi1BT9+FAURVEUpabMbKNSpbh8B8YKU87Lq/Hj4HZ/PM9VD/NdMN7U+qaYZPy3q1cE+2U9s6WDFM4vyP1SoyOboytzL/frwDIuZ8Vw+qxOk5FtR+S8ruy0fN81T1CyV7S2ZAPtcXt1BmzC9Syz6HAoH+XQyPa5/XwIwl0PsPDfA6H0eXinaKWXR/PS5+JdJovNgR9Hgs3Nfgh9PjIs229rtXb+89sOibKWhPVPeDolw7QPMf+EK9qeFWU8hPddg78jyrgPwslp6WOCElLfk+HPOUfztuz/L5wVe1xvAaWn9l4MF+V849LifAHGNGHnTRiifwCkDCgk2LFyLuSLtt50Us43fmfyQRLK+DMkikSmXpRSY+h3Pt8wtH9rwvpEpCHUfYE9Q4Oh9COp960PSOjwhYrgCsXukPJ7kJLhU4tXlrcTLXLOmLztaxbeC6elbBv47IvsuK5wAHQc4ddZBmADc+p4sgNXgq58KIqiKIpSU/TjQ1EURVGUmqJml9GotdS2Fu25opjWoj2+gleU0j9/QEpvfSbFI4zayutxZJ00RZACMgldxJDkMnONsdwpG3VInfk+XpNDBl00AdsOocyelwM5Zz+YTwZCufTP4ZEz5yUHYssSsAw+n8lUu3NyqZmbFrhElIjoWErKeZsycgldtM9MS50NMnMsN5mcnzkaW8cn570o9k9N22PPzxwTZd2BXL/en1tU3n6nJKWn7SnbftaT18gly7/JzRdl9Qlr2smFUjI6yKKhvtQrpc0fmfNGeZvfFyKiH7/2YVnPoL3/zU0joqyFZfxtzshnrzkl9zknZ+31LslKmXGTb9vAccJr5NmA+f0lknMMpbY8U3MxklXXjilGP82y6KeBQXMovEPYsGIGWud5fB+e73BEjn/FuN49E2QSMdW83yYYXflQFEVRFKWmVPXxsWnTJvrIRz5CTU1NNH/+fLr66qvpwIED4phcLkfr16+nOXPmUGNjI61du5Z6enomtNOKoiiKopy4VPXxsWPHDlq/fj3t2rWLHnvsMSoWi/R7v/d7NDQ0VD7mlltuoYceeoi2bt1KO3bsoCNHjtA111wz4R1XFEVRFOXEpCqfj0cffVTs33PPPTR//nzat28fffzjH6e+vj66++676d5776WLLrqIiIi2bNlCZ5xxBu3atYsuuOCCyhtL+FYSWWk2wWrgckuU2rr8IcZZ5spAi2WRFKXTFkc/Uc7KAZuolwd5Y87avcOsnKKew89D+IAUwOeD25bTIOXlfa3mc7wKabXHJdKuAx1zqOjIaos+HhhC3RXiu4nJWeckZLjxczNW+jpUJ2333SzL7bOpU2T7RdY+uJv010upLZdiDpVkWZ5JiDH0+bK6tyiONLs3p6e7RdmchP1nqRHCdM8Dqe2StPVtSICfwdkZG9K9yZP+AW8zOTOGEF+efb283QbtvcH8JeanThdln2r+ZXl7GPwoXph7sth/PWUzCXc0Sj8e7tfRmpL+CM1Ju49ZdT+YteO9JBUfWv5t8BvCucl9QHBe+iykPM+2jESSTfPtSHgEu5kAf5CIz4PDl2Lbkf3l7TWLlouyR9604dWvXH6pKHvwTZvJNoCs0Qk2T4W0FsGUDFX028kkZ153cVw+H3197zsgtbW9P9H37dtHxWKRVq9eXT5m2bJl1NnZSTt37hy1jnw+T/39/eJHURRFUZSZy7g/PsIwpJtvvpkuvPBCOuus9wPsdHd3UzqdptbWVnFse3s7dXd3j1LL+34kLS0t5Z/FixePt0uKoiiKopwAjFtqu379enr++efpqaeeOq4ObNy4kTZs2FDe7+/vr/0HiGt56TgkqVFzSkwTaIaYDPlTLeS8HLwGlJcyzAhKbdm0TMrsndwEFxkntm/weot22dJzSegQ7LcrGu54cZmSGPW+XIbOmsr7kmJmAZSF8v1WXy7Dt7PspQHJ85qYyWAolKastxIyyyoHl9O78y3l7ZFALkvnWebcHETcfKdk5b2/huyw/JqOlGRfuku2vXrvoChLO14FGBmWS52L8A7hUlvMXFvMxJsTuCmtKSGfi7cD+ywcLsprOikts6VSS3xZR8auLvMIskREc5O2bD6YTxYne23ffDn3Ava4NflSvlyEiKdDzNRyLJBRc12ZbDkJOK6qqKYuKnxPbmNmlvdhZlWo4+3Amq/mJeQc8qez4HQ8fzOqOGdcHx833ngjPfzww/Tkk0/SokVWB9/R0UGFQoF6e3vF6kdPTw91dHSMUhNRJpOhTCYzapmiKIqiKDOPqj67jDF044030v3330+PP/44LV26VJQvX76cUqkUbd++vfy7AwcO0MGDB6mrq2tieqwoiqIoyglNVSsf69evp3vvvZd+8pOfUFNTU9mPo6Wlherq6qilpYWuv/562rBhA7W1tVFzczN9+ctfpq6uruqULoqiKIqizFiq+vi48847iYjod37nd8Tvt2zZQp///OeJiOjb3/42+b5Pa9eupXw+T2vWrKHvfe971fcsCKksoJoMf4VqMtnOBMbKzus6diLgIcSTIJ/F8OKDzEbdCj4frG8G5LzeSIW+HHh9XMqdhkcC+8akgF7R0Z5rfFE25xjvFEtXWQQfjxSzc/NsoERECS/ejyTq82Gvo8mXZS6vko6ELT2HyU6JiBoc/iBpCA3eyHwb3ivJ+92dt/4SPlzTO0XrL/DU0P+KrfOtQqso4/JdHDeEy4lfz80VZc3MPwbDnfPswDyLKxHRb4o23Hp3IOWs3UXrrNEDviLvlJaVtwPwo6hLyPs2r9GGkMe+nZa1QR87mB8HkfT5qQf5cINvxx+zs3LXqOjMk7/xA9vXXijLGWuCHzJpKMuzbXlewvG8jdevgktriYgC1uZlILUVfZHR9MUVXrloJcXhOZJ5z0Sq+vhwvSR/Szabpc2bN9PmzZvH3SlFURRFUWYu09jVVlEURVGUmcj0zWprTO3MH5NlgnCeO7nR46LNee79icCVuZWXQcRaA8vChoXr9+aDZJPV42HkW16WwEiGITtM3hePmeCwzCRABg2BU+Pad2URNnj9zHzzyOFnRFnekdWWk4UxzLIonlieqEKWyJfXMVtoi29lsPWelIWmPLu0/y5k1E2DMWdx6t3y9tuBNDUc8G3kzr6SrGeYmXMO5eQ8ybCItiWQ9vLMpnuGTiUX3ETTX5Lmk1/n22PP43JiXgcR0dtMItzryUi0/JpQdswjpdZDZFSMRtqStOakxSkpfT09be/NvISsh2eOxf9M0dTC4dJXFBIXYb4VWc2h4/9fNBfxbLg5mMJZ1kYK+pk3jocWnyl2bgBlI4aNlcOMbYalfDmHmXTjAHMsz8Q9E9GVD0VRFEVRaop+fCiKoiiKUlP040NRFEVRlJoyfX0+pinVZac9QeE20/Fm+EX7MD82iXJWeSz3gYhYS1nodQO2XHEs2ku5LBZtqyGXz4L/icvuinJt7leCWXVdWSdZGYaJLjL/CFcQdvT5QL+OJjbGPowqz5Y7APbpYVbW5rD5ZyA7bL1n7d5DTkcZImKyUZSQtjApLJalWIhvnhmXiMhnGWh9mKd1zM8B/TG4rwiR9KXwIastl9CGMG71rI2WpPQBGA6snBQl0fwam5PSj4aTBUl0C0iGebbYJgihnmJtHgO/kgL7f7QBpLY8nD+G+ueZZH2YJwGkD+Dh1jGcOk8DkECJLpvTrmchgLnvjzP0OmbAFdXA9YvnuyjvTSurxsOM2vxvBrwjPPbuMfjOmgHoyoeiKIqiKDVFPz4URVEURakp+vGhKIqiKEpNOTF8PrjNdrLjU4y276BiPw9HnVPiKzLe+CXjPY/HtsAYIOhHw+ybPvhgVHz/8ThuM0V/DO67AX4cnqs99FUpMBt5iP4gPM5JvP0WYxLkmR0Y/1NoYO0PwBiiLb3A7hvGa+ChqbMReznflj3gfcXQ77xvIckYFDmIu/GusfE70AdiHkvrnjPSPyEMbBvz0jL9O/flGCZpZ0+yMOEYL6Me4mVw3xks43EoMGQ9L0uB70SOhQ3vC2TskpOSNj5LPpTXy+tB/xMe6p2IqMDGGH1lhlm9Wehbmvh8wzllCeE9UO/bOnMGr5diafWlrwoft1a4NymHzwefw1gm+oqPs+N9hs+iK87OtsPPxpa943LXcPmCVRPng/unuOqshkp9/8aJrnwoiqIoilJT9ONDURRFUZSacmKYXTjjXP5xmjYmK4y7y9SCocHHUUdVTJS5arwyXG5qwHsBJgqPmzMgc2zYaJep/TA+NDKGcBddA7OHV2JtQCZNrMdzZEPm2XkNjrfrM5+NFcoEOXhFOXGe+/7ycxNwbJYt2aaglUAsdaNk0rBtzE5aed+4pBLNF/3GylnbEoOijC/R95VkmPKQzaGT/PiMv2iSQFNHhklaUb7M91H66rNrGg4zoqyJZdxFiS4Py57x4yXKDb40ARXAlBU6Qpij+YrDxzQNJrAGlJ4y+kJrIsGjinD/AxGKPX6+F+HeJIQpSx5b6X/RaErBdAZcXhvAmGaYqcsDk+uaBefG1lnPZOgemFJC9n5LNMqMzoa9lyISXcyobeQTLovi5bwC198IV+iEcaIrH4qiKIqi1BT9+FAURVEUpabox4eiKIqiKDVl+vp8GHPcdqVxS1gnys/CJRGebPnwWEyWn8tvwbH3+Xcu+E1gqGIXCYdfh4m3bQr7KVRp2HnR8PlVjJNoH+rJWRt9mJP2+vvf3FPezsN5rv8OXGVoS+dW/ohfCRsrTH+e4f4gHoaUtvdRXhFRwKrBvmD7DczPI/B9OJZJKMEHoJWFFG+NhBePl5pyn4eicc893ncffSCY34Uf8Yexr1ZMDR84UqxLnwtp169n7aFvDG/v/X17Xb2B9IfhzEv0i/0m1j766gyxeeIaNfSKQg+TJtb3osOPJFpvvL/ZANtPRPxBbFk6MhdBPu2YD4PMX+Snb+wRZe+GXOos5dMC8FPzeLoIlOA7/Ou8hBw3oXSfKKmt62/UBMhwdeVDURRFUZSaoh8fiqIoiqLUlOlrdvG84zZHmAo/rbwAlo2qabfCYw1Is1ySzRlJ0i5neri8CPIzD7PeMkyS3dTU+KYvN8EQEXk84mkaJGywvFmxuSwita0sAmEKlqH5kjHKWdOsDTTX4JL5ADeDREw78cumvs/bLMUel4rId22dGOEy4aFJiC3nwzWiyUSW2TYbIBomN0ugtJS3UQBzBUpthanDIX1FEtyUBGYWLn1FkwxvIxo11Y4/ymeHQinF5FFUUc5bYGZONNfw6KQ4L7hhKwVlWdYE3u9oNNJ482jI5lEKzFy8TTSt8Fc4vs4hT3VsnUREIWszama0m5ctWi6K7nnjF7Zs8UdE2SOH9todMCuKzLVgjvXS8ZLocRMxc1UovY1k+D1+046ufCiKoiiKUlP040NRFEVRlJqiHx+KoiiKotSU6evzUUtctvvjkaQKCWdlx43Zn+kM7zdmrq3GP4LZQT0II8zDlgv/D4pKaCtFyHDRH6QEdl/uq4NZbbn0F6VxDvtpxrOPYQg26KKQs8pJxGtpgr4E0D63w2O464JD+pkVtl3ZPoZpj6Ma6zDKcoXfA/yrFIRW0jgQZkUZSl/jQB+PiYL7eaB/hugbDGGO9ScHIlUeFj4H/cbr57LkuUmZ8ZefO+RLX5H6iGjaInxXHNlhxxp57meRAv8fnJtxFOGVmWGnYfvoA+KuN773PGXAw28+LcoO8tdGFSHM+XvheIIfcOmtQcUu89syGAJhMrLhVkhVKx933nknnXPOOdTc3EzNzc3U1dVFP/vZz8rluVyO1q9fT3PmzKHGxkZau3Yt9fT0THinFUVRFEU5canq42PRokV0++230759++jpp5+miy66iK666ip64YUXiIjolltuoYceeoi2bt1KO3bsoCNHjtA111wzKR1XFEVRFOXEpCqzyxVXXCH2b7vtNrrzzjtp165dtGjRIrr77rvp3nvvpYsuuoiIiLZs2UJnnHEG7dq1iy644ILqelbLCKfVtDNRx051hFOOq5/VmEtc8GNBWhtZiuTljr6FGTl9fYdEl4NLj17RLu2bopQ3YhZKQpkwh5ts4DgRRRUkwkK2B9e77cj+8vZgEC/1rEdZINwabj4ZgOXVIbacPgzSy6yxElbMJMrBqKVFUSZPHA5R3hl/37j0NBfEm0hQkhs4/q/i0tsURBFF6ak4rwoTDW8fTTsi+iqMDZfeYkRXV7RXV6RWLOPX7LreqCQ63rQipK/wyOK8ybLnHU13OTY38UkTbVbxGubt4yj58O4J2fOX8eR9y7J740O/53JTMWSg/dSpH2Wdwciktk6MWireISDRjexjhm8OM614fvxxJsQ6+R2YeJPMuB1OgyCg++67j4aGhqirq4v27dtHxWKRVq9eXT5m2bJl1NnZSTt37oytJ5/PU39/v/hRFEVRFGXmUvXHx3PPPUeNjY2UyWTohhtuoPvvv5/OPPNM6u7upnQ6Ta2treL49vZ26u7ujq1v06ZN1NLSUv5ZvHhx1RehKIqiKMqJQ9UfHx/84Adp//79tHv3bvrSl75E69atoxdffHHcHdi4cSP19fWVfw4dOjTuuhRFURRFmf5ULbVNp9N02mmnERHR8uXLae/evfSd73yHPvOZz1ChUKDe3l6x+tHT00MdHR2x9WUyGcpkMtX3fKpAW9tkZ4etBeP1OZkkibDwj4DMtX6J2YSzMH15OGIv3rYayThbsv1Gqa3Jy7DdLjkahm2Pw8NxStp+Y2bLNQvPK2/fd/A/Y+tE+zTapDPcfh9KOWXR4/4gGAo8npRDpsf9A3JwHvo5FJglfjiU74KCiS+TvhPyfid4yHbQuYfMtl0ELwD0jwhC7p8QH8Le5YOB/ijDLBQ6hlfnfXWFlg/HkKTy0OgYXj7FtJjo85Jm4+YKoZ5wPOuFyHkYer+yLNY5g9Juiys5RURa6/D5CCOSdNYmPCe9rKjeg/eCrFTs8tsYGTXefjV+HONlEsKkj5fjDjIWhiHl83lavnw5pVIp2r59e7nswIEDdPDgQerq6jreZhRFURRFmSFUtfKxceNGuvTSS6mzs5MGBgbo3nvvpSeeeIK2bdtGLS0tdP3119OGDRuora2Nmpub6ctf/jJ1dXVVr3RRFEVRFGXGUtXHx9GjR+lzn/scvfXWW9TS0kLnnHMObdu2jT75yU8SEdG3v/1t8n2f1q5dS/l8ntasWUPf+973xtezWma1rWblqVL57FhlUy2vrbT98UqLsf5qxoYtN0akrwWW2bNZStpMnV2W94ZlGV9u3HZ4nyi65JSV8X3DbMQsGmmkbzxaoeOaXKC0l0crrPcrl3omYHn1sgXnlrcfPLxXlPWy5WWXaQFlkT5bOM1EsmXa+4TL55itNMuEuRjVM2eybBuiegZ1VAloSulznFeNnJZni8XMsdwMhGXcROPDWPjMPIYmGb7vktYSEZ2UtDloMTvuvKRVFM5LyOin9b4jczGb35h9WRwHc4hH8CWSJhsfFt/53AhCadrgEUYxEmqDiHCKWZvjQaltpRLeOk++X/jz5sxGOxmmFAAluyaosE00wbiy2gr4eV7FY1jVx8fdd9/tLM9ms7R582bavHlzNdUqiqIoijKL0MRyiqIoiqLUFP34UBRFURSlppx4WW1RjsRBexq3WU2UJLYaX4bxcqLUWU0bLrsjnsflpnkpd/OKLPx0ErLKpliI4zrIcsrssEVI+2hKPLy6tNd6KfAdSbNHBqW1LGx6JBsvn7eOEO0oteV22GREKGhBO3cENm+xngbm19LnSEmAIdS5LR99TEL2LGYjTlVybFJcFpsYim0f/TGKQoabji0bLEmJbn8p3ucjHzrGGJzICuzYqF9HvAyX+3k0puT8zvjBqMcRETUm4jPOJiFsdj07dkn6HVHG/TxafelXwUOR45uW+3nUg88Dl3bn8f6CtBZl4ASl48GP2SaKyms5efBz4FJz9FUartQfyCWZdZW5UjdU0yb8HYyEbWeY0gkstVUURVEURakG/fhQFEVRFKWmTF+zS1xW2+NZmmJUnPF2zIqmWDLLqbW5ZpxjiJFAPcxGyzNENjRAd3j0U4gkmLTf0gbkbl6dXWr/1GKQ1jIzjCmAfDYll6WN75CfleyxpiDPCwvxGWmFxM0x3iL7LRzLs98SEa1h0tr/Obi8hSaSFt8uoftJ2e8iayMB/6uM8Iy3sLjNpYiBD9lRYTm7gfUtDfLKJpbVdn5iUJS9m3rPbgeNoqw3sPNmOClNBMOBNcOguQajobqktyMsy27JoGkh/j42J0fK22hK4eYSlMhyqS32s8nPwb5tYyGT1o7VN361qUhk0vg/F3lTGnWbKCq15aC5kJto6j00sxVYmTxvrIivvwUjszZiZGA235p8aa5LBfbeXLZIxq7iEnkv4zBloGuAK8KpaKCKvzNQD38XYUbvbW/KsAOcYBzRT/sHQpr7wcqO1ZUPRVEURVFqin58KIqiKIpSU/TjQ1EURVGUmjJ9fT4mgAnz63AxlVltJ6vtiajXIZ+N+HgA3H4a8cEY5rbtZjiRhTfPgi23oZ7VMSzKHnp1T2xfrlgikyL6fE6BnJcSlWXrjOAI/c7trpctPF+UcT8PtM+iD8glnStG3SaSPjgYer0oMqDGXx+GyeZ+JQmQoaJgtNL/gLj/BxGR71lZbivzcSAiyiWtPwhmvB0IWch28PnAjLvc5yNnwHcEzuVgmPo42pLSj4X7ajT4cqR4nXi9kZD1zD8kU4W7QJbdN/TV4PcY5wLfx/uN8LkRguy9Uj8DnIkFxzsrx4vguBSEk//DpZ+wh8Lfjzte/YXdcYUbR7/EasJDxOGqA+qJpHbgwPhy3zB8Z0w2uvKhKIqiKEpN0Y8PRVEURVFqin58KIqiKIpSU2a0z4dgKn0zjofp7NfhAnXp3GaJZbifckzLPNOsJ0HPzkNwYJ0s7ocPvhqfWmR9IB58U/p/PPT6TrHPfUB8Vz8nAbTJcvs4xu5Ys/A8sS9M9HAs98G56hTp48JxxQjAuCK8r5h+PAMxGvLG+nIkfAx3bfej4b6tvT4HfgYJ1tcC+qOwNoqejCtSgHgdw56N9ZCA629mfhd4Xsja9EleU4JdfxP4bjSzeB31fnw49Ywn/QowhH2CDUfaESMCx5SnmMeYGK6w6Hy+bTv8bOxxRHKuPAw+TjzuRwjjhnFHBOx9lnO82obhPrWCb06ifb6tcig+1L/ny74Yl0+bw6/D6Z/B24M6MFaSywclkrKBwZ9T1zPsYjzxQIh05UNRFEVRlBqjHx+KoiiKotSU2WN2mWpOVLNPLcAlRLG8DRloi3KZPA4DS7QmycIfZyHjbc4ubwdwn3IE4dVZ+yYXvyyO8GXaaOba+LnBzSnjXd6M4OPytUMizJeTwcx0ySk8TD2GZWfZWWG53p3lVNbT5lhOzok2YPmcyXJRPssZgPDpXIZLRBSSracA/ebZarMgA+ay2Cxkju1I2HDnmFWWX20KLYdsTqfgmvxI3+LnlC/qiZfM4n1D014cESk3muuOWFPLcCifBW5qKcJ853v4nHKKFYZaJyL6bOeFYj+5iJuHUT5uYsvwPVUpEfNwDGiewTElZrqM9K3Cvz0uM8uEvXsYuvKhKIqiKEpN0Y8PRVEURVFqin58KIqiKIpSU2aUz0dNwqlXw3Ty86h1X9CPgwNhyI1DRoayNRPYY/F+c+mth5ebYN/ZWZkq22P7n160UpT9+5u7xD4PP37lYkdabfCP4NeBPh9SbijHhttaXanKIzZZvN8V2us9DBHPbdIY4pnJO410eXDKMosm3j6e8eJT2KP0MhB+HrIszZp3+QfUJ6TPBYZw5z4hXNpL5A6hnvVsPSmQxc5jbeLVcjlpFvw4+Nig3wze/37DJbuQakDIgMfn1+EEpdwOl6Ii+EpwX5UAxpeHUEcPBN5iALLrIiudB+HU/cZGWRF7FwW9vaLoleKc8vaPX3tKlHFZMr4XOJHni/lfof8H9/MwRdlvLnNHJkoyGzrmN583Uh5d+d8ZXflQFEVRFKWm6MeHoiiKoig15YQzu0w700qtGa/5JJggqVSiwu/VKjK8RpYbuRkGZaGszC+AuYb1LSJFYyYDLrslIvLq6uxhsLyJ0Tm56ePBQ9Ikc+XCj1AlYARILjdE00KSmWGwrERcauq+LyICo+947F1ZNtE85piLrkiVCDfD4HhLqbGcC1m2m3BJHR1qRlzaT4D0M8GksFkvvo1oVllbL5pWWnx7jdEoovZ6P7VwuSjzUmm2jeZI2befvmoj87pMKWsWyTZ41lOcpyLDMp7nApbzeURblNqC9U6WVfjqKzqehfmJerHvyrD9o0P/JfY3Hzu3vP2BpBybeQnWOTSB8vGPvM+YacX1CI8hdeX3pppIyJVKaKPP8PGvW+jKh6IoiqIoNeW4Pj5uv/128jyPbr755vLvcrkcrV+/nubMmUONjY20du1a6unpOd5+KoqiKIoyQxj3x8fevXvpH//xH+mcc84Rv7/lllvooYceoq1bt9KOHTvoyJEjdM011xx3RxVFURRFmRmMy+djcHCQrrvuOvr+979P3/rWt8q/7+vro7vvvpvuvfdeuuiii4iIaMuWLXTGGWfQrl276IIL4iVIiGfM8ft31Fpeiu252ue23ogsssLwwNX4cYx3LLAvvE1XnSjL5BSlZdeZ2RH9QQpMwviezAhaapISWmjEVonzitl9Pch4e/kSOWd/8vp/lrdRMipkuOj/wa7DJTV1geGmhZ8H+Byg3VdkuQX/AJfdmxO5T45smVLCKedCNFup9WupRurJ/WGSLj2ng0EjQ+RHfDDYNaNfR4qHUIfHpIn51bj8WDD7MMdD3yTm0zTWPbtsMZt/Trs+zBvm5+GUXuJc8G1fTUk+34+ALPTd0PrRDJjKQ6G7yLFstUVwnuAh7C9bLMOp+w3Sx4tnj30TogX817EPlLdTINk9M3u4vH3X67+A9m1/TgKfE5cfDZfl4hgWXe/McYZCd0tr3c/wWL8fvc5xsH79err88stp9erV4vf79u2jYrEofr9s2TLq7OyknTt3YjVERJTP56m/v1/8KIqiKIoyc6l65eO+++6jZ555hvbu3Rsp6+7upnQ6Ta2treL37e3t1N3dPWp9mzZtom9+85vVdkNRFEVRlBOUqj4+Dh06RDfddBM99thjlIXMoONl48aNtGHDhvJ+f38/LV68eELqrgkTYdpB84lLzlqpqWWiTE7jNQk5JJu4fB/J7MhkhF5GmlJ++vpj5e0rP9ou66lny9sOk52B8fVCtg8SYR/MMFed0lXefuiN3aKMm1N+9KZc6ePZQ/OOZVHsNpeC5iLn2aXf1BiLmHw5PbLU7zB1iCV0uG+uyIlFEY3SPWddMmGM1hjXviuqo6uOHxz6T7FfqIGpVpgzHM9XJPsxI2ICg4nDsyg/ejBeMotmrkpNgo8cfkbs83ryEO6Wm1mIiN4NbN+GjfwTxKPGYjZiNHtxpNlFPsNtzESCkUEvO/MTsXWi+aY3ZyX5Lw4uEGV5Fgk32yCvf0myr7xd77vExBLX/a+GyxaeH1vGoytHIgg7pfSj3ws0Dbuoyuyyb98+Onr0KJ1//vmUTCYpmUzSjh076Lvf/S4lk0lqb2+nQqFAvRCWtqenhzo6OkatM5PJUHNzs/hRFEVRFGXmUtXKx8UXX0zPPfec+N0XvvAFWrZsGd166620ePFiSqVStH37dlq7di0RER04cIAOHjxIXV1do1WpKIqiKMoso6qPj6amJjrrrLPE7xoaGmjOnDnl319//fW0YcMGamtro+bmZvryl79MXV1dVSldFEVRFEWZuUx4ePVvf/vb5Ps+rV27lvL5PK1Zs4a+973vTXQzk0s1dt/x2ohd5403FHotpMW8DVcoboRLbwsgtcWQw7xsYFDsPzZi7a4//M+touzTn72hvO2BhM+kWMbZUrwtNZJFF/rGfVCuOGWVKBM+IHAreAZalHdiiGkOHym0kfoO/xu01w6yLKf3H5K+KvUs3Pd4M2KinZ/7DmCmWszIKkJ8o83YIdmWvivxcx/9E7ikcDgS01q2P8yqxfDuwiUBxjvLrsMnKcvkmVxd41tNllEMxc6lr+jHwe31+VD2jacP4HMWwXuYZ34dmKmW+3gQEQ2Y+MzFLoZFhuEAyuxz2eTnRBn3m3ovGJaVOnzD6kFO2zts3z1HUi2i7L28lNByXk4OlbcvaXhJlLWw9wuGs38nsOeF5Pax5NL2ESN9bHgIAJd/Fb4zXL5afEwTrvwFDo774+OJJ54Q+9lsljZv3kybN28+3qoVRVEURZmBaG4XRVEURVFqyomR1XayzQnVRCathonKJBtHrSO4VoFBkwzfj8ix4qNT3v38I2J/S69dXl6c3C/KWCJRMpHIrBVGUcVl/pSMTulaYORL0dGl7vhlSh5VE6WeotvQeF/Is9pKcER5b3D0m9hSO0qEA2OXs3HZny/LJqFF3yHfxbHh8kc0NcistpU/T8PMnBCREDrMF9GZye6bV/nzVmTnpRzL1y6TCJbxJXM0e3DJ5PvH2snyViAjAafZfHP998lNMAiaVgbYXBwIZa09QWNsPb5DPhs60rwOGflcZj1r9mvzC1Bm/8zhuEWyzLLnrx7ud6lk+9NfkCEAQjbe3XlpkhkIrMnkhdR8UbYw2cv6Ju9TlvUV+z0M8mVuIkMzJ59TLjltNdFJRR1CHl35M6IrH4qiKIqi1BT9+FAURVEUpabox4eiKIqiKDXlxPD54EyGn8M09p2YtL65ZLKujLTCd8PRN5SwMduqKUImSZC3/uSFx8vbDw7JMMZH8q3l7UOlVlHmFa3dGUOoU5LtF+PtvGgD9qAew8NBg0/PJaesLG8/+sYeigNtq1ykF4CdPXQ4meSY9BNloOifwENFY9jqwLNtYnbW0JOyRQ6X+uJ53F8B7cAYgrme2ajRz4D7TuCs5DZxVxupSAhxFl4cxgJ9bjKsuAhTusjOHYKQ3iHzOfH9eCl1n4kfXyTB/UHIHXo7ZNcxDHOD++40OXxzcEz5WGGKAO7n0RdKf4j+UMpE00wmW+9J2XmOyWmL4EfE/TqQJubn0QYpEhp92/4lS6U83m+Uxxr2TLf50q/EsHEcysuygF3/64k2UdaWsZLZX5gPirK5KRtK4DeZo6JsWbqnvH1aUvp4DKO0nc0HnNPcB2QYZLgumaxTljvG/KsEXflQFEVRFKWm6MeHoiiKoig15cQzu0wUk2HOcElrp7NpB+GmFTTB8P0qIpx6Kbv0FxkJWCb9ddEuxXaXpGxtKLDLne+ChM8vsKX+Ouh3NdFYOWASEouUJZAipu01chMMkcwO+whk1uTL2ymQCAeO5XNuaikSmhZk10JuooGl1gH2GsDl/JwwX8g606IejJRpzxsrU2zR8OiYEm5aSsDMGWBtNoC5LMfazDokhMdCNC1I8JrlsfHXVTDcDCHb53cKe8bby8JcGGD1JMYIKskl2gFIVrkpa6AKeSWfiwOQjbaXmVa46YRIRh8lIiKfmVrGMNFwsgk7Oxo8aT7gmWtbfPleuKRzRXnbq5Pmkghs3l5z6v8WRZkf2vbzeXmNxaIdjwBspcXAzqpSKGfYOwXb175SnSjj82tB4i1RVu/FR4mNSqRtv8NIFmXe3vgilY4XXflQFEVRFKWm6MeHoiiKoig1ZdqZXcz/LAuVeAQ3vlQ0UeaLyTCDuJb2p5vZxZFQSeJYinPUYSBZmseODcHj2oNl2sEBO44jI3IJsTBozx1JybJSYJdzA0geZwJ7nheA2iZurhFF7ym7LgPXYdhyp4GlT8O80/sHZJ3cRDECKgJuPhhxml3cuMwuKRZlsgQRJ5PCJCTr5JEy81BWqMLswhei8TryDrMLbzJ0mF2KjgR8g3B7sY0kOxWD5PLdIbhvPKppGsZU5KOD/vD2sN9D7BmqxuySh0Z4f5JVrLTzOgfheofYc5ILMekbKCOY2qUUSRAXr6LIJlgZjOlA0u73J2VZiT17HqiSPHyHsTEPQVESDNv3S5iH94vPonxC9NESSyRZDODdx8xF+aJsj4/FQAmuyREZtgD3ht/jEpR5Hn++5GRwrUwUY8x1A//zQJkK/t55ppKjasibb75JixcvnupuKIqiKIoyDg4dOkSLFi1yHjPtPj7CMKQjR46QMYY6Ozvp0KFD1NzcPNXdmlb09/fT4sWLdWxGQccmHh2beHRsRkfHJR4dmyjGGBoYGKAFCxaQ74oXRdPQ7OL7Pi1atIj6+/uJiKi5uVlvbAw6NvHo2MSjYxOPjs3o6LjEo2MjaWlpGfsgUodTRVEURVFqjH58KIqiKIpSU6btx0cmk6G/+qu/okwmM/bBswwdm3h0bOLRsYlHx2Z0dFzi0bE5Pqadw6miKIqiKDObabvyoSiKoijKzEQ/PhRFURRFqSn68aEoiqIoSk3Rjw9FURRFUWrKtP342Lx5My1ZsoSy2SytWrWK9uzZM9VdqimbNm2ij3zkI9TU1ETz58+nq6++mg4cOCCOyeVytH79epozZw41NjbS2rVrqaenZ4p6PHXcfvvt5Hke3XzzzeXfzeaxOXz4MP3hH/4hzZkzh+rq6ujss8+mp59+ulxujKFvfOMbdPLJJ1NdXR2tXr2aXnnllSnscW0IgoC+/vWv09KlS6muro5OPfVU+uu//muRh2K2jM2TTz5JV1xxBS1YsIA8z6MHHnhAlFcyDseOHaPrrruOmpubqbW1la6//noaHBys4VVMDq6xKRaLdOutt9LZZ59NDQ0NtGDBAvrc5z5HR44cEXXM1LGZUMw05L777jPpdNr88z//s3nhhRfMn/zJn5jW1lbT09Mz1V2rGWvWrDFbtmwxzz//vNm/f7+57LLLTGdnpxkcHCwfc8MNN5jFixeb7du3m6efftpccMEF5qMf/egU9rr27NmzxyxZssScc8455qabbir/fraOzbFjx8wpp5xiPv/5z5vdu3ebV1991Wzbts38+te/Lh9z++23m5aWFvPAAw+YX/7yl+bKK680S5cuNSMjI1PY88nntttuM3PmzDEPP/ywee2118zWrVtNY2Oj+c53vlM+ZraMzSOPPGK+9rWvmR//+MeGiMz9998vyisZh0suucR8+MMfNrt27TK/+MUvzGmnnWauvfbaGl/JxOMam97eXrN69Wrzwx/+0Lz88stm586dZuXKlWb58uWijpk6NhPJtPz4WLlypVm/fn15PwgCs2DBArNp06Yp7NXUcvToUUNEZseOHcaY9x+CVCpltm7dWj7mpZdeMkRkdu7cOVXdrCkDAwPm9NNPN4899pj5xCc+Uf74mM1jc+utt5qPfexjseVhGJqOjg7zd3/3d+Xf9fb2mkwmY/7t3/6tFl2cMi6//HLzx3/8x+J311xzjbnuuuuMMbN3bPAPbCXj8OKLLxoiMnv37i0f87Of/cx4nmcOHz5cs75PNqN9mCF79uwxRGTeeOMNY8zsGZvjZdqZXQqFAu3bt49Wr15d/p3v+7R69WrauXPnFPZsaunr6yMiora2NiIi2rdvHxWLRTFOy5Yto87OzlkzTuvXr6fLL79cjAHR7B6bBx98kFasWEG///u/T/Pnz6fzzjuPvv/975fLX3vtNeru7hZj09LSQqtWrZrxY/PRj36Utm/fTr/61a+IiOiXv/wlPfXUU3TppZcS0eweG04l47Bz505qbW2lFStWlI9ZvXo1+b5Pu3fvrnmfp5K+vj7yPI9aW1uJSMemUqZdYrl33nmHgiCg9vZ28fv29nZ6+eWXp6hXU0sYhnTzzTfThRdeSGeddRYREXV3d1M6nS5P+N/S3t5O3d3dU9DL2nLffffRM888Q3v37o2UzeaxefXVV+nOO++kDRs20F/8xV/Q3r176c/+7M8onU7TunXrytc/2vM108fmq1/9KvX399OyZcsokUhQEAR022230XXXXUdENKvHhlPJOHR3d9P8+fNFeTKZpLa2tlk1Vrlcjm699Va69tpry8nldGwqY9p9fChR1q9fT88//zw99dRTU92VacGhQ4fopptuoscee4yy2exUd2daEYYhrVixgv7mb/6GiIjOO+88ev755+muu+6idevWTXHvppYf/ehH9IMf/IDuvfde+tCHPkT79++nm2++mRYsWDDrx0apnmKxSH/wB39Axhi68847p7o7JxzTzuwyd+5cSiQSEWVCT08PdXR0TFGvpo4bb7yRHn74Yfr5z39OixYtKv++o6ODCoUC9fb2iuNnwzjt27ePjh49Sueffz4lk0lKJpO0Y8cO+u53v0vJZJLa29tn7dicfPLJdOaZZ4rfnXHGGXTw4EEiovL1z8bn68///M/pq1/9Kn32s5+ls88+m/7oj/6IbrnlFtq0aRMRze6x4VQyDh0dHXT06FFRXiqV6NixY7NirH774fHGG2/QY489Vl71INKxqZRp9/GRTqdp+fLltH379vLvwjCk7du3U1dX1xT2rLYYY+jGG2+k+++/nx5//HFaunSpKF++fDmlUikxTgcOHKCDBw/O+HG6+OKL6bnnnqP9+/eXf1asWEHXXXddeXu2js2FF14YkWT/6le/olNOOYWIiJYuXUodHR1ibPr7+2n37t0zfmyGh4fJ9+UrL5FIUBiGRDS7x4ZTyTh0dXVRb28v7du3r3zM448/TmEY0qpVq2re51ry2w+PV155hf7jP/6D5syZI8pn89hUxVR7vI7GfffdZzKZjLnnnnvMiy++aL74xS+a1tZW093dPdVdqxlf+tKXTEtLi3niiSfMW2+9Vf4ZHh4uH3PDDTeYzs5O8/jjj5unn37adHV1ma6urins9dTB1S7GzN6x2bNnj0kmk+a2224zr7zyivnBD35g6uvrzb/+67+Wj7n99ttNa2ur+clPfmL++7//21x11VUzUk6KrFu3zixcuLAstf3xj39s5s6da77yla+Uj5ktYzMwMGCeffZZ8+yzzxoiMn//939vnn322bJio5JxuOSSS8x5551ndu/ebZ566ilz+umnzwg5qWtsCoWCufLKK82iRYvM/v37xbs5n8+X65ipYzORTMuPD2OM+Yd/+AfT2dlp0um0Wblypdm1a9dUd6mmENGoP1u2bCkfMzIyYv70T//UnHTSSaa+vt58+tOfNm+99dbUdXoKwY+P2Tw2Dz30kDnrrLNMJpMxy5YtM//0T/8kysMwNF//+tdNe3u7yWQy5uKLLzYHDhyYot7Wjv7+fnPTTTeZzs5Ok81mzQc+8AHzta99TfzRmC1j8/Of/3zU98u6deuMMZWNw7vvvmuuvfZa09jYaJqbm80XvvAFMzAwMAVXM7G4xua1116LfTf//Oc/L9cxU8dmIvGMYeH9FEVRFEVRJplp5/OhKIqiKMrMRj8+FEVRFEWpKfrxoSiKoihKTdGPD0VRFEVRaop+fCiKoiiKUlP040NRFEVRlJqiHx+KoiiKotQU/fhQFEVRFKWm6MeHoiiKoig1RT8+FEVRFEWpKfrxoSiKoihKTdGPD0VRFEVRasr/A1EXL/BESGnOAAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"code","source":"alignments","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:33.258327Z","iopub.execute_input":"2023-10-23T03:51:33.258582Z","iopub.status.idle":"2023-10-23T03:51:33.264797Z","shell.execute_reply.started":"2023-10-23T03:51:33.258558Z","shell.execute_reply":"2023-10-23T03:51:33.263914Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(21,), dtype=int64, numpy=\narray([ 2,  9, 14, 39,  2, 12, 21,  5, 39,  1, 20, 39, 12, 39, 19,  9, 24,\n       39, 14, 15, 23])>"},"metadata":{}}]},{"cell_type":"code","source":"tf.strings.reduce_join([bytes.decode(x) for x in num_to_char(alignments.numpy()).numpy()])","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:33.265992Z","iopub.execute_input":"2023-10-23T03:51:33.266326Z","iopub.status.idle":"2023-10-23T03:51:33.279302Z","shell.execute_reply.started":"2023-10-23T03:51:33.266300Z","shell.execute_reply":"2023-10-23T03:51:33.278361Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'bin blue at l six now'>"},"metadata":{}}]},{"cell_type":"code","source":"def mappable_function(path:str) ->List[str]:\n    result = tf.py_function(load_data, [path], (tf.float32, tf.int64))\n    return result","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:33.280454Z","iopub.execute_input":"2023-10-23T03:51:33.280735Z","iopub.status.idle":"2023-10-23T03:51:33.285379Z","shell.execute_reply.started":"2023-10-23T03:51:33.280711Z","shell.execute_reply":"2023-10-23T03:51:33.284507Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"markdown","source":"# 2. Create Data Pipeline","metadata":{"tags":[]}},{"cell_type":"code","source":"from matplotlib import pyplot as plt","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:33.286610Z","iopub.execute_input":"2023-10-23T03:51:33.286968Z","iopub.status.idle":"2023-10-23T03:51:33.294022Z","shell.execute_reply.started":"2023-10-23T03:51:33.286936Z","shell.execute_reply":"2023-10-23T03:51:33.293187Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"data = tf.data.Dataset.list_files('./data/s1/*.mpg')\ndata = data.shuffle(500, reshuffle_each_iteration=False)\ndata = data.map(mappable_function)\ndata = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\ndata = data.prefetch(tf.data.AUTOTUNE)\n# Added for split \ntrain = data.take(450)\ntest = data.skip(450)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:33.302765Z","iopub.execute_input":"2023-10-23T03:51:33.303048Z","iopub.status.idle":"2023-10-23T03:51:33.408139Z","shell.execute_reply.started":"2023-10-23T03:51:33.303025Z","shell.execute_reply":"2023-10-23T03:51:33.407256Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"len(train)","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:33.409348Z","iopub.execute_input":"2023-10-23T03:51:33.410052Z","iopub.status.idle":"2023-10-23T03:51:33.416215Z","shell.execute_reply.started":"2023-10-23T03:51:33.410018Z","shell.execute_reply":"2023-10-23T03:51:33.415325Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"450"},"metadata":{}}]},{"cell_type":"code","source":"frames, alignments = data.as_numpy_iterator().next()","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:33.417341Z","iopub.execute_input":"2023-10-23T03:51:33.417671Z","iopub.status.idle":"2023-10-23T03:51:34.114331Z","shell.execute_reply.started":"2023-10-23T03:51:33.417637Z","shell.execute_reply":"2023-10-23T03:51:34.113131Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"len(frames)","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:34.115514Z","iopub.execute_input":"2023-10-23T03:51:34.115949Z","iopub.status.idle":"2023-10-23T03:51:34.123816Z","shell.execute_reply.started":"2023-10-23T03:51:34.115909Z","shell.execute_reply":"2023-10-23T03:51:34.122803Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"2"},"metadata":{}}]},{"cell_type":"code","source":"sample = data.as_numpy_iterator()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:34.125058Z","iopub.execute_input":"2023-10-23T03:51:34.125334Z","iopub.status.idle":"2023-10-23T03:51:34.137103Z","shell.execute_reply.started":"2023-10-23T03:51:34.125311Z","shell.execute_reply":"2023-10-23T03:51:34.136311Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"val = sample.next(); val[0]","metadata":{"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:34.138138Z","iopub.execute_input":"2023-10-23T03:51:34.138431Z","iopub.status.idle":"2023-10-23T03:51:34.590537Z","shell.execute_reply.started":"2023-10-23T03:51:34.138407Z","shell.execute_reply":"2023-10-23T03:51:34.588704Z"},"trusted":true},"execution_count":33,"outputs":[{"execution_count":33,"output_type":"execute_result","data":{"text/plain":"array([[[[[1.38044   ],\n          [1.38044   ],\n          [1.4550583 ],\n          ...,\n          [0.2611643 ],\n          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\nplt.imshow(val[0][0][35])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:34.602396Z","iopub.execute_input":"2023-10-23T03:51:34.602741Z","iopub.status.idle":"2023-10-23T03:51:34.912874Z","shell.execute_reply.started":"2023-10-23T03:51:34.602711Z","shell.execute_reply":"2023-10-23T03:51:34.910657Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7fbe97f32380>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:34.914706Z","iopub.execute_input":"2023-10-23T03:51:34.915019Z","iopub.status.idle":"2023-10-23T03:51:34.992786Z","shell.execute_reply.started":"2023-10-23T03:51:34.914994Z","shell.execute_reply":"2023-10-23T03:51:34.990966Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'place white by d six now'>"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3. Design the Deep Neural Network","metadata":{"tags":[]}},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential \nfrom tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:34.994515Z","iopub.execute_input":"2023-10-23T03:51:34.995022Z","iopub.status.idle":"2023-10-23T03:51:35.007957Z","shell.execute_reply.started":"2023-10-23T03:51:34.994990Z","shell.execute_reply":"2023-10-23T03:51:35.005594Z"},"trusted":true},"execution_count":37,"outputs":[]},{"cell_type":"code","source":"data.as_numpy_iterator().next()[0][0].shape","metadata":{"execution":{"iopub.status.busy":"2023-10-23T03:51:35.009666Z","iopub.execute_input":"2023-10-23T03:51:35.009962Z","iopub.status.idle":"2023-10-23T03:51:35.927378Z","shell.execute_reply.started":"2023-10-23T03:51:35.009938Z","shell.execute_reply":"2023-10-23T03:51:35.926445Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"(75, 46, 140, 1)"},"metadata":{}}]},{"cell_type":"code","source":"model = Sequential()\nmodel.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPool3D((1,2,2)))\n\nmodel.add(Conv3D(256, 3, padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPool3D((1,2,2)))\n\nmodel.add(Conv3D(75, 3, padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPool3D((1,2,2)))\n\nmodel.add(TimeDistributed(Flatten()))\n\nmodel.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\nmodel.add(Dropout(.5))\n\nmodel.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\nmodel.add(Dropout(.5))\n\nmodel.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T03:51:35.928466Z","iopub.execute_input":"2023-10-23T03:51:35.928743Z","iopub.status.idle":"2023-10-23T03:51:37.995692Z","shell.execute_reply.started":"2023-10-23T03:51:35.928718Z","shell.execute_reply":"2023-10-23T03:51:37.994705Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"# url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n# url = 'https://drive.google.com/file/d/1fyZoYgqZw_aRa66kiOR6J8XLu3Y9wfMg/view?usp=sharing'\nurl = 'https://drive.google.com/u/0/uc?id=1JAmcd2v0JcZStgs69VytgqwGJMlhNeaT&export=download'\noutput = 'check.zip'\ngdown.download(url, output, quiet=False)\ngdown.extractall('check.zip', 'models')","metadata":{"execution":{"iopub.status.busy":"2023-10-23T04:07:39.576826Z","iopub.execute_input":"2023-10-23T04:07:39.577733Z","iopub.status.idle":"2023-10-23T04:07:47.162795Z","shell.execute_reply.started":"2023-10-23T04:07:39.577700Z","shell.execute_reply":"2023-10-23T04:07:47.161811Z"},"trusted":true},"execution_count":45,"outputs":[{"name":"stderr","text":"Downloading...\nFrom (uriginal): https://drive.google.com/u/0/uc?id=1JAmcd2v0JcZStgs69VytgqwGJMlhNeaT&export=download\nFrom (redirected): https://drive.google.com/uc?id=1JAmcd2v0JcZStgs69VytgqwGJMlhNeaT&export=download&confirm=t&uuid=12248fe3-0f3e-4e6f-9f67-86c3c103778b\nTo: /kaggle/working/check.zip\n100%|██████████| 95.3M/95.3M [00:03<00:00, 31.5MB/s]\n","output_type":"stream"},{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"['models/models/checkpoint',\n 'models/models/checkpoint.index',\n 'models/models/checkpoint.data-00000-of-00001']"},"metadata":{}}]},{"cell_type":"code","source":"model.load_weights('./models/models/checkpoint')","metadata":{"execution":{"iopub.status.busy":"2023-10-23T04:08:25.032167Z","iopub.execute_input":"2023-10-23T04:08:25.032990Z","iopub.status.idle":"2023-10-23T04:08:25.130791Z","shell.execute_reply.started":"2023-10-23T04:08:25.032955Z","shell.execute_reply":"2023-10-23T04:08:25.129910Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x7fbea54f02e0>"},"metadata":{}}]},{"cell_type":"code","source":"model.summary()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:08:29.042756Z","iopub.execute_input":"2023-10-23T04:08:29.043584Z","iopub.status.idle":"2023-10-23T04:08:29.088836Z","shell.execute_reply.started":"2023-10-23T04:08:29.043553Z","shell.execute_reply":"2023-10-23T04:08:29.087911Z"},"trusted":true},"execution_count":48,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n conv3d (Conv3D)             (None, 75, 46, 140, 128)  3584      \n                                                                 \n activation (Activation)     (None, 75, 46, 140, 128)  0         \n                                                                 \n max_pooling3d (MaxPooling3D  (None, 75, 23, 70, 128)  0         \n )                                                               \n                                                                 \n conv3d_1 (Conv3D)           (None, 75, 23, 70, 256)   884992    \n                                                                 \n activation_1 (Activation)   (None, 75, 23, 70, 256)   0         \n                                                                 \n max_pooling3d_1 (MaxPooling  (None, 75, 11, 35, 256)  0         \n 3D)                                                             \n                                                                 \n conv3d_2 (Conv3D)           (None, 75, 11, 35, 75)    518475    \n                                                                 \n activation_2 (Activation)   (None, 75, 11, 35, 75)    0         \n                                                                 \n max_pooling3d_2 (MaxPooling  (None, 75, 5, 17, 75)    0         \n 3D)                                                             \n                                                                 \n time_distributed (TimeDistr  (None, 75, 6375)         0         \n ibuted)                                                         \n                                                                 \n bidirectional (Bidirectiona  (None, 75, 256)          6660096   \n l)                                                              \n                                                                 \n dropout (Dropout)           (None, 75, 256)           0         \n                                                                 \n bidirectional_1 (Bidirectio  (None, 75, 256)          394240    \n nal)                                                            \n                                                                 \n dropout_1 (Dropout)         (None, 75, 256)           0         \n                                                                 \n dense (Dense)               (None, 75, 41)            10537     \n                                                                 \n=================================================================\nTotal params: 8,471,924\nTrainable params: 8,471,924\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"5*17*75","metadata":{"execution":{"iopub.status.busy":"2023-10-23T04:08:31.940393Z","iopub.execute_input":"2023-10-23T04:08:31.941004Z","iopub.status.idle":"2023-10-23T04:08:31.946965Z","shell.execute_reply.started":"2023-10-23T04:08:31.940965Z","shell.execute_reply":"2023-10-23T04:08:31.946033Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"6375"},"metadata":{}}]},{"cell_type":"code","source":"yhat = model.predict(val[0])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:08:33.038583Z","iopub.execute_input":"2023-10-23T04:08:33.039547Z","iopub.status.idle":"2023-10-23T04:08:38.284277Z","shell.execute_reply.started":"2023-10-23T04:08:33.039502Z","shell.execute_reply":"2023-10-23T04:08:38.283379Z"},"trusted":true},"execution_count":50,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 5s 5s/step\n","output_type":"stream"}]},{"cell_type":"code","source":"tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)])","metadata":{"execution":{"iopub.status.busy":"2023-10-23T04:08:39.358977Z","iopub.execute_input":"2023-10-23T04:08:39.359327Z","iopub.status.idle":"2023-10-23T04:08:39.455673Z","shell.execute_reply.started":"2023-10-23T04:08:39.359302Z","shell.execute_reply":"2023-10-23T04:08:39.454740Z"},"trusted":true},"execution_count":51,"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'place wwhiittee  bby       ssiiixx   nnoowww'>"},"metadata":{}}]},{"cell_type":"code","source":"tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:08:40.541017Z","iopub.execute_input":"2023-10-23T04:08:40.541394Z","iopub.status.idle":"2023-10-23T04:08:40.634315Z","shell.execute_reply.started":"2023-10-23T04:08:40.541365Z","shell.execute_reply":"2023-10-23T04:08:40.633373Z"},"trusted":true},"execution_count":52,"outputs":[{"execution_count":52,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'place wwhiittee  bby       ssiiixx   nnoowww'>"},"metadata":{}}]},{"cell_type":"code","source":"model.input_shape","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:08:48.187625Z","iopub.execute_input":"2023-10-23T04:08:48.188345Z","iopub.status.idle":"2023-10-23T04:08:48.194060Z","shell.execute_reply.started":"2023-10-23T04:08:48.188310Z","shell.execute_reply":"2023-10-23T04:08:48.193097Z"},"trusted":true},"execution_count":53,"outputs":[{"execution_count":53,"output_type":"execute_result","data":{"text/plain":"(None, 75, 46, 140, 1)"},"metadata":{}}]},{"cell_type":"code","source":"model.output_shape","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:08:49.589693Z","iopub.execute_input":"2023-10-23T04:08:49.590438Z","iopub.status.idle":"2023-10-23T04:08:49.596715Z","shell.execute_reply.started":"2023-10-23T04:08:49.590402Z","shell.execute_reply":"2023-10-23T04:08:49.595782Z"},"trusted":true},"execution_count":54,"outputs":[{"execution_count":54,"output_type":"execute_result","data":{"text/plain":"(None, 75, 41)"},"metadata":{}}]},{"cell_type":"markdown","source":"# 4. Setup Training Options and Train","metadata":{"tags":[]}},{"cell_type":"code","source":"def scheduler(epoch, lr):\n    if epoch < 30:\n        return lr\n    else:\n        return lr * tf.math.exp(-0.1)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:09:05.446810Z","iopub.execute_input":"2023-10-23T04:09:05.447209Z","iopub.status.idle":"2023-10-23T04:09:05.452381Z","shell.execute_reply.started":"2023-10-23T04:09:05.447182Z","shell.execute_reply":"2023-10-23T04:09:05.451408Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"def CTCLoss(y_true, y_pred):\n    batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n    input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n    label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n\n    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n\n    loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n    return loss","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:09:06.686410Z","iopub.execute_input":"2023-10-23T04:09:06.687227Z","iopub.status.idle":"2023-10-23T04:09:06.693606Z","shell.execute_reply.started":"2023-10-23T04:09:06.687194Z","shell.execute_reply":"2023-10-23T04:09:06.692457Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"code","source":"class ProduceExample(tf.keras.callbacks.Callback): \n    def __init__(self, dataset) -> None: \n        self.dataset = dataset.as_numpy_iterator()\n    \n    def on_epoch_end(self, epoch, logs=None) -> None:\n        data = self.dataset.next()\n        yhat = self.model.predict(data[0])\n        decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n        for x in range(len(yhat)):           \n            print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n            print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n            print('~'*100)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:09:08.438049Z","iopub.execute_input":"2023-10-23T04:09:08.438421Z","iopub.status.idle":"2023-10-23T04:09:08.446266Z","shell.execute_reply.started":"2023-10-23T04:09:08.438393Z","shell.execute_reply":"2023-10-23T04:09:08.445242Z"},"trusted":true},"execution_count":57,"outputs":[]},{"cell_type":"code","source":"model.compile(optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.00001), loss=CTCLoss)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:09:11.780081Z","iopub.execute_input":"2023-10-23T04:09:11.780454Z","iopub.status.idle":"2023-10-23T04:09:11.798941Z","shell.execute_reply.started":"2023-10-23T04:09:11.780425Z","shell.execute_reply":"2023-10-23T04:09:11.798035Z"},"trusted":true},"execution_count":58,"outputs":[]},{"cell_type":"code","source":"checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True) ","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:09:22.815255Z","iopub.execute_input":"2023-10-23T04:09:22.815632Z","iopub.status.idle":"2023-10-23T04:09:22.820839Z","shell.execute_reply.started":"2023-10-23T04:09:22.815603Z","shell.execute_reply":"2023-10-23T04:09:22.819706Z"},"trusted":true},"execution_count":59,"outputs":[]},{"cell_type":"code","source":"schedule_callback = LearningRateScheduler(scheduler)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:09:24.036839Z","iopub.execute_input":"2023-10-23T04:09:24.037234Z","iopub.status.idle":"2023-10-23T04:09:24.041384Z","shell.execute_reply.started":"2023-10-23T04:09:24.037208Z","shell.execute_reply":"2023-10-23T04:09:24.040415Z"},"trusted":true},"execution_count":60,"outputs":[]},{"cell_type":"code","source":"example_callback = ProduceExample(test)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:09:28.974979Z","iopub.execute_input":"2023-10-23T04:09:28.975356Z","iopub.status.idle":"2023-10-23T04:09:28.991327Z","shell.execute_reply.started":"2023-10-23T04:09:28.975328Z","shell.execute_reply":"2023-10-23T04:09:28.990451Z"},"trusted":true},"execution_count":61,"outputs":[]},{"cell_type":"code","source":"model.fit(train, validation_data=test, epochs=46, callbacks=[checkpoint_callback, schedule_callback, example_callback])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T04:09:42.774582Z","iopub.execute_input":"2023-10-23T04:09:42.774971Z","iopub.status.idle":"2023-10-23T11:00:18.961932Z","shell.execute_reply.started":"2023-10-23T04:09:42.774938Z","shell.execute_reply":"2023-10-23T11:00:18.960718Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"Epoch 1/46\n169/450 [==========>...................] - ETA: 3:15 - loss: 5.2740","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb000d1c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb000d1c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.2137","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe0c017d80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe0c017d80] Warning MVs not available\n[mpeg1video @ 0x7fbf641f2300] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf641f2300] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 3s 3s/step\nOriginal: place red in p three again\nPrediction: place red in thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay green with t three again\nPrediction: lay gren with thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 744s 2s/step - loss: 5.2137 - val_loss: 2.6833 - lr: 1.2246e-05\nEpoch 2/46\n 71/450 [===>..........................] - ETA: 4:29 - loss: 5.1794","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbddc0a8b80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbddc0a8b80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.1321","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbeb0092540] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbeb0092540] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: set white by o nine soon\nPrediction: set white by o nine son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay green in z five again\nPrediction: lay gren in five again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 539s 1s/step - loss: 5.1321 - val_loss: 2.7730 - lr: 1.2246e-05\nEpoch 3/46\n309/450 [===================>..........] - ETA: 1:38 - loss: 5.0318","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdc0092640] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdc0092640] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.0286","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe000436c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe000436c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 120ms/step\nOriginal: set red at u five again\nPrediction: set red at five again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place white with k five soon\nPrediction: place white with five son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 535s 1s/step - loss: 5.0286 - val_loss: 2.6671 - lr: 1.2246e-05\nEpoch 4/46\n 89/450 [====>.........................] - ETA: 4:10 - loss: 4.9611","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x5618bc4be600] ac-tex damaged at 22 17\n[mpeg1video @ 0x5618bc4be600] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.9753","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdc41eb040] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdc41eb040] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: bin blue with g three again\nPrediction: bin blue with g thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set red at h six please\nPrediction: set red at h six please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 533s 1s/step - loss: 4.9753 - val_loss: 2.4756 - lr: 1.2246e-05\nEpoch 5/46\n363/450 [=======================>......] - ETA: 1:00 - loss: 4.8378","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf64019f80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf64019f80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.8542","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb0011d40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb0011d40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: lay green by f eight now\nPrediction: lay gren by eight now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place red with j nine soon\nPrediction: place red with nine son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 532s 1s/step - loss: 4.8542 - val_loss: 2.4459 - lr: 1.2246e-05\nEpoch 6/46\n192/450 [===========>..................] - ETA: 2:59 - loss: 4.7620","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe1400f080] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe1400f080] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.7239","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf780ac200] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf780ac200] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: set white in h nine again\nPrediction: set white in nine again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin green at u one again\nPrediction: bin gren at one again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 4.7239 - val_loss: 2.5749 - lr: 1.2246e-05\nEpoch 7/46\n210/450 [=============>................] - ETA: 2:46 - loss: 4.7540","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbd05cd8800] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbd05cd8800] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.6704","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe100789c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe100789c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: set green at p two please\nPrediction: set gren at p two please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin red at f nine soon\nPrediction: bin red at nine son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 4.6704 - val_loss: 2.3397 - lr: 1.2246e-05\nEpoch 8/46\n 60/450 [===>..........................] - ETA: 4:32 - loss: 4.3961","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb000c5c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb000c5c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.5729","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe000a8080] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe000a8080] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 120ms/step\nOriginal: lay red at r one soon\nPrediction: lay red at one son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay red in d eight now\nPrediction: lay red in d eight now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 4.5729 - val_loss: 2.4326 - lr: 1.2246e-05\nEpoch 9/46\n147/450 [========>.....................] - ETA: 3:29 - loss: 4.5537","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf8042b180] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf8042b180] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.5538","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbc13b1ba00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbc13b1ba00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\nOriginal: place green by k eight please\nPrediction: place gren by eight please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place red at v eight now\nPrediction: place red at v eight now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 528s 1s/step - loss: 4.5538 - val_loss: 2.2854 - lr: 1.2246e-05\nEpoch 10/46\n408/450 [==========================>...] - ETA: 29s - loss: 4.5661","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbeb00a4140] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbeb00a4140] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.5261","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdac20bc40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdac20bc40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 114ms/step\nOriginal: bin green in a five again\nPrediction: bin gren in a five again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place blue with x one soon\nPrediction: place blue with x one son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 4.5261 - val_loss: 2.1919 - lr: 1.2246e-05\nEpoch 11/46\n163/450 [=========>....................] - ETA: 3:21 - loss: 4.5595","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe04048640] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe04048640] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.4498","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf6c0679c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf6c0679c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: bin blue in z one soon\nPrediction: bin blue in z one son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set red by b six please\nPrediction: set red by b six please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 532s 1s/step - loss: 4.4498 - val_loss: 2.2497 - lr: 1.2246e-05\nEpoch 12/46\n168/450 [==========>...................] - ETA: 3:16 - loss: 4.3368","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdbc0149c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdbc0149c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.3156","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe0c070bc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe0c070bc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\nOriginal: lay red with f three again\nPrediction: lay red with thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place white in j four please\nPrediction: place white in j four please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 4.3156 - val_loss: 2.1456 - lr: 1.2246e-05\nEpoch 13/46\n198/450 [============>.................] - ETA: 2:54 - loss: 4.2967","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf840a2e00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf840a2e00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.3023","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdd803b280] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdd803b280] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\nOriginal: bin green in n zero now\nPrediction: bin gren in n zero now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place blue at v four please\nPrediction: place blue at v four please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 4.3023 - val_loss: 2.1781 - lr: 1.2246e-05\nEpoch 14/46\n206/450 [============>.................] - ETA: 2:48 - loss: 4.2250","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf6c0493c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf6c0493c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.1881","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb0084f80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb0084f80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: lay blue by k five soon\nPrediction: lay blue by k five son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin blue with t one again\nPrediction: bin blue with t one again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 4.1881 - val_loss: 1.8220 - lr: 1.2246e-05\nEpoch 15/46\n 18/450 [>.............................] - ETA: 4:59 - loss: 4.8640","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe1402f640] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe1402f640] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.1526","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf74000bc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf74000bc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: set blue by u two please\nPrediction: set blue by u two please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set blue at n five again\nPrediction: set blue at n five again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 4.1526 - val_loss: 2.0990 - lr: 1.2246e-05\nEpoch 16/46\n 80/450 [====>.........................] - ETA: 4:16 - loss: 4.1440","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbd05bddc00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbd05bddc00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.0558","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe183ddc00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe183ddc00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 122ms/step\nOriginal: place blue in b six now\nPrediction: place blue in b six now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set green by x one again\nPrediction: set gren by x one again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 4.0558 - val_loss: 1.8186 - lr: 1.2246e-05\nEpoch 17/46\n295/450 [==================>...........] - ETA: 1:47 - loss: 4.0680","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb40720c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb40720c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.0338","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe0c01d3c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe0c01d3c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 114ms/step\nOriginal: bin blue with m five soon\nPrediction: bin blue with m five son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set red with i five again\nPrediction: set red with i five again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 4.0338 - val_loss: 1.8875 - lr: 1.2246e-05\nEpoch 18/46\n252/450 [===============>..............] - ETA: 2:17 - loss: 3.9541","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf80403f00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf80403f00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.9605","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdc416e000] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdc416e000] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 120ms/step\nOriginal: lay red by l two please\nPrediction: lay red by l two please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place green by k seven soon\nPrediction: place gren by seven son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 532s 1s/step - loss: 3.9605 - val_loss: 2.0343 - lr: 1.2246e-05\nEpoch 19/46\n349/450 [======================>.......] - ETA: 1:10 - loss: 3.8358","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf68053b40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf68053b40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.8620","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb0027a40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb0027a40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: set red with c zero please\nPrediction: set red with c zero please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place white by k zero now\nPrediction: place white by k zero now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 534s 1s/step - loss: 3.8620 - val_loss: 1.9150 - lr: 1.2246e-05\nEpoch 20/46\n133/450 [=======>......................] - ETA: 3:41 - loss: 3.9868","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe0c08c740] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe0c08c740] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.8776","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf6c0ad2c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf6c0ad2c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\nOriginal: place white in x zero now\nPrediction: place white in x zero now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place green at q six now\nPrediction: place gren at q six now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 532s 1s/step - loss: 3.8776 - val_loss: 1.5901 - lr: 1.2246e-05\nEpoch 21/46\n189/450 [===========>..................] - ETA: 3:00 - loss: 3.8448","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdc4205840] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdc4205840] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.8377","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe10001740] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe10001740] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 122ms/step\nOriginal: lay green by f nine soon\nPrediction: lay gren by f nine son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set red at u four please\nPrediction: set red at u four please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 3.8377 - val_loss: 1.5290 - lr: 1.2246e-05\nEpoch 22/46\n143/450 [========>.....................] - ETA: 3:34 - loss: 3.7091","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb05c4300] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb05c4300] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.7183","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbddc0d3740] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbddc0d3740] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 120ms/step\nOriginal: set white at o six please\nPrediction: set white at o six please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin green at h two please\nPrediction: bin gren at t two please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 537s 1s/step - loss: 3.7183 - val_loss: 1.7072 - lr: 1.2246e-05\nEpoch 23/46\n423/450 [===========================>..] - ETA: 18s - loss: 3.6501","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf75db4800] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf75db4800] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.6493","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb8078140] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb8078140] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: set white in h seven soon\nPrediction: set white in h seven son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place white at d three soon\nPrediction: place white at d thre son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 536s 1s/step - loss: 3.6493 - val_loss: 1.5772 - lr: 1.2246e-05\nEpoch 24/46\n390/450 [=========================>....] - ETA: 42s - loss: 3.6815","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe14033580] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe14033580] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.6521","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf8006b680] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf8006b680] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\nOriginal: place blue in o four now\nPrediction: place blue in o four now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay white at z zero now\nPrediction: lay white at z zero now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 537s 1s/step - loss: 3.6521 - val_loss: 1.3235 - lr: 1.2246e-05\nEpoch 25/46\n405/450 [==========================>...] - ETA: 31s - loss: 3.5769","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdd4003ec0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdd4003ec0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.6083","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf68086d00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf68086d00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\nOriginal: lay red by l one soon\nPrediction: lay red by l one son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin white by h one again\nPrediction: bin white by h one again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 537s 1s/step - loss: 3.6083 - val_loss: 1.3979 - lr: 1.2246e-05\nEpoch 26/46\n366/450 [=======================>......] - ETA: 58s - loss: 3.5868","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb8043480] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb8043480] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.5716","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe0001c0c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe0001c0c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: bin blue in r seven soon\nPrediction: bin blue in r seven son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin green with b five soon\nPrediction: bin gren with b five son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 3.5716 - val_loss: 1.3331 - lr: 1.2246e-05\nEpoch 27/46\n346/450 [======================>.......] - ETA: 1:12 - loss: 3.4658","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf7c0c6d40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf7c0c6d40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.4204","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbd059b9e40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbd059b9e40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: set white with p two now\nPrediction: set white with p two now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place white by k one soon\nPrediction: place white by k one son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 3.4204 - val_loss: 1.2439 - lr: 1.2246e-05\nEpoch 28/46\n353/450 [======================>.......] - ETA: 1:07 - loss: 3.4302","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbeb019a000] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbeb019a000] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.4211","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb4060340] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb4060340] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\nOriginal: place blue with x three again\nPrediction: place blue with x thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay red with f one soon\nPrediction: lay red with f one son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 3.4211 - val_loss: 1.4693 - lr: 1.2246e-05\nEpoch 29/46\n 88/450 [====>.........................] - ETA: 4:11 - loss: 3.5110","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe080611c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe080611c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.3457","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf75eb4c80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf75eb4c80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\nOriginal: set red by b four now\nPrediction: set red by b four now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place green by y six please\nPrediction: place gren by y six please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 3.3457 - val_loss: 1.3107 - lr: 1.2246e-05\nEpoch 30/46\n316/450 [====================>.........] - ETA: 1:33 - loss: 3.4248","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbd0896b440] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbd0896b440] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.3678","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbeb003a940] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbeb003a940] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: place blue in i two please\nPrediction: place blue in i two please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay red at y four now\nPrediction: lay red at y four now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 532s 1s/step - loss: 3.3678 - val_loss: 1.2599 - lr: 1.2246e-05\nEpoch 31/46\n117/450 [======>.......................] - ETA: 3:52 - loss: 3.1613","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdc0014880] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdc0014880] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.2488","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe0c008480] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe0c008480] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: place green at k four please\nPrediction: place gren at k four please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set green with p eight now\nPrediction: set gren with p eight now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 3.2488 - val_loss: 1.3006 - lr: 1.1080e-05\nEpoch 32/46\n258/450 [================>.............] - ETA: 2:12 - loss: 3.2517","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf80045bc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf80045bc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.2357","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdc422bf00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdc422bf00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 123ms/step\nOriginal: place red in c four please\nPrediction: place red in c four please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place blue in i one soon\nPrediction: place blue in i one son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 531s 1s/step - loss: 3.2357 - val_loss: 1.4156 - lr: 1.0026e-05\nEpoch 33/46\n358/450 [======================>.......] - ETA: 1:03 - loss: 3.1031","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf680866c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf680866c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.1733","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb0008340] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb0008340] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 118ms/step\nOriginal: bin red by t three again\nPrediction: bin red by t thre again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay white at l four please\nPrediction: lay white at l four please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 3.1733 - val_loss: 1.1682 - lr: 9.0718e-06\nEpoch 34/46\n206/450 [============>.................] - ETA: 2:48 - loss: 3.1007","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe0c062f80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe0c062f80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.1393","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf75de0cc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf75de0cc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 122ms/step\nOriginal: place red at j two please\nPrediction: place red at j two please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place green by k six now\nPrediction: place gren by k six now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 3.1393 - val_loss: 1.2601 - lr: 8.2085e-06\nEpoch 35/46\n400/450 [=========================>....] - ETA: 34s - loss: 3.0943","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbc13b16880] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbc13b16880] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.1090","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe18004d00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe18004d00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: bin white by a five soon\nPrediction: bin white by a five son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place red with x eight please\nPrediction: place red with x eight please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 3.1090 - val_loss: 1.1206 - lr: 7.4273e-06\nEpoch 36/46\n293/450 [==================>...........] - ETA: 1:49 - loss: 2.9560","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb0481d40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb0481d40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 2.9874","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbddc1e70c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbddc1e70c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 123ms/step\nOriginal: bin blue at l eight please\nPrediction: bin blue at l eight please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set green in o seven soon\nPrediction: set gren in o seven son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 2.9874 - val_loss: 1.3359 - lr: 6.7205e-06\nEpoch 37/46\n 44/450 [=>............................] - ETA: 4:41 - loss: 3.0787","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf75dbaa40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf75dbaa40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.0282","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdbc05fa00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdbc05fa00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: set blue in n zero please\nPrediction: set blue in n zero please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin red with g nine again\nPrediction: bin red with g nine again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 3.0282 - val_loss: 1.0365 - lr: 6.0810e-06\nEpoch 38/46\n235/450 [==============>...............] - ETA: 2:28 - loss: 3.0233","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe100aeb00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe100aeb00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 3.0131","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf7c213b40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf7c213b40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: place red by p nine soon\nPrediction: place red by p nine son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay blue in q zero now\nPrediction: lay blue in q zero now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 3.0131 - val_loss: 1.1646 - lr: 5.5023e-06\nEpoch 39/46\n102/450 [=====>........................] - ETA: 4:02 - loss: 2.9749","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbddc090440] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbddc090440] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 2.9787","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe9808bf40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe9808bf40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: set red by u eight please\nPrediction: set red by u eight please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: lay red at y six please\nPrediction: lay red at y six please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 2.9787 - val_loss: 1.0505 - lr: 4.9787e-06\nEpoch 40/46\n255/450 [================>.............] - ETA: 2:15 - loss: 3.0243","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdc005b440] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdc005b440] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 2.9101","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe0c0fc800] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe0c0fc800] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 114ms/step\nOriginal: set blue in m nine soon\nPrediction: set blue in m nine son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin red with t four now\nPrediction: bin red with t four now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 528s 1s/step - loss: 2.9101 - val_loss: 1.0040 - lr: 4.5049e-06\nEpoch 41/46\n224/450 [=============>................] - ETA: 2:36 - loss: 3.0213","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf80091ac0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf80091ac0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 2.9357","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdc4181b40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdc4181b40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 118ms/step\nOriginal: place white by q six please\nPrediction: place white by q six please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set green in o eight please\nPrediction: set gren in o eight please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 2.9357 - val_loss: 1.0662 - lr: 4.0762e-06\nEpoch 42/46\n112/450 [======>.......................] - ETA: 3:54 - loss: 2.8908","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf64078e80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf64078e80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 2.9135","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdb0065640] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdb0065640] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: bin white by t seven soon\nPrediction: bin white by t seven son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set blue in t five again\nPrediction: set blue in t five again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 530s 1s/step - loss: 2.9135 - val_loss: 1.1787 - lr: 3.6883e-06\nEpoch 43/46\n101/450 [=====>........................] - ETA: 4:02 - loss: 2.8074","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe000a4780] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe000a4780] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 2.8363","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf80087080] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf80087080] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 122ms/step\nOriginal: lay blue in j seven soon\nPrediction: lay blue in j seven son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set red at o one again\nPrediction: set red at o one again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 2.8363 - val_loss: 1.0196 - lr: 3.3373e-06\nEpoch 44/46\n167/450 [==========>...................] - ETA: 3:17 - loss: 2.8182","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbc13b26c00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbc13b26c00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 2.8241","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbe9802c800] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe9802c800] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\nOriginal: place white at x four now\nPrediction: place white at x four now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: place white with k seven again\nPrediction: place white with k seven again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 532s 1s/step - loss: 2.8241 - val_loss: 0.9925 - lr: 3.0197e-06\nEpoch 45/46\n391/450 [=========================>....] - ETA: 40s - loss: 2.8155","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbda0095f40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbda0095f40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 2.8608","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdd842f4c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdd842f4c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 120ms/step\nOriginal: place red by x three soon\nPrediction: place red by x thre son\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: set white with v six now\nPrediction: set white with v six now\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 528s 1s/step - loss: 2.8608 - val_loss: 1.1915 - lr: 2.7324e-06\nEpoch 46/46\n160/450 [=========>....................] - ETA: 3:21 - loss: 2.8397","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbf800f5f80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbf800f5f80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 2.7980","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7fbdbc027c00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbdbc027c00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 131ms/step\nOriginal: bin white in m six please\nPrediction: bin white in m six please\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nOriginal: bin green by u five again\nPrediction: bin gren by u five again\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n450/450 [==============================] - 529s 1s/step - loss: 2.7980 - val_loss: 0.9335 - lr: 2.4723e-06\n","output_type":"stream"},{"execution_count":62,"output_type":"execute_result","data":{"text/plain":"<keras.callbacks.History at 0x7fbe47183400>"},"metadata":{}}]},{"cell_type":"markdown","source":"# 5. Make a Prediction ","metadata":{"tags":[]}},{"cell_type":"code","source":"# url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n# output = 'checkpoints.zip'\n# gdown.download(url, output, quiet=False)\n# gdown.extractall('checkpoints.zip', 'models')","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T11:12:02.263797Z","iopub.execute_input":"2023-10-23T11:12:02.264594Z","iopub.status.idle":"2023-10-23T11:12:02.268729Z","shell.execute_reply.started":"2023-10-23T11:12:02.264562Z","shell.execute_reply":"2023-10-23T11:12:02.267787Z"},"trusted":true},"execution_count":63,"outputs":[]},{"cell_type":"code","source":"# model.load_weights('./models/checkpoint')","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T11:12:03.419346Z","iopub.execute_input":"2023-10-23T11:12:03.419723Z","iopub.status.idle":"2023-10-23T11:12:03.424476Z","shell.execute_reply.started":"2023-10-23T11:12:03.419693Z","shell.execute_reply":"2023-10-23T11:12:03.423256Z"},"trusted":true},"execution_count":64,"outputs":[]},{"cell_type":"code","source":"test_data = test.as_numpy_iterator()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T11:12:09.690685Z","iopub.execute_input":"2023-10-23T11:12:09.691575Z","iopub.status.idle":"2023-10-23T11:12:09.702459Z","shell.execute_reply.started":"2023-10-23T11:12:09.691540Z","shell.execute_reply":"2023-10-23T11:12:09.701391Z"},"trusted":true},"execution_count":65,"outputs":[]},{"cell_type":"code","source":"sample = test_data.next()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T11:12:11.186390Z","iopub.execute_input":"2023-10-23T11:12:11.186765Z","iopub.status.idle":"2023-10-23T11:15:22.765113Z","shell.execute_reply.started":"2023-10-23T11:12:11.186735Z","shell.execute_reply":"2023-10-23T11:15:22.763373Z"},"trusted":true},"execution_count":66,"outputs":[{"name":"stderr","text":"[mpeg1video @ 0x7fbe10012a00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7fbe10012a00] Warning MVs not available\n","output_type":"stream"}]},{"cell_type":"code","source":"sample[0].shape","metadata":{"execution":{"iopub.status.busy":"2023-10-23T11:15:22.768767Z","iopub.execute_input":"2023-10-23T11:15:22.769088Z","iopub.status.idle":"2023-10-23T11:15:22.779040Z","shell.execute_reply.started":"2023-10-23T11:15:22.769062Z","shell.execute_reply":"2023-10-23T11:15:22.777635Z"},"trusted":true},"execution_count":67,"outputs":[{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"(2, 75, 46, 140, 1)"},"metadata":{}}]},{"cell_type":"code","source":"sample[1].shape","metadata":{"execution":{"iopub.status.busy":"2023-10-23T11:15:22.780119Z","iopub.execute_input":"2023-10-23T11:15:22.780384Z","iopub.status.idle":"2023-10-23T11:15:22.792031Z","shell.execute_reply.started":"2023-10-23T11:15:22.780361Z","shell.execute_reply":"2023-10-23T11:15:22.791057Z"},"trusted":true},"execution_count":68,"outputs":[{"execution_count":68,"output_type":"execute_result","data":{"text/plain":"(2, 40)"},"metadata":{}}]},{"cell_type":"code","source":"sample[1]","metadata":{"execution":{"iopub.status.busy":"2023-10-23T11:15:22.795569Z","iopub.execute_input":"2023-10-23T11:15:22.796287Z","iopub.status.idle":"2023-10-23T11:15:22.805768Z","shell.execute_reply.started":"2023-10-23T11:15:22.796262Z","shell.execute_reply":"2023-10-23T11:15:22.803904Z"},"trusted":true},"execution_count":69,"outputs":[{"execution_count":69,"output_type":"execute_result","data":{"text/plain":"array([[12,  1, 25, 39,  2, 12, 21,  5, 39,  1, 20, 39, 17, 39, 19,  9,\n        24, 39, 16, 12,  5,  1, 19,  5,  0,  0,  0,  0,  0,  0,  0,  0,\n         0,  0,  0,  0,  0,  0,  0,  0],\n       [12,  1, 25, 39, 18,  5,  4, 39, 23,  9, 20,  8, 39, 12, 39,  6,\n        15, 21, 18, 39, 14, 15, 23,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n         0,  0,  0,  0,  0,  0,  0,  0]])"},"metadata":{}}]},{"cell_type":"code","source":"yhat = model.predict(sample[0])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T11:15:22.807961Z","iopub.execute_input":"2023-10-23T11:15:22.808232Z","iopub.status.idle":"2023-10-23T11:15:23.011762Z","shell.execute_reply.started":"2023-10-23T11:15:22.808209Z","shell.execute_reply":"2023-10-23T11:15:23.010876Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 0s 131ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"print('~'*100, 'REAL TEXT')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T11:15:23.014754Z","iopub.execute_input":"2023-10-23T11:15:23.015066Z","iopub.status.idle":"2023-10-23T11:15:23.147374Z","shell.execute_reply.started":"2023-10-23T11:15:23.015040Z","shell.execute_reply":"2023-10-23T11:15:23.146294Z"},"trusted":true},"execution_count":71,"outputs":[{"name":"stdout","text":"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REAL TEXT\n","output_type":"stream"},{"execution_count":71,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'lay blue at q six please'>,\n <tf.Tensor: shape=(), dtype=string, numpy=b'lay red with l four now'>]"},"metadata":{}}]},{"cell_type":"code","source":"decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T11:15:23.149082Z","iopub.execute_input":"2023-10-23T11:15:23.149370Z","iopub.status.idle":"2023-10-23T11:15:23.164902Z","shell.execute_reply.started":"2023-10-23T11:15:23.149345Z","shell.execute_reply":"2023-10-23T11:15:23.163452Z"},"trusted":true},"execution_count":72,"outputs":[]},{"cell_type":"code","source":"print('~'*100, 'PREDICTIONS')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-10-23T11:15:23.166410Z","iopub.execute_input":"2023-10-23T11:15:23.166694Z","iopub.status.idle":"2023-10-23T11:15:23.390489Z","shell.execute_reply.started":"2023-10-23T11:15:23.166670Z","shell.execute_reply":"2023-10-23T11:15:23.388901Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PREDICTIONS\n","output_type":"stream"},{"execution_count":73,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'lay blue at q six please'>,\n <tf.Tensor: shape=(), dtype=string, numpy=b'lay red with l four now'>]"},"metadata":{}}]},{"cell_type":"markdown","source":"# Test on a Video","metadata":{}},{"cell_type":"code","source":"sample = load_data(tf.convert_to_tensor('./data/s1/bras9a.mpg'))","metadata":{"execution":{"iopub.status.busy":"2023-10-23T11:15:23.392359Z","iopub.execute_input":"2023-10-23T11:15:23.392732Z","iopub.status.idle":"2023-10-23T11:15:23.806060Z","shell.execute_reply.started":"2023-10-23T11:15:23.392698Z","shell.execute_reply":"2023-10-23T11:15:23.804363Z"},"trusted":true},"execution_count":74,"outputs":[]},{"cell_type":"code","source":"print('~'*100, 'REAL TEXT')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in [sample[1]]]","metadata":{"execution":{"iopub.status.busy":"2023-10-23T11:15:23.810191Z","iopub.execute_input":"2023-10-23T11:15:23.810488Z","iopub.status.idle":"2023-10-23T11:15:23.865144Z","shell.execute_reply.started":"2023-10-23T11:15:23.810463Z","shell.execute_reply":"2023-10-23T11:15:23.864006Z"},"trusted":true},"execution_count":75,"outputs":[{"name":"stdout","text":"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REAL TEXT\n","output_type":"stream"},{"execution_count":75,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'bin red at s nine again'>]"},"metadata":{}}]},{"cell_type":"code","source":"yhat = model.predict(tf.expand_dims(sample[0], axis=0))","metadata":{"execution":{"iopub.status.busy":"2023-10-23T11:15:23.866410Z","iopub.execute_input":"2023-10-23T11:15:23.866697Z","iopub.status.idle":"2023-10-23T11:15:24.451127Z","shell.execute_reply.started":"2023-10-23T11:15:23.866673Z","shell.execute_reply":"2023-10-23T11:15:24.449136Z"},"trusted":true},"execution_count":76,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 1s 513ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()","metadata":{"execution":{"iopub.status.busy":"2023-10-23T11:15:24.452822Z","iopub.execute_input":"2023-10-23T11:15:24.453151Z","iopub.status.idle":"2023-10-23T11:15:24.466350Z","shell.execute_reply.started":"2023-10-23T11:15:24.453125Z","shell.execute_reply":"2023-10-23T11:15:24.464819Z"},"trusted":true},"execution_count":77,"outputs":[]},{"cell_type":"code","source":"print('~'*100, 'PREDICTIONS')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]","metadata":{"execution":{"iopub.status.busy":"2023-10-23T11:15:24.468335Z","iopub.execute_input":"2023-10-23T11:15:24.468604Z","iopub.status.idle":"2023-10-23T11:15:24.590474Z","shell.execute_reply.started":"2023-10-23T11:15:24.468580Z","shell.execute_reply":"2023-10-23T11:15:24.589300Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PREDICTIONS\n","output_type":"stream"},{"execution_count":78,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'bin red at s nine again'>]"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/Kaggle Output/V1.50.96/model-checkpoint/checkpoint b/Kaggle Output/V1.50.96/model-checkpoint/checkpoint
new file mode 100644
index 0000000000000000000000000000000000000000..5be71fcccd7de38d938cc0eba59a43e4021ad153
--- /dev/null
+++ b/Kaggle Output/V1.50.96/model-checkpoint/checkpoint	
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint"
+all_model_checkpoint_paths: "checkpoint"
diff --git a/Kaggle Output/V1.50.96/model-checkpoint/checkpoint.data-00000-of-00001 b/Kaggle Output/V1.50.96/model-checkpoint/checkpoint.data-00000-of-00001
new file mode 100644
index 0000000000000000000000000000000000000000..67a641ea0b324cf5490337a479d1cfaba0274d10
--- /dev/null
+++ b/Kaggle Output/V1.50.96/model-checkpoint/checkpoint.data-00000-of-00001	
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:623f6fbe259cf8c7c5ace0b17a06186699913e3af645c192df3a950f402a2448
+size 101674390
diff --git a/Kaggle Output/V1.50.96/model-checkpoint/checkpoint.index b/Kaggle Output/V1.50.96/model-checkpoint/checkpoint.index
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diff --git a/Kaggle Output/V1.50.96/models - checkpoint 96.zip b/Kaggle Output/V1.50.96/models - checkpoint 96.zip
new file mode 100644
index 0000000000000000000000000000000000000000..9ad15aa0e6d3707942f9956f45e4846008fba352
--- /dev/null
+++ b/Kaggle Output/V1.50.96/models - checkpoint 96.zip	
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+version https://git-lfs.github.com/spec/v1
+oid sha256:d67a1b84159c8e884e677da62a8f8fdd78b4ba67791f5fc92a55eff5d068309f
+size 94462143
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..bfbd3f7f051ceba77d6b3c700855b442fecc9f6a
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Monkey-D-Coders
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/Notebooks/complete-2-models-with-detailed-accuracy-analysis.ipynb b/Notebooks/complete-2-models-with-detailed-accuracy-analysis.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..fd55586fea7247c9ab12e7ae780967d168380183
--- /dev/null
+++ b/Notebooks/complete-2-models-with-detailed-accuracy-analysis.ipynb
@@ -0,0 +1 @@
+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[],"dockerImageVersionId":30558,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 0. Install and Import Dependencies","metadata":{"tags":[]}},{"cell_type":"code","source":"!pip list","metadata":{"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:01:15.733384Z","iopub.execute_input":"2023-11-16T01:01:15.733704Z","iopub.status.idle":"2023-11-16T01:01:19.284684Z","shell.execute_reply.started":"2023-11-16T01:01:15.733678Z","shell.execute_reply":"2023-11-16T01:01:19.283662Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Package                                  Version               Editable project location\n---------------------------------------- --------------------- -------------------------\nabsl-py                                  1.4.0\naccelerate                               0.22.0\naccess                                   1.1.9\naffine                                   2.4.0\naiobotocore                              2.5.4\naiofiles                                 22.1.0\naiohttp                                  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6.0.0\nblessed                                  1.20.0\nblinker                                  1.6.2\nblis                                     0.7.10\nblosc2                                   2.0.0\nbokeh                                    3.2.2\nboltons                                  23.0.0\nBoruta                                   0.3\nboto3                                    1.26.100\nbotocore                                 1.31.17\nbq-helper                                0.4.1                 /root/src/BigQuery_Helper\nbqplot                                   0.12.40\nbranca                                   0.6.0\nbrewer2mpl                               1.4.1\nbrotlipy                                 0.7.0\ncachetools                               4.2.4\nCartopy                                  0.22.0\ncatalogue                                2.0.9\ncatalyst                                 22.4\ncatboost                                 1.2.1\ncategory-encoders               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                 0.8.2\nfasttext                                 0.9.2\nfbpca                                    1.0\nfeather-format                           0.4.1\nfeaturetools                             1.27.0\nfilelock                                 3.12.2\nFiona                                    1.9.4.post1\nfitter                                   1.6.0\nflake8                                   6.0.0\nflashtext                                2.7\nFlask                                    2.3.3\nflatbuffers                              23.5.26\nflax                                     0.7.2\nflit_core                                3.9.0\nfolium                                   0.14.0\nfonttools                                4.40.0\nfqdn                                     1.5.1\nfrozendict                               2.3.8\nfrozenlist                               1.3.3\nfsspec                                   2023.9.0\nfuncy                                    2.0\nfury                                     0.9.0\nfuture                                   0.18.3\nfuzzywuzzy                               0.18.0\ngast                                     0.4.0\ngatspy                                   0.3\ngcsfs                                    2023.6.0\ngensim                                   4.3.2\ngeographiclib                            2.0\nGeohash                                  1.0\ngeojson                                  3.0.1\ngeopandas                                0.13.2\ngeoplot                                  0.5.1\ngeopy                                    2.4.0\ngeoviews                                 1.10.1\nggplot                                   0.11.5\ngiddy                                    2.3.4\ngitdb                                    4.0.10\nGitPython                                3.1.31\ngoogle-api-core                          2.11.1\ngoogle-api-python-client                 2.97.0\ngoogle-apitools                          0.5.31\ngoogle-auth                              2.20.0\ngoogle-auth-httplib2                     0.1.0\ngoogle-auth-oauthlib                     1.0.0\ngoogle-cloud-aiplatform                  0.6.0a1\ngoogle-cloud-artifact-registry           1.8.1\ngoogle-cloud-automl                      1.0.1\ngoogle-cloud-bigquery                    2.34.4\ngoogle-cloud-bigtable                    1.7.3\ngoogle-cloud-core                        2.3.2\ngoogle-cloud-datastore                   2.17.0\ngoogle-cloud-dlp                         3.12.1\ngoogle-cloud-language                    2.11.0\ngoogle-cloud-monitoring                  2.15.0\ngoogle-cloud-pubsub                      2.17.1\ngoogle-cloud-pubsublite                  1.8.2\ngoogle-cloud-recommendations-ai          0.7.1\ngoogle-cloud-resource-manager            1.10.1\ngoogle-cloud-spanner                     3.36.0\ngoogle-cloud-storage                     1.44.0\ngoogle-cloud-translate                   3.12.0\ngoogle-cloud-videointelligence           2.11.3\ngoogle-cloud-vision                      2.8.0\ngoogle-crc32c                            1.5.0\ngoogle-pasta                             0.2.0\ngoogle-resumable-media                   2.5.0\ngoogleapis-common-protos                 1.59.1\ngplearn                                  0.4.2\ngpustat                                  1.0.0\ngpxpy                                    1.5.0\ngraphviz                                 0.20.1\ngreenlet                                 2.0.2\ngrpc-google-iam-v1                       0.12.6\ngrpcio                                   1.51.1\ngrpcio-status                            1.46.3\ngviz-api                                 1.10.0\ngym                                      0.26.2\ngym-notices                              0.0.8\nGymnasium                                0.26.3\ngymnasium-notices                        0.0.1\nh11                                      0.14.0\nh2o                                      3.42.0.3\nh5py                                     3.9.0\nhaversine                                2.8.0\nhdfs                                     2.7.0\nhep-ml                                   0.7.2\nhijri-converter                          2.3.1\nhmmlearn                                 0.3.0\nholidays                                 0.24\nholoviews                                1.17.1\nhpsklearn                                0.1.0\nhtml5lib                                 1.1\nhtmlmin                                  0.1.12\nhttplib2                                 0.21.0\nhttptools                                0.6.0\nhuggingface-hub                          0.16.4\nhumanize                                 4.8.0\nhunspell                                 0.5.5\nhusl                                     4.0.3\nhydra-slayer                             0.4.1\nhyperopt                                 0.2.7\nhypertools                               0.8.0\nibis-framework                           6.2.0\nidna                                     3.4\nigraph                                   0.10.6\nimagecodecs                              2023.9.4\nImageHash                                4.3.1\nimageio                                  2.31.1\nimbalanced-learn                         0.11.0\nimgaug                                   0.4.0\nimportlib-metadata                       6.7.0\nimportlib-resources                      5.12.0\ninequality                               1.0.0\niniconfig                                2.0.0\nipydatawidgets                           4.3.5\nipykernel                                6.23.3\nipyleaflet                               0.17.3\nipympl                                   0.7.0\nipython                                  8.14.0\nipython-genutils                         0.2.0\nipython-sql                              0.5.0\nipyvolume                                0.6.3\nipyvue                                   1.10.0\nipyvuetify                               1.8.10\nipywebrtc                                0.6.0\nipywidgets                               7.7.1\nisoduration                              20.11.0\nisort                                    5.12.0\nisoweek                                  1.3.3\nitsdangerous                             2.1.2\nJanome                                   0.5.0\njaraco.classes                           3.2.3\njax                                      0.4.13\njaxlib                                   0.4.13+cuda11.cudnn86\njedi                                     0.18.2\njeepney                                  0.8.0\njieba                                    0.42.1\nJinja2                                   3.1.2\njmespath                                 1.0.1\njoblib                                   1.3.2\njson5                                    0.9.14\njsonpatch                                1.32\njsonpointer                              2.0\njsonschema                               4.17.3\njupyter_client                           7.4.9\njupyter-console                          6.6.3\njupyter_core                             5.3.1\njupyter-events                           0.6.3\njupyter-http-over-ws                     0.0.8\njupyter-lsp                              1.5.1\njupyter_server                           2.6.0\njupyter_server_fileid                    0.9.0\njupyter-server-mathjax                   0.2.6\njupyter_server_proxy                     4.0.0\njupyter_server_terminals                 0.4.4\njupyter_server_ydoc                      0.8.0\njupyter-ydoc                             0.2.5\njupyterlab                               3.6.5\njupyterlab-git                           0.41.0\njupyterlab-lsp                           4.2.0\njupyterlab-pygments                      0.2.2\njupyterlab_server                        2.23.0\njupyterlab-widgets                       3.0.7\njupytext                                 1.14.6\nkaggle                                   1.5.16\nkaggle-environments                      1.12.0\nkeras                                    2.12.0\nkeras-core                               0.1.5\nkeras-cv                                 0.6.1\nkeras-nlp                                0.6.1\nkeras-tuner                              1.3.5\nkeyring                                  24.2.0\nkeyrings.google-artifactregistry-auth    1.1.2\nkfp                                      2.0.1\nkfp-pipeline-spec                        0.2.2\nkfp-server-api                           2.0.0\nkiwisolver                               1.4.4\nkmapper                                  2.0.1\nkmodes                                   0.12.2\nkorean-lunar-calendar                    0.3.1\nkornia                                   0.7.0\nkt-legacy                                1.0.5\nkubernetes                               26.1.0\nlangcodes                                3.3.0\nlangid                                   1.1.6\nlazy_loader                              0.2\nlazy-object-proxy                        1.9.0\nlearntools                               0.3.4\nleven                                    1.0.4\nLevenshtein                              0.21.1\nlibclang                                 16.0.0\nlibmambapy                               1.4.9\nlibpysal                                 4.7.0\nlibrosa                                  0.10.1\nlightgbm                                 3.3.2\nlightning-utilities                      0.9.0\nlime                                     0.2.0.1\nline-profiler                            4.1.1\nlinkify-it-py                            2.0.2\nllvmlite                                 0.40.1\nlml                                      0.1.0\nlocket                                   1.0.0\nLunarCalendar                            0.0.9\nlxml                                     4.9.3\nlz4                                      4.3.2\nMako                                     1.2.4\nmamba                                    1.4.9\nmapclassify                              2.6.0\nmarisa-trie                              1.0.0\nMarkdown                                 3.4.3\nmarkdown-it-py                           2.2.0\nmarkovify                                0.9.4\nMarkupSafe                               2.1.3\nmarshmallow                              3.20.1\nmatplotlib                               3.7.2\nmatplotlib-inline                        0.1.6\nmatplotlib-venn                          0.11.9\nmccabe                                   0.7.0\nmdit-py-plugins                          0.4.0\nmdurl                                    0.1.0\nmemory-profiler                          0.61.0\nmercantile                               1.2.1\nmgwr                                     2.1.2\nmissingno                                0.5.2\nmistune                                  0.8.4\nmizani                                   0.10.0\nml-dtypes                                0.2.0\nmlcrate                                  0.2.0\nmlens                                    0.2.3\nmlxtend                                  0.22.0\nmmh3                                     4.0.1\nmne                                      1.5.0\nmnist                                    0.2.2\nmock                                     5.1.0\nmomepy                                   0.6.0\nmore-itertools                           9.1.0\nmpld3                                    0.5.9\nmpmath                                   1.3.0\nmsgpack                                  1.0.5\nmsgpack-numpy                            0.4.8\nmultidict                                6.0.4\nmultimethod                              1.9.1\nmultipledispatch                         1.0.0\nmultiprocess                             0.70.15\nmunkres                                  1.1.4\nmurmurhash                               1.0.9\nmypy-extensions                          1.0.0\nnamex                                    0.0.7\nnb-conda                                 2.2.1\nnb-conda-kernels                         2.3.1\nnbclassic                                1.0.0\nnbclient                                 0.5.13\nnbconvert                                6.4.5\nnbdime                                   3.2.0\nnbformat                                 5.9.0\nnest-asyncio                             1.5.6\nnetCDF4                                  1.6.4\nnetworkx                                 3.1\nnibabel                                  5.1.0\nnilearn                                  0.10.1\nninja                                    1.11.1\nnltk                                     3.2.4\nnose                                     1.3.7\nnotebook                                 6.5.4\nnotebook-executor                        0.2\nnotebook_shim                            0.2.3\nnumba                                    0.57.1\nnumexpr                                  2.8.5\nnumpy                                    1.23.5\nnvidia-ml-py                             11.495.46\nnvtx                                     0.2.5\noauth2client                             4.1.3\noauthlib                                 3.2.2\nobjsize                                  0.6.1\nodfpy                                    1.4.1\nolefile                                  0.46\nonnx                                     1.14.1\nopencensus                               0.11.2\nopencensus-context                       0.1.3\nopencv-contrib-python                    4.8.0.76\nopencv-python                            4.8.0.76\nopencv-python-headless                   4.8.0.76\nopenpyxl                                 3.1.2\nopenslide-python                         1.3.0\nopentelemetry-api                        1.18.0\nopentelemetry-exporter-otlp              1.18.0\nopentelemetry-exporter-otlp-proto-common 1.18.0\nopentelemetry-exporter-otlp-proto-grpc   1.18.0\nopentelemetry-exporter-otlp-proto-http   1.18.0\nopentelemetry-proto                      1.18.0\nopentelemetry-sdk                        1.18.0\nopentelemetry-semantic-conventions       0.39b0\nopt-einsum                               3.3.0\noptax                                    0.1.7\noptuna                                   3.3.0\norbax-checkpoint                         0.3.5\norderedmultidict                         1.0.1\norjson                                   3.9.1\nortools                                  9.4.1874\nosmnx                                    1.1.1\noverrides                                6.5.0\npackaging                                21.3\npandas                                   2.0.2\npandas-datareader                        0.10.0\npandas-profiling                         3.6.6\npandas-summary                           0.2.0\npandasql                                 0.7.3\npandocfilters                            1.5.0\npanel                                    1.2.2\npapermill                                2.4.0\nparam                                    1.13.0\nparso                                    0.8.3\nparsy                                    2.1\npartd                                    1.4.0\npath                                     16.7.1\npath.py                                  12.5.0\npathos                                   0.3.1\npathtools                                0.1.2\npathy                                    0.10.1\npatsy                                    0.5.3\npdf2image                                1.16.3\npexpect                                  4.8.0\nphik                                     0.12.3\npickleshare                              0.7.5\nPillow                                   9.5.0\npip                                      23.1.2\npkgutil_resolve_name                     1.3.10\nplatformdirs                             3.10.0\nplotly                                   5.15.0\nplotly-express                           0.4.1\nplotnine                                 0.10.1\npluggy                                   1.0.0\npointpats                                2.3.0\npolars                                   0.19.2\npolyglot                                 16.7.4\npooch                                    1.7.0\npox                                      0.3.3\nppca                                     0.0.4\nppft                                     1.7.6.7\npreprocessing                            0.1.13\npreshed                                  3.0.8\nprettytable                              3.8.0\nprogressbar2                             4.2.0\nprometheus-client                        0.17.0\npromise                                  2.3\nprompt-toolkit                           3.0.38\npronouncing                              0.2.0\nprophet                                  1.1.1\nproto-plus                               1.22.3\nprotobuf                                 3.20.3\npsutil                                   5.9.3\nptxcompiler                              0.8.1\nptxcompiler-cu11                         0.7.0.post1\nptyprocess                               0.7.0\npudb                                     2022.1.3\nPuLP                                     2.7.0\npure-eval                                0.2.2\npy-cpuinfo                               9.0.0\npy-lz4framed                             0.14.0\npy-spy                                   0.3.14\npy4j                                     0.10.9.7\npyaml                                    23.9.1\nPyArabic                                 0.6.15\npyarrow                                  11.0.0\npyasn1                                   0.4.8\npyasn1-modules                           0.2.7\nPyAstronomy                              0.19.0\npybind11                                 2.11.1\npyclipper                                1.3.0.post4\npycodestyle                              2.10.0\npycolmap                                 0.4.0\npycosat                                  0.6.4\npycparser                                2.21\npycryptodome                             3.18.0\npyct                                     0.5.0\npycuda                                   2022.2.2\npydantic                                 1.10.9\npydantic_core                            2.6.3\npydegensac                               0.1.2\npydicom                                  2.4.3\npydocstyle                               6.3.0\npydot                                    1.4.2\npydub                                    0.25.1\npyemd                                    1.0.0\npyerfa                                   2.0.0.3\npyexcel-io                               0.6.6\npyexcel-ods                              0.6.0\npyfasttext                               0.4.6\npyflakes                                 3.0.1\npygltflib                                1.16.0\nPygments                                 2.15.1\nPyJWT                                    2.7.0\npykalman                                 0.9.5\npyLDAvis                                 3.2.2\npylibraft                                23.8.0\npylint                                   2.17.5\npymc3                                    3.11.5\nPyMeeus                                  0.5.12\npymongo                                  3.13.0\nPympler                                  1.0.1\npynndescent                              0.5.10\npynvml                                   11.4.1\npynvrtc                                  9.2\npyocr                                    0.8.3\npyOpenSSL                                23.2.0\npyparsing                                3.0.9\npypdf                                    3.15.5\npyproj                                   3.6.0\npyrsistent                               0.19.3\npysal                                    23.7\npyshp                                    2.3.1\nPySocks                                  1.7.1\npytesseract                              0.3.10\npytest                                   7.4.1\npython-bidi                              0.4.2\npython-dateutil                          2.8.2\npython-dotenv                            1.0.0\npython-json-logger                       2.0.7\npython-Levenshtein                       0.21.1\npython-louvain                           0.16\npython-lsp-jsonrpc                       1.0.0\npython-lsp-server                        1.7.4\npython-slugify                           8.0.1\npython-utils                             3.7.0\npythreejs                                2.4.2\npytoolconfig                             1.2.5\npytools                                  2023.1.1\npytorch-ignite                           0.4.12\npytorch-lightning                        2.0.8\npytz                                     2023.3\npyu2f                                    0.1.5\nPyUpSet                                  0.1.1.post7\npyviz_comms                              3.0.0\nPyWavelets                               1.4.1\nPyYAML                                   6.0\npyzmq                                    25.1.0\nqgrid                                    1.3.1\nqtconsole                                5.4.4\nQtPy                                     2.4.0\nquantecon                                0.7.1\nquantities                               0.14.1\nqudida                                   0.0.4\nraft-dask                                23.8.0\nrandomgen                                1.23.1\nrapidfuzz                                3.2.0\nrasterio                                 1.3.8\nrasterstats                              0.19.0\nray                                      2.5.1\nray-cpp                                  2.5.1\nregex                                    2023.6.3\nrequests                                 2.31.0\nrequests-oauthlib                        1.3.1\nrequests-toolbelt                        0.10.1\nresponses                                0.18.0\nretrying                                 1.3.3\nrfc3339-validator                        0.1.4\nrfc3986-validator                        0.1.1\nrgf-python                               3.12.0\nrich                                     13.4.2\nrmm                                      23.8.0\nrope                                     1.9.0\nrsa                                      4.9\nRtree                                    1.0.1\nruamel.yaml                              0.17.32\nruamel.yaml.clib                         0.2.7\nruamel-yaml-conda                        0.15.100\ns2sphere                                 0.2.5\ns3fs                                     2023.9.0\ns3transfer                               0.6.2\nsafetensors                              0.3.3\nscattertext                              0.1.19\nscikit-image                             0.21.0\nscikit-learn                             1.2.2\nscikit-learn-intelex                     2023.2.1\nscikit-multilearn                        0.2.0\nscikit-optimize                          0.9.0\nscikit-plot                              0.3.7\nscikit-surprise                          1.1.3\nscipy                                    1.11.2\nseaborn                                  0.12.2\nSecretStorage                            3.3.3\nsegment-anything                         1.0\nsegregation                              2.5\nsemver                                   3.0.1\nSend2Trash                               1.8.2\nsentencepiece                            0.1.99\nsentry-sdk                               1.30.0\nsetproctitle                             1.3.2\nsetuptools                               68.0.0\nsetuptools-git                           1.2\nsetuptools-scm                           7.1.0\nshap                                     0.42.1\nShapely                                  1.8.5.post1\nshellingham                              1.5.3\nsimpervisor                              1.0.0\nSimpleITK                                2.2.1\nsimplejson                               3.19.1\nsix                                      1.16.0\nsklearn-pandas                           2.2.0\nslicer                                   0.0.7\nsmart-open                               6.3.0\nsmhasher                                 0.150.1\nsmmap                                    5.0.0\nsniffio                                  1.3.0\nsnowballstemmer                          2.2.0\nsnuggs                                   1.4.7\nsortedcontainers                         2.4.0\nsoundfile                                0.12.1\nsoupsieve                                2.3.2.post1\nsoxr                                     0.3.6\nspacy                                    3.6.1\nspacy-legacy                             3.0.12\nspacy-loggers                            1.0.4\nspaghetti                                1.7.4\nspectral                                 0.23.1\nspglm                                    1.0.8\nsphinx-rtd-theme                         0.2.4\nspint                                    1.0.7\nsplot                                    1.1.5.post1\nspopt                                    0.5.0\nspreg                                    1.3.2\nspvcm                                    0.3.0\nSQLAlchemy                               2.0.17\nsqlglot                                  17.16.2\nsqlparse                                 0.4.4\nsquarify                                 0.4.3\nsrsly                                    2.4.7\nstack-data                               0.6.2\nstarlette                                0.27.0\nstatsmodels                              0.14.0\nstemming                                 1.0.1\nstop-words                               2018.7.23\nstopit                                   1.1.2\nstumpy                                   1.12.0\nsympy                                    1.12\ntables                                   3.8.0\ntabulate                                 0.9.0\ntangled-up-in-unicode                    0.2.0\ntbb                                      2021.10.0\ntblib                                    1.7.0\ntenacity                                 8.2.2\ntensorboard                              2.12.3\ntensorboard-data-server                  0.7.1\ntensorboard-plugin-profile               2.13.0\ntensorboardX                             2.6\ntensorflow                               2.12.0\ntensorflow-addons                        0.21.0\ntensorflow-cloud                         0.1.16\ntensorflow-datasets                      4.9.2\ntensorflow-decision-forests              1.4.0\ntensorflow-estimator                     2.12.0\ntensorflow-hub                           0.12.0\ntensorflow-io                            0.32.0\ntensorflow-io-gcs-filesystem             0.32.0\ntensorflow-metadata                      0.14.0\ntensorflow-probability                   0.20.1\ntensorflow-serving-api                   2.12.1\ntensorflow-text                          2.12.1\ntensorflow-transform                     0.14.0\ntensorflowjs                             3.15.0\ntensorpack                               0.11\ntensorstore                              0.1.41\ntermcolor                                2.3.0\nterminado                                0.17.1\ntestpath                                 0.6.0\ntext-unidecode                           1.3\ntextblob                                 0.17.1\ntexttable                                1.6.7\ntextwrap3                                0.9.2\nTheano                                   1.0.5\nTheano-PyMC                              1.1.2\nthinc                                    8.1.12\nthreadpoolctl                            3.1.0\ntifffile                                 2023.4.12\ntimm                                     0.9.7\ntinycss2                                 1.2.1\ntobler                                   0.11.1\ntokenizers                               0.13.3\ntoml                                     0.10.2\ntomli                                    2.0.1\ntomlkit                                  0.12.1\ntoolz                                    0.12.0\ntorch                                    2.0.0\ntorchaudio                               2.0.1\ntorchdata                                0.6.0\ntorchinfo                                1.8.0\ntorchmetrics                             1.1.1\ntorchtext                                0.15.1\ntorchvision                              0.15.1\ntornado                                  6.3.2\nTPOT                                     0.12.1\ntqdm                                     4.66.1\ntraceml                                  1.0.8\ntraitlets                                5.9.0\ntraittypes                               0.2.1\ntransformers                             4.33.0\ntreelite                                 3.2.0\ntreelite-runtime                         3.2.0\ntrueskill                                0.4.5\ntsfresh                                  0.20.1\ntypeguard                                2.13.3\ntyper                                    0.9.0\ntyping_extensions                        4.6.3\ntyping-inspect                           0.9.0\ntyping-utils                             0.1.0\ntzdata                                   2023.3\ntzlocal                                  5.0.1\nuc-micro-py                              1.0.2\nucx-py                                   0.33.0\nujson                                    5.8.0\numap-learn                               0.5.3\nunicodedata2                             15.0.0\nUnidecode                                1.3.6\nupdate-checker                           0.18.0\nuri-template                             1.3.0\nuritemplate                              3.0.1\nurllib3                                  1.26.15\nurwid                                    2.1.2\nurwid-readline                           0.13\nuvicorn                                  0.22.0\nuvloop                                   0.17.0\nvaex                                     4.17.0\nvaex-astro                               0.9.3\nvaex-core                                4.17.1\nvaex-hdf5                                0.14.1\nvaex-jupyter                             0.8.2\nvaex-ml                                  0.18.3\nvaex-server                              0.9.0\nvaex-viz                                 0.5.4\nvecstack                                 0.4.0\nvirtualenv                               20.21.0\nvisions                                  0.7.5\nvowpalwabbit                             9.9.0\nvtk                                      9.2.6\nWand                                     0.6.11\nwandb                                    0.15.9\nwasabi                                   1.1.2\nwatchfiles                               0.20.0\nwavio                                    0.0.7\nwcwidth                                  0.2.6\nwebcolors                                1.13\nwebencodings                             0.5.1\nwebsocket-client                         1.6.0\nwebsockets                               11.0.3\nWerkzeug                                 2.3.7\nwfdb                                     4.1.2\nwhatthepatch                             1.0.5\nwheel                                    0.40.0\nwidgetsnbextension                       3.6.5\nwitwidget                                1.8.1\nwoodwork                                 0.26.0\nWordbatch                                1.4.9\nwordcloud                                1.9.2\nwordsegment                              1.3.1\nwrapt                                    1.14.1\nwurlitzer                                3.0.3\nxarray                                   2023.8.0\nxarray-einstats                          0.6.0\nxgboost                                  1.7.6\nxvfbwrapper                              0.2.9\nxxhash                                   3.3.0\nxyzservices                              2023.7.0\ny-py                                     0.6.0\nyapf                                     0.40.1\nyarl                                     1.9.2\nydata-profiling                          4.3.1\nyellowbrick                              1.5\nypy-websocket                            0.8.4\nzict                                     3.0.0\nzipp                                     3.15.0\nzstandard                                0.19.0\n","output_type":"stream"}]},{"cell_type":"code","source":"%pip install opencv-python matplotlib imageio gdown tensorflow","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:01:19.286574Z","iopub.execute_input":"2023-11-16T01:01:19.286879Z","iopub.status.idle":"2023-11-16T01:01:31.565919Z","shell.execute_reply.started":"2023-11-16T01:01:19.286851Z","shell.execute_reply":"2023-11-16T01:01:31.564810Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Requirement already satisfied: opencv-python in /opt/conda/lib/python3.10/site-packages (4.8.0.76)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (3.7.2)\nRequirement already satisfied: imageio in /opt/conda/lib/python3.10/site-packages (2.31.1)\nCollecting gdown\n  Downloading gdown-4.7.1-py3-none-any.whl (15 kB)\nRequirement already satisfied: tensorflow in /opt/conda/lib/python3.10/site-packages (2.12.0)\nRequirement already satisfied: numpy>=1.21.2 in /opt/conda/lib/python3.10/site-packages (from opencv-python) (1.23.5)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.1.0)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (0.11.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (4.40.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.4.4)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (21.3)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (9.5.0)\nRequirement already satisfied: pyparsing<3.1,>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (2.8.2)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from gdown) (3.12.2)\nRequirement already satisfied: requests[socks] in /opt/conda/lib/python3.10/site-packages (from gdown) (2.31.0)\nRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from gdown) (1.16.0)\nRequirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from gdown) (4.66.1)\nRequirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.10/site-packages (from gdown) (4.12.2)\nRequirement already satisfied: absl-py>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.4.0)\nRequirement already satisfied: astunparse>=1.6.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.6.3)\nRequirement already satisfied: flatbuffers>=2.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (23.5.26)\nRequirement already satisfied: gast<=0.4.0,>=0.2.1 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.4.0)\nRequirement already satisfied: google-pasta>=0.1.1 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.2.0)\nRequirement already satisfied: grpcio<2.0,>=1.24.3 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.51.1)\nRequirement already satisfied: h5py>=2.9.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (3.9.0)\nRequirement already satisfied: jax>=0.3.15 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.4.13)\nRequirement already satisfied: keras<2.13,>=2.12.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.12.0)\nRequirement already satisfied: libclang>=13.0.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (16.0.0)\nRequirement already satisfied: opt-einsum>=2.3.2 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (3.3.0)\nRequirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (3.20.3)\nRequirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from tensorflow) (68.0.0)\nRequirement already satisfied: tensorboard<2.13,>=2.12 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.12.3)\nRequirement already satisfied: tensorflow-estimator<2.13,>=2.12.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.12.0)\nRequirement already satisfied: termcolor>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.3.0)\nRequirement already satisfied: typing-extensions>=3.6.6 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (4.6.3)\nRequirement already satisfied: wrapt<1.15,>=1.11.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.14.1)\nRequirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.32.0)\nRequirement already satisfied: wheel<1.0,>=0.23.0 in /opt/conda/lib/python3.10/site-packages (from astunparse>=1.6.0->tensorflow) (0.40.0)\nRequirement already satisfied: ml-dtypes>=0.1.0 in /opt/conda/lib/python3.10/site-packages (from jax>=0.3.15->tensorflow) (0.2.0)\nRequirement already satisfied: scipy>=1.7 in /opt/conda/lib/python3.10/site-packages (from jax>=0.3.15->tensorflow) (1.11.2)\nRequirement already satisfied: google-auth<3,>=1.6.3 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.20.0)\nRequirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (1.0.0)\nRequirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (3.4.3)\nRequirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (0.7.1)\nRequirement already satisfied: werkzeug>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.3.7)\nRequirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.10/site-packages (from beautifulsoup4->gdown) (2.3.2.post1)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (3.1.0)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (3.4)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (1.26.15)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (2023.7.22)\nRequirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (1.7.1)\nRequirement already satisfied: cachetools<6.0,>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (4.2.4)\nRequirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.2.7)\nRequirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (4.9)\nRequirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (1.3.1)\nRequirement already satisfied: MarkupSafe>=2.1.1 in /opt/conda/lib/python3.10/site-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow) (2.1.3)\nRequirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.4.8)\nRequirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.10/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (3.2.2)\nInstalling collected packages: gdown\nSuccessfully installed gdown-4.7.1\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"import os\nimport cv2\nimport tensorflow as tf\nimport numpy as np\nfrom typing import List\nfrom matplotlib import pyplot as plt\n","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:01:31.568504Z","iopub.execute_input":"2023-11-16T01:01:31.569275Z","iopub.status.idle":"2023-11-16T01:01:39.424403Z","shell.execute_reply.started":"2023-11-16T01:01:31.569244Z","shell.execute_reply":"2023-11-16T01:01:39.423624Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"code","source":"import imageio","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:01:39.426957Z","iopub.execute_input":"2023-11-16T01:01:39.427676Z","iopub.status.idle":"2023-11-16T01:01:39.495305Z","shell.execute_reply.started":"2023-11-16T01:01:39.427634Z","shell.execute_reply":"2023-11-16T01:01:39.494577Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"tf.config.list_physical_devices('GPU')","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:01:39.496389Z","iopub.execute_input":"2023-11-16T01:01:39.496715Z","iopub.status.idle":"2023-11-16T01:01:39.827526Z","shell.execute_reply.started":"2023-11-16T01:01:39.496687Z","shell.execute_reply":"2023-11-16T01:01:39.826489Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"},"metadata":{}}]},{"cell_type":"code","source":"physical_devices = tf.config.list_physical_devices('GPU')\ntry:\n    tf.config.experimental.set_memory_growth(physical_devices[0], True)\nexcept:\n    pass","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:01:39.828983Z","iopub.execute_input":"2023-11-16T01:01:39.829843Z","iopub.status.idle":"2023-11-16T01:01:39.836424Z","shell.execute_reply.started":"2023-11-16T01:01:39.829805Z","shell.execute_reply":"2023-11-16T01:01:39.835564Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"# 1. Build Data Loading Functions","metadata":{"tags":[]}},{"cell_type":"code","source":"import gdown","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:01:39.837750Z","iopub.execute_input":"2023-11-16T01:01:39.838469Z","iopub.status.idle":"2023-11-16T01:01:40.019714Z","shell.execute_reply.started":"2023-11-16T01:01:39.838420Z","shell.execute_reply":"2023-11-16T01:01:40.018899Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\noutput = 'data.zip'\ngdown.download(url, output, quiet=False)\ngdown.extractall('data.zip')","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:01:40.021138Z","iopub.execute_input":"2023-11-16T01:01:40.021974Z","iopub.status.idle":"2023-11-16T01:02:02.192867Z","shell.execute_reply.started":"2023-11-16T01:01:40.021938Z","shell.execute_reply":"2023-11-16T01:02:02.191833Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"Downloading...\nFrom (uriginal): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL\nFrom (redirected): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL&confirm=t&uuid=726b159c-fd8e-4e68-9532-6707a56660df\nTo: /kaggle/working/data.zip\n100%|██████████| 423M/423M [00:16<00:00, 26.0MB/s] \n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"['data/',\n 'data/alignments/',\n 'data/alignments/s1/',\n 'data/alignments/s1/bbaf2n.align',\n 'data/alignments/s1/bbaf3s.align',\n 'data/alignments/s1/bbaf4p.align',\n 'data/alignments/s1/bbaf5a.align',\n 'data/alignments/s1/bbal6n.align',\n 'data/alignments/s1/bbal7s.align',\n 'data/alignments/s1/bbal8p.align',\n 'data/alignments/s1/bbal9a.align',\n 'data/alignments/s1/bbas1s.align',\n 'data/alignments/s1/bbas2p.align',\n 'data/alignments/s1/bbas3a.align',\n 'data/alignments/s1/bbaszn.align',\n 'data/alignments/s1/bbaz4n.align',\n 'data/alignments/s1/bbaz5s.align',\n 'data/alignments/s1/bbaz6p.align',\n 'data/alignments/s1/bbaz7a.align',\n 'data/alignments/s1/bbbf6n.align',\n 'data/alignments/s1/bbbf7s.align',\n 'data/alignments/s1/bbbf8p.align',\n 'data/alignments/s1/bbbf9a.align',\n 'data/alignments/s1/bbbm1s.align',\n 'data/alignments/s1/bbbm2p.align',\n 'data/alignments/s1/bbbm3a.align',\n 'data/alignments/s1/bbbmzn.align',\n 'data/alignments/s1/bbbs4n.align',\n 'data/alignments/s1/bbbs5s.align',\n 'data/alignments/s1/bbbs6p.align',\n 'data/alignments/s1/bbbs7a.align',\n 'data/alignments/s1/bbbz8n.align',\n 'data/alignments/s1/bbbz9s.align',\n 'data/alignments/s1/bbie8n.align',\n 'data/alignments/s1/bbie9s.align',\n 'data/alignments/s1/bbif1a.align',\n 'data/alignments/s1/bbifzp.align',\n 'data/alignments/s1/bbil2n.align',\n 'data/alignments/s1/bbil3s.align',\n 'data/alignments/s1/bbil4p.align',\n 'data/alignments/s1/bbil5a.align',\n 'data/alignments/s1/bbir6n.align',\n 'data/alignments/s1/bbir7s.align',\n 'data/alignments/s1/bbir8p.align',\n 'data/alignments/s1/bbir9a.align',\n 'data/alignments/s1/bbiz1s.align',\n 'data/alignments/s1/bbiz2p.align',\n 'data/alignments/s1/bbiz3a.align',\n 'data/alignments/s1/bbizzn.align',\n 'data/alignments/s1/bbwg1s.align',\n 'data/alignments/s1/bbwg2p.align',\n 'data/alignments/s1/bbwg3a.align',\n 'data/alignments/s1/bbwgzn.align',\n 'data/alignments/s1/bbwm4n.align',\n 'data/alignments/s1/bbwm5s.align',\n 'data/alignments/s1/bbwm6p.align',\n 'data/alignments/s1/bbwm7a.align',\n 'data/alignments/s1/bbws8n.align',\n 'data/alignments/s1/bbws9s.align',\n 'data/alignments/s1/bbwt1a.align',\n 'data/alignments/s1/bbwtzp.align',\n 'data/alignments/s1/bgaa6n.align',\n 'data/alignments/s1/bgaa7s.align',\n 'data/alignments/s1/bgaa8p.align',\n 'data/alignments/s1/bgaa9a.align',\n 'data/alignments/s1/bgah1s.align',\n 'data/alignments/s1/bgah2p.align',\n 'data/alignments/s1/bgah3a.align',\n 'data/alignments/s1/bgahzn.align',\n 'data/alignments/s1/bgan4n.align',\n 'data/alignments/s1/bgan5s.align',\n 'data/alignments/s1/bgan6p.align',\n 'data/alignments/s1/bgan7a.align',\n 'data/alignments/s1/bgat8n.align',\n 'data/alignments/s1/bgat9s.align',\n 'data/alignments/s1/bgau1a.align',\n 'data/alignments/s1/bgauzp.align',\n 'data/alignments/s1/bgbb1s.align',\n 'data/alignments/s1/bgbb2p.align',\n 'data/alignments/s1/bgbb3a.align',\n 'data/alignments/s1/bgbbzn.align',\n 'data/alignments/s1/bgbh4n.align',\n 'data/alignments/s1/bgbh5s.align',\n 'data/alignments/s1/bgbh6p.align',\n 'data/alignments/s1/bgbh7a.align',\n 'data/alignments/s1/bgbn8n.align',\n 'data/alignments/s1/bgbn9s.align',\n 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'data/alignments/s1/swau8n.align',\n 'data/alignments/s1/swau9s.align',\n 'data/alignments/s1/swav1a.align',\n 'data/alignments/s1/swavzp.align',\n 'data/alignments/s1/swbc1s.align',\n 'data/alignments/s1/swbc2p.align',\n 'data/alignments/s1/swbc3a.align',\n 'data/alignments/s1/swbczn.align',\n 'data/alignments/s1/swbi4n.align',\n 'data/alignments/s1/swbi5s.align',\n 'data/alignments/s1/swbi6p.align',\n 'data/alignments/s1/swbi7a.align',\n 'data/alignments/s1/swbo8n.align',\n 'data/alignments/s1/swbo9s.align',\n 'data/alignments/s1/swbp1a.align',\n 'data/alignments/s1/swbpzp.align',\n 'data/alignments/s1/swbv2n.align',\n 'data/alignments/s1/swbv3s.align',\n 'data/alignments/s1/swbv4p.align',\n 'data/alignments/s1/swbv5a.align',\n 'data/alignments/s1/swib2n.align',\n 'data/alignments/s1/swib3s.align',\n 'data/alignments/s1/swib4p.align',\n 'data/alignments/s1/swib5a.align',\n 'data/alignments/s1/swih6n.align',\n 'data/alignments/s1/swih7s.align',\n 'data/alignments/s1/swih8p.align',\n 'data/alignments/s1/swih9a.align',\n 'data/alignments/s1/swio1s.align',\n 'data/alignments/s1/swio2p.align',\n 'data/alignments/s1/swio3a.align',\n 'data/alignments/s1/swiozn.align',\n 'data/alignments/s1/swiu4n.align',\n 'data/alignments/s1/swiu5s.align',\n 'data/alignments/s1/swiu6p.align',\n 'data/alignments/s1/swiu7a.align',\n 'data/alignments/s1/swwc4n.align',\n 'data/alignments/s1/swwc5s.align',\n 'data/alignments/s1/swwc6p.align',\n 'data/alignments/s1/swwc7a.align',\n 'data/alignments/s1/swwi8n.align',\n 'data/alignments/s1/swwi9s.align',\n 'data/alignments/s1/swwj1a.align',\n 'data/alignments/s1/swwjzp.align',\n 'data/alignments/s1/swwp2n.align',\n 'data/alignments/s1/swwp3s.align',\n 'data/alignments/s1/swwp4p.align',\n 'data/alignments/s1/swwp5a.align',\n 'data/alignments/s1/swwv6n.align',\n ...]"},"metadata":{}}]},{"cell_type":"code","source":"def load_video(path:str) -> List[float]: \n\n    cap = cv2.VideoCapture(path)\n    frames = []\n    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))): \n        ret, frame = cap.read()\n        frame = tf.image.rgb_to_grayscale(frame)\n        frames.append(frame[190:236,80:220,:])\n    cap.release()\n    \n    mean = tf.math.reduce_mean(frames)\n    std = tf.math.reduce_std(tf.cast(frames, tf.float32))\n    return tf.cast((frames - mean), tf.float32) / std","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:02.194276Z","iopub.execute_input":"2023-11-16T01:02:02.195068Z","iopub.status.idle":"2023-11-16T01:02:02.202688Z","shell.execute_reply.started":"2023-11-16T01:02:02.195028Z","shell.execute_reply":"2023-11-16T01:02:02.201627Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"vocab = [x for x in \"abcdefghijklmnopqrstuvwxyz'?!123456789 \"]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:02.207148Z","iopub.execute_input":"2023-11-16T01:02:02.207747Z","iopub.status.idle":"2023-11-16T01:02:02.212605Z","shell.execute_reply.started":"2023-11-16T01:02:02.207718Z","shell.execute_reply":"2023-11-16T01:02:02.211502Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\nnum_to_char = tf.keras.layers.StringLookup(\n    vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True\n)\n\nprint(\n    f\"The vocabulary is: {char_to_num.get_vocabulary()} \"\n    f\"(size ={char_to_num.vocabulary_size()})\"\n)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:02.213908Z","iopub.execute_input":"2023-11-16T01:02:02.214219Z","iopub.status.idle":"2023-11-16T01:02:04.369046Z","shell.execute_reply.started":"2023-11-16T01:02:02.214191Z","shell.execute_reply":"2023-11-16T01:02:04.368025Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', '1', '2', '3', '4', '5', '6', '7', '8', '9', ' '] (size =40)\n","output_type":"stream"}]},{"cell_type":"code","source":"char_to_num.get_vocabulary()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:04.370200Z","iopub.execute_input":"2023-11-16T01:02:04.370490Z","iopub.status.idle":"2023-11-16T01:02:04.379020Z","shell.execute_reply.started":"2023-11-16T01:02:04.370459Z","shell.execute_reply":"2023-11-16T01:02:04.378119Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"['',\n 'a',\n 'b',\n 'c',\n 'd',\n 'e',\n 'f',\n 'g',\n 'h',\n 'i',\n 'j',\n 'k',\n 'l',\n 'm',\n 'n',\n 'o',\n 'p',\n 'q',\n 'r',\n 's',\n 't',\n 'u',\n 'v',\n 'w',\n 'x',\n 'y',\n 'z',\n \"'\",\n '?',\n '!',\n '1',\n '2',\n '3',\n '4',\n '5',\n '6',\n '7',\n '8',\n '9',\n ' ']"},"metadata":{}}]},{"cell_type":"code","source":"char_to_num(['n','i','c','k'])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:04.380175Z","iopub.execute_input":"2023-11-16T01:02:04.380486Z","iopub.status.idle":"2023-11-16T01:02:04.390993Z","shell.execute_reply.started":"2023-11-16T01:02:04.380453Z","shell.execute_reply":"2023-11-16T01:02:04.390020Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(4,), dtype=int64, numpy=array([14,  9,  3, 11])>"},"metadata":{}}]},{"cell_type":"code","source":"num_to_char([14,  9,  3, 11])","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:04.392256Z","iopub.execute_input":"2023-11-16T01:02:04.392841Z","iopub.status.idle":"2023-11-16T01:02:04.406456Z","shell.execute_reply.started":"2023-11-16T01:02:04.392809Z","shell.execute_reply":"2023-11-16T01:02:04.405663Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(4,), dtype=string, numpy=array([b'n', b'i', b'c', b'k'], dtype=object)>"},"metadata":{}}]},{"cell_type":"code","source":"def load_alignments(path:str) -> List[str]: \n    with open(path, 'r') as f: \n        lines = f.readlines() \n    tokens = []\n    for line in lines:\n        line = line.split()\n        if line[2] != 'sil': \n            tokens = [*tokens,' ',line[2]]\n    return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:04.407449Z","iopub.execute_input":"2023-11-16T01:02:04.407722Z","iopub.status.idle":"2023-11-16T01:02:04.413358Z","shell.execute_reply.started":"2023-11-16T01:02:04.407699Z","shell.execute_reply":"2023-11-16T01:02:04.412495Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"def load_data(path: str): \n#     path = bytes.decode(path.numpy())\n#     print(path)\n    file_name = path.numpy().decode('utf-8').split('/')[-1].split('.')[0]\n    # File name splitting for windows\n#     file_name = path.split('\\\\')[-1].split('.')[0]\n    video_path = os.path.join('data','s1',f'{file_name}.mpg')\n    alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')\n    frames = load_video(video_path) \n    alignments = load_alignments(alignment_path)\n    \n    return frames, alignments\n#     return file_name","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:04.414492Z","iopub.execute_input":"2023-11-16T01:02:04.414812Z","iopub.status.idle":"2023-11-16T01:02:04.422524Z","shell.execute_reply.started":"2023-11-16T01:02:04.414781Z","shell.execute_reply":"2023-11-16T01:02:04.421748Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"test_path = './data/s1/bbal6n.mpg'","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:04.423603Z","iopub.execute_input":"2023-11-16T01:02:04.424480Z","iopub.status.idle":"2023-11-16T01:02:04.433202Z","shell.execute_reply.started":"2023-11-16T01:02:04.424448Z","shell.execute_reply":"2023-11-16T01:02:04.432474Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"# test_path = bytes.decode(test_path.numpy())","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:04.434154Z","iopub.execute_input":"2023-11-16T01:02:04.434441Z","iopub.status.idle":"2023-11-16T01:02:04.441157Z","shell.execute_reply.started":"2023-11-16T01:02:04.434409Z","shell.execute_reply":"2023-11-16T01:02:04.440440Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"# hi = tf.convert_to_tensor(test_path).numpy().decode('utf-8')\n# hi","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:04.442400Z","iopub.execute_input":"2023-11-16T01:02:04.442760Z","iopub.status.idle":"2023-11-16T01:02:04.449944Z","shell.execute_reply.started":"2023-11-16T01:02:04.442734Z","shell.execute_reply":"2023-11-16T01:02:04.449118Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('/')[-1].split('.')[0]","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:04.450947Z","iopub.execute_input":"2023-11-16T01:02:04.451255Z","iopub.status.idle":"2023-11-16T01:02:04.460280Z","shell.execute_reply.started":"2023-11-16T01:02:04.451231Z","shell.execute_reply":"2023-11-16T01:02:04.459452Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"'bbal6n'"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"frames, alignments = load_data(tf.convert_to_tensor(test_path))","metadata":{"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:04.461196Z","iopub.execute_input":"2023-11-16T01:02:04.461507Z","iopub.status.idle":"2023-11-16T01:02:07.211166Z","shell.execute_reply.started":"2023-11-16T01:02:04.461477Z","shell.execute_reply":"2023-11-16T01:02:07.210200Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"alignments","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:07.212518Z","iopub.execute_input":"2023-11-16T01:02:07.213171Z","iopub.status.idle":"2023-11-16T01:02:07.219601Z","shell.execute_reply.started":"2023-11-16T01:02:07.213125Z","shell.execute_reply":"2023-11-16T01:02:07.218675Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(21,), dtype=int64, numpy=\narray([ 2,  9, 14, 39,  2, 12, 21,  5, 39,  1, 20, 39, 12, 39, 19,  9, 24,\n       39, 14, 15, 23])>"},"metadata":{}}]},{"cell_type":"code","source":"plt.imshow(frames[40])","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:07.220912Z","iopub.execute_input":"2023-11-16T01:02:07.221257Z","iopub.status.idle":"2023-11-16T01:02:07.507113Z","shell.execute_reply.started":"2023-11-16T01:02:07.221218Z","shell.execute_reply":"2023-11-16T01:02:07.506374Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7e6a4d80bfd0>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"alignments","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:07.508655Z","iopub.execute_input":"2023-11-16T01:02:07.509308Z","iopub.status.idle":"2023-11-16T01:02:07.515693Z","shell.execute_reply.started":"2023-11-16T01:02:07.509270Z","shell.execute_reply":"2023-11-16T01:02:07.514752Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(21,), dtype=int64, numpy=\narray([ 2,  9, 14, 39,  2, 12, 21,  5, 39,  1, 20, 39, 12, 39, 19,  9, 24,\n       39, 14, 15, 23])>"},"metadata":{}}]},{"cell_type":"code","source":"tf.strings.reduce_join([bytes.decode(x) for x in num_to_char(alignments.numpy()).numpy()])","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:07.516841Z","iopub.execute_input":"2023-11-16T01:02:07.517148Z","iopub.status.idle":"2023-11-16T01:02:07.527980Z","shell.execute_reply.started":"2023-11-16T01:02:07.517117Z","shell.execute_reply":"2023-11-16T01:02:07.527058Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'bin blue at l six now'>"},"metadata":{}}]},{"cell_type":"code","source":"def mappable_function(path:str) ->List[str]:\n    result = tf.py_function(load_data, [path], (tf.float32, tf.int64))\n    return result","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:07.528969Z","iopub.execute_input":"2023-11-16T01:02:07.529207Z","iopub.status.idle":"2023-11-16T01:02:07.534332Z","shell.execute_reply.started":"2023-11-16T01:02:07.529185Z","shell.execute_reply":"2023-11-16T01:02:07.533492Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"markdown","source":"# 2. Create Data Pipeline","metadata":{"tags":[]}},{"cell_type":"code","source":"from matplotlib import pyplot as plt","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:07.535419Z","iopub.execute_input":"2023-11-16T01:02:07.535709Z","iopub.status.idle":"2023-11-16T01:02:07.543321Z","shell.execute_reply.started":"2023-11-16T01:02:07.535685Z","shell.execute_reply":"2023-11-16T01:02:07.542480Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"data = tf.data.Dataset.list_files('./data/s1/*.mpg')\ndata = data.shuffle(500, reshuffle_each_iteration=False)\ndata = data.map(mappable_function)\ndata = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\ndata = data.prefetch(tf.data.AUTOTUNE)\n# Added for split \ntrain = data.take(450)\ntest = data.skip(450)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:07.552753Z","iopub.execute_input":"2023-11-16T01:02:07.553068Z","iopub.status.idle":"2023-11-16T01:02:07.660047Z","shell.execute_reply.started":"2023-11-16T01:02:07.553045Z","shell.execute_reply":"2023-11-16T01:02:07.659302Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"len(train)","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:07.661133Z","iopub.execute_input":"2023-11-16T01:02:07.661413Z","iopub.status.idle":"2023-11-16T01:02:07.667877Z","shell.execute_reply.started":"2023-11-16T01:02:07.661387Z","shell.execute_reply":"2023-11-16T01:02:07.666701Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"450"},"metadata":{}}]},{"cell_type":"code","source":"frames, alignments = data.as_numpy_iterator().next()","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:07.668868Z","iopub.execute_input":"2023-11-16T01:02:07.669157Z","iopub.status.idle":"2023-11-16T01:02:08.373507Z","shell.execute_reply.started":"2023-11-16T01:02:07.669123Z","shell.execute_reply":"2023-11-16T01:02:08.372488Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"len(frames)","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:08.374766Z","iopub.execute_input":"2023-11-16T01:02:08.375065Z","iopub.status.idle":"2023-11-16T01:02:08.381548Z","shell.execute_reply.started":"2023-11-16T01:02:08.375038Z","shell.execute_reply":"2023-11-16T01:02:08.380485Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"2"},"metadata":{}}]},{"cell_type":"code","source":"sample = data.as_numpy_iterator()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:08.382959Z","iopub.execute_input":"2023-11-16T01:02:08.383352Z","iopub.status.idle":"2023-11-16T01:02:08.393436Z","shell.execute_reply.started":"2023-11-16T01:02:08.383319Z","shell.execute_reply":"2023-11-16T01:02:08.392611Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"# len(sample)","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:08.394839Z","iopub.execute_input":"2023-11-16T01:02:08.395420Z","iopub.status.idle":"2023-11-16T01:02:08.399362Z","shell.execute_reply.started":"2023-11-16T01:02:08.395387Z","shell.execute_reply":"2023-11-16T01:02:08.398384Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"val = sample.next(); val[0]","metadata":{"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:08.400409Z","iopub.execute_input":"2023-11-16T01:02:08.400690Z","iopub.status.idle":"2023-11-16T01:02:08.845733Z","shell.execute_reply.started":"2023-11-16T01:02:08.400667Z","shell.execute_reply":"2023-11-16T01:02:08.844693Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"array([[[[[1.2794168 ],\n          [1.2794168 ],\n          [1.2794168 ],\n          ...,\n          [9.266079  ],\n          [9.304849  ],\n          [8.917147  ]],\n\n         [[1.2794168 ],\n          [1.2794168 ],\n          [1.2794168 ],\n          ...,\n          [8.645756  ],\n          [8.917147  ],\n          [8.917147  ]],\n\n         [[1.2018763 ],\n          [1.2018763 ],\n          [1.2018763 ],\n          ...,\n          [9.653781  ],\n          [9.770091  ],\n          [9.770091  ]],\n\n         ...,\n\n         [[0.8529445 ],\n     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sample.next()\n    \n#     print(i,val[0][0][0][0][0])\n#     print(tf.strings.reduce_join([num_to_char(word) for word in val[1][0]]))\n#     yhat = model.predict(val[0])\n#     print(tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)]))\n#     print(tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]]))","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:08.849213Z","iopub.execute_input":"2023-11-16T01:02:08.849536Z","iopub.status.idle":"2023-11-16T01:02:08.854538Z","shell.execute_reply.started":"2023-11-16T01:02:08.849509Z","shell.execute_reply":"2023-11-16T01:02:08.853313Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"# imageio.mimsave('./animation.gif', val[0][0], fps=10)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:08.856444Z","iopub.execute_input":"2023-11-16T01:02:08.857382Z","iopub.status.idle":"2023-11-16T01:02:08.867939Z","shell.execute_reply.started":"2023-11-16T01:02:08.857348Z","shell.execute_reply":"2023-11-16T01:02:08.865876Z"},"trusted":true},"execution_count":36,"outputs":[]},{"cell_type":"code","source":"# 0:videos, 0: 1st video out of the batch,  0: return the first frame in the video \nplt.imshow(val[0][0][35])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:08.870680Z","iopub.execute_input":"2023-11-16T01:02:08.871024Z","iopub.status.idle":"2023-11-16T01:02:09.177830Z","shell.execute_reply.started":"2023-11-16T01:02:08.870991Z","shell.execute_reply":"2023-11-16T01:02:09.176227Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7e6a4c2c9540>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:09.179875Z","iopub.execute_input":"2023-11-16T01:02:09.180156Z","iopub.status.idle":"2023-11-16T01:02:09.252441Z","shell.execute_reply.started":"2023-11-16T01:02:09.180131Z","shell.execute_reply":"2023-11-16T01:02:09.250826Z"},"trusted":true},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'bin green with b six please'>"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3. Design the Deep Neural Network","metadata":{"tags":[]}},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential \nfrom tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:09.254567Z","iopub.execute_input":"2023-11-16T01:02:09.254830Z","iopub.status.idle":"2023-11-16T01:02:09.267015Z","shell.execute_reply.started":"2023-11-16T01:02:09.254807Z","shell.execute_reply":"2023-11-16T01:02:09.265945Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"data.as_numpy_iterator().next()[0][0].shape","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:09.269705Z","iopub.execute_input":"2023-11-16T01:02:09.269962Z","iopub.status.idle":"2023-11-16T01:02:10.183317Z","shell.execute_reply.started":"2023-11-16T01:02:09.269939Z","shell.execute_reply":"2023-11-16T01:02:10.182371Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"(75, 46, 140, 1)"},"metadata":{}}]},{"cell_type":"code","source":"model = Sequential()\nmodel.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPool3D((1,2,2)))\n\nmodel.add(Conv3D(256, 3, padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPool3D((1,2,2)))\n\nmodel.add(Conv3D(75, 3, padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPool3D((1,2,2)))\n\nmodel.add(TimeDistributed(Flatten()))\n\nmodel.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\nmodel.add(Dropout(.5))\n\nmodel.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\nmodel.add(Dropout(.5))\n\nmodel.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:10.184620Z","iopub.execute_input":"2023-11-16T01:02:10.185713Z","iopub.status.idle":"2023-11-16T01:02:12.367267Z","shell.execute_reply.started":"2023-11-16T01:02:10.185683Z","shell.execute_reply":"2023-11-16T01:02:12.366243Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"# url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n# url = 'https://drive.google.com/file/d/1fyZoYgqZw_aRa66kiOR6J8XLu3Y9wfMg/view?usp=sharing'\n# url = 'https://drive.google.com/u/0/uc?id=1JAmcd2v0JcZStgs69VytgqwGJMlhNeaT&export=download'\n# output = 'check.zip'\n# gdown.download(url, output, quiet=False)\n# gdown.extractall('check.zip', 'models')","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:12.368701Z","iopub.execute_input":"2023-11-16T01:02:12.369086Z","iopub.status.idle":"2023-11-16T01:02:12.374115Z","shell.execute_reply.started":"2023-11-16T01:02:12.369050Z","shell.execute_reply":"2023-11-16T01:02:12.373081Z"},"trusted":true},"execution_count":42,"outputs":[]},{"cell_type":"code","source":"# model.load_weights('./models/models/checkpoint')","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:12.375308Z","iopub.execute_input":"2023-11-16T01:02:12.375650Z","iopub.status.idle":"2023-11-16T01:02:12.385450Z","shell.execute_reply.started":"2023-11-16T01:02:12.375624Z","shell.execute_reply":"2023-11-16T01:02:12.384584Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:12.386652Z","iopub.execute_input":"2023-11-16T01:02:12.387025Z","iopub.status.idle":"2023-11-16T01:02:12.432834Z","shell.execute_reply.started":"2023-11-16T01:02:12.386994Z","shell.execute_reply":"2023-11-16T01:02:12.432029Z"},"trusted":true},"execution_count":44,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n conv3d (Conv3D)             (None, 75, 46, 140, 128)  3584      \n                                                                 \n activation (Activation)     (None, 75, 46, 140, 128)  0         \n                                                                 \n max_pooling3d (MaxPooling3D  (None, 75, 23, 70, 128)  0         \n )                                                               \n                                                                 \n conv3d_1 (Conv3D)           (None, 75, 23, 70, 256)   884992    \n                                                                 \n activation_1 (Activation)   (None, 75, 23, 70, 256)   0         \n                                                                 \n max_pooling3d_1 (MaxPooling  (None, 75, 11, 35, 256)  0         \n 3D)                                                             \n                                                                 \n conv3d_2 (Conv3D)           (None, 75, 11, 35, 75)    518475    \n                                                                 \n activation_2 (Activation)   (None, 75, 11, 35, 75)    0         \n                                                                 \n max_pooling3d_2 (MaxPooling  (None, 75, 5, 17, 75)    0         \n 3D)                                                             \n                                                                 \n time_distributed (TimeDistr  (None, 75, 6375)         0         \n ibuted)                                                         \n                                                                 \n bidirectional (Bidirectiona  (None, 75, 256)          6660096   \n l)                                                              \n                                                                 \n dropout (Dropout)           (None, 75, 256)           0         \n                                                                 \n bidirectional_1 (Bidirectio  (None, 75, 256)          394240    \n nal)                                                            \n                                                                 \n dropout_1 (Dropout)         (None, 75, 256)           0         \n                                                                 \n dense (Dense)               (None, 75, 41)            10537     \n                                                                 \n=================================================================\nTotal params: 8,471,924\nTrainable params: 8,471,924\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"5*17*75","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:12.433949Z","iopub.execute_input":"2023-11-16T01:02:12.434271Z","iopub.status.idle":"2023-11-16T01:02:12.440389Z","shell.execute_reply.started":"2023-11-16T01:02:12.434239Z","shell.execute_reply":"2023-11-16T01:02:12.439478Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"6375"},"metadata":{}}]},{"cell_type":"code","source":"yhat = model.predict(val[0])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:12.441390Z","iopub.execute_input":"2023-11-16T01:02:12.441738Z","iopub.status.idle":"2023-11-16T01:02:17.867857Z","shell.execute_reply.started":"2023-11-16T01:02:12.441708Z","shell.execute_reply":"2023-11-16T01:02:17.866905Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 5s 5s/step\n","output_type":"stream"}]},{"cell_type":"code","source":"tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)])","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:17.869368Z","iopub.execute_input":"2023-11-16T01:02:17.869776Z","iopub.status.idle":"2023-11-16T01:02:18.011056Z","shell.execute_reply.started":"2023-11-16T01:02:17.869743Z","shell.execute_reply":"2023-11-16T01:02:18.009234Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'yyyyyyy888888888888888888888888888888888888888eeeeeeee888888888888888888888'>"},"metadata":{}}]},{"cell_type":"code","source":"tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.013854Z","iopub.execute_input":"2023-11-16T01:02:18.014624Z","iopub.status.idle":"2023-11-16T01:02:18.198215Z","shell.execute_reply.started":"2023-11-16T01:02:18.014568Z","shell.execute_reply":"2023-11-16T01:02:18.196422Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'yyyyyyy888888888888888888888888888888888888888eeeeeeee888888888888888888888'>"},"metadata":{}}]},{"cell_type":"code","source":"model.input_shape","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.200916Z","iopub.execute_input":"2023-11-16T01:02:18.201696Z","iopub.status.idle":"2023-11-16T01:02:18.212821Z","shell.execute_reply.started":"2023-11-16T01:02:18.201612Z","shell.execute_reply":"2023-11-16T01:02:18.210996Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"(None, 75, 46, 140, 1)"},"metadata":{}}]},{"cell_type":"code","source":"model.output_shape","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.215246Z","iopub.execute_input":"2023-11-16T01:02:18.216022Z","iopub.status.idle":"2023-11-16T01:02:18.227781Z","shell.execute_reply.started":"2023-11-16T01:02:18.215963Z","shell.execute_reply":"2023-11-16T01:02:18.226185Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"(None, 75, 41)"},"metadata":{}}]},{"cell_type":"markdown","source":"# 4. Setup Training Options and Train","metadata":{"tags":[]}},{"cell_type":"code","source":"def scheduler(epoch, lr):\n    if epoch < 30:\n        return lr\n    else:\n        return lr * tf.math.exp(-0.1)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.230038Z","iopub.execute_input":"2023-11-16T01:02:18.230390Z","iopub.status.idle":"2023-11-16T01:02:18.238048Z","shell.execute_reply.started":"2023-11-16T01:02:18.230356Z","shell.execute_reply":"2023-11-16T01:02:18.236699Z"},"trusted":true},"execution_count":51,"outputs":[]},{"cell_type":"code","source":"def CTCLoss(y_true, y_pred):\n    batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n    input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n    label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n\n    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n\n    loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n    return loss","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.239359Z","iopub.execute_input":"2023-11-16T01:02:18.239706Z","iopub.status.idle":"2023-11-16T01:02:18.251918Z","shell.execute_reply.started":"2023-11-16T01:02:18.239678Z","shell.execute_reply":"2023-11-16T01:02:18.250056Z"},"trusted":true},"execution_count":52,"outputs":[]},{"cell_type":"code","source":"class ProduceExample(tf.keras.callbacks.Callback): \n    def __init__(self, dataset) -> None: \n        self.dataset = dataset.as_numpy_iterator()\n    \n    def on_epoch_end(self, epoch, logs=None) -> None:\n        data = self.dataset.next()\n        yhat = self.model.predict(data[0])\n        decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n        for x in range(len(yhat)):           \n            print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n            print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n            print('~'*100)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.253816Z","iopub.execute_input":"2023-11-16T01:02:18.254204Z","iopub.status.idle":"2023-11-16T01:02:18.267950Z","shell.execute_reply.started":"2023-11-16T01:02:18.254167Z","shell.execute_reply":"2023-11-16T01:02:18.265822Z"},"trusted":true},"execution_count":53,"outputs":[]},{"cell_type":"code","source":"model.compile(optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.00001), loss=CTCLoss)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.270718Z","iopub.execute_input":"2023-11-16T01:02:18.272093Z","iopub.status.idle":"2023-11-16T01:02:18.310185Z","shell.execute_reply.started":"2023-11-16T01:02:18.272012Z","shell.execute_reply":"2023-11-16T01:02:18.308020Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True) ","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.312893Z","iopub.execute_input":"2023-11-16T01:02:18.313619Z","iopub.status.idle":"2023-11-16T01:02:18.323468Z","shell.execute_reply.started":"2023-11-16T01:02:18.313555Z","shell.execute_reply":"2023-11-16T01:02:18.321547Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"schedule_callback = LearningRateScheduler(scheduler)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.325921Z","iopub.execute_input":"2023-11-16T01:02:18.326646Z","iopub.status.idle":"2023-11-16T01:02:18.336578Z","shell.execute_reply.started":"2023-11-16T01:02:18.326572Z","shell.execute_reply":"2023-11-16T01:02:18.334992Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"code","source":"example_callback = ProduceExample(test)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.337812Z","iopub.execute_input":"2023-11-16T01:02:18.338182Z","iopub.status.idle":"2023-11-16T01:02:18.375157Z","shell.execute_reply.started":"2023-11-16T01:02:18.338137Z","shell.execute_reply":"2023-11-16T01:02:18.373543Z"},"trusted":true},"execution_count":57,"outputs":[]},{"cell_type":"code","source":"# model.fit(train, validation_data=test, epochs=46, callbacks=[checkpoint_callback, schedule_callback, example_callback])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.377394Z","iopub.execute_input":"2023-11-16T01:02:18.378054Z","iopub.status.idle":"2023-11-16T01:02:18.388987Z","shell.execute_reply.started":"2023-11-16T01:02:18.378005Z","shell.execute_reply":"2023-11-16T01:02:18.387121Z"},"trusted":true},"execution_count":58,"outputs":[]},{"cell_type":"markdown","source":"# 5. Make a Prediction ","metadata":{"tags":[]}},{"cell_type":"code","source":"# url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n# output = 'checkpoints.zip'\n# gdown.download(url, output, quiet=False)\n# gdown.extractall('checkpoints.zip', 'models')","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.391934Z","iopub.execute_input":"2023-11-16T01:02:18.392824Z","iopub.status.idle":"2023-11-16T01:02:18.401515Z","shell.execute_reply.started":"2023-11-16T01:02:18.392766Z","shell.execute_reply":"2023-11-16T01:02:18.399554Z"},"trusted":true},"execution_count":59,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# model.load_weights('./models/checkpoint')","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.404344Z","iopub.execute_input":"2023-11-16T01:02:18.405551Z","iopub.status.idle":"2023-11-16T01:02:18.414322Z","shell.execute_reply.started":"2023-11-16T01:02:18.405466Z","shell.execute_reply":"2023-11-16T01:02:18.412503Z"},"trusted":true},"execution_count":60,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_data = data.as_numpy_iterator()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.417118Z","iopub.execute_input":"2023-11-16T01:02:18.417827Z","iopub.status.idle":"2023-11-16T01:02:18.438233Z","shell.execute_reply.started":"2023-11-16T01:02:18.417761Z","shell.execute_reply":"2023-11-16T01:02:18.436777Z"},"trusted":true},"execution_count":61,"outputs":[]},{"cell_type":"code","source":"sample = test_data.next()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.440330Z","iopub.execute_input":"2023-11-16T01:02:18.440743Z","iopub.status.idle":"2023-11-16T01:02:18.909674Z","shell.execute_reply.started":"2023-11-16T01:02:18.440706Z","shell.execute_reply":"2023-11-16T01:02:18.908517Z"},"trusted":true},"execution_count":62,"outputs":[]},{"cell_type":"code","source":"sample[0].shape","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:18.911098Z","iopub.execute_input":"2023-11-16T01:02:18.911724Z","iopub.status.idle":"2023-11-16T01:02:18.920760Z","shell.execute_reply.started":"2023-11-16T01:02:18.911696Z","shell.execute_reply":"2023-11-16T01:02:18.919667Z"},"trusted":true},"execution_count":63,"outputs":[{"execution_count":63,"output_type":"execute_result","data":{"text/plain":"(2, 75, 46, 140, 1)"},"metadata":{}}]},{"cell_type":"code","source":"sample[1].shape","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:18.922780Z","iopub.execute_input":"2023-11-16T01:02:18.923037Z","iopub.status.idle":"2023-11-16T01:02:18.934067Z","shell.execute_reply.started":"2023-11-16T01:02:18.923014Z","shell.execute_reply":"2023-11-16T01:02:18.932549Z"},"trusted":true},"execution_count":64,"outputs":[{"execution_count":64,"output_type":"execute_result","data":{"text/plain":"(2, 40)"},"metadata":{}}]},{"cell_type":"code","source":"sample[1]","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:18.935968Z","iopub.execute_input":"2023-11-16T01:02:18.936239Z","iopub.status.idle":"2023-11-16T01:02:18.946044Z","shell.execute_reply.started":"2023-11-16T01:02:18.936215Z","shell.execute_reply":"2023-11-16T01:02:18.944884Z"},"trusted":true},"execution_count":65,"outputs":[{"execution_count":65,"output_type":"execute_result","data":{"text/plain":"array([[12,  1, 25, 39, 18,  5,  4, 39,  2, 25, 39, 12, 39, 15, 14,  5,\n        39, 19, 15, 15, 14,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n         0,  0,  0,  0,  0,  0,  0,  0],\n       [16, 12,  1,  3,  5, 39, 23,  8,  9, 20,  5, 39, 23,  9, 20,  8,\n        39,  5, 39, 15, 14,  5, 39, 19, 15, 15, 14,  0,  0,  0,  0,  0,\n         0,  0,  0,  0,  0,  0,  0,  0]])"},"metadata":{}}]},{"cell_type":"code","source":"yhat = model.predict(sample[0])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:18.948346Z","iopub.execute_input":"2023-11-16T01:02:18.948652Z","iopub.status.idle":"2023-11-16T01:02:20.930136Z","shell.execute_reply.started":"2023-11-16T01:02:18.948627Z","shell.execute_reply":"2023-11-16T01:02:20.929211Z"},"trusted":true},"execution_count":66,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 2s 2s/step\n","output_type":"stream"}]},{"cell_type":"code","source":"print('~'*100, 'REAL TEXT')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:20.931667Z","iopub.execute_input":"2023-11-16T01:02:20.932015Z","iopub.status.idle":"2023-11-16T01:02:21.011348Z","shell.execute_reply.started":"2023-11-16T01:02:20.931982Z","shell.execute_reply":"2023-11-16T01:02:21.010469Z"},"trusted":true},"execution_count":67,"outputs":[{"name":"stdout","text":"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REAL TEXT\n","output_type":"stream"},{"execution_count":67,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'lay red by l one soon'>,\n <tf.Tensor: shape=(), dtype=string, numpy=b'place white with e one soon'>]"},"metadata":{}}]},{"cell_type":"code","source":"# for i in range (50):\n#     val = sample.next()\n    \n# #     print(i,val[0][0][0][0][0])\n#     og = tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])\n#     print(og)\n#     yhat = model.predict(val[0])\n# #     print(tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)]))\n# #     print(tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]]))\n#     decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()\n#     predicted = [tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]\n#     print(predicted)","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:21.012632Z","iopub.execute_input":"2023-11-16T01:02:21.012975Z","iopub.status.idle":"2023-11-16T01:02:21.858647Z","shell.execute_reply.started":"2023-11-16T01:02:21.012942Z","shell.execute_reply":"2023-11-16T01:02:21.857344Z"},"trusted":true},"execution_count":68,"outputs":[]},{"cell_type":"code","source":"decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:21.860217Z","iopub.execute_input":"2023-11-16T01:02:21.860657Z","iopub.status.idle":"2023-11-16T01:02:21.884725Z","shell.execute_reply.started":"2023-11-16T01:02:21.860604Z","shell.execute_reply":"2023-11-16T01:02:21.883652Z"},"trusted":true},"execution_count":69,"outputs":[]},{"cell_type":"code","source":"print('~'*100, 'PREDICTIONS')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-16T01:02:21.885892Z","iopub.execute_input":"2023-11-16T01:02:21.886180Z","iopub.status.idle":"2023-11-16T01:02:22.038020Z","shell.execute_reply.started":"2023-11-16T01:02:21.886153Z","shell.execute_reply":"2023-11-16T01:02:22.036993Z"},"trusted":true},"execution_count":70,"outputs":[{"name":"stdout","text":"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PREDICTIONS\n","output_type":"stream"},{"execution_count":70,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'y18'>,\n <tf.Tensor: shape=(), dtype=string, numpy=b'1n8'>]"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Test on a Video","metadata":{}},{"cell_type":"code","source":"sample1 = load_data(tf.convert_to_tensor('./data/s1/bras9a.mpg'))","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:22.039230Z","iopub.execute_input":"2023-11-16T01:02:22.039539Z","iopub.status.idle":"2023-11-16T01:02:22.282420Z","shell.execute_reply.started":"2023-11-16T01:02:22.039513Z","shell.execute_reply":"2023-11-16T01:02:22.281358Z"},"trusted":true},"execution_count":71,"outputs":[]},{"cell_type":"code","source":"print('~'*100, 'REAL TEXT')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in [sample1[1]]]","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:22.283682Z","iopub.execute_input":"2023-11-16T01:02:22.284011Z","iopub.status.idle":"2023-11-16T01:02:22.321027Z","shell.execute_reply.started":"2023-11-16T01:02:22.283979Z","shell.execute_reply":"2023-11-16T01:02:22.320135Z"},"trusted":true},"execution_count":72,"outputs":[{"name":"stdout","text":"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ REAL TEXT\n","output_type":"stream"},{"execution_count":72,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'bin red at s nine again'>]"},"metadata":{}}]},{"cell_type":"code","source":"yhat = model.predict(tf.expand_dims(sample1[0], axis=0))","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:22.322717Z","iopub.execute_input":"2023-11-16T01:02:22.323090Z","iopub.status.idle":"2023-11-16T01:02:22.876313Z","shell.execute_reply.started":"2023-11-16T01:02:22.323054Z","shell.execute_reply":"2023-11-16T01:02:22.875386Z"},"trusted":true},"execution_count":73,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 1s 504ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:22.877877Z","iopub.execute_input":"2023-11-16T01:02:22.878224Z","iopub.status.idle":"2023-11-16T01:02:22.888161Z","shell.execute_reply.started":"2023-11-16T01:02:22.878191Z","shell.execute_reply":"2023-11-16T01:02:22.887390Z"},"trusted":true},"execution_count":74,"outputs":[]},{"cell_type":"code","source":"print('~'*100, 'PREDICTIONS')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:22.889404Z","iopub.execute_input":"2023-11-16T01:02:22.889733Z","iopub.status.idle":"2023-11-16T01:02:22.965500Z","shell.execute_reply.started":"2023-11-16T01:02:22.889707Z","shell.execute_reply":"2023-11-16T01:02:22.964501Z"},"trusted":true},"execution_count":75,"outputs":[{"name":"stdout","text":"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PREDICTIONS\n","output_type":"stream"},{"execution_count":75,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'y18'>]"},"metadata":{}}]},{"cell_type":"code","source":"len(test)","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:22.966690Z","iopub.execute_input":"2023-11-16T01:02:22.966963Z","iopub.status.idle":"2023-11-16T01:02:22.973697Z","shell.execute_reply.started":"2023-11-16T01:02:22.966937Z","shell.execute_reply":"2023-11-16T01:02:22.972704Z"},"trusted":true},"execution_count":76,"outputs":[{"execution_count":76,"output_type":"execute_result","data":{"text/plain":"50"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Accuracy Analysis","metadata":{}},{"cell_type":"markdown","source":"### On 50-Epoch Trained Model","metadata":{}},{"cell_type":"code","source":"url = 'https://drive.google.com/u/0/uc?id=1JAmcd2v0JcZStgs69VytgqwGJMlhNeaT&export=download'\noutput = 'check.zip'\ngdown.download(url, output, quiet=False)\ngdown.extractall('check.zip', 'models')","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:22.974883Z","iopub.execute_input":"2023-11-16T01:02:22.975204Z","iopub.status.idle":"2023-11-16T01:02:30.165129Z","shell.execute_reply.started":"2023-11-16T01:02:22.975168Z","shell.execute_reply":"2023-11-16T01:02:30.164142Z"},"trusted":true},"execution_count":77,"outputs":[{"name":"stderr","text":"Downloading...\nFrom (uriginal): https://drive.google.com/u/0/uc?id=1JAmcd2v0JcZStgs69VytgqwGJMlhNeaT&export=download\nFrom (redirected): https://drive.google.com/uc?id=1JAmcd2v0JcZStgs69VytgqwGJMlhNeaT&export=download&confirm=t&uuid=7723e342-c7d6-49f5-a2ce-8865a8d420ca\nTo: /kaggle/working/check.zip\n100%|██████████| 95.3M/95.3M [00:02<00:00, 32.4MB/s]\n","output_type":"stream"},{"execution_count":77,"output_type":"execute_result","data":{"text/plain":"['models/models/checkpoint',\n 'models/models/checkpoint.index',\n 'models/models/checkpoint.data-00000-of-00001']"},"metadata":{}}]},{"cell_type":"code","source":"model.load_weights('./models/models/checkpoint')","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:30.166689Z","iopub.execute_input":"2023-11-16T01:02:30.167125Z","iopub.status.idle":"2023-11-16T01:02:30.379332Z","shell.execute_reply.started":"2023-11-16T01:02:30.167075Z","shell.execute_reply":"2023-11-16T01:02:30.378388Z"},"trusted":true},"execution_count":78,"outputs":[{"execution_count":78,"output_type":"execute_result","data":{"text/plain":"<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x7e69f07b7f70>"},"metadata":{}}]},{"cell_type":"code","source":"data = tf.data.Dataset.list_files('./data/s1/*.mpg')\ndata = data.shuffle(500, reshuffle_each_iteration=False)\ndata = data.map(mappable_function)\ndata = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\ndata = data.prefetch(tf.data.AUTOTUNE)","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:30.380814Z","iopub.execute_input":"2023-11-16T01:02:30.381503Z","iopub.status.idle":"2023-11-16T01:02:30.407552Z","shell.execute_reply.started":"2023-11-16T01:02:30.381465Z","shell.execute_reply":"2023-11-16T01:02:30.406681Z"},"trusted":true},"execution_count":79,"outputs":[]},{"cell_type":"code","source":"test_data = data.as_numpy_iterator()","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:30.408666Z","iopub.execute_input":"2023-11-16T01:02:30.409011Z","iopub.status.idle":"2023-11-16T01:02:30.426119Z","shell.execute_reply.started":"2023-11-16T01:02:30.408978Z","shell.execute_reply":"2023-11-16T01:02:30.425257Z"},"trusted":true},"execution_count":80,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def levenshtein_distance(str1, str2):\n    len_str1 = len(str1) + 1\n    len_str2 = len(str2) + 1\n\n    # Initialize a matrix to store edit distances\n    matrix = [[0] * len_str2 for _ in range(len_str1)]\n\n    # Initialize the first row and column of the matrix\n    for i in range(len_str1):\n        matrix[i][0] = i\n    for j in range(len_str2):\n        matrix[0][j] = j\n\n    # Fill in the matrix using dynamic programming\n    for i in range(1, len_str1):\n        for j in range(1, len_str2):\n            cost = 0 if str1[i - 1] == str2[j - 1] else 1\n            matrix[i][j] = min(\n                matrix[i - 1][j] + 1,      # Deletion\n                matrix[i][j - 1] + 1,      # Insertion\n                matrix[i - 1][j - 1] + cost  # Substitution\n            )\n\n    return matrix[len_str1 - 1][len_str2 - 1]\n\ndef calculate_word_error_rate(reference, hypothesis):\n    reference_words = reference.split()\n    hypothesis_words = hypothesis.split()\n\n    # Calculate Levenshtein distance between reference and hypothesis words\n    distance = levenshtein_distance(reference_words, hypothesis_words)\n\n    # Calculate Word Error Rate\n    wer = distance / len(reference_words)\n    return wer\n\ndef calculate_sentence_error_rate(reference, hypothesis):\n    # Calculate Levenshtein distance between reference and hypothesis sentences\n    distance = levenshtein_distance(reference, hypothesis)\n\n    # Calculate Sentence Error Rate\n    ser = distance / len(reference)\n    return ser","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:30.427259Z","iopub.execute_input":"2023-11-16T01:02:30.427568Z","iopub.status.idle":"2023-11-16T01:02:30.437569Z","shell.execute_reply.started":"2023-11-16T01:02:30.427541Z","shell.execute_reply":"2023-11-16T01:02:30.436456Z"},"trusted":true},"execution_count":81,"outputs":[]},{"cell_type":"code","source":"word_error_rates_for_model1=[]\nsentence_error_rates_for_model1=[]","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:30.449087Z","iopub.execute_input":"2023-11-16T01:02:30.449401Z","iopub.status.idle":"2023-11-16T01:02:30.453964Z","shell.execute_reply.started":"2023-11-16T01:02:30.449374Z","shell.execute_reply":"2023-11-16T01:02:30.452913Z"},"trusted":true},"execution_count":82,"outputs":[]},{"cell_type":"code","source":"for i in range (500):\n    try:\n        sample = test_data.next()\n\n        #     print(i,val[0][0][0][0][0])\n        og = [tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]\n        #     og = tf.strings.reduce_join([num_to_char(word) for word in sample[1][0]])\n\n        yhat = model.predict(sample[0])\n        #     print(tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)]))\n        #     print(tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]]))\n        decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()\n        predicted = [tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]\n\n\n        reference_string = str(og[1])[11:-25]\n        hypothesis_string = str(predicted[1])[11:-25]\n\n        wer = calculate_word_error_rate(reference_string, hypothesis_string)\n        ser = calculate_sentence_error_rate(reference_string, hypothesis_string)\n\n        word_error_rates_for_model1.append(wer)\n        sentence_error_rates_for_model1.append(ser)\n\n        if i<10: # print results for first 10 sentences\n\n            print(og[1])\n            print(\"Real Sentence\",str(og[1])[11:-25])\n\n            print(predicted[1])\n            print(\"Sentence Predicted\",str(predicted[1])[11:-25])\n\n            print(f\"Word Error Rate: {wer * 100:.2f}%\")\n            print(f\"Sentence Error Rate: {ser * 100:.2f}%\")\n\n            print()\n    except:\n        pass\n    ","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:02:30.455065Z","iopub.execute_input":"2023-11-16T01:02:30.455389Z","iopub.status.idle":"2023-11-16T01:08:45.275142Z","shell.execute_reply.started":"2023-11-16T01:02:30.455363Z","shell.execute_reply":"2023-11-16T01:08:45.274331Z"},"trusted":true},"execution_count":83,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\ntf.Tensor(b'set green at i nine again', shape=(), dtype=string)\nReal Sentence 'set green at i nine again'\ntf.Tensor(b'set green at nine again', shape=(), dtype=string)\nSentence Predicted 'set green at nine again'\nWord Error Rate: 16.67%\nSentence Error Rate: 7.41%\n\n1/1 [==============================] - 0s 118ms/step\ntf.Tensor(b'set blue with h seven soon', shape=(), dtype=string)\nReal Sentence 'set blue with h seven soon'\ntf.Tensor(b'set blue with seven soon', shape=(), dtype=string)\nSentence Predicted 'set blue with seven soon'\nWord Error Rate: 16.67%\nSentence Error Rate: 7.14%\n\n1/1 [==============================] - 0s 116ms/step\ntf.Tensor(b'place sp blue at i six please', shape=(), dtype=string)\nReal Sentence 'place sp blue at i six please'\ntf.Tensor(b'place blue at six please', shape=(), dtype=string)\nSentence Predicted 'place blue at six please'\nWord Error Rate: 28.57%\nSentence Error Rate: 16.13%\n\n1/1 [==============================] - 0s 117ms/step\ntf.Tensor(b'place blue with j four please', shape=(), dtype=string)\nReal Sentence 'place blue with j four please'\ntf.Tensor(b'place blue with four please', shape=(), dtype=string)\nSentence Predicted 'place blue with four please'\nWord Error Rate: 16.67%\nSentence Error Rate: 6.45%\n\n1/1 [==============================] - 0s 117ms/step\ntf.Tensor(b'lay red with r nine soon', shape=(), dtype=string)\nReal Sentence 'lay red with r nine soon'\ntf.Tensor(b'lay red with nine sooon', shape=(), dtype=string)\nSentence Predicted 'lay red with nine sooon'\nWord Error Rate: 33.33%\nSentence Error Rate: 11.54%\n\n1/1 [==============================] - 0s 133ms/step\ntf.Tensor(b'lay red sp with l five soon', shape=(), dtype=string)\nReal Sentence 'lay red sp with l five soon'\ntf.Tensor(b'lay red with five soon', shape=(), dtype=string)\nSentence Predicted 'lay red with five soon'\nWord Error Rate: 28.57%\nSentence Error Rate: 17.24%\n\n1/1 [==============================] - 0s 119ms/step\ntf.Tensor(b'place white at j seven soon', shape=(), dtype=string)\nReal Sentence 'place white at j seven soon'\ntf.Tensor(b'place white at seven soon', shape=(), dtype=string)\nSentence Predicted 'place white at seven soon'\nWord Error Rate: 16.67%\nSentence Error Rate: 6.90%\n\n1/1 [==============================] - 0s 127ms/step\ntf.Tensor(b'set blue at h one again', shape=(), dtype=string)\nReal Sentence 'set blue at h one again'\ntf.Tensor(b'set blue at h one again', shape=(), dtype=string)\nSentence Predicted 'set blue at h one again'\nWord Error Rate: 0.00%\nSentence Error Rate: 0.00%\n\n1/1 [==============================] - 0s 121ms/step\ntf.Tensor(b'place green with r five soon', shape=(), dtype=string)\nReal Sentence 'place green with r five soon'\ntf.Tensor(b'place green with five soon', shape=(), dtype=string)\nSentence Predicted 'place green with five soon'\nWord Error Rate: 16.67%\nSentence Error Rate: 6.67%\n\n1/1 [==============================] - 0s 120ms/step\ntf.Tensor(b'lay blue by q eight now', shape=(), dtype=string)\nReal Sentence 'lay blue by q eight now'\ntf.Tensor(b'lay blue by eight now', shape=(), dtype=string)\nSentence Predicted 'lay blue by eight now'\nWord Error Rate: 16.67%\nSentence Error Rate: 8.00%\n\n1/1 [==============================] - 0s 123ms/step\n1/1 [==============================] - 0s 123ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 120ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 122ms/step\n1/1 [==============================] - 0s 130ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 114ms/step\n1/1 [==============================] - 0s 125ms/step\n1/1 [==============================] - 0s 120ms/step\n1/1 [==============================] - 0s 122ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 129ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 128ms/step\n1/1 [==============================] - 0s 114ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 114ms/step\n1/1 [==============================] - 0s 114ms/step\n1/1 [==============================] - 0s 119ms/step\n1/1 [==============================] - 0s 119ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 116ms/step\n","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7e69500610c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7e69500610c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 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[==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 123ms/step\n1/1 [==============================] - 0s 120ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 119ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 122ms/step\n1/1 [==============================] - 0s 120ms/step\n1/1 [==============================] - 0s 125ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 122ms/step\n1/1 [==============================] - 0s 123ms/step\n1/1 [==============================] - 0s 122ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 120ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 123ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 121ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"mean_wer=sum(word_error_rates_for_model1)/len(word_error_rates_for_model1)\nmean_ser=sum(sentence_error_rates_for_model1)/len(sentence_error_rates_for_model1)\n\nprint(\"For Model 1\")\nprint(f\"Mean Word Error Rate: {mean_wer * 100:.2f}%\")\nprint(f\"Mean Sentence Error Rate: {mean_ser * 100:.2f}%\")","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:17:31.428170Z","iopub.execute_input":"2023-11-16T01:17:31.428961Z","iopub.status.idle":"2023-11-16T01:17:31.434840Z","shell.execute_reply.started":"2023-11-16T01:17:31.428928Z","shell.execute_reply":"2023-11-16T01:17:31.433817Z"},"trusted":true},"execution_count":97,"outputs":[{"name":"stdout","text":"For Model 1\nMean Word Error Rate: 14.40%\nMean Sentence Error Rate: 6.06%\n","output_type":"stream"}]},{"cell_type":"code","source":"# print(\"Max For Model 1\")\n# print(f\"Mean Word Error Rate: {max(word_error_rates_for_model1) * 100:.2f}%\")\n# print(f\"Mean Sentence Error Rate: {max(sentence_error_rates_for_model1) * 100:.2f}%\")","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:20:38.432927Z","iopub.execute_input":"2023-11-16T01:20:38.433324Z","iopub.status.idle":"2023-11-16T01:20:38.437901Z","shell.execute_reply.started":"2023-11-16T01:20:38.433293Z","shell.execute_reply":"2023-11-16T01:20:38.436807Z"},"trusted":true},"execution_count":100,"outputs":[]},{"cell_type":"code","source":"len(word_error_rates_for_model1)","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:08:45.284741Z","iopub.execute_input":"2023-11-16T01:08:45.285427Z","iopub.status.idle":"2023-11-16T01:08:45.293842Z","shell.execute_reply.started":"2023-11-16T01:08:45.285382Z","shell.execute_reply":"2023-11-16T01:08:45.292912Z"},"trusted":true},"execution_count":85,"outputs":[{"execution_count":85,"output_type":"execute_result","data":{"text/plain":"498"},"metadata":{}}]},{"cell_type":"markdown","source":"### On 96-Epoch trained model","metadata":{}},{"cell_type":"code","source":"url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\noutput = 'checkpoints.zip'\ngdown.download(url, output, quiet=False)\ngdown.extractall('checkpoints.zip', 'models')","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:08:45.294932Z","iopub.execute_input":"2023-11-16T01:08:45.295406Z","iopub.status.idle":"2023-11-16T01:08:51.651582Z","shell.execute_reply.started":"2023-11-16T01:08:45.295373Z","shell.execute_reply":"2023-11-16T01:08:51.650623Z"},"trusted":true},"execution_count":86,"outputs":[{"name":"stderr","text":"Downloading...\nFrom (uriginal): https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y\nFrom (redirected): https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y&confirm=t&uuid=20684f54-1d09-4fa2-bb80-33ebd517253d\nTo: /kaggle/working/checkpoints.zip\n100%|██████████| 94.5M/94.5M [00:02<00:00, 31.9MB/s]\n","output_type":"stream"},{"execution_count":86,"output_type":"execute_result","data":{"text/plain":"['models/checkpoint.index',\n 'models/__MACOSX/._checkpoint.index',\n 'models/checkpoint.data-00000-of-00001',\n 'models/__MACOSX/._checkpoint.data-00000-of-00001',\n 'models/checkpoint',\n 'models/__MACOSX/._checkpoint']"},"metadata":{}}]},{"cell_type":"code","source":"model.load_weights('./models/checkpoint')","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:08:51.652610Z","iopub.execute_input":"2023-11-16T01:08:51.652862Z","iopub.status.idle":"2023-11-16T01:08:51.817726Z","shell.execute_reply.started":"2023-11-16T01:08:51.652839Z","shell.execute_reply":"2023-11-16T01:08:51.816736Z"},"trusted":true},"execution_count":87,"outputs":[{"execution_count":87,"output_type":"execute_result","data":{"text/plain":"<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x7e69f078b4f0>"},"metadata":{}}]},{"cell_type":"code","source":"data = tf.data.Dataset.list_files('./data/s1/*.mpg')\ndata = data.shuffle(500, reshuffle_each_iteration=False)\ndata = data.map(mappable_function)\ndata = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\ndata = data.prefetch(tf.data.AUTOTUNE)","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:08:51.818827Z","iopub.execute_input":"2023-11-16T01:08:51.819081Z","iopub.status.idle":"2023-11-16T01:08:51.842835Z","shell.execute_reply.started":"2023-11-16T01:08:51.819058Z","shell.execute_reply":"2023-11-16T01:08:51.842147Z"},"trusted":true},"execution_count":88,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_data = data.as_numpy_iterator()","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:08:51.843776Z","iopub.execute_input":"2023-11-16T01:08:51.844038Z","iopub.status.idle":"2023-11-16T01:08:51.858546Z","shell.execute_reply.started":"2023-11-16T01:08:51.844014Z","shell.execute_reply":"2023-11-16T01:08:51.857684Z"},"trusted":true},"execution_count":89,"outputs":[]},{"cell_type":"code","source":"word_error_rates_for_model2=[]\nsentence_error_rates_for_model2=[]","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:08:51.859786Z","iopub.execute_input":"2023-11-16T01:08:51.860386Z","iopub.status.idle":"2023-11-16T01:08:51.864329Z","shell.execute_reply.started":"2023-11-16T01:08:51.860353Z","shell.execute_reply":"2023-11-16T01:08:51.863477Z"},"trusted":true},"execution_count":90,"outputs":[]},{"cell_type":"code","source":"","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:16:37.292489Z","iopub.execute_input":"2023-11-16T01:16:37.293245Z","iopub.status.idle":"2023-11-16T01:16:37.299621Z","shell.execute_reply.started":"2023-11-16T01:16:37.293213Z","shell.execute_reply":"2023-11-16T01:16:37.298444Z"},"trusted":true},"execution_count":95,"outputs":[{"execution_count":95,"output_type":"execute_result","data":{"text/plain":"0.34615384615384615"},"metadata":{}}]},{"cell_type":"code","source":"for i in range (500):\n    sample = test_data.next()\n    \n#     print(i,val[0][0][0][0][0])\n    og = [tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]\n#     og = tf.strings.reduce_join([num_to_char(word) for word in sample[1][0]])\n\n    yhat = model.predict(sample[0])\n#     print(tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)]))\n#     print(tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]]))\n    decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()\n    predicted = [tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]\n\n    \n    reference_string = str(og[1])[11:-25]\n    hypothesis_string = str(predicted[1])[11:-25]\n\n    wer = calculate_word_error_rate(reference_string, hypothesis_string)\n    ser = calculate_sentence_error_rate(reference_string, hypothesis_string)\n    \n    word_error_rates_for_model2.append(wer)\n    sentence_error_rates_for_model2.append(ser)\n    \n    if i<10: # print results for first 10 sentences\n    \n        print(og[1])\n        print(\"Real Sentence\",str(og[1])[11:-25])\n\n        print(predicted[1])\n        print(\"Sentence Predicted\",str(predicted[1])[11:-25])\n\n        print(f\"Word Error Rate: {wer * 100:.2f}%\")\n        print(f\"Sentence Error Rate: {ser * 100:.2f}%\")\n\n        print()\n    ","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:08:51.865516Z","iopub.execute_input":"2023-11-16T01:08:51.865774Z","iopub.status.idle":"2023-11-16T01:15:11.159198Z","shell.execute_reply.started":"2023-11-16T01:08:51.865750Z","shell.execute_reply":"2023-11-16T01:15:11.158384Z"},"trusted":true},"execution_count":91,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 0s 124ms/step\ntf.Tensor(b'bin red at m five again', shape=(), dtype=string)\nReal Sentence 'bin red at m five again'\ntf.Tensor(b'bin red at m five again', shape=(), dtype=string)\nSentence Predicted 'bin red at m five again'\nWord Error Rate: 0.00%\nSentence Error Rate: 0.00%\n\n1/1 [==============================] - 0s 117ms/step\ntf.Tensor(b'place red with d six please', shape=(), dtype=string)\nReal Sentence 'place red with d six please'\ntf.Tensor(b'place red with d six please', shape=(), dtype=string)\nSentence Predicted 'place red with d six please'\nWord Error Rate: 0.00%\nSentence Error Rate: 0.00%\n\n1/1 [==============================] - 0s 115ms/step\ntf.Tensor(b'place white by q seven again', shape=(), dtype=string)\nReal Sentence 'place white by q seven again'\ntf.Tensor(b'place white by q seven again', shape=(), dtype=string)\nSentence Predicted 'place white by q seven again'\nWord Error Rate: 0.00%\nSentence Error Rate: 0.00%\n\n1/1 [==============================] - 0s 123ms/step\ntf.Tensor(b'bin red at f nine soon', shape=(), dtype=string)\nReal Sentence 'bin red at f nine soon'\ntf.Tensor(b'bin red at f nine soon', shape=(), dtype=string)\nSentence Predicted 'bin red at f nine soon'\nWord Error Rate: 0.00%\nSentence Error Rate: 0.00%\n\n1/1 [==============================] - 0s 116ms/step\ntf.Tensor(b'place green with e seven soon', shape=(), dtype=string)\nReal Sentence 'place green with e seven soon'\ntf.Tensor(b'place green with seven soon', shape=(), dtype=string)\nSentence Predicted 'place green with seven soon'\nWord Error Rate: 16.67%\nSentence Error Rate: 6.45%\n\n1/1 [==============================] - 0s 115ms/step\ntf.Tensor(b'lay red at y four now', shape=(), dtype=string)\nReal Sentence 'lay red at y four now'\ntf.Tensor(b'lay red at y four now', shape=(), dtype=string)\nSentence Predicted 'lay red at y four now'\nWord Error Rate: 0.00%\nSentence Error Rate: 0.00%\n\n1/1 [==============================] - 0s 114ms/step\ntf.Tensor(b'place blue with p six now', shape=(), dtype=string)\nReal Sentence 'place blue with p six now'\ntf.Tensor(b'place blue with p six now', shape=(), dtype=string)\nSentence Predicted 'place blue with p six now'\nWord Error Rate: 0.00%\nSentence Error Rate: 0.00%\n\n1/1 [==============================] - 0s 118ms/step\ntf.Tensor(b'place white at x five soon', shape=(), dtype=string)\nReal Sentence 'place white at x five soon'\ntf.Tensor(b'place white at x five soon', shape=(), dtype=string)\nSentence Predicted 'place white at x five soon'\nWord Error Rate: 0.00%\nSentence Error Rate: 0.00%\n\n1/1 [==============================] - 0s 116ms/step\ntf.Tensor(b'set green by j one soon', shape=(), dtype=string)\nReal Sentence 'set green by j one soon'\ntf.Tensor(b'set green by j one soon', shape=(), dtype=string)\nSentence Predicted 'set green by j one soon'\nWord Error Rate: 0.00%\nSentence Error Rate: 0.00%\n\n1/1 [==============================] - 0s 115ms/step\ntf.Tensor(b'place white by k two please', shape=(), dtype=string)\nReal Sentence 'place white by k two please'\ntf.Tensor(b'place white by k two please', shape=(), dtype=string)\nSentence Predicted 'place white by k two please'\nWord Error Rate: 0.00%\nSentence Error Rate: 0.00%\n\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 120ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 114ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 121ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 114ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 119ms/step\n1/1 [==============================] - 0s 119ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 119ms/step\n1/1 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[==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 114ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 125ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 127ms/step\n1/1 [==============================] - 0s 121ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 120ms/step\n1/1 [==============================] - 0s 114ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 114ms/step\n","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7e6b340c4600] ac-tex damaged at 22 17\n[mpeg1video @ 0x7e6b340c4600] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\n1/1 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[==============================] - 0s 120ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 122ms/step\n1/1 [==============================] - 0s 118ms/step\n1/1 [==============================] - 0s 123ms/step\n1/1 [==============================] - 0s 119ms/step\n1/1 [==============================] - 0s 120ms/step\n1/1 [==============================] - 0s 123ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 121ms/step\n1/1 [==============================] - 0s 121ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 126ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 127ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 120ms/step\n1/1 [==============================] - 0s 120ms/step\n1/1 [==============================] - 0s 130ms/step\n1/1 [==============================] - 0s 125ms/step\n1/1 [==============================] - 0s 125ms/step\n1/1 [==============================] - 0s 115ms/step\n1/1 [==============================] - 0s 123ms/step\n1/1 [==============================] - 0s 122ms/step\n1/1 [==============================] - 0s 116ms/step\n1/1 [==============================] - 0s 117ms/step\n1/1 [==============================] - 0s 113ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"len(word_error_rates_for_model2)","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:21:08.161014Z","iopub.execute_input":"2023-11-16T01:21:08.161746Z","iopub.status.idle":"2023-11-16T01:21:08.167589Z","shell.execute_reply.started":"2023-11-16T01:21:08.161713Z","shell.execute_reply":"2023-11-16T01:21:08.166625Z"},"trusted":true},"execution_count":102,"outputs":[{"execution_count":102,"output_type":"execute_result","data":{"text/plain":"500"},"metadata":{}}]},{"cell_type":"code","source":"mean_wer=sum(word_error_rates_for_model2)/len(word_error_rates_for_model2)\nmean_ser=sum(sentence_error_rates_for_model2)/len(sentence_error_rates_for_model2)\n\nprint(\"For Model 2\")\nprint(f\"Mean Word Error Rate: {mean_wer * 100:.8f}%\")\nprint(f\"Mean Sentence Error Rate: {mean_ser * 100:.8f}%\")","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:17:06.298549Z","iopub.execute_input":"2023-11-16T01:17:06.299406Z","iopub.status.idle":"2023-11-16T01:17:06.305063Z","shell.execute_reply.started":"2023-11-16T01:17:06.299374Z","shell.execute_reply":"2023-11-16T01:17:06.304136Z"},"trusted":true},"execution_count":96,"outputs":[{"name":"stdout","text":"For Model 2\nMean Word Error Rate: 1.76666667%\nMean Sentence Error Rate: 0.67408067%\n","output_type":"stream"}]},{"cell_type":"code","source":"# print(\"Max For Model 2\")\n# print(f\"Mean Word Error Rate: {max(word_error_rates_for_model2) * 100:.8f}%\")\n# print(f\"Mean Sentence Error Rate: {max(sentence_error_rates_for_model2) * 100:.8f}%\")","metadata":{"execution":{"iopub.status.busy":"2023-11-16T01:20:49.458710Z","iopub.execute_input":"2023-11-16T01:20:49.459506Z","iopub.status.idle":"2023-11-16T01:20:49.463873Z","shell.execute_reply.started":"2023-11-16T01:20:49.459473Z","shell.execute_reply":"2023-11-16T01:20:49.462695Z"},"trusted":true},"execution_count":101,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/Notebooks/project-phase-1-v1-2.ipynb b/Notebooks/project-phase-1-v1-2.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..b6bad8e21eaf4a998fb678a91545a84b2ad08d95
--- /dev/null
+++ b/Notebooks/project-phase-1-v1-2.ipynb
@@ -0,0 +1 @@
+{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":5,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# 0. Install and Import Dependencies","metadata":{"tags":[]}},{"cell_type":"code","source":"# !pip list","metadata":{"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:19.578487Z","iopub.execute_input":"2023-11-04T20:00:19.579320Z","iopub.status.idle":"2023-11-04T20:00:19.584264Z","shell.execute_reply.started":"2023-11-04T20:00:19.579285Z","shell.execute_reply":"2023-11-04T20:00:19.583411Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"%pip install opencv-python matplotlib imageio gdown tensorflow","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:19.585946Z","iopub.execute_input":"2023-11-04T20:00:19.586196Z","iopub.status.idle":"2023-11-04T20:00:31.620285Z","shell.execute_reply.started":"2023-11-04T20:00:19.586174Z","shell.execute_reply":"2023-11-04T20:00:31.619227Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Requirement already satisfied: opencv-python in /opt/conda/lib/python3.10/site-packages (4.8.0.76)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.10/site-packages (3.7.2)\nRequirement already satisfied: imageio in /opt/conda/lib/python3.10/site-packages (2.31.1)\nRequirement already satisfied: gdown in /opt/conda/lib/python3.10/site-packages (4.7.1)\nRequirement already satisfied: tensorflow in /opt/conda/lib/python3.10/site-packages (2.12.0)\nRequirement already satisfied: numpy>=1.21.2 in /opt/conda/lib/python3.10/site-packages (from opencv-python) (1.23.5)\nRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.1.0)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (0.11.0)\nRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (4.40.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (1.4.4)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (21.3)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (9.5.0)\nRequirement already satisfied: pyparsing<3.1,>=2.3.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.10/site-packages (from matplotlib) (2.8.2)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from gdown) (3.12.2)\nRequirement already satisfied: requests[socks] in /opt/conda/lib/python3.10/site-packages (from gdown) (2.31.0)\nRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from gdown) (1.16.0)\nRequirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from gdown) (4.66.1)\nRequirement already satisfied: beautifulsoup4 in /opt/conda/lib/python3.10/site-packages (from gdown) (4.12.2)\nRequirement already satisfied: absl-py>=1.0.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.4.0)\nRequirement already satisfied: astunparse>=1.6.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.6.3)\nRequirement already satisfied: flatbuffers>=2.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (23.5.26)\nRequirement already satisfied: gast<=0.4.0,>=0.2.1 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.4.0)\nRequirement already satisfied: google-pasta>=0.1.1 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.2.0)\nRequirement already satisfied: grpcio<2.0,>=1.24.3 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.51.1)\nRequirement already satisfied: h5py>=2.9.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (3.9.0)\nRequirement already satisfied: jax>=0.3.15 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.4.13)\nRequirement already satisfied: keras<2.13,>=2.12.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.12.0)\nRequirement already satisfied: libclang>=13.0.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (16.0.0)\nRequirement already satisfied: opt-einsum>=2.3.2 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (3.3.0)\nRequirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (3.20.3)\nRequirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from tensorflow) (68.0.0)\nRequirement already satisfied: tensorboard<2.13,>=2.12 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.12.3)\nRequirement already satisfied: tensorflow-estimator<2.13,>=2.12.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.12.0)\nRequirement already satisfied: termcolor>=1.1.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (2.3.0)\nRequirement already satisfied: typing-extensions>=3.6.6 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (4.6.3)\nRequirement already satisfied: wrapt<1.15,>=1.11.0 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (1.14.1)\nRequirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /opt/conda/lib/python3.10/site-packages (from tensorflow) (0.32.0)\nRequirement already satisfied: wheel<1.0,>=0.23.0 in /opt/conda/lib/python3.10/site-packages (from astunparse>=1.6.0->tensorflow) (0.40.0)\nRequirement already satisfied: ml-dtypes>=0.1.0 in /opt/conda/lib/python3.10/site-packages (from jax>=0.3.15->tensorflow) (0.2.0)\nRequirement already satisfied: scipy>=1.7 in /opt/conda/lib/python3.10/site-packages (from jax>=0.3.15->tensorflow) (1.11.2)\nRequirement already satisfied: google-auth<3,>=1.6.3 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.20.0)\nRequirement already satisfied: google-auth-oauthlib<1.1,>=0.5 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (1.0.0)\nRequirement already satisfied: markdown>=2.6.8 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (3.4.3)\nRequirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (0.7.1)\nRequirement already satisfied: werkzeug>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from tensorboard<2.13,>=2.12->tensorflow) (2.3.7)\nRequirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.10/site-packages (from beautifulsoup4->gdown) (2.3.2.post1)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (3.1.0)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (3.4)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (1.26.15)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (2023.7.22)\nRequirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /opt/conda/lib/python3.10/site-packages (from requests[socks]->gdown) (1.7.1)\nRequirement already satisfied: cachetools<6.0,>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (4.2.4)\nRequirement already satisfied: pyasn1-modules>=0.2.1 in /opt/conda/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.2.7)\nRequirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.10/site-packages (from google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (4.9)\nRequirement already satisfied: requests-oauthlib>=0.7.0 in /opt/conda/lib/python3.10/site-packages (from google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (1.3.1)\nRequirement already satisfied: MarkupSafe>=2.1.1 in /opt/conda/lib/python3.10/site-packages (from werkzeug>=1.0.1->tensorboard<2.13,>=2.12->tensorflow) (2.1.3)\nRequirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.10/site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.13,>=2.12->tensorflow) (0.4.8)\nRequirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.10/site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<1.1,>=0.5->tensorboard<2.13,>=2.12->tensorflow) (3.2.2)\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"import os\nimport cv2\nimport tensorflow as tf\nimport numpy as np\nfrom typing import List\nfrom matplotlib import pyplot as plt\n","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:31.621563Z","iopub.execute_input":"2023-11-04T20:00:31.621851Z","iopub.status.idle":"2023-11-04T20:00:35.167577Z","shell.execute_reply.started":"2023-11-04T20:00:31.621827Z","shell.execute_reply":"2023-11-04T20:00:35.166208Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"code","source":"import imageio","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:35.169573Z","iopub.execute_input":"2023-11-04T20:00:35.170794Z","iopub.status.idle":"2023-11-04T20:00:35.205446Z","shell.execute_reply.started":"2023-11-04T20:00:35.170724Z","shell.execute_reply":"2023-11-04T20:00:35.204769Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"tf.config.list_physical_devices('GPU')","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:35.208811Z","iopub.execute_input":"2023-11-04T20:00:35.209060Z","iopub.status.idle":"2023-11-04T20:00:35.282538Z","shell.execute_reply.started":"2023-11-04T20:00:35.209039Z","shell.execute_reply":"2023-11-04T20:00:35.281580Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"},"metadata":{}}]},{"cell_type":"code","source":"physical_devices = tf.config.list_physical_devices('GPU')\ntry:\n    tf.config.experimental.set_memory_growth(physical_devices[0], True)\nexcept:\n    pass","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:35.283924Z","iopub.execute_input":"2023-11-04T20:00:35.284357Z","iopub.status.idle":"2023-11-04T20:00:35.292408Z","shell.execute_reply.started":"2023-11-04T20:00:35.284323Z","shell.execute_reply":"2023-11-04T20:00:35.291489Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"# 1. Build Data Loading Functions","metadata":{"tags":[]}},{"cell_type":"code","source":"import gdown","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:35.294033Z","iopub.execute_input":"2023-11-04T20:00:35.294315Z","iopub.status.idle":"2023-11-04T20:00:35.419842Z","shell.execute_reply.started":"2023-11-04T20:00:35.294283Z","shell.execute_reply":"2023-11-04T20:00:35.419068Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"code","source":"url = 'https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL'\noutput = 'data.zip'\ngdown.download(url, output, quiet=False)\ngdown.extractall('data.zip')","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:35.420909Z","iopub.execute_input":"2023-11-04T20:00:35.421406Z","iopub.status.idle":"2023-11-04T20:00:45.232065Z","shell.execute_reply.started":"2023-11-04T20:00:35.421379Z","shell.execute_reply":"2023-11-04T20:00:45.231152Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"Downloading...\nFrom (uriginal): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL\nFrom (redirected): https://drive.google.com/uc?id=1YlvpDLix3S-U8fd-gqRwPcWXAXm8JwjL&confirm=t&uuid=287568f3-bc5a-43f5-a634-e82d54f84f57\nTo: /kaggle/working/data.zip\n100%|██████████| 423M/423M [00:04<00:00, 96.4MB/s] \n","output_type":"stream"},{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"['data/',\n 'data/alignments/',\n 'data/alignments/s1/',\n 'data/alignments/s1/bbaf2n.align',\n 'data/alignments/s1/bbaf3s.align',\n 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'data/alignments/s1/sgwq1a.align',\n 'data/alignments/s1/sgwqzp.align',\n 'data/alignments/s1/sgwx2n.align',\n 'data/alignments/s1/sgwx3s.align',\n 'data/alignments/s1/sgwx4p.align',\n 'data/alignments/s1/sgwx5a.align',\n 'data/alignments/s1/srab1s.align',\n 'data/alignments/s1/srab2p.align',\n 'data/alignments/s1/srab3a.align',\n 'data/alignments/s1/srabzn.align',\n 'data/alignments/s1/srah4n.align',\n 'data/alignments/s1/srah5s.align',\n 'data/alignments/s1/srah6p.align',\n 'data/alignments/s1/srah7a.align',\n 'data/alignments/s1/sran8n.align',\n 'data/alignments/s1/sran9s.align',\n 'data/alignments/s1/srao1a.align',\n 'data/alignments/s1/sraozp.align',\n 'data/alignments/s1/srau2n.align',\n 'data/alignments/s1/srau3s.align',\n 'data/alignments/s1/srau4p.align',\n 'data/alignments/s1/srau5a.align',\n 'data/alignments/s1/srbb4n.align',\n 'data/alignments/s1/srbb5s.align',\n 'data/alignments/s1/srbb6p.align',\n 'data/alignments/s1/srbb7a.align',\n 'data/alignments/s1/srbh8n.align',\n 'data/alignments/s1/srbh9s.align',\n 'data/alignments/s1/srbi1a.align',\n 'data/alignments/s1/srbizp.align',\n 'data/alignments/s1/srbo2n.align',\n 'data/alignments/s1/srbo3s.align',\n 'data/alignments/s1/srbo4p.align',\n 'data/alignments/s1/srbo5a.align',\n 'data/alignments/s1/srbu6n.align',\n 'data/alignments/s1/srbu7s.align',\n 'data/alignments/s1/srbu8p.align',\n 'data/alignments/s1/srbu9a.align',\n 'data/alignments/s1/sria6n.align',\n 'data/alignments/s1/sria7s.align',\n 'data/alignments/s1/sria8p.align',\n 'data/alignments/s1/sria9a.align',\n 'data/alignments/s1/srih1s.align',\n 'data/alignments/s1/srih2p.align',\n 'data/alignments/s1/srih3a.align',\n 'data/alignments/s1/srihzn.align',\n 'data/alignments/s1/srin4n.align',\n 'data/alignments/s1/srin5s.align',\n 'data/alignments/s1/srin6p.align',\n 'data/alignments/s1/srin7a.align',\n 'data/alignments/s1/srit8n.align',\n 'data/alignments/s1/srit9s.align',\n 'data/alignments/s1/sriu1a.align',\n 'data/alignments/s1/sriuzp.align',\n 'data/alignments/s1/srwb8n.align',\n 'data/alignments/s1/srwb9s.align',\n 'data/alignments/s1/srwc1a.align',\n 'data/alignments/s1/srwczp.align',\n 'data/alignments/s1/srwi2n.align',\n 'data/alignments/s1/srwi3s.align',\n 'data/alignments/s1/srwi4p.align',\n 'data/alignments/s1/srwi5a.align',\n 'data/alignments/s1/srwo6n.align',\n 'data/alignments/s1/srwo7s.align',\n 'data/alignments/s1/srwo8p.align',\n 'data/alignments/s1/srwo9a.align',\n 'data/alignments/s1/srwv1s.align',\n 'data/alignments/s1/srwv2p.align',\n 'data/alignments/s1/srwv3a.align',\n 'data/alignments/s1/srwvzn.align',\n 'data/alignments/s1/swab6n.align',\n 'data/alignments/s1/swab7s.align',\n 'data/alignments/s1/swab8p.align',\n 'data/alignments/s1/swab9a.align',\n 'data/alignments/s1/swai1s.align',\n 'data/alignments/s1/swai2p.align',\n 'data/alignments/s1/swai3a.align',\n 'data/alignments/s1/swaizn.align',\n 'data/alignments/s1/swao4n.align',\n 'data/alignments/s1/swao5s.align',\n 'data/alignments/s1/swao6p.align',\n 'data/alignments/s1/swao7a.align',\n 'data/alignments/s1/swau8n.align',\n 'data/alignments/s1/swau9s.align',\n 'data/alignments/s1/swav1a.align',\n 'data/alignments/s1/swavzp.align',\n 'data/alignments/s1/swbc1s.align',\n 'data/alignments/s1/swbc2p.align',\n 'data/alignments/s1/swbc3a.align',\n 'data/alignments/s1/swbczn.align',\n 'data/alignments/s1/swbi4n.align',\n 'data/alignments/s1/swbi5s.align',\n 'data/alignments/s1/swbi6p.align',\n 'data/alignments/s1/swbi7a.align',\n 'data/alignments/s1/swbo8n.align',\n 'data/alignments/s1/swbo9s.align',\n 'data/alignments/s1/swbp1a.align',\n 'data/alignments/s1/swbpzp.align',\n 'data/alignments/s1/swbv2n.align',\n 'data/alignments/s1/swbv3s.align',\n 'data/alignments/s1/swbv4p.align',\n 'data/alignments/s1/swbv5a.align',\n 'data/alignments/s1/swib2n.align',\n 'data/alignments/s1/swib3s.align',\n 'data/alignments/s1/swib4p.align',\n 'data/alignments/s1/swib5a.align',\n 'data/alignments/s1/swih6n.align',\n 'data/alignments/s1/swih7s.align',\n 'data/alignments/s1/swih8p.align',\n 'data/alignments/s1/swih9a.align',\n 'data/alignments/s1/swio1s.align',\n 'data/alignments/s1/swio2p.align',\n 'data/alignments/s1/swio3a.align',\n 'data/alignments/s1/swiozn.align',\n 'data/alignments/s1/swiu4n.align',\n 'data/alignments/s1/swiu5s.align',\n 'data/alignments/s1/swiu6p.align',\n 'data/alignments/s1/swiu7a.align',\n 'data/alignments/s1/swwc4n.align',\n 'data/alignments/s1/swwc5s.align',\n 'data/alignments/s1/swwc6p.align',\n 'data/alignments/s1/swwc7a.align',\n 'data/alignments/s1/swwi8n.align',\n 'data/alignments/s1/swwi9s.align',\n 'data/alignments/s1/swwj1a.align',\n 'data/alignments/s1/swwjzp.align',\n 'data/alignments/s1/swwp2n.align',\n 'data/alignments/s1/swwp3s.align',\n 'data/alignments/s1/swwp4p.align',\n 'data/alignments/s1/swwp5a.align',\n 'data/alignments/s1/swwv6n.align',\n ...]"},"metadata":{}}]},{"cell_type":"code","source":"def load_video(path:str) -> List[float]: \n\n    cap = cv2.VideoCapture(path)                        # read frames of the video\n    frames = []\n    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))): \n        ret, frame = cap.read()                         # ret-wheather frame is captured or not  , frame actual matrix representation of captured frame\n        frame = tf.image.rgb_to_grayscale(frame)        # convert to grayscale\n        frames.append(frame[190:236,80:220,:])          # Crop only mouth region and add pixel matrix to frame list\n    cap.release()\n    \n    mean = tf.math.reduce_mean(frames )\n    std = tf.math.reduce_std(tf.cast(frames, tf.float32))\n    return tf.cast((frames - mean), tf.float32) / std   # standarrdize pixel values and make it float16 to reduce furthur computation times","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:45.233363Z","iopub.execute_input":"2023-11-04T20:00:45.233647Z","iopub.status.idle":"2023-11-04T20:00:45.240328Z","shell.execute_reply.started":"2023-11-04T20:00:45.233622Z","shell.execute_reply":"2023-11-04T20:00:45.239507Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"vocab = [x for x in \"abcdefghijklmnopqrstuvwxyz'?!123456789 \"]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:45.241586Z","iopub.execute_input":"2023-11-04T20:00:45.241870Z","iopub.status.idle":"2023-11-04T20:00:45.251898Z","shell.execute_reply.started":"2023-11-04T20:00:45.241846Z","shell.execute_reply":"2023-11-04T20:00:45.250992Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"# encode vocabs to its index and set every character that is not in vocab to empty strig \nchar_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token=\"\")\n# decode do the opposite of above \nnum_to_char = tf.keras.layers.StringLookup(vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True)\n\nprint(f\"The vocabulary is: {char_to_num.get_vocabulary()} \"\n    f\"(size ={char_to_num.vocabulary_size()})\")","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:45.252989Z","iopub.execute_input":"2023-11-04T20:00:45.253230Z","iopub.status.idle":"2023-11-04T20:00:46.321755Z","shell.execute_reply.started":"2023-11-04T20:00:45.253209Z","shell.execute_reply":"2023-11-04T20:00:46.320717Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', '1', '2', '3', '4', '5', '6', '7', '8', '9', ' '] (size =40)\n","output_type":"stream"}]},{"cell_type":"code","source":"char_to_num.get_vocabulary()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:46.322841Z","iopub.execute_input":"2023-11-04T20:00:46.323133Z","iopub.status.idle":"2023-11-04T20:00:46.330944Z","shell.execute_reply.started":"2023-11-04T20:00:46.323108Z","shell.execute_reply":"2023-11-04T20:00:46.330098Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"['',\n 'a',\n 'b',\n 'c',\n 'd',\n 'e',\n 'f',\n 'g',\n 'h',\n 'i',\n 'j',\n 'k',\n 'l',\n 'm',\n 'n',\n 'o',\n 'p',\n 'q',\n 'r',\n 's',\n 't',\n 'u',\n 'v',\n 'w',\n 'x',\n 'y',\n 'z',\n \"'\",\n '?',\n '!',\n '1',\n '2',\n '3',\n '4',\n '5',\n '6',\n '7',\n '8',\n '9',\n ' ']"},"metadata":{}}]},{"cell_type":"code","source":"char_to_num(['o', 'm', 'm'])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:46.332074Z","iopub.execute_input":"2023-11-04T20:00:46.332411Z","iopub.status.idle":"2023-11-04T20:00:46.345939Z","shell.execute_reply.started":"2023-11-04T20:00:46.332379Z","shell.execute_reply":"2023-11-04T20:00:46.344920Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(3,), dtype=int64, numpy=array([15, 13, 13])>"},"metadata":{}}]},{"cell_type":"code","source":"num_to_char([14,  9,  3, 11])","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:46.353203Z","iopub.execute_input":"2023-11-04T20:00:46.353478Z","iopub.status.idle":"2023-11-04T20:00:46.365271Z","shell.execute_reply.started":"2023-11-04T20:00:46.353455Z","shell.execute_reply":"2023-11-04T20:00:46.364375Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(4,), dtype=string, numpy=array([b'n', b'i', b'c', b'k'], dtype=object)>"},"metadata":{}}]},{"cell_type":"code","source":"def load_alignments(path:str) -> List[str]: \n    with open(path, 'r') as f: \n        lines = f.readlines() \n    tokens = []\n    # in every line there are 3 elemnts\n    # 1st and 2nd are the time stamps during which word is said\n    # 3rd is actual word that is said\n    for line in lines:\n        line = line.split()\n        if line[2] != 'sil':                     # dont consider silence\n            tokens = [*tokens,' ',line[2]] \n    # all encoded words are stored in tokens list so return their concatination to train model\n    return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:46.366334Z","iopub.execute_input":"2023-11-04T20:00:46.366571Z","iopub.status.idle":"2023-11-04T20:00:46.373805Z","shell.execute_reply.started":"2023-11-04T20:00:46.366550Z","shell.execute_reply":"2023-11-04T20:00:46.372918Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"def load_data(path: str): \n#     path = bytes.decode(path.numpy())\n#     print(path)\n    file_name = path.numpy().decode('utf-8').split('/')[-1].split('.')[0]\n#     file_name = path.split('\\\\')[-1].split('.')[0]\n    video_path = os.path.join('data','s1',f'{file_name}.mpg')\n    alignment_path = os.path.join('data','alignments','s1',f'{file_name}.align')\n    frames = load_video(video_path) \n    alignments = load_alignments(alignment_path)\n    \n    return frames, alignments\n#     return file_name","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:46.374922Z","iopub.execute_input":"2023-11-04T20:00:46.375197Z","iopub.status.idle":"2023-11-04T20:00:46.386472Z","shell.execute_reply.started":"2023-11-04T20:00:46.375174Z","shell.execute_reply":"2023-11-04T20:00:46.385548Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"test_path = './data/s1/srwv1s.mpg'","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:46.387470Z","iopub.execute_input":"2023-11-04T20:00:46.387784Z","iopub.status.idle":"2023-11-04T20:00:46.396509Z","shell.execute_reply.started":"2023-11-04T20:00:46.387760Z","shell.execute_reply":"2023-11-04T20:00:46.395696Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"# test_path = bytes.decode(test_path.numpy())","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:46.397767Z","iopub.execute_input":"2023-11-04T20:00:46.398081Z","iopub.status.idle":"2023-11-04T20:00:46.407323Z","shell.execute_reply.started":"2023-11-04T20:00:46.398057Z","shell.execute_reply":"2023-11-04T20:00:46.406425Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"# hi = tf.convert_to_tensor(test_path).numpy().decode('utf-8')\n# hi","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:46.408405Z","iopub.execute_input":"2023-11-04T20:00:46.408728Z","iopub.status.idle":"2023-11-04T20:00:46.417131Z","shell.execute_reply.started":"2023-11-04T20:00:46.408699Z","shell.execute_reply":"2023-11-04T20:00:46.416301Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"tf.convert_to_tensor(test_path).numpy().decode('utf-8').split('/')[-1].split('.')[0]","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:46.418385Z","iopub.execute_input":"2023-11-04T20:00:46.418701Z","iopub.status.idle":"2023-11-04T20:00:46.431050Z","shell.execute_reply.started":"2023-11-04T20:00:46.418672Z","shell.execute_reply":"2023-11-04T20:00:46.430097Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"'srwv1s'"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"frames, alignments = load_data(tf.convert_to_tensor(test_path))","metadata":{"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:46.432276Z","iopub.execute_input":"2023-11-04T20:00:46.432876Z","iopub.status.idle":"2023-11-04T20:00:47.488844Z","shell.execute_reply.started":"2023-11-04T20:00:46.432844Z","shell.execute_reply":"2023-11-04T20:00:47.487907Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"code","source":"alignments","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:47.489965Z","iopub.execute_input":"2023-11-04T20:00:47.492006Z","iopub.status.idle":"2023-11-04T20:00:47.498695Z","shell.execute_reply.started":"2023-11-04T20:00:47.491978Z","shell.execute_reply":"2023-11-04T20:00:47.497813Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(23,), dtype=int64, numpy=\narray([19,  5, 20, 39, 18,  5,  4, 39, 23,  9, 20,  8, 39, 22, 39, 15, 14,\n        5, 39, 19, 15, 15, 14])>"},"metadata":{}}]},{"cell_type":"code","source":"plt.imshow(frames[0])","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:47.499860Z","iopub.execute_input":"2023-11-04T20:00:47.500097Z","iopub.status.idle":"2023-11-04T20:00:47.984766Z","shell.execute_reply.started":"2023-11-04T20:00:47.500076Z","shell.execute_reply":"2023-11-04T20:00:47.983722Z"},"trusted":true},"execution_count":23,"outputs":[{"execution_count":23,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 0x7d7781d40610>"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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PnQbC5IqUmYuXLLUsovIcluJWjEBGRO17DMh6LG3tMmXULVrMScSmdQyjjo7LufpI/tiy3zL0rLdJTe+SJNUB2fZA6EjjHIXVh5R9MP+MNnFkcvZPDJpXrnhFoKwzixJF1ktkXgW+YaX6Yy7XE7RZVySaVByTZLVWdCDqrMKM3fXpw+/LMr4/dfZSg2TEyLyDM/AKkuElKdll0IFkUOTImquasX2vtu+Z7Znk78oFrk5gromY8vizdN96xeTudqa48OiiLvhrlogo816bfLZtEpCZWK0zFatRDbDJRqsfAAAAACgpmDyAQAAAICagskHAAAAAGrKlLX5qClaAx0nNzZhW6AyznI7C3dEhoYO6lh2Vpvup11thc3HNNILdV+rtt3hYzwxrsU2V9iJpqBcFnnm0rHCbQfMTVGHUJfZaeOvL0k6LDsfb1nncJCOOY4oo2xHGtxwX9t/cFffiKstC5sedcNl9iBqNFKijfLv54jKiPrTw3tK2zrU/mWdS8MWUrJMuH0rOwpuR7L1sAy9rkOD29C2S+POSdgj8JDqNhuPqusvyPvEw6JH7G8q+U5OQF8FM9zGQ1PxE/rCCy/QVVddRfPmzSPHcejxxx8X5cYYuuuuu+i0006juro6WrFiBb355pvj1V8AAAAATHMqnnwMDw/T+eefTxs3bjxh+d/93d/R/fffTw899BDt2rWLGhoaaOXKlZTNZk94PAAAAABmFxXLLpdffjldfvnlJywzxtB9991Hf/3Xf03XXHMNERH967/+K7W3t9Pjjz9OX/7yl8tvyJjKl9/Hy9W2EmzTN766W1QuZayvjs6QyC4jSMkGEsdZPSpwJJdyfJ0N19f7bOlT9c3l8o0tc61tvPWY8v0KMs5GoiOyJWvt0mb4BFdlDdbHcnjm0mi/42/wk2zZnYho1ITLvcdVc3VOGElRtEdy+X40KD86I9/11a0YNvLVHmQRR7OBlAGkO6uOBsqyyqrzuKvtCJNZPiyLd9nMkowqWfDDenQG2gY3d8JtIqI2N7zfTa6WVphrsXpO+RXqu6sjh3J3Vy2tiDq1y6glou5TB/fGlvF69BPL29duuDaK6h3ics4XTpcRVm3voijR0UZ5hFF97YHF9dTm2quxfSdYmz889P+Loq8s+r2wb5GIqjoUMOtPoK6DtV9hwnXWnOWbqV9ijs7uHa24vPYt2dQrIk6CquBv8LgKgwcOHKDe3l5asWJF6XctLS20fPly2rFjx3g2BQAAAIBpyrganPb29hIRUXt7u/h9e3t7qUyTy+Uolwv/oxkcHBzPLgEAAABgijHprrYbNmyglpaW0s+CBQsmu0sAAAAAmEDGdeWjo6ODiIj6+vrotNNOK/2+r6+PLrjgghOes379elq3bl1pf3BwcFZMQITuqXQytxDqcpHMuEJ3VGUT7Qo2XpyMSxnXZGuRqdcS0jogbUsQP/78WB16fYTdN1sdmhzrW2GM04QrqvqXw7PYD3C7joJyp+V2HdoeRNuAcNKuNlYKN3U9PGw6DwNPRJQzrB51CR4Lxe2rkPEBb9DVGngQe2zB5CgObSvyv97eWdrWtiL8XkUz1/Lsw/Gu1WO1b8vGbH39pss3RMP6fVJfhfGyiQBjMq4rH4sWLaKOjg7atm1b6XeDg4O0a9cu6urqOuE56XSampubxQ8AAAAAZi4Vr3wcP36cfvWrX5X2Dxw4QPv27aO2tjbq7OyktWvX0ve+9z0666yzaNGiRXTnnXfSvHnz6Nprrx3PfgMAAABgmlLx5GPv3r30+7//+6X9jyST1atX0yOPPELf+MY3aHh4mL72ta9Rf38/fe5zn6OnnnqKMpnM+PUaAAAAANOWiicfn//8562+yo7j0He+8x36zne+c1Idqwo3XkWypVE/qZTy1Z6bCTVxk1AxOFjcDe1Oz69D9ztiA8KJ2IeEbVR9/eMVFn0iCHTchdA+wJYafaxr4PYauQpSnEttP16Pj8TyYAypuAND7OEYUbYSOkx6iocpd2XAvwKziciq+CC+sHmQZTyE+lAg/7nIBZaYGKpvGWbX0ZY4Lso8ZgOj29d9FdjCKbD2m5RdRVbd/1S55kmW56agnpN6N8nKdPs+K4uv09M2Hvr1ZoEorKHWxysNQwXvgpVq7cHYefWO+p4m2HOix9S1xBlx1LEsTDtZ4rjY7GacKsd7Cn1Zx41J93YBAAAAwOwCkw8AAAAA1BRktZ0smETkaJfR4XBZ3MsrWxm+LFlQ59nkMJskU4lcYnERti7h1jjLbkQaFBlg5bhxKUVIMBR1i+XL5JFMsrwNtVCa5PKBdqFkkojN1fbdQL6u/Uzq0LKHzvLa6o6yvmh3Ut5v+f8ID6+u6+Rh2bl0QkRUYJ8Wnak2GsK9eMJtve+qfueZfKXLPHFNKmQ6z+Ib78n+YRvsnqbUM5y3vDf6WE7WhNekJZkR7mpreS2T+vmaTlmsJwCTD5/NrJKyrCHNgwpcayvIKgzGBqMJAAAAgJqCyQcAAAAAagomHwAAAACoKVPX5sOYcXXdtLqTnkw71aZWtqWuzoZp1Z1ifN+MJ+eO3HbEsaVnHi/GS2eOpLF34ss4egy5e60fn0Z81EjbhTSFro9PHn5JlOUirpChXq/dYgvW8OrxodC57m+r45jfJPb7g/qwXzrdvStfbW6vkXHK17lbmVtuStmqDLvxIdRdS85xbfORccLnvcGVIcy57Ya2HbGFhed2HoGJ/x+rELEH0bYU4bZ2w60W3oa+Av7aalsV0a8xnC9t7rXC7TviesraVO+Q4XZquuOVuMjykAgVjKnNTZWXZbSrrWdzp7WEC7C5D1cZhn6sMBWzCax8AAAAAKCmYPIBAAAAgJoydWWXGQ53fdUSjMmGS89eVroeBkk2X9SrdGJZNN7V9MOKyu9rLLZlQtty6sksL5Z5rlFLxjwDqnZn5cvQbiRzq6/22cAp1ztfuNpKuHtrUl0Cl1q0+y4nb2TfeLZWLXPUO1K+aGCyS72SPQK+r56LVjd8/qJyzQelLS4BEcnop1o+8CJuomEbp3sDosxlUSa1i3DKiR9Tfmdsj3pWhxBW2NRLW738Pno6UiZDS3B8rHzVN5mpV9Wj2zDcfVk+p54lwq6QE7wK3tNJzoZr8qF0N6LeWSHHWqJgj8nJZOMGEbDyAQAAAICagskHAAAAAGoKJh8AAAAAqClT1+bDcSq3DajWFe4k7B+qdeEVdh7aFYxlTHQKUr90mA6rXW2DhCVkuw1LKHIrNg10rLjVEwHXc6sMhRwoewRtH1IQbpKBOpbXI+F2HW6VOSozbnx4cw0Pp05E1OaF57aoTJ7c9TOtQqhn2Duo++15o+w4aZs0zDK3Zo1yA7Zko21S18jvYkY9QjykeNSugfc7/lkYCPJi3xamXGeZHbG6xZoTbhIRDdmy8VoIhB2JcnsOtB1NuN/myPb4eOj0AZd1LhVHVoN2GTXazsKvIKQ5x5a1vGDJMsu+rxG328h3Ypyy84IxwcoHAAAAAGoKJh8AAAAAqClTV3aZ4QhX21T8sqSWXSjFlg21xxwPDGqJjDorMFoSYXJJJBtvfDX6WC4K6OGvdsHW5qLLaVbusz5b3tbuqy1KvuBSS4tbp9oPe15Ptoiu+pniLoyF2CJ9Xl5JBjw6a15FIxVuqhE5kO+r0WfL6b5lVNs8GaU1oe5qkZ3Lx4KIqBDwDMeyXinDyMKsiXd1FRFd9Xjz4KPqodVXGHnGZzjctT7irs5lHlu0UyJ7ZnD2vs2u0Z0YsPIBAAAAgJqCyQcAAAAAagomHwAAAACoKVPW5sMpBuTYsgpOByxueyYd6tzaFc2tDzV5k5OugC5zp803pmRZnume9fLWesNSk+dja82Aq93m0l5sGRe+K8rQaHP1rSALpAniQ58nmZafI6ndcy1fa+VZbTvCit8sStuJJje8V03K9TTPVOK8Cv/MVegG1e80c5PMKDfYEdbXejUWnvq/YoDZJ/T5w6KMh/EeUm6xWRNe41CQEWXcZVb3rdUdYWVyLJoc+UwXmC3LoWILxRGoa+IuvL4lc21StZ9iNifnp94XZRk1/vXMZTijXFZ5d9xAh+Hn/ZbwfuuMu8MmfKeTypJjrne8tD2i3HVdFV69jYXFH1FZnIntaxflpw7uLW1ftmi5KHPY+2V1n418F5RFCnd9TaXijw3kyDlNjbF1OszV9qZPXCLK3Eb2nuq+BHqf2eKpcAVmeIQdF/+3yZZFd8Iy15aZ6sKa4bcCTJwd1RjpCjhY+QAAAABATcHkAwAAAAA1ZcrKLjMetrznuHLJ1PBlMh25LxsumUYy14r6K+iLTfZQy3lcojEJtcTGs2DWQDEz4+ROaHNLtLm+NiipgZONyADhgOhkoVxqaXaltHHF/CWl7Sff7pE1BmGE0QH1LLwfyOXsXiZn6IijHB19lLvBjgTSLTXLjk0q99kmN8vKLNEnFUOBlLJ0vZyALfHqvkUjjp6Y9/1Gsd/qSUmqwxssbX/Mk3KRlrpE39i2dq3lEtWIkf0eVtcRR0qNS70r3bALhmcjjq/HK3OcxhX+vmn5wiJniMy5lURwBlMSrHwAAAAAoKZUNPnYsGEDfeYzn6GmpiaaO3cuXXvttbR//35xTDabpe7ubpozZw41NjbSqlWrqK+vb1w7DQAAAIDpS0WTj+3bt1N3dzft3LmTnnnmGSoUCvSHf/iHNDwcLlXefvvt9MQTT9CWLVto+/btdOTIEbruuuvGveMAAAAAmJ5UZPPx1FNPif1HHnmE5s6dSz09PfS7v/u7NDAwQA8//DBt3ryZLrnkQ3enTZs20dlnn007d+6kiy66aPx6Pt0R2qbUhIU7Vr4QWxZxkS3T3Sqyr8u4tqqnp+W6ikVCmFtjmJffN+GKV77NB89AqzPVcnTm0qibZDgg2r1R1KMHjh3apLLRumxsciqE90/f3l3a/nVR6vq9fn1p+1BhjixTLqvvFZpK29qOoiUxQnFwV9CCsl3g9iCBsh34gBpK2zobrq4nF4SfoUC56qWZy2jSlf3m9fI69L5v+R/rcO4Usd/oyTE+NTlU2l6Yek+ULUyEbro6nH3a4duq37b3yw/v05Av7V8KwsZGPietakwbWHmTeoqrtvOw2Vjx93ssWyyLXYd2bxVNsGzfJi/tb2qSNXsqMw3D6Z+UzcfAwAAREbW1tRERUU9PDxUKBVqxYkXpmMWLF1NnZyft2LHjhHXkcjkaHBwUPwAAAACYuVQ9+QiCgNauXUsXX3wxnXPOOURE1NvbS6lUilpbW8Wx7e3t1Nvbe8J6NmzYQC0tLaWfBQsWVNslAAAAAEwDqna17e7upldffZVefPHFk+rA+vXrad26daX9wcHBWTcBMdqdlZcVlDtnMVzCdVXG2yBZZfQ6m+yhV0EtUoOVCpYFHb70apVkgrLLuNSipRU+ivpyC6p5LqdoiUDWqSKOOrx9eewxJiUNqTq5m+aukd8WZSPMnVZLGQMq+upgMXTh5VIGEdFxP3TvtMkXSSd+SVyfx8fGJk8RERWZtOOr6+dt1nnxrs0F5dpcDOLfhQSTb/Kq38fy9WL/1yOhnPWad7ooO6MulF06lSTTkRgobTczt2MiKXtF3JeZlKVdogt+eE1aOtOut+W6GicdyzdDv3v8/ltlVBUZ1PbuR97v+CjFvMzkpDw2ZrZaMOWoavKxZs0a2rp1K73wwgs0f/780u87Ojoon89Tf3+/WP3o6+ujjo6OE9aVTqcpnS7Ptx0AAAAA05+KZBdjDK1Zs4Yee+wxevbZZ2nRokWifMmSJZRMJmnbtm2l3+3fv58OHjxIXV1d49NjAAAAAExrKlr56O7ups2bN9NPfvITampqKtlxtLS0UF1dHbW0tNDNN99M69ato7a2NmpubqZbb72Vurq64OkCAAAAACKqcPLx4IMPEhHR5z//efH7TZs20Ve/+lUiIrr33nvJdV1atWoV5XI5WrlyJT3wwAPj0tkZRZINvY63nQllKEfKxWSKZYaqHmtNy+LO6jC7EpNQWmq57naVUIEbsHDFi7gIh2VPH35ZFH3A3JKz+jS2rbXyEWUTMMx0eB2KPM/sLnQG1rjjPmwjvN/vFptF2QBzt+zNSfdZbrugbQCGfRW2uxjuD5Ms4zYXH2SlzcNwIbQraUxJnT3jhc9i3mJjkVG2GilP2SpZ7EO4Q+VQUfU7CPtdVGPKyxKutEFIsLFqTMpryvrSzoLXc7wg2+f9OZSMd9ltTCj33USYnVY/b8f90DZHZ+pt8sKPQVq59upnio+pttQp1zrCFKQ7q2E2J65KRkssy63NXTbSRlGFErDZbrAyo2xFXJ3qQRRyN+BxcsnVWX0nmslwpS031D2ngn5WNPkoJ5dGJpOhjRs30saNGyupGgAAAACzBOR2AQAAAEBNQVbbycIWEZCXaSmDRTx1R5XrIVsKM2O53fIVNYvswSUYIiLjjcN8dazIpLw/vspqauLdcLkklTNybIbZeQWKd2fVLpvDRq4vc4lEZ0TlGUn58vmH54X12NxgBwqyLM+WuusT8pq4XJItSrngvdEGsT+SD8vrUrKeTCIct5GCrKc+GR6rJYr6RLgsP1KU46RdWMtFXweXcwpK2uF9Hc3L8/wg/jnl0o6jZB49No3J8Brr9PizNt7PyfF+14TPhpaSTkmF2Yh11FYuCWkpzbO4OkckQPZ/ZVa931yFKBgdNTY878nDL6n2w7LLzlgmO8DaMPqdVThu/LePyykRL2BW79ZDu0XR1Z+42NommHpg5QMAAAAANQWTDwAAAADUFEw+AAAAAFBTYPMxSYiQ6jpKOHNvdTIq+ivL5ugU5YlOkumuukFtO8KnnTYvJkvGW92+SbgnPG5MqnUjU9oy15q1ls1Dmmv3WamPS9sBHf6a23UMKbsOnj3VVfo8d5vUochHmXtnUdkq8KyyRWWPwu0jBvKyLwOjqm8+cz31ZN+4zUdSuaWe3tBf2p6f6RdlLYnQduFovkmU8ZDtnrJ5SKnw7twt9O2RVtk31lftMivcSZULZb4YvkNFP94N9fiQtLEpNkrfdj42+p5yWw7dPndDTnvyettSw+FxbnzIeJ0NuN4N333taquz3KbYR0WH8w9YV3WG50CcJ8t4NtynfiNtLi7rXBru6LQHKkw6f0+1XcmVC8N4UE5S/XnS4dZF56ZHeHWbx6hTbaiCk8HmTjvBYOUDAAAAADUFkw8AAAAA1BRMPgAAAABQU2DzMQVwtO7GdUGtA3I7h7wOTRweK3zpx2zfEt58MnRIji30uuXYqJZNrExeE7er0KnpI2nNWfnHEkOirMEN42C4ypBnJBXaQBxVIdSPFsJ9HQOE24e8l5NxRXicjYyKQaHtI0ZZTIy6pDyWx48Yzst4He9lwzYbEzLcNrd50OPGU9qnVXhxbrug0aHXud2D68h+izgjSdnvUWYPU/Dj7QESqr1T60fEfnv9YGlbX7+oR8XkmJMM7TraEsOibF7yg9K2jt3BbYqGlb1Ritl16NDrKdU+vzf6ec+z9ySp3pMChfXod4jYPfbV8y3SPpzMN0Pbi8SgbbrA9AMrHwAAAACoKZh8AAAAAKCmTF3ZxZjJyeRXI8TCpM4CyZYtTVreIqeRLb1n5TKww/ZNh8yy6eSlK56TY/u2cS6oLLrM/c2obLxOli2L6zDsLEy7o+SiajPl2sI4J1XuzrQIW62Wuo1O0RlS7yjJgGUrbXalW2YTkxOaXC0fhAwlj4qyQ8kwW+27vpRkjhXD+92RGhRlAXuKRnx5DR0ZeSx35x1W2WGzfnhPtezB3WL781IS0uHO4/p2LC9Dj/c7sh5+P3R4dU7Cjb/f2n03lSov+/OiZnl/W5KjYn9uMpTW2lg2WiKZZZZLbkRErW4o39Srsg5P7nO4W+ywCpnO0wBoOdDTMl8QlmfUPc2xNgJ1Xj2TWuod2UbaCftzxfwlosxJsmOV5Gty6nrZO63rcevi30X+TdH9tmaZ5d+QxBh/8oR0Hf8sRtI+cCoJM8CwJm4dS8qynctk/UoyDtvbi6mnTNmMCCsfAAAAAKgxmHwAAAAAoKZg8gEAAACAmjJ1bT5mODy8ekWOaTZtz+d2FWNo3lyX1XpitbY2Fbj3jguWfnrqmjJsP6PcEpuZXUdehXD2ooHqY+vh+/Wq/SSrNxNJjT5Q2tIukxnmXppNSA06y3T9EU/acQz40q5ixA219IjNCysrBPGhz3WYcJ0OnuMy7VeHCde2Gzz8enNK2lyI9iwp5XXa+nL72ZqQrrU8ZDwR0SnMTbbVky6z3M6jwZH2V61uWE+TCoVu+4+vFv8NltsGd7slitpRCbjWr1IEkOvFH2t5h422NxvNnvjAqYb+DtpsQHiYBZvdygxkdl0tAAAAACYdTD4AAAAAUFMgu0wSjk3asC2/sYy3ERddfp4v6zdptfTJZYGiXF51yo1wqlfBHYuUU0nUQ+syZXWSEM/IqWUPz5JZNGlJBqyzhXLPY0+JafUOcyFUdXoULt97joyaymWX9wPpspp1uJQil6gjGVhFpFDZ8TovlAx0pNKhQpgdV58n69TtuXxHoLPc8uigbSkpg4jz1APH+6P7po/lcBnmtGS/KNNusVwGy6gIq3xfn8ddu/XbbMu/ylvQkh+PxBuoCKf6meJvtE06rBr1/RJu7+peRLLTsnfYFFWU5lQoJUZc6dn3bdWCLlHktVb5p2y6hHMYq5+TmJ22WrDyAQAAAICagskHAAAAAGoKJh8AAAAAqCmw+ZgK6FDk3JZD2zhw+wyl85l8qN27w9ItM/DqZT3VZp5kbUay8U5EBtwywwYTEf308J7Sts56yTN06hl3WthqSOqVmyB3NxwxUq8ulKkfJx1ZZ6MbuskWSLoTZlkb/epZyLBw7jpzqcZLhGPVpMJ22859z2uKr9OJd6cNLHUmnXjX17TF/iZjsWvRdXKbD505ltOkQuRr2xmOdoPmbaaUjQm3a9F2Q0nLe1Lgz6l6nnh7+p7Z7DoiNidlvqaBaj9g4/j0kX2ibOW8C/iRosxR12vYu/nk2z2i7Au/9Tul7acO7BJll3UuHavLtUO55FcSVrxspqEdRyVUtPLx4IMP0nnnnUfNzc3U3NxMXV1d9LOf/axUns1mqbu7m+bMmUONjY20atUq6uvrG/dOAwAAAGD6UtHkY/78+XTPPfdQT08P7d27ly655BK65ppr6LXXXiMiottvv52eeOIJ2rJlC23fvp2OHDlC11133YR0HAAAAADTk4pkl6uuukrs33333fTggw/Szp07af78+fTwww/T5s2b6ZJLLiEiok2bNtHZZ59NO3fupIsuumj8ej1dsC3DW11t2TKldqflrrZaJCiwJWvlPmu0+y7P3qjbt0g7DutPJAtj1VJOBe5uzP1Ot8+lloJaBuWSiF7M5COjl8Sj2XHDV0Zn1rS5d/K+uRXEtE2yOptcGX1TZDat4N8ILUmN0YESEfdOC1wSqcTV0ya76PHlEol23+XHRiQZiwwTWAbSt9xfW4RVfZZtMZ2Lpfqa+DDqMdWuzry8oJ43LqfUWx5F1/I+++r9evLwS6Vtnak2ApMsPCVfOF74vuk2+HlOSn37ah1dOdIeD4Fgy3irrol/w2znzUCqNjj1fZ8effRRGh4epq6uLurp6aFCoUArVqwoHbN48WLq7OykHTt2xNaTy+VocHBQ/AAAAABg5lLx5OOVV16hxsZGSqfTdMstt9Bjjz1Gn/rUp6i3t5dSqRS1traK49vb26m3tze2vg0bNlBLS0vpZ8GCBRVfBAAAAACmDxVPPj75yU/Svn37aNeuXfT1r3+dVq9eTa+//nrVHVi/fj0NDAyUfg4dOlR1XQAAAACY+lTsaptKpejMM88kIqIlS5bQnj176Pvf/z596Utfonw+T/39/WL1o6+vjzo6OmLrS6fTlE6nY8tBBTD90GRluGdH6YnGY7de225wXVLrkEU2X9VZJ1lo5JNyu+XXocM4WzRS7k7rK03cpqbaNHht11Ekbrsh5+7apVKW2YJqh+j/BhrcsP2MI0OPcy2/P0iJsqS64hEnfMeGA/m++SxzrbZ5aPOOx5blmcuutsfwLG6wGt5+xM6B1xmx+Qjr1XYcruWu8mOzatxsx9rCq+sMx/WW6ygXbdeh27Ady21QImVltq9dbUWW20jaAVardtHNqW+RF/8u8GMHA5XFVtiAlPc+TRg6U29geca5fUixgudihme8PekrCoKAcrkcLVmyhJLJJG3btq1Utn//fjp48CB1dXVZagAAAADAbKKilY/169fT5ZdfTp2dnTQ0NESbN2+m559/np5++mlqaWmhm2++mdatW0dtbW3U3NxMt956K3V1dc1OTxcAAAAAnJCKJh9Hjx6lG2+8kd555x1qaWmh8847j55++mn6gz/4AyIiuvfee8l1XVq1ahXlcjlauXIlPfDAAxPS8SlDtVkRuTurXlKzuY3Zlt+41KHrsGWg1bDlPqNcfR3m3uioyKyGZ6+01a/LdF/96saUZ5LVETb50rNNZtFRSpM6k6vlfmsZJo5ALYNz19doNly2o4ZphPWlQUXmzCqX1SAI++Yr90af3Q/tTsulDeHaS0S+4S6T8ZKIlis0hXFYQs+rbLye5V7wbMT9fn3scURETd5obFlGXKN29Q3RC/K2p4S7tyYj0qHF1Vcda5OvbNiEDS5lJlUZlyd5pGEiIl+9M9ct/GxpW0ZGJXKS8f12Ukwi09LNRERXrpZKop/OcGnFRkWTj4cffthanslkaOPGjbRx48aT6hQAAAAAZi6za6oFAAAAgEkHkw8AAAAA1BRktZ0sqrUV4Wid04sP8asz0BpRpsM4s32lV5qAhWVXIdx5mHSjUmdWrcjqvnF7lEDbZ8TbDgg3XO0KWEl32NFZFaY8qbVefp5F99VuwRxuA5KNhIwnVuapsvix0JlbhX4fMRXiLpSxVQr7jw/bmPhQ0fwaffV/lN3KJGRA2XxEwpSz/WaVAdcGv/pKwquX+9/gWDYe2l5kvNEh+m3vXs7EZwrW3zBTDO9c5L2YSnYdGu56G1iu18YMz2KrwcoHAAAAAGoKJh8AAAAAqClTV3bxA6psQXyakbQMPZcT9LJkIn550+GuWsptS7vMZj+WKW3X5eUyocsjl2p5KB8ui2rZQ3iF6n4yicYU1KK4LautJXKgo8YwwZwDXYsEkrGs3uql3iHVfs4idfClcB6ZlIiohS3LandaHUmSw6Wcdy3jpKNffswbFvu8r9pldoRFPNUuq3yfRyIlkpKEll04/hiiG5dMtFxki1Rqo8Cir+o6eXstnowaq12GuZuwvo4hS3RUno04perUsh+n3Ksdy5WWR7+1RTjNqmqEXKM94kVmaFnI35svdn5OnqjeIScZ9mDr27t010t84fSLZfv1YRuORXKOlDH0d5B8i0SiQgkIWXs0XoIzOiq0NYM5a+NkZJcy3XSdCtx5jY5gPc5g5QMAAAAANQWTDwAAAADUFEw+AAAAAFBTpq7NBxhXnLy0s3D8eLsSgbYz0Homh9t56Dpt52ls4YgteNzOw+hw3/G2Mjwses7IcUpFxiYcD62Xcx28oMoKrD9emRluiaSWPhLEv67Dyo6D2zwQSdsNHc6c22vozLXZIKxX12lz5+XGC4Uxrpe3P6Iy7mq3YA7P3BooexSbzYfopqNdhJX9kxtv16LdezncJkNnOy73Pz79DGn32nKpheUct1vaemi3KNPvFHdRP6Bub7u2sxCNMDf7ojzRYfuRUdIZaDm29rStBm9jPEIlTCeq/CbbwMoHAAAAAGoKJh8AAAAAqCmYfAAAAACgpsDmg2h8Qp1XSpVp463YrqOgY3mwUOgJNQfl8TPy+er6ojRZ0TdbXA9NBceuPP3CE7dHRE8f2Rd7ns/0zLSjk4VLePyGIaUu5yy2BfWGp1+XZTyNesHEx4Rwlf2JLXbHsLKdGArqWJ3lh6nmNhi6jYDZQOgYGJWEV+f1DPh1scfpOrkNhI47wYnE+WDttSRknI/IdbD7pu1hODqWBt+3/Ydns4bxximauLZN4thi3tj6rZ9FG/qZ/lUhjDH0ftAgypqc92LrEXYW2oYsbwuoH5ZFYoCkLO+7aiMSI4TDY3To7zAv03E2yo3tMRmh1yfAzoODlQ8AAAAA1BRMPgAAAABQUyC7zBa0axoPlZyWj4FbFy61OzkpuxguyVgkEaMz3laCJauuVVoSbpPyvJXzLqiqK08efknsp5kLZSoYFWVcdilEwo3HjwcPt24LW91gcTstOMqdUclHXGrhEsyHfQ37Hah+HyuGy+K5IN7VthiosOhO+Uu2RVZPzlfPIndZdeUYJizSjs0NN7DILhoebl2HeveIl8nnMs2ajLprs37q9tj9t2VJ1mhpg+NX6WxrO8uWEuA99V70B/I6erILS9sDRZlVuMMbLG3/x9s7RNkXF3y2tO2kZGh7J8GeGy2XsL5qF1nHKqVY5BPbd8gmkVjKrO67lUjVNYan2ajEBRkrHwAAAACoKZh8AAAAAKCmYPIBAAAAgJoCm4/Jwmq7UKWPnRfvuGdyObHv5kPtMUjKOWiQCh8LT6Wtd/zQlsBkZVppw91ytStcwvKo2bRtm+6qq2F9jaaDLk/3dtQYXjF/iTyA3bdHDr4oivJlupe6as7vWVxtORE3UHZNDcrmw3OH5X4i7Len3LyH/ND1saA+CdxeQttO5JkNSEHZfOhjRZm6Dn5u2lUu4RbbEe56m3Dj7TFs1LvSpimjxrHeDd+blLq/vP2U6mfGibfd4PYSuoynAdDPiahDXZ8+lpcHlmfK5upr+8/Ut4R6P6aehV8XTxH7ewcXlrZHfWmblHHD8f91SrrdfvetMGz7x1z5Pfv6b13KOqfeQ8t3MQL7vuhvgXCTtaSLqNp2wx0n3+oaYMbBBgUrHwAAAACoKZh8AAAAAKCmQHaZJRjlMuvmWIZGV0X541NSLQGx/Yi0EZE62Gl8R0swelm4StetahHLq2oZ3NHRX1lf61W2TO6W6at+82N1hl2+RJ524l/JY6SkFeZOWq/ccNMmXiJocqUr5JAXut7mVTTQNFsGzwXyORkJQnfHrCoTGX7VMrzOBltkrpjcRVb3W0syfIleyyc8O62ukzM3MSj2dVZbLsM0uVJmbGVt1qvXpJ65OnuRdyjc1HKJywo9ixypM976Flfuav/DLFjevcg1MT7myfd5X05GMX17uLW0XVRuuL9KzS1t62i3PMJuvyfv20/eCiVQHaX4skXLY/sakXEt1+ywDLimYImoOhFusRMcbfTETU7stxcrHwAAAACoKSc1+bjnnnvIcRxau3Zt6XfZbJa6u7tpzpw51NjYSKtWraK+vr6T7ScAAAAAZghVTz727NlD//RP/0TnnXee+P3tt99OTzzxBG3ZsoW2b99OR44coeuuu+6kOwoAAACAmUFVNh/Hjx+nG264gX7wgx/Q9773vdLvBwYG6OGHH6bNmzfTJZdcQkREmzZtorPPPpt27txJF1100fj0eryZhKy2trC+xuZyZXPDrSCsr1O0HMtcykxC6vVOmoU1VuMWdW89cZ0Rs4aIts36ZnOtVWNhtQ/hZfo8pm06SVkWyYLJ5ustrtSkcyZeB05YnBr5FWotnZ/X5Ei7Bq7yaxsAT9k5eMx2od7I+8TddLU77+mJ/tL2sJEhrXnGW51Ft2DJuJs38e68BVXGbTC4jQeRtMHQdizcLdbmrqtda3V22gyrp1652jaw9zTtaPufsKwSuw6RmVm/F0z33/r2XlGkXW+5y7bN0TSpnjdei7ZbEldvedf+9OyVYv8rL70h9vtH4zMX//p4W2n7aKJRlB0vhs9YnSfvW0C/LG13WOxBrp6/LLZtIvkNczOqkNmGWTPc2uq3uOhqsx1uizbR9hcnBbdHqcA2paqVj+7ubrryyitpxYoV4vc9PT1UKBTE7xcvXkydnZ20Y8cOXQ0REeVyORocHBQ/AAAAAJi5VLzy8eijj9JLL71Ee/bsiZT19vZSKpWi1tZW8fv29nbq7e09YX0bNmygb3/725V2AwAAAADTlIomH4cOHaLbbruNnnnmGcpk9JpUdaxfv57WrVtX2h8cHKQFCxaMS90gREfr44t4xlNLryziqasinPLzHOUyKyKcamySkIYvTU6EJKbrrDKw4EoV/fTpt3vKOlYfV2RusXqJ/orTP13a/ulhOeHPMvkkp5Y7hyPLtFx2kiXaTZcjs/PK+xtwWUCtoXKpxVV9Syo3YO6m2+RJd1YuuzQrV1cefbTZkREv00wiiUhQbCy0zKRlF1t22jS7/ozSEvl9dHUb7DwhsxDZo/1WAI9AqqU8LaeMT3shbmuLak+2P5Jjz4Yr+zKYC/+ujBSkzJdiWY11lNz2ZLhi7qbl83amGQp3tCu9lmD5jv5mVRt5ukqsEs0MoKInvaenh44ePUqf/vSnKZFIUCKRoO3bt9P9999PiUSC2tvbKZ/PU39/vzivr6+POjo6TlhnOp2m5uZm8QMAAACAmUtFKx+XXnopvfLKK+J3N910Ey1evJjuuOMOWrBgASWTSdq2bRutWrWKiIj2799PBw8epK6urvHrNQAAAACmLRVNPpqamuicc84Rv2toaKA5c+aUfn/zzTfTunXrqK2tjZqbm+nWW2+lrq6uqevpAgAAAICaMu7h1e+9915yXZdWrVpFuVyOVq5cSQ888EDlFRlTOxdYm5ZXSR8qONawUL2OclF1Ckxr9JQyxo9VmqQphhqh01gvy47LLKfO22Hgt4QnJTG/LtRkg3qpu3rHmO7fIiUypxj2LRiVro9mNHvC44iInJRsQ+iw2laFXb8pWkIca8ZLr2Uuw26DHOPLP/k74Y66N24qvDeV2IqIOpRKWu+E45ZR7qSNys4hx+xDbBlJNUMBt0dRrpfM5sIPlF0B0+R9I/vtKffWDHOb7GCuvUQqLLw+j11zgxtvj6GNXDy2r0Pda8q23Zh3gTyRP28WOw5H2Vtxl8qth3aLMu5Oq0Ofjyg7mn72OGRVyPwGZkeTJl1P2J+hQL6XnHmetP+Zr9xiOTobcCEf/2dnkD23xaLs93A+7E9elfEw7f+Vmi/KftEQOjusf/NlUbbhzPPFPreNC/IFVRYOqiko+zaWPsFRdnIi3Lp2RWX38ekj+0SReKZqbG9CRNX9/a3gnJOefDz//PNiP5PJ0MaNG2njxo0nWzUAAAAAZiDI7QIAAACAmoKstieiFnKPrQ0ttXBc98TbRHKpXy37b31ju9jnLpzuvI/JepjsQr6KYsqWtx211O2kwvMcq9utTskpl2UNX2KcCNlrgpYwedbLSBTVKt3m+FKsjmJaCa7DZRDtesrK1DJ8E7vHnloy9kx4j1Mqwmae/V8T6OfUQr2rpRWfbatMweyaMko+4ZlNtVxSCbbMsmJZXGU4Lj/qo1324RSYtJJVMks+8uw77Nj4NnQ23JEgwcrkuIlor+p6L+tcWtr2PiavV0emJXYfjcpqa5jsY5SLLpf29NV+kAsl0KLKotyaDOXhw8lTRNn/OiSDX/4/Cz8XdlPJJ08d2FXajrhIc8YpGil/963tTVOw8gEAAACAmoLJBwAAAABqCiYfAAAAAKgpU9fmo5autidDlX0UWW1tths6eyJ3U1XhzSkXuj5y11Yiop681Ha/eyAM1f0//vDjosy0hfqpo0MMcx1Ua5vpMOuko9zURMZbpYFHsuFasvqOh+1ExC2S2yvo+vUYl2kvEsmwy7P62rIWW9C2CwFTvrUbLulMrlz3V80HrK+eKuS2IvXKroO73npa1xfHSZR3qfU/oIyw65D3oo65GleUHXa8qLLaJ5lr9ZVnyCyrOg0Ch9t5aFdbmz1QRrm68hDyBeUGze08goiLdPieJrWtCh9jo21zZOh7zwvL80Vl1+G7bFuW5Qrh/df2IL0DTaXtD5Iya24+iB/T0xMfiP1/P/BCafv6Rb8rygYCFj5Av9/cBke939aMtOXan03C30Lt+lsOg0MBnfJb5R2LlQ8AAAAA1BRMPgAAAABQU6au7DJVqWD5y7Ett7FopCadFEWOsSzFcYlGu+Sy9vyhIVH0Zl5FMWVtOCNSojE22YMvC3tyOVdkubXIFSYnl2EjUgrbjUgUwoVRju/Ww/GRQnnW1ycPvyTKrFlGFbZFUmtGTLZvVIRX3+qKGaKlBb6YrOtw1bK468RLK7aIpzwyqq2XNmklo5aWkxZpJyL7sJrTSna5gkWK1XKFk+AyQAUZlRXWJfMgXgLkS9b63hTZAx559tl9GjHSXX2I9SWrJBGd5ZWjZRcutWjZJWu4q20F/5taxrjVGxH7Ho8U6sfLLoFyw+WvjaPcrgM/vP86gup7rL1DKelq+0paZlBv8/ZH+v8RX5wf5ieLZPRm9zHyzFT5/JX7XZiuYOUDAAAAADUFkw8AAAAA1JQpJ7t85CVQDHJjHDlJVCK72I4NWII05dEiPEz00q6lzLBlWt9I74PR43qpP1zu1GNdLIYyjOur+8Ainjq+XBZ2WD1GLRkbZg1uVN8iniG8TiVB8Xr0vRgcil+m5Em5EpEEYeF2UfXNUUufYl9HjjSWpX7W70C1Yet3XD81Yy3Rcs+YQAkoOuIpJ8/qLSh5hkfV1N4WvK+FMWQXxyq7hKSVBw+/V/o+iTNPRnaxvcMmXnbh9zQqu4T7+nnj/dbPxRD7TuRUt2yyS1KNG79X+r6NsL7qq0u7YdlgQrbHr8ME8t0fVtfhj4TfiWBUySfsuxj48n9jntiNlOxi/Pj/o/1E2F4hKfuWTcjv4vEg/t4YJkHqb7v4Lun/6ct8/vT95s9N9DmZeMr9Lolzjn94jvW9+b84ppyjasjbb79NCxYsGPtAAAAAAEw5Dh06RPPnz7ceM+UmH0EQ0JEjR8gYQ52dnXTo0CFqbm4e+8RZxODgIC1YsABjcwIwNvFgbOLB2JwYjEs8GJsoxhgaGhqiefPmkTtGTqcpJ7u4rkvz58+nwcFBIiJqbm7GjY0BYxMPxiYejE08GJsTg3GJB2MjaWlpKes4GJwCAAAAoKZg8gEAAACAmjJlJx/pdJr+5m/+htIsXwj4EIxNPBibeDA28WBsTgzGJR6Mzckx5QxOAQAAADCzmbIrHwAAAACYmWDyAQAAAICagskHAAAAAGoKJh8AAAAAqClTdvKxceNGWrhwIWUyGVq+fDnt3r17srtUUzZs2ECf+cxnqKmpiebOnUvXXnst7d8v0z1ns1nq7u6mOXPmUGNjI61atYr6+vomqceTxz333EOO49DatWtLv5vNY3P48GH6yle+QnPmzKG6ujo699xzae/evaVyYwzddddddNppp1FdXR2tWLGC3nzzzUnscW3wfZ/uvPNOWrRoEdXV1dEnPvEJ+u53vyvyUMyWsXnhhRfoqquuonnz5pHjOPT444+L8nLG4dixY3TDDTdQc3Mztba20s0330zHjx+v4VVMDLaxKRQKdMcdd9C5555LDQ0NNG/ePLrxxhvpyJEjoo6ZOjbjipmCPProoyaVSpl/+Zd/Ma+99pr5sz/7M9Pa2mr6+vomu2s1Y+XKlWbTpk3m1VdfNfv27TNXXHGF6ezsNMePHy8dc8stt5gFCxaYbdu2mb1795qLLrrIfPazn53EXtee3bt3m4ULF5rzzjvP3HbbbaXfz9axOXbsmDnjjDPMV7/6VbNr1y7z1ltvmaefftr86le/Kh1zzz33mJaWFvP444+bX/ziF+bqq682ixYtMqOjo5PY84nn7rvvNnPmzDFbt241Bw4cMFu2bDGNjY3m+9//fumY2TI2Tz75pPnWt75lfvzjHxsiMo899pgoL2ccLrvsMnP++eebnTt3mp///OfmzDPPNNdff32Nr2T8sY1Nf3+/WbFihfnRj35k3njjDbNjxw6zbNkys2TJElHHTB2b8WRKTj6WLVtmuru7S/u+75t58+aZDRs2TGKvJpejR48aIjLbt283xnz4EiSTSbNly5bSMf/93/9tiMjs2LFjsrpZU4aGhsxZZ51lnnnmGfN7v/d7pcnHbB6bO+64w3zuc5+LLQ+CwHR0dJi///u/L/2uv7/fpNNp8+///u+16OKkceWVV5o//dM/Fb+77rrrzA033GCMmb1jo//AljMOr7/+uiEis2fPntIxP/vZz4zjOObw4cM16/tEc6KJmWb37t2GiMxvfvMbY8zsGZuTZcrJLvl8nnp6emjFihWl37muSytWrKAdO3ZMYs8ml4GBASIiamtrIyKinp4eKhQKYpwWL15MnZ2ds2acuru76corrxRjQDS7x+anP/0pLV26lP74j/+Y5s6dSxdeeCH94Ac/KJUfOHCAent7xdi0tLTQ8uXLZ/zYfPazn6Vt27bRL3/5SyIi+sUvfkEvvvgiXX755UQ0u8eGU8447Nixg1pbW2np0qWlY1asWEGu69KuXbtq3ufJZGBggBzHodbWViLC2JTLlEss995775Hv+9Te3i5+397eTm+88cYk9WpyCYKA1q5dSxdffDGdc845RETU29tLqVSq9MB/RHt7O/X29k5CL2vLo48+Si+99BLt2bMnUjabx+att96iBx98kNatW0d/9Vd/RXv27KG/+Iu/oFQqRatXry5d/4ner5k+Nt/85jdpcHCQFi9eTJ7nke/7dPfdd9MNN9xARDSrx4ZTzjj09vbS3LlzRXkikaC2trZZNVbZbJbuuOMOuv7660vJ5TA25THlJh8gSnd3N7366qv04osvTnZXpgSHDh2i2267jZ555hnKZDKT3Z0pRRAEtHTpUvrbv/1bIiK68MIL6dVXX6WHHnqIVq9ePcm9m1z+4z/+g374wx/S5s2b6bd/+7dp3759tHbtWpo3b96sHxtQOYVCgb74xS+SMYYefPDBye7OtGPKyS6nnnoqeZ4X8Uzo6+ujjo6OSerV5LFmzRraunUrPffcczR//vzS7zs6Oiifz1N/f784fjaMU09PDx09epQ+/elPUyKRoEQiQdu3b6f777+fEokEtbe3z9qxOe200+hTn/qU+N3ZZ59NBw8eJCIqXf9sfL/+8i//kr75zW/Sl7/8ZTr33HPpT/7kT+j222+nDRs2ENHsHhtOOePQ0dFBR48eFeXFYpGOHTs2K8bqo4nHb37zG3rmmWdKqx5EGJtymXKTj1QqRUuWLKFt27aVfhcEAW3bto26uromsWe1xRhDa9asoccee4yeffZZWrRokShfsmQJJZNJMU779++ngwcPzvhxuvTSS+mVV16hffv2lX6WLl1KN9xwQ2l7to7NxRdfHHHJ/uUvf0lnnHEGEREtWrSIOjo6xNgMDg7Srl27ZvzYjIyMkOvKT57neRQEARHN7rHhlDMOXV1d1N/fTz09PaVjnn32WQqCgJYvX17zPteSjyYeb775Jv3nf/4nzZkzR5TP5rGpiMm2eD0Rjz76qEmn0+aRRx4xr7/+uvna175mWltbTW9v72R3rWZ8/etfNy0tLeb5558377zzTulnZGSkdMwtt9xiOjs7zbPPPmv27t1rurq6TFdX1yT2evLg3i7GzN6x2b17t0kkEubuu+82b775pvnhD39o6uvrzb/927+VjrnnnntMa2ur+clPfmL+67/+y1xzzTUz0p1Us3r1anP66aeXXG1//OMfm1NPPdV84xvfKB0zW8ZmaGjIvPzyy+bll182RGT+4R/+wbz88sslj41yxuGyyy4zF154odm1a5d58cUXzVlnnTUj3EltY5PP583VV19t5s+fb/bt2ye+zblcrlTHTB2b8WRKTj6MMeYf//EfTWdnp0mlUmbZsmVm586dk92lmkJEJ/zZtGlT6ZjR0VHz53/+5+aUU04x9fX15o/+6I/MO++8M3mdnkT05GM2j80TTzxhzjnnHJNOp83ixYvNP//zP4vyIAjMnXfeadrb2006nTaXXnqp2b9//yT1tnYMDg6a2267zXR2dppMJmM+/vGPm29961vij8ZsGZvnnnvuhN+X1atXG2PKG4f333/fXH/99aaxsdE0Nzebm266yQwNDU3C1YwvtrE5cOBA7Lf5ueeeK9UxU8dmPHGMYeH9AAAAAAAmmCln8wEAAACAmQ0mHwAAAACoKZh8AAAAAKCmYPIBAAAAgJqCyQcAAAAAagomHwAAAACoKZh8AAAAAKCmYPIBAAAAgJqCyQcAAAAAagomHwAAAACoKZh8AAAAAKCmYPIBAAAAgJryfwC/z4o52djPqQAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"code","source":"plt.imshow(frames[23])","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:47.986388Z","iopub.execute_input":"2023-11-04T20:00:47.986899Z","iopub.status.idle":"2023-11-04T20:00:48.235551Z","shell.execute_reply.started":"2023-11-04T20:00:47.986859Z","shell.execute_reply":"2023-11-04T20:00:48.234678Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage 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"},"metadata":{}}]},{"cell_type":"code","source":"plt.imshow(frames[40])","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:48.237093Z","iopub.execute_input":"2023-11-04T20:00:48.237481Z","iopub.status.idle":"2023-11-04T20:00:48.481573Z","shell.execute_reply.started":"2023-11-04T20:00:48.237446Z","shell.execute_reply":"2023-11-04T20:00:48.480679Z"},"trusted":true},"execution_count":25,"outputs":[{"execution_count":25,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage 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shape=(23,), dtype=int64, numpy=\narray([19,  5, 20, 39, 18,  5,  4, 39, 23,  9, 20,  8, 39, 22, 39, 15, 14,\n        5, 39, 19, 15, 15, 14])>"},"metadata":{}}]},{"cell_type":"code","source":"tf.strings.reduce_join([bytes.decode(x) for x in num_to_char(alignments.numpy()).numpy()])","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:48.493792Z","iopub.execute_input":"2023-11-04T20:00:48.494073Z","iopub.status.idle":"2023-11-04T20:00:48.508347Z","shell.execute_reply.started":"2023-11-04T20:00:48.494050Z","shell.execute_reply":"2023-11-04T20:00:48.507314Z"},"trusted":true},"execution_count":27,"outputs":[{"execution_count":27,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'set red with v one soon'>"},"metadata":{}}]},{"cell_type":"code","source":"def mappable_function(path:str) ->List[str]:\n    result = tf.py_function(load_data, [path], (tf.float32, tf.int64))\n    return result","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:48.509644Z","iopub.execute_input":"2023-11-04T20:00:48.509992Z","iopub.status.idle":"2023-11-04T20:00:48.516332Z","shell.execute_reply.started":"2023-11-04T20:00:48.509962Z","shell.execute_reply":"2023-11-04T20:00:48.515538Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"markdown","source":"# 2. Create Data Pipeline","metadata":{"tags":[]}},{"cell_type":"code","source":"from matplotlib import pyplot as plt","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:48.517727Z","iopub.execute_input":"2023-11-04T20:00:48.518154Z","iopub.status.idle":"2023-11-04T20:00:48.528574Z","shell.execute_reply.started":"2023-11-04T20:00:48.518120Z","shell.execute_reply":"2023-11-04T20:00:48.527662Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"code","source":"data = tf.data.Dataset.list_files('./data/s1/*.mpg')\ndata = data.shuffle(500, reshuffle_each_iteration=False)\ndata = data.map(mappable_function)\ndata = data.padded_batch(2, padded_shapes=([75,None,None,None],[40]))\ndata = data.prefetch(tf.data.AUTOTUNE)\n","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:48.529754Z","iopub.execute_input":"2023-11-04T20:00:48.530027Z","iopub.status.idle":"2023-11-04T20:00:48.623016Z","shell.execute_reply.started":"2023-11-04T20:00:48.530004Z","shell.execute_reply":"2023-11-04T20:00:48.622179Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"# Train - Test Split\ntrain = data.take(450)\ntest = data.skip(450)","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:48.624131Z","iopub.execute_input":"2023-11-04T20:00:48.624398Z","iopub.status.idle":"2023-11-04T20:00:48.632316Z","shell.execute_reply.started":"2023-11-04T20:00:48.624374Z","shell.execute_reply":"2023-11-04T20:00:48.631438Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"len(train)","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:48.633556Z","iopub.execute_input":"2023-11-04T20:00:48.633881Z","iopub.status.idle":"2023-11-04T20:00:48.649690Z","shell.execute_reply.started":"2023-11-04T20:00:48.633844Z","shell.execute_reply":"2023-11-04T20:00:48.648787Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"450"},"metadata":{}}]},{"cell_type":"code","source":"frames, alignments = data.as_numpy_iterator().next()","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:48.650918Z","iopub.execute_input":"2023-11-04T20:00:48.651181Z","iopub.status.idle":"2023-11-04T20:00:49.314383Z","shell.execute_reply.started":"2023-11-04T20:00:48.651158Z","shell.execute_reply":"2023-11-04T20:00:49.313532Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"len(frames)","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:49.315643Z","iopub.execute_input":"2023-11-04T20:00:49.316170Z","iopub.status.idle":"2023-11-04T20:00:49.321884Z","shell.execute_reply.started":"2023-11-04T20:00:49.316141Z","shell.execute_reply":"2023-11-04T20:00:49.321028Z"},"trusted":true},"execution_count":34,"outputs":[{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"2"},"metadata":{}}]},{"cell_type":"code","source":"sample = data.as_numpy_iterator()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:49.323307Z","iopub.execute_input":"2023-11-04T20:00:49.323623Z","iopub.status.idle":"2023-11-04T20:00:49.337725Z","shell.execute_reply.started":"2023-11-04T20:00:49.323597Z","shell.execute_reply":"2023-11-04T20:00:49.336885Z"},"trusted":true},"execution_count":35,"outputs":[]},{"cell_type":"code","source":"val = sample.next()\nval[0]","metadata":{"scrolled":true,"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:49.339184Z","iopub.execute_input":"2023-11-04T20:00:49.339513Z","iopub.status.idle":"2023-11-04T20:00:49.782356Z","shell.execute_reply.started":"2023-11-04T20:00:49.339482Z","shell.execute_reply":"2023-11-04T20:00:49.781146Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"array([[[[[1.3697747 ],\n          [1.3697747 ],\n          [1.3697747 ],\n          ...,\n          [0.03702094],\n          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fps=10)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:49.798463Z","iopub.execute_input":"2023-11-04T20:00:49.798786Z","iopub.status.idle":"2023-11-04T20:00:49.806083Z","shell.execute_reply.started":"2023-11-04T20:00:49.798755Z","shell.execute_reply":"2023-11-04T20:00:49.804984Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"# 0:videos, 0: 1st video out of the batch,  0: return the first frame in the video \nplt.imshow(val[0][0][23])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:49.808316Z","iopub.execute_input":"2023-11-04T20:00:49.808565Z","iopub.status.idle":"2023-11-04T20:00:50.092652Z","shell.execute_reply.started":"2023-11-04T20:00:49.808544Z","shell.execute_reply":"2023-11-04T20:00:50.091526Z"},"trusted":true},"execution_count":39,"outputs":[{"execution_count":39,"output_type":"execute_result","data":{"text/plain":"<matplotlib.image.AxesImage at 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Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"tf.strings.reduce_join([num_to_char(word) for word in val[1][0]])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:50.095193Z","iopub.execute_input":"2023-11-04T20:00:50.095494Z","iopub.status.idle":"2023-11-04T20:00:50.169049Z","shell.execute_reply.started":"2023-11-04T20:00:50.095468Z","shell.execute_reply":"2023-11-04T20:00:50.168039Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'bin white with b one again'>"},"metadata":{}}]},{"cell_type":"markdown","source":"# 3. Design the Deep Neural Network","metadata":{"tags":[]}},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential \nfrom tensorflow.keras.layers import Conv3D, LSTM, Dense, Dropout, Bidirectional, MaxPool3D, Activation, Reshape, SpatialDropout3D, BatchNormalization, TimeDistributed, Flatten\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:50.181079Z","iopub.execute_input":"2023-11-04T20:00:50.181454Z","iopub.status.idle":"2023-11-04T20:00:50.194539Z","shell.execute_reply.started":"2023-11-04T20:00:50.181429Z","shell.execute_reply":"2023-11-04T20:00:50.192467Z"},"trusted":true},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"data.as_numpy_iterator().next()[0][0].shape","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:50.196732Z","iopub.execute_input":"2023-11-04T20:00:50.198037Z","iopub.status.idle":"2023-11-04T20:00:51.070693Z","shell.execute_reply.started":"2023-11-04T20:00:50.198003Z","shell.execute_reply":"2023-11-04T20:00:51.069707Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"(75, 46, 140, 1)"},"metadata":{}}]},{"cell_type":"code","source":"model = Sequential()\nmodel.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPool3D((1,2,2)))\n\nmodel.add(Conv3D(256, 3, padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPool3D((1,2,2)))\n\nmodel.add(Conv3D(75, 3, padding='same'))\nmodel.add(Activation('relu'))\nmodel.add(MaxPool3D((1,2,2)))\n\nmodel.add(TimeDistributed(Flatten()))\n\nmodel.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\nmodel.add(Dropout(.5))\n\nmodel.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))\nmodel.add(Dropout(.5))\n\nmodel.add(Dense(char_to_num.vocabulary_size()+1, kernel_initializer='he_normal', activation='softmax'))","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:51.072027Z","iopub.execute_input":"2023-11-04T20:00:51.072327Z","iopub.status.idle":"2023-11-04T20:00:53.125310Z","shell.execute_reply.started":"2023-11-04T20:00:51.072302Z","shell.execute_reply":"2023-11-04T20:00:53.124526Z"},"trusted":true},"execution_count":43,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:53.126871Z","iopub.execute_input":"2023-11-04T20:00:53.127229Z","iopub.status.idle":"2023-11-04T20:00:53.172782Z","shell.execute_reply.started":"2023-11-04T20:00:53.127195Z","shell.execute_reply":"2023-11-04T20:00:53.171895Z"},"trusted":true},"execution_count":44,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n conv3d (Conv3D)             (None, 75, 46, 140, 128)  3584      \n                                                                 \n activation (Activation)     (None, 75, 46, 140, 128)  0         \n                                                                 \n max_pooling3d (MaxPooling3D  (None, 75, 23, 70, 128)  0         \n )                                                               \n                                                                 \n conv3d_1 (Conv3D)           (None, 75, 23, 70, 256)   884992    \n                                                                 \n activation_1 (Activation)   (None, 75, 23, 70, 256)   0         \n                                                                 \n max_pooling3d_1 (MaxPooling  (None, 75, 11, 35, 256)  0         \n 3D)                                                             \n                                                                 \n conv3d_2 (Conv3D)           (None, 75, 11, 35, 75)    518475    \n                                                                 \n activation_2 (Activation)   (None, 75, 11, 35, 75)    0         \n                                                                 \n max_pooling3d_2 (MaxPooling  (None, 75, 5, 17, 75)    0         \n 3D)                                                             \n                                                                 \n time_distributed (TimeDistr  (None, 75, 6375)         0         \n ibuted)                                                         \n                                                                 \n bidirectional (Bidirectiona  (None, 75, 256)          6660096   \n l)                                                              \n                                                                 \n dropout (Dropout)           (None, 75, 256)           0         \n                                                                 \n bidirectional_1 (Bidirectio  (None, 75, 256)          394240    \n nal)                                                            \n                                                                 \n dropout_1 (Dropout)         (None, 75, 256)           0         \n                                                                 \n dense (Dense)               (None, 75, 41)            10537     \n                                                                 \n=================================================================\nTotal params: 8,471,924\nTrainable params: 8,471,924\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"5*17*75","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:53.173878Z","iopub.execute_input":"2023-11-04T20:00:53.174140Z","iopub.status.idle":"2023-11-04T20:00:53.179704Z","shell.execute_reply.started":"2023-11-04T20:00:53.174116Z","shell.execute_reply":"2023-11-04T20:00:53.178862Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":"6375"},"metadata":{}}]},{"cell_type":"code","source":"yhat = model.predict(val[0])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:53.180647Z","iopub.execute_input":"2023-11-04T20:00:53.180937Z","iopub.status.idle":"2023-11-04T20:00:56.846381Z","shell.execute_reply.started":"2023-11-04T20:00:53.180913Z","shell.execute_reply":"2023-11-04T20:00:56.845430Z"},"trusted":true},"execution_count":46,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 4s 4s/step\n","output_type":"stream"}]},{"cell_type":"code","source":"tf.strings.reduce_join([num_to_char(x) for x in tf.argmax(yhat[0],axis=1)])","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:56.847935Z","iopub.execute_input":"2023-11-04T20:00:56.848304Z","iopub.status.idle":"2023-11-04T20:00:56.938652Z","shell.execute_reply.started":"2023-11-04T20:00:56.848269Z","shell.execute_reply":"2023-11-04T20:00:56.937808Z"},"trusted":true},"execution_count":47,"outputs":[{"execution_count":47,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'555555555555555555555555555555555555555555555555555555555555555555555555ccc'>"},"metadata":{}}]},{"cell_type":"code","source":"tf.strings.reduce_join([num_to_char(tf.argmax(x)) for x in yhat[0]])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:56.940137Z","iopub.execute_input":"2023-11-04T20:00:56.940485Z","iopub.status.idle":"2023-11-04T20:00:57.015533Z","shell.execute_reply.started":"2023-11-04T20:00:56.940453Z","shell.execute_reply":"2023-11-04T20:00:57.014717Z"},"trusted":true},"execution_count":48,"outputs":[{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"<tf.Tensor: shape=(), dtype=string, numpy=b'555555555555555555555555555555555555555555555555555555555555555555555555ccc'>"},"metadata":{}}]},{"cell_type":"code","source":"model.input_shape","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:57.016838Z","iopub.execute_input":"2023-11-04T20:00:57.017173Z","iopub.status.idle":"2023-11-04T20:00:57.023337Z","shell.execute_reply.started":"2023-11-04T20:00:57.017142Z","shell.execute_reply":"2023-11-04T20:00:57.022460Z"},"trusted":true},"execution_count":49,"outputs":[{"execution_count":49,"output_type":"execute_result","data":{"text/plain":"(None, 75, 46, 140, 1)"},"metadata":{}}]},{"cell_type":"code","source":"model.output_shape","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:57.024442Z","iopub.execute_input":"2023-11-04T20:00:57.024704Z","iopub.status.idle":"2023-11-04T20:00:57.035417Z","shell.execute_reply.started":"2023-11-04T20:00:57.024680Z","shell.execute_reply":"2023-11-04T20:00:57.034468Z"},"trusted":true},"execution_count":50,"outputs":[{"execution_count":50,"output_type":"execute_result","data":{"text/plain":"(None, 75, 41)"},"metadata":{}}]},{"cell_type":"markdown","source":"# 4. Setup Training Options and Train","metadata":{"tags":[]}},{"cell_type":"code","source":"def scheduler(epoch, lr):\n    if epoch < 30:\n        return lr\n    else:\n        return lr * tf.math.exp(-0.1)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:57.036568Z","iopub.execute_input":"2023-11-04T20:00:57.036854Z","iopub.status.idle":"2023-11-04T20:00:57.045583Z","shell.execute_reply.started":"2023-11-04T20:00:57.036831Z","shell.execute_reply":"2023-11-04T20:00:57.044662Z"},"trusted":true},"execution_count":51,"outputs":[]},{"cell_type":"code","source":"# define our special CTC loss function\ndef CTCLoss(y_true, y_pred):\n    batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")\n    input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")\n    label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")\n\n    input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n    label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")\n\n    loss = tf.keras.backend.ctc_batch_cost(y_true, y_pred, input_length, label_length)\n    return loss","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:57.046636Z","iopub.execute_input":"2023-11-04T20:00:57.046944Z","iopub.status.idle":"2023-11-04T20:00:57.056924Z","shell.execute_reply.started":"2023-11-04T20:00:57.046921Z","shell.execute_reply":"2023-11-04T20:00:57.056001Z"},"trusted":true},"execution_count":52,"outputs":[]},{"cell_type":"code","source":"# produce example original lable and predicted lable after each epoch to see model performance\nclass ProduceExample(tf.keras.callbacks.Callback): \n    def __init__(self, dataset) -> None: \n        self.dataset = dataset.as_numpy_iterator()\n    \n    def on_epoch_end(self, epoch, logs=None) -> None:\n#         if epoh == 1 or epoch % 4 == 0:\n        data = self.dataset.next()\n        yhat = self.model.predict(data[0])\n        decoded = tf.keras.backend.ctc_decode(yhat, [75,75], greedy=False)[0][0].numpy()\n        for x in range(len(yhat)):           \n            print('Original:', tf.strings.reduce_join(num_to_char(data[1][x])).numpy().decode('utf-8'))\n            print('Prediction:', tf.strings.reduce_join(num_to_char(decoded[x])).numpy().decode('utf-8'))\n            print('-'*100)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:57.058118Z","iopub.execute_input":"2023-11-04T20:00:57.058394Z","iopub.status.idle":"2023-11-04T20:00:57.069386Z","shell.execute_reply.started":"2023-11-04T20:00:57.058370Z","shell.execute_reply":"2023-11-04T20:00:57.068428Z"},"trusted":true},"execution_count":53,"outputs":[]},{"cell_type":"code","source":"# use adam optimizer\nmodel.compile(optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=0.0001), loss=CTCLoss)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:57.070547Z","iopub.execute_input":"2023-11-04T20:00:57.070873Z","iopub.status.idle":"2023-11-04T20:00:57.093280Z","shell.execute_reply.started":"2023-11-04T20:00:57.070849Z","shell.execute_reply":"2023-11-04T20:00:57.092544Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"code","source":"checkpoint_callback = ModelCheckpoint(os.path.join('models','checkpoint'), monitor='loss', save_weights_only=True) ","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:57.094354Z","iopub.execute_input":"2023-11-04T20:00:57.094614Z","iopub.status.idle":"2023-11-04T20:00:57.099362Z","shell.execute_reply.started":"2023-11-04T20:00:57.094591Z","shell.execute_reply":"2023-11-04T20:00:57.098493Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"code","source":"schedule_callback = LearningRateScheduler(scheduler)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:57.100517Z","iopub.execute_input":"2023-11-04T20:00:57.101375Z","iopub.status.idle":"2023-11-04T20:00:57.110387Z","shell.execute_reply.started":"2023-11-04T20:00:57.101344Z","shell.execute_reply":"2023-11-04T20:00:57.109361Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"code","source":"example_callback = ProduceExample(test)","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-04T20:00:57.111513Z","iopub.execute_input":"2023-11-04T20:00:57.111831Z","iopub.status.idle":"2023-11-04T20:00:57.132512Z","shell.execute_reply.started":"2023-11-04T20:00:57.111791Z","shell.execute_reply":"2023-11-04T20:00:57.131844Z"},"trusted":true},"execution_count":57,"outputs":[]},{"cell_type":"code","source":"history = model.fit(train, validation_data=test, epochs=96, callbacks=[checkpoint_callback, schedule_callback, example_callback])","metadata":{"execution":{"iopub.status.busy":"2023-11-04T20:00:57.133583Z","iopub.execute_input":"2023-11-04T20:00:57.133868Z","iopub.status.idle":"2023-11-05T03:35:01.748972Z","shell.execute_reply.started":"2023-11-04T20:00:57.133844Z","shell.execute_reply":"2023-11-05T03:35:01.746997Z"},"trusted":true},"execution_count":58,"outputs":[{"name":"stdout","text":"Epoch 1/96\n448/450 [============================>.] - ETA: 1s - loss: 85.3319","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76940c7700] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76940c7700] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 85.2746","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76ec030440] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76ec030440] Warning MVs not available\n[mpeg1video @ 0x7d7768032a80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7768032a80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 2s 2s/step\nOriginal: bin white at t two now\nPrediction: le e e e e eo\n----------------------------------------------------------------------------------------------------\nOriginal: set white by o eight now\nPrediction: le e e e e eo\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 739s 2s/step - loss: 85.2746 - val_loss: 69.1930 - lr: 1.0000e-04\nEpoch 2/96\n 68/450 [===>..........................] - ETA: 4:28 - loss: 73.4134","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76b8053980] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76b8053980] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 70.9716","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d77940ad200] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d77940ad200] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 118ms/step\nOriginal: lay white at r nine again\nPrediction: la e e e o o\n----------------------------------------------------------------------------------------------------\nOriginal: place blue with j three soon\nPrediction: la e e e o o\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 533s 1s/step - loss: 70.9716 - val_loss: 65.6452 - lr: 1.0000e-04\nEpoch 3/96\n169/450 [==========>...................] - ETA: 3:16 - loss: 68.2471","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7698203d00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7698203d00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 66.9147","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76e40a8280] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76e40a8280] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 118ms/step\nOriginal: place white by d six now\nPrediction: la e e i e eo\n----------------------------------------------------------------------------------------------------\nOriginal: lay red with l seven again\nPrediction: la e e i e eo\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 533s 1s/step - loss: 66.9147 - val_loss: 62.2782 - lr: 1.0000e-04\nEpoch 4/96\n335/450 [=====================>........] - ETA: 1:20 - loss: 64.3567","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d786819c100] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d786819c100] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 63.9592","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d74efbb6340] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d74efbb6340] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\nOriginal: lay blue in d five again\nPrediction: la re t e eo\n----------------------------------------------------------------------------------------------------\nOriginal: place blue in u nine soon\nPrediction: la re t e eo\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 534s 1s/step - loss: 63.9592 - val_loss: 58.9128 - lr: 1.0000e-04\nEpoch 5/96\n104/450 [=====>........................] - ETA: 4:01 - loss: 62.2432","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d77681e9fc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d77681e9fc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 61.8173","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76940a2e00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76940a2e00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: set red in h one soon\nPrediction: la re t e on\n----------------------------------------------------------------------------------------------------\nOriginal: bin white at m eight now\nPrediction: la re t e on\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 533s 1s/step - loss: 61.8173 - val_loss: 58.2501 - lr: 1.0000e-04\nEpoch 6/96\n 95/450 [=====>........................] - ETA: 4:08 - loss: 60.6522","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76e800bbc0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76e800bbc0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 60.1350","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d785009fe00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d785009fe00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: set blue at a five soon\nPrediction: la re i e on\n----------------------------------------------------------------------------------------------------\nOriginal: place white by d seven soon\nPrediction: la re t e oa\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 534s 1s/step - loss: 60.1350 - val_loss: 55.6897 - lr: 1.0000e-04\nEpoch 7/96\n  3/450 [..............................] - ETA: 5:12 - loss: 61.2685","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d75e1f81940] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d75e1f81940] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 58.5690","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76f8038e00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76f8038e00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 114ms/step\nOriginal: set green by p seven again\nPrediction: la re i e on\n----------------------------------------------------------------------------------------------------\nOriginal: lay blue at d nine again\nPrediction: la re i e on\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 535s 1s/step - loss: 58.5690 - val_loss: 55.3017 - lr: 1.0000e-04\nEpoch 8/96\n128/450 [=======>......................] - ETA: 3:45 - loss: 57.1586","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7684b6c000] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7684b6c000] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 55.7670","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76b800cf00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76b800cf00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 122ms/step\nOriginal: set white in o three again\nPrediction: la re t e ae\n----------------------------------------------------------------------------------------------------\nOriginal: set green with q zero please\nPrediction: la re t e ae\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 534s 1s/step - loss: 55.7670 - val_loss: 50.9557 - lr: 1.0000e-04\nEpoch 9/96\n  6/450 [..............................] - ETA: 5:05 - loss: 51.8333","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d78640a13c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d78640a13c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 52.7824","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7698001600] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7698001600] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\nOriginal: bin blue in l three soon\nPrediction: la re i e on\n----------------------------------------------------------------------------------------------------\nOriginal: bin green at u zero please\nPrediction: la re t e aon\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 536s 1s/step - loss: 52.7824 - val_loss: 48.8146 - lr: 1.0000e-04\nEpoch 10/96\n117/450 [======>.......................] - ETA: 3:52 - loss: 51.7781","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76f8069700] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76f8069700] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 50.8946","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x5646a2e28980] ac-tex damaged at 22 17\n[mpeg1video @ 0x5646a2e28980] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: place red by x five again\nPrediction: la re i e an\n----------------------------------------------------------------------------------------------------\nOriginal: lay white by f four please\nPrediction: la re t e ae\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 534s 1s/step - loss: 50.8946 - val_loss: 46.5846 - lr: 1.0000e-04\nEpoch 11/96\n 71/450 [===>..........................] - ETA: 4:23 - loss: 49.8170","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76b8015b00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76b8015b00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 49.0681","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7794036140] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7794036140] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\nOriginal: bin blue at f four please\nPrediction: set re b e pae\n----------------------------------------------------------------------------------------------------\nOriginal: bin green at t eight now\nPrediction: set re i ie on\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 530s 1s/step - loss: 49.0681 - val_loss: 45.2084 - lr: 1.0000e-04\nEpoch 12/96\n450/450 [==============================] - ETA: 0s - loss: 47.0584","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76f80a7680] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76f80a7680] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\nOriginal: lay blue in x six please\nPrediction: la re t ie ae\n----------------------------------------------------------------------------------------------------\nOriginal: place red by x four please\nPrediction: la re y e pase\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 531s 1s/step - loss: 47.0584 - val_loss: 41.4461 - lr: 1.0000e-04\nEpoch 13/96\n246/450 [===============>..............] - ETA: 2:22 - loss: 44.8250","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7684d65180] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7684d65180] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 44.3900","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76aa01d540] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76aa01d540] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 130ms/step\nOriginal: place white at d five again\nPrediction: la re it ie ain\n----------------------------------------------------------------------------------------------------\nOriginal: lay red by e nine again\nPrediction: la re y e aon\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 532s 1s/step - loss: 44.3900 - val_loss: 40.2157 - lr: 1.0000e-04\nEpoch 14/96\n377/450 [========================>.....] - ETA: 50s - loss: 42.6432","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d78500a1d80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d78500a1d80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 42.5115","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d768806e180] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d768806e180] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 120ms/step\nOriginal: lay green in f three again\nPrediction: la gre it ie ain\n----------------------------------------------------------------------------------------------------\nOriginal: bin blue with g two please\nPrediction: la blue it oe plase\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 530s 1s/step - loss: 42.5115 - val_loss: 36.6420 - lr: 1.0000e-04\nEpoch 15/96\n 81/450 [====>.........................] - ETA: 4:16 - loss: 40.0415","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76ec0af680] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76ec0af680] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 39.7808","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d78580c7300] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d78580c7300] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 114ms/step\nOriginal: set red at u two now\nPrediction: la re it on ow\n----------------------------------------------------------------------------------------------------\nOriginal: lay green by m four please\nPrediction: la re iy oe plese\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 532s 1s/step - loss: 39.7808 - val_loss: 34.9563 - lr: 1.0000e-04\nEpoch 16/96\n269/450 [================>.............] - ETA: 2:06 - loss: 38.0795","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76b295e5c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76b295e5c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 37.8971","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76fc0b7340] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76fc0b7340] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: place red with d four now\nPrediction: la gre it o ow\n----------------------------------------------------------------------------------------------------\nOriginal: lay green in l six please\nPrediction: set gre it o plase\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 531s 1s/step - loss: 37.8971 - val_loss: 31.5592 - lr: 1.0000e-04\nEpoch 17/96\n 42/450 [=>............................] - ETA: 4:40 - loss: 37.2339","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7694076200] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7694076200] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 35.7804","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76e804c340] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76e804c340] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 125ms/step\nOriginal: place green with l two please\nPrediction: pla gre it o plase\n----------------------------------------------------------------------------------------------------\nOriginal: set white by v five again\nPrediction: sit whie by ie ain\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 526s 1s/step - loss: 35.7804 - val_loss: 30.6613 - lr: 1.0000e-04\nEpoch 18/96\n317/450 [====================>.........] - ETA: 1:31 - loss: 33.3841","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7864073480] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7864073480] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 33.3151","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d75e1fa4540] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d75e1fa4540] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 123ms/step\nOriginal: place white at q zero now\nPrediction: plac blue it o now\n----------------------------------------------------------------------------------------------------\nOriginal: place white in x zero now\nPrediction: plac whie it o now\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 527s 1s/step - loss: 33.3151 - val_loss: 27.6424 - lr: 1.0000e-04\nEpoch 19/96\n 45/450 [==>...........................] - ETA: 4:39 - loss: 31.6894","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7768044b80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7768044b80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 30.8727","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7684b7ea80] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7684b7ea80] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: bin red in f seven again\nPrediction: set red it ne ain\n----------------------------------------------------------------------------------------------------\nOriginal: lay blue with y six now\nPrediction: pla blue it ie now\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 526s 1s/step - loss: 30.8727 - val_loss: 26.3044 - lr: 1.0000e-04\nEpoch 20/96\n 49/450 [==>...........................] - ETA: 4:37 - loss: 29.1554","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76e402d840] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76e402d840] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 28.5955","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d784c0c5140] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d784c0c5140] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\nOriginal: bin white by n three soon\nPrediction: bit white bt hre son\n----------------------------------------------------------------------------------------------------\nOriginal: set white with v seven soon\nPrediction: set whie it ie son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 527s 1s/step - loss: 28.5955 - val_loss: 23.0497 - lr: 1.0000e-04\nEpoch 21/96\n 80/450 [====>.........................] - ETA: 4:15 - loss: 25.9200","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76a404f1c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76a404f1c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 26.3424","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76f4088780] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76f4088780] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: set white at v zero please\nPrediction: sit white at zo please\n----------------------------------------------------------------------------------------------------\nOriginal: place white with k four now\nPrediction: place whie wit ro now\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 529s 1s/step - loss: 26.3424 - val_loss: 20.3814 - lr: 1.0000e-04\nEpoch 22/96\n 75/450 [====>.........................] - ETA: 4:20 - loss: 24.0886","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76880ba700] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76880ba700] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 24.4883","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76bc0c2680] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76bc0c2680] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: place blue at p zero please\nPrediction: place blue at ro please\n----------------------------------------------------------------------------------------------------\nOriginal: place green at e one again\nPrediction: place gren an ne ain\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 529s 1s/step - loss: 24.4883 - val_loss: 18.4825 - lr: 1.0000e-04\nEpoch 23/96\n223/450 [=============>................] - ETA: 2:37 - loss: 22.7817","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d785009eac0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d785009eac0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 22.5235","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76a4078680] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76a4078680] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 124ms/step\nOriginal: lay green with t zero now\nPrediction: place gre wit er now\n----------------------------------------------------------------------------------------------------\nOriginal: lay red at y four now\nPrediction: lay red at or now\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 531s 1s/step - loss: 22.5235 - val_loss: 17.3327 - lr: 1.0000e-04\nEpoch 24/96\n 52/450 [==>...........................] - ETA: 4:38 - loss: 21.2628","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76f4037f40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76f4037f40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 20.8805","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d785803cc40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d785803cc40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 114ms/step\nOriginal: place red in v seven again\nPrediction: place red in eve again\n----------------------------------------------------------------------------------------------------\nOriginal: set white in o one soon\nPrediction: set white in oe son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 531s 1s/step - loss: 20.8805 - val_loss: 15.4639 - lr: 1.0000e-04\nEpoch 25/96\n 34/450 [=>............................] - ETA: 4:46 - loss: 20.7000","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76b8079e00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76b8079e00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 19.3281","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d776808e3c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d776808e3c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 118ms/step\nOriginal: set white with c four now\nPrediction: bit white wit or now\n----------------------------------------------------------------------------------------------------\nOriginal: place green in k zero please\nPrediction: place gren in zor please\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 528s 1s/step - loss: 19.3281 - val_loss: 15.1650 - lr: 1.0000e-04\nEpoch 26/96\n 52/450 [==>...........................] - ETA: 4:38 - loss: 17.5392","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d769405f000] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d769405f000] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 17.7996","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76e40858c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76e40858c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 122ms/step\nOriginal: place red by j six please\nPrediction: place red by six please\n----------------------------------------------------------------------------------------------------\nOriginal: bin white in g zero now\nPrediction: bin white in zr now\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 527s 1s/step - loss: 17.7996 - val_loss: 12.4896 - lr: 1.0000e-04\nEpoch 27/96\n395/450 [=========================>....] - ETA: 38s - loss: 16.5832","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d78640b03c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d78640b03c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 16.5271","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76a001ce00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76a001ce00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: place blue by j zero please\nPrediction: place blue by eor please\n----------------------------------------------------------------------------------------------------\nOriginal: set green with p nine soon\nPrediction: set gren with ine son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 527s 1s/step - loss: 16.5271 - val_loss: 11.0701 - lr: 1.0000e-04\nEpoch 28/96\n257/450 [================>.............] - ETA: 2:13 - loss: 14.8020","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d776804a0c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d776804a0c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 15.0045","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7694076100] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7694076100] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 122ms/step\nOriginal: lay blue by k seven again\nPrediction: lay blue by seve again\n----------------------------------------------------------------------------------------------------\nOriginal: set blue at g eight now\nPrediction: set blue at eght now\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 529s 1s/step - loss: 15.0045 - val_loss: 10.2751 - lr: 1.0000e-04\nEpoch 29/96\n339/450 [=====================>........] - ETA: 1:16 - loss: 14.0662","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76e40240c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76e40240c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 14.0592","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d78481301c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d78481301c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\nOriginal: lay blue in q one soon\nPrediction: lay blue in ne son\n----------------------------------------------------------------------------------------------------\nOriginal: lay green in f one soon\nPrediction: lay gren in ne son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 529s 1s/step - loss: 14.0592 - val_loss: 9.1001 - lr: 1.0000e-04\nEpoch 30/96\n412/450 [==========================>...] - ETA: 26s - loss: 13.1332","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d74efc73340] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d74efc73340] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 13.1728","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76f800a380] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76f800a380] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 119ms/step\nOriginal: place blue by c five soon\nPrediction: place blue by five son\n----------------------------------------------------------------------------------------------------\nOriginal: place green by e three soon\nPrediction: place gren by tre son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 530s 1s/step - loss: 13.1728 - val_loss: 9.4078 - lr: 1.0000e-04\nEpoch 31/96\n118/450 [======>.......................] - ETA: 3:50 - loss: 12.5134","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x5646a3268900] ac-tex damaged at 22 17\n[mpeg1video @ 0x5646a3268900] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 12.3935","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76b2406900] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76b2406900] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\nOriginal: set blue at n three soon\nPrediction: set blue at tre son\n----------------------------------------------------------------------------------------------------\nOriginal: place white at j eight please\nPrediction: place white at eiht please\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 530s 1s/step - loss: 12.3935 - val_loss: 8.2575 - lr: 9.0484e-05\nEpoch 32/96\n  8/450 [..............................] - ETA: 5:05 - loss: 10.7112","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d785001af00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d785001af00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 11.1808","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76a40aa880] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76a40aa880] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 120ms/step\nOriginal: set white in b four please\nPrediction: set white in b for please\n----------------------------------------------------------------------------------------------------\nOriginal: set blue at t nine again\nPrediction: set blue at t ne again\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 528s 1s/step - loss: 11.1808 - val_loss: 7.8103 - lr: 8.1873e-05\nEpoch 33/96\n450/450 [==============================] - ETA: 0s - loss: 10.4560","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x5646ae05ac00] ac-tex damaged at 22 17\n[mpeg1video @ 0x5646ae05ac00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 120ms/step\nOriginal: bin blue by f nine again\nPrediction: bin blue by nine again\n----------------------------------------------------------------------------------------------------\nOriginal: lay red at e two now\nPrediction: lay red at two now\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 531s 1s/step - loss: 10.4560 - val_loss: 6.9572 - lr: 7.4082e-05\nEpoch 34/96\n232/450 [==============>...............] - ETA: 2:31 - loss: 9.7544","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76bc038640] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76bc038640] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 9.8701","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7768196a00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7768196a00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: lay red at k seven soon\nPrediction: lay red at seve son\n----------------------------------------------------------------------------------------------------\nOriginal: set blue in g five soon\nPrediction: set blue in five son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 530s 1s/step - loss: 9.8701 - val_loss: 6.6227 - lr: 6.7032e-05\nEpoch 35/96\n 49/450 [==>...........................] - ETA: 4:38 - loss: 9.0996","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d769008d9c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d769008d9c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 9.0793","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76ec057840] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76ec057840] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\nOriginal: lay white sp by f three soon\nPrediction: lay white by tre son\n----------------------------------------------------------------------------------------------------\nOriginal: lay red at k six now\nPrediction: lay red at six now\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 530s 1s/step - loss: 9.0793 - val_loss: 5.7344 - lr: 6.0653e-05\nEpoch 36/96\n399/450 [=========================>....] - ETA: 35s - loss: 8.5103","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x5646a30a45c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x5646a30a45c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 8.4954","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76b2946780] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76b2946780] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 125ms/step\nOriginal: bin red in s two now\nPrediction: bin red in wo now\n----------------------------------------------------------------------------------------------------\nOriginal: place green at d nine soon\nPrediction: place gren at ne son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 533s 1s/step - loss: 8.4954 - val_loss: 4.9208 - lr: 5.4881e-05\nEpoch 37/96\n 92/450 [=====>........................] - ETA: 4:09 - loss: 8.1721","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7794003400] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7794003400] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 8.0987","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76880bfc00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76880bfc00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\nOriginal: bin green by b one soon\nPrediction: bin gren by b one son\n----------------------------------------------------------------------------------------------------\nOriginal: lay blue by k five soon\nPrediction: lay blue by five son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 533s 1s/step - loss: 8.0987 - val_loss: 5.0810 - lr: 4.9659e-05\nEpoch 38/96\n268/450 [================>.............] - ETA: 2:07 - loss: 7.7783","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76e40b6b40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76e40b6b40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 7.6377","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7858023c00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7858023c00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\nOriginal: set green with p eight now\nPrediction: set gren with p eight now\n----------------------------------------------------------------------------------------------------\nOriginal: lay white with m zero now\nPrediction: lay white with zero now\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 531s 1s/step - loss: 7.6377 - val_loss: 4.6688 - lr: 4.4933e-05\nEpoch 39/96\n194/450 [===========>..................] - ETA: 2:57 - loss: 7.4151","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76b24df880] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76b24df880] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 7.3532","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76fc06c080] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76fc06c080] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 113ms/step\nOriginal: place green by y four now\nPrediction: place gren by y four now\n----------------------------------------------------------------------------------------------------\nOriginal: set green with x three soon\nPrediction: set gren with thre son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 530s 1s/step - loss: 7.3532 - val_loss: 4.1540 - lr: 4.0657e-05\nEpoch 40/96\n 53/450 [==>...........................] - ETA: 4:35 - loss: 6.7174","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7688005140] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7688005140] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 6.9693","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76b802b2c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76b802b2c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 114ms/step\nOriginal: set green by j two please\nPrediction: set gren by two please\n----------------------------------------------------------------------------------------------------\nOriginal: set red with v one soon\nPrediction: set red with one son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 528s 1s/step - loss: 6.9693 - val_loss: 3.7644 - lr: 3.6788e-05\nEpoch 41/96\n 49/450 [==>...........................] - ETA: 4:38 - loss: 6.5841","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7858b4be40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7858b4be40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 6.5354","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76a4017d00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76a4017d00] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\nOriginal: bin green at a seven soon\nPrediction: bin gren at a seven son\n----------------------------------------------------------------------------------------------------\nOriginal: lay blue at q four now\nPrediction: lay blue at four now\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 529s 1s/step - loss: 6.5354 - val_loss: 3.7980 - lr: 3.3287e-05\nEpoch 42/96\n354/450 [======================>.......] - ETA: 1:06 - loss: 6.2998","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d77681a79c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d77681a79c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 6.3091","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d786819a040] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d786819a040] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 120ms/step\nOriginal: set red by b seven again\nPrediction: set red by b seven again\n----------------------------------------------------------------------------------------------------\nOriginal: bin green in a three soon\nPrediction: bin gren in a thre son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 529s 1s/step - loss: 6.3091 - val_loss: 3.4402 - lr: 3.0119e-05\nEpoch 43/96\n222/450 [=============>................] - ETA: 2:38 - loss: 6.1450","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76bc0112c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76bc0112c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 6.1269","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d78501a6200] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d78501a6200] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 127ms/step\nOriginal: bin green by h seven again\nPrediction: bin gren by seven again\n----------------------------------------------------------------------------------------------------\nOriginal: set red with b eight now\nPrediction: set red with b eight now\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 529s 1s/step - loss: 6.1269 - val_loss: 3.5841 - lr: 2.7253e-05\nEpoch 44/96\n450/450 [==============================] - ETA: 0s - loss: 5.8158","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76f00ab4c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76f00ab4c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 121ms/step\nOriginal: place white at x four now\nPrediction: place white at four now\n----------------------------------------------------------------------------------------------------\nOriginal: place green by k eight please\nPrediction: place gren by eight please\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 529s 1s/step - loss: 5.8158 - val_loss: 2.9137 - lr: 2.4660e-05\nEpoch 45/96\n 49/450 [==>...........................] - ETA: 4:39 - loss: 5.7414","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d78600ae000] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d78600ae000] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.5908","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76aa2270c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76aa2270c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 124ms/step\nOriginal: set red at h six please\nPrediction: set red at h six please\n----------------------------------------------------------------------------------------------------\nOriginal: bin white at t four please\nPrediction: bin white at t four please\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 529s 1s/step - loss: 5.5908 - val_loss: 3.5077 - lr: 2.2313e-05\nEpoch 46/96\n 31/450 [=>............................] - ETA: 4:49 - loss: 6.2382","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7794062580] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7794062580] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.5060","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76980640c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76980640c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: place red in c four please\nPrediction: place red in c four please\n----------------------------------------------------------------------------------------------------\nOriginal: bin blue in r nine again\nPrediction: bin blue in r nine again\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 528s 1s/step - loss: 5.5060 - val_loss: 2.7765 - lr: 2.0190e-05\nEpoch 47/96\n115/450 [======>.......................] - ETA: 3:52 - loss: 5.1303","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76f0060900] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76f0060900] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.3135","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d78640597c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d78640597c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 115ms/step\nOriginal: place white at j six now\nPrediction: place white at six now\n----------------------------------------------------------------------------------------------------\nOriginal: place blue in o five soon\nPrediction: place blue in five son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 528s 1s/step - loss: 5.3135 - val_loss: 2.7981 - lr: 1.8268e-05\nEpoch 48/96\n424/450 [===========================>..] - ETA: 18s - loss: 5.0985","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76bc027000] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76bc027000] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.0879","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76fc082d40] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76fc082d40] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\nOriginal: set white with p two now\nPrediction: set white with p two now\n----------------------------------------------------------------------------------------------------\nOriginal: lay red by r five soon\nPrediction: lay red by five son\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 529s 1s/step - loss: 5.0879 - val_loss: 2.4640 - lr: 1.6530e-05\nEpoch 49/96\n 86/450 [====>.........................] - ETA: 4:12 - loss: 5.0373","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76880410c0] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76880410c0] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 5.0412","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76c0072080] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76c0072080] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 117ms/step\nOriginal: bin white with h four please\nPrediction: bin white with four please\n----------------------------------------------------------------------------------------------------\nOriginal: place blue in o four now\nPrediction: place blue in o four now\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 527s 1s/step - loss: 5.0412 - val_loss: 2.2985 - lr: 1.4957e-05\nEpoch 50/96\n222/450 [=============>................] - ETA: 2:38 - loss: 5.0053","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7864210200] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7864210200] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.9653","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76a0059700] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76a0059700] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"1/1 [==============================] - 0s 116ms/step\nOriginal: bin green by u five again\nPrediction: bin gren by u five again\n----------------------------------------------------------------------------------------------------\nOriginal: bin blue by s six please\nPrediction: bin blue by s six please\n----------------------------------------------------------------------------------------------------\n450/450 [==============================] - 528s 1s/step - loss: 4.9653 - val_loss: 2.2633 - lr: 1.3534e-05\nEpoch 51/96\n 75/450 [====>.........................] - ETA: 4:19 - loss: 4.6747","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d76f4059200] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d76f4059200] Warning MVs not available\n","output_type":"stream"},{"name":"stdout","text":"450/450 [==============================] - ETA: 0s - loss: 4.8409","output_type":"stream"},{"name":"stderr","text":"[mpeg1video @ 0x7d7688207f00] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7688207f00] Warning MVs not available\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)","Cell \u001b[0;32mIn[58], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m96\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mschedule_callback\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexample_callback\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n","Cell \u001b[0;32mIn[53], line 8\u001b[0m, in \u001b[0;36mProduceExample.on_epoch_end\u001b[0;34m(self, epoch, logs)\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mon_epoch_end\u001b[39m(\u001b[38;5;28mself\u001b[39m, epoch, logs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m#         if epoh == 1 or epoch % 4 == 0:\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m         yhat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel\u001b[38;5;241m.\u001b[39mpredict(data[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m     10\u001b[0m         decoded \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mkeras\u001b[38;5;241m.\u001b[39mbackend\u001b[38;5;241m.\u001b[39mctc_decode(yhat, [\u001b[38;5;241m75\u001b[39m,\u001b[38;5;241m75\u001b[39m], greedy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mnumpy()\n","\u001b[0;31mStopIteration\u001b[0m: "],"ename":"StopIteration","evalue":"","output_type":"error"}]},{"cell_type":"code","source":"# history","metadata":{"execution":{"iopub.status.busy":"2023-11-05T04:15:52.578094Z","iopub.execute_input":"2023-11-05T04:15:52.578489Z","iopub.status.idle":"2023-11-05T04:15:52.583205Z","shell.execute_reply.started":"2023-11-05T04:15:52.578457Z","shell.execute_reply":"2023-11-05T04:15:52.582141Z"},"trusted":true},"execution_count":75,"outputs":[]},{"cell_type":"code","source":"training_loss = [85.2746 ,70.9716 ,66.9147 ,63.9592 ,61.8173 ,60.1350 ,58.5690 ,55.7670 ,52.7824 ,50.8946 ,49.0681 ,47.0584 ,44.3900 ,42.5115 ,39.7808 ,37.8971 ,35.7804 ,33.3151 ,30.8727 ,28.5955 ,26.3424 ,24.4883 ,22.5235 ,20.8805 ,19.3281 ,17.7996 ,16.5271 ,15.0045 ,14.0592,13.1728 ,12.3935 ,11.1808 ,10.4560 ,9.8701 ,9.0793 ,8.4954 ,8.0987 ,7.6377 ,7.3532 ,6.9693 ,6.5354 ,6.3091 ,6.1269 ,5.8158 ,5.5908 ,5.5060 ,5.3135 ,5.0879 ,5.0412 ,4.9653 ]\nvalidation_loss = [69.1930 ,65.6452 ,62.2782 ,63.9592 ,58.2501 ,55.6897 ,55.3017 ,50.9557 ,48.8146 ,46.5846 ,45.2084 ,41.4461 ,40.2157 ,36.6420 ,34.9563 ,31.5592 ,35.7804 ,27.6424 ,26.3044 ,23.0497 ,20.3814 ,18.4825 ,17.3327 ,15.4639 ,15.1650 ,12.4896 ,11.0701 ,10.2751 ,9.1001 ,9.4078 ,8.2575 ,7.8103 ,6.9572 ,6.6227 ,5.7344 ,4.9208 ,5.0810 ,4.6688 ,4.1540 ,3.7644 ,3.7980 ,3.4402 ,3.5841 ,2.9137 ,3.5077 ,2.7765 ,2.7981 ,2.4640 ,2.2985 ,2.2633 ]","metadata":{"execution":{"iopub.status.busy":"2023-11-05T04:33:18.848842Z","iopub.execute_input":"2023-11-05T04:33:18.849760Z","iopub.status.idle":"2023-11-05T04:33:18.858447Z","shell.execute_reply.started":"2023-11-05T04:33:18.849712Z","shell.execute_reply":"2023-11-05T04:33:18.857478Z"},"trusted":true},"execution_count":78,"outputs":[]},{"cell_type":"code","source":"\n# Plot the loss\nplt.figure()\nplt.plot(training_loss, label='Training Loss')\nplt.plot(validation_loss, label='Validation Loss')\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-11-05T04:33:21.781973Z","iopub.execute_input":"2023-11-05T04:33:21.782772Z","iopub.status.idle":"2023-11-05T04:33:22.026187Z","shell.execute_reply.started":"2023-11-05T04:33:21.782731Z","shell.execute_reply":"2023-11-05T04:33:22.025267Z"},"trusted":true},"execution_count":79,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAjIAAAGwCAYAAACzXI8XAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABk3ElEQVR4nO3dd3gU5d7G8e9uem+QBqH3FpAOdlBARRAUVESsKAKKyrG8Kraj2EVFxQpiAcQjigIiICLSe5HeAyGhp5K2O+8fExYiLWU3m3J/rmuvzM7MPvPbOZHcZ+aZ57EYhmEgIiIiUg5Z3V2AiIiISHEpyIiIiEi5pSAjIiIi5ZaCjIiIiJRbCjIiIiJSbinIiIiISLmlICMiIiLllqe7C3A1u91OYmIiQUFBWCwWd5cjIiIihWAYBmlpacTGxmK1nv+6S4UPMomJicTFxbm7DBERESmGhIQEqlevft7tFT7IBAUFAeaJCA4OdnM1IiIiUhipqanExcU5/o6fT4UPMqduJwUHByvIiIiIlDMX6xaizr4iIiJSbinIiIiISLmlICMiIiLlVoXvIyMiIiVjs9nIzc11dxlSwXh5eeHh4VHidhRkRETknAzDICkpiRMnTri7FKmgQkNDiY6OLtE4bwoyIiJyTqdCTGRkJP7+/hpUVJzGMAwyMzM5dOgQADExMcVuS0FGRETOYrPZHCEmIiLC3eVIBeTn5wfAoUOHiIyMLPZtJnX2FRGRs5zqE+Pv7+/mSqQiO/X7VZI+WAoyIiJyXrqdJK7kjN8vBRkREREptxRkREREpNxSkBEREbmIWrVqMWbMmELv/+eff2KxWPToeilQkCmmPJud3UcyOJKe7e5SREQkn8ViueDrhRdeKFa7K1asYPDgwYXev1OnThw8eJCQkJBiHa+wFJj0+HWxPTJ5LTM2HGTUDU2459La7i5HRESAgwcPOpanTJnCqFGj2Lp1q2NdYGCgY9kwDGw2G56eF/9TWLVq1SLV4e3tTXR0dJE+I8WjKzLFVLtKAAA7Dqe7uRIRkdJhGAaZOXlueRmGUagao6OjHa+QkBAsFovj/ZYtWwgKCmLWrFm0bt0aHx8f/v77b3bu3EmvXr2IiooiMDCQtm3bMnfu3ALt/vvWksVi4fPPP+emm27C39+f+vXrM336dMf2f18pmTBhAqGhocyePZvGjRsTGBhI9+7dCwSvvLw8Hn74YUJDQ4mIiODJJ59k0KBB9O7du9j/mx0/fpw777yTsLAw/P396dGjB9u3b3ds37t3Lz179iQsLIyAgACaNm3KzJkzHZ8dMGAAVatWxc/Pj/r16zN+/Phi1+IquiJTTHUj84PMIQUZEakcTubaaDJqtluOvemlbvh7O+dP1lNPPcVbb71FnTp1CAsLIyEhgeuuu45XXnkFHx8fJk6cSM+ePdm6dSs1atQ4bzsvvvgib7zxBm+++SYffPABAwYMYO/evYSHh59z/8zMTN566y2+/vprrFYrd9xxByNHjuTbb78F4PXXX+fbb79l/PjxNG7cmPfee4+ffvqJq666qtjf9a677mL79u1Mnz6d4OBgnnzySa677jo2bdqEl5cXQ4cOJScnh7/++ouAgAA2bdrkuGr13HPPsWnTJmbNmkWVKlXYsWMHJ0+eLHYtrqIgU0z1qgYBsEtXZEREypWXXnqJa665xvE+PDyc+Ph4x/uXX36ZadOmMX36dIYNG3bedu666y5uu+02AF599VXef/99li9fTvfu3c+5f25uLuPGjaNu3boADBs2jJdeesmx/YMPPuDpp5/mpptuAmDs2LGOqyPFcSrALFq0iE6dOgHw7bffEhcXx08//cQtt9zCvn376Nu3L82bNwegTp06js/v27ePVq1a0aZNG8C8KlUWKcgU06krMkfScziRmUOov7ebKxIRcS0/Lw82vdTNbcd2llN/mE9JT0/nhRdeYMaMGRw8eJC8vDxOnjzJvn37LthOixYtHMsBAQEEBwc75g46F39/f0eIAXN+oVP7p6SkkJycTLt27RzbPTw8aN26NXa7vUjf75TNmzfj6elJ+/btHesiIiJo2LAhmzdvBuDhhx9myJAh/P7773Tt2pW+ffs6vteQIUPo27cvq1ev5tprr6V3796OQFSWqI9MMfl7e1It1JwnQreXRKQysFgs+Ht7uuXlzBGGAwICCrwfOXIk06ZN49VXX2XhwoWsXbuW5s2bk5OTc8F2vLy8zjo/Fwod59q/sH1/XOW+++5j165dDBw4kA0bNtCmTRs++OADAHr06MHevXt59NFHSUxMpEuXLowcOdKt9Z6LgkwJ1Klq/sewU7eXRETKrUWLFnHXXXdx00030bx5c6Kjo9mzZ0+p1hASEkJUVBQrVqxwrLPZbKxevbrYbTZu3Ji8vDyWLVvmWHf06FG2bt1KkyZNHOvi4uJ48MEH+fHHH3n88cf57LPPHNuqVq3KoEGD+OabbxgzZgyffvppsetxFd1aKoF6kYEs3H5EV2RERMqx+vXr8+OPP9KzZ08sFgvPPfdcsW/nlMTw4cMZPXo09erVo1GjRnzwwQccP368UFejNmzYQFBQkOO9xWIhPj6eXr16cf/99/PJJ58QFBTEU089RbVq1ejVqxcAI0aMoEePHjRo0IDjx48zf/58GjduDMCoUaNo3bo1TZs2JTs7m19//dWxrSxRkCmBepFmz24FGRGR8uudd97hnnvuoVOnTlSpUoUnn3yS1NTUUq/jySefJCkpiTvvvBMPDw8GDx5Mt27d8PC4eP+gyy+/vMB7Dw8P8vLyGD9+PI888gg33HADOTk5XH755cycOdNxm8tmszF06FD2799PcHAw3bt359133wXMsXCefvpp9uzZg5+fH5dddhmTJ092/hcvIYvh7ht0LpaamkpISAgpKSkEBwc7te2lu45y66dLqRHuz19PFP/xOBGRsiYrK4vdu3dTu3ZtfH193V1OpWS322ncuDH9+vXj5Zdfdnc5LnGh37PC/v3WFZkSOHVFJuF4Jlm5Nnyd2KteREQql7179/L7779zxRVXkJ2dzdixY9m9eze33367u0sr09TZtwQiArwJ9ffCMGDX4Qx3lyMiIuWY1WplwoQJtG3bls6dO7Nhwwbmzp1bJvullCW6IlMCFouFulUDWbX3ODsPp9Mk1rm3rkREpPKIi4tj0aJF7i6j3NEVmRKqV1UdfkVERNxFQaaEHE8uaSwZERGRUqcgU0KnpirYqSsyIiIipU5BpoQck0ceycBmr9BPsouIiJQ5CjIlVC3MDx9PKzl5dvYfz3R3OSIiIpWKgkwJeVgt1K5i3l5Sh18RkYrhyiuvZMSIEY73tWrVYsyYMRf8jMVi4aeffirxsZ3VTmXh1iBjs9l47rnnqF27Nn5+ftStW5eXX365wGyghmEwatQoYmJi8PPzo2vXrmzfvt2NVZ/tVIdfTR4pIuJePXv2pHv37ufctnDhQiwWC+vXry9yuytWrGDw4MElLa+AF154gZYtW561/uDBg/To0cOpx/q3CRMmEBoa6tJjlBa3BpnXX3+djz/+mLFjx7J582Zef/113njjDccU4gBvvPEG77//PuPGjWPZsmUEBATQrVs3srKy3Fh5QZpzSUSkbLj33nuZM2cO+/fvP2vb+PHjadOmDS1atChyu1WrVsXf398ZJV5UdHQ0Pj4+pXKsisCtQWbx4sX06tWL66+/nlq1anHzzTdz7bXXsnz5csC8GjNmzBieffZZevXqRYsWLZg4cSKJiYnnveyWnZ1NampqgZer1dVYMiIiZcINN9xA1apVmTBhQoH16enpTJ06lXvvvZejR49y2223Ua1aNfz9/WnevDmTJk26YLv/vrW0fft2Lr/8cnx9fWnSpAlz5sw56zNPPvkkDRo0wN/fnzp16vDcc8+Rm5sLmFdEXnzxRdatW4fFYsFisThq/vetpQ0bNnD11Vfj5+dHREQEgwcPJj399N+bu+66i969e/PWW28RExNDREQEQ4cOdRyrOPbt20evXr0IDAwkODiYfv36kZyc7Ni+bt06rrrqKoKCgggODqZ169asXLkSMKda6NmzJ2FhYQQEBNC0aVNmzpxZ7Fouxq0j+3bq1IlPP/2Ubdu20aBBA9atW8fff//NO++8A8Du3btJSkqia9eujs+EhITQvn17lixZwq233npWm6NHj+bFF18ste8AZ95aysAwjEJNuS4iUu4YBuS66aEGL38oxL+tnp6e3HnnnUyYMIFnnnnG8e/x1KlTsdls3HbbbaSnp9O6dWuefPJJgoODmTFjBgMHDqRu3bq0a9fuosew2+306dOHqKgoli1bRkpKSoH+NKcEBQUxYcIEYmNj2bBhA/fffz9BQUE88cQT9O/fn40bN/Lbb78xd+5cwPz79m8ZGRl069aNjh07smLFCg4dOsR9993HsGHDCoS1+fPnExMTw/z589mxYwf9+/enZcuW3H///Rf9Puf6fqdCzIIFC8jLy2Po0KH079+fP//8E4ABAwbQqlUrPv74Yzw8PFi7dq1jRu2hQ4eSk5PDX3/9RUBAAJs2bSIwMLDIdRSWW4PMU089RWpqKo0aNcLDwwObzcYrr7zCgAEDAEhKSgIgKiqqwOeioqIc2/7t6aef5rHHHnO8T01NJS4uzkXfwFS7SgAWC6SczOVIeg5Vg3RJUEQqoNxMeDXWPcf+v0TwDijUrvfccw9vvvkmCxYs4MorrwTM20p9+/YlJCSEkJAQRo4c6dh/+PDhzJ49m++//75QQWbu3Lls2bKF2bNnExtrno9XX331rH4tzz77rGO5Vq1ajBw5ksmTJ/PEE0/g5+dHYGAgnp6eREdHn/dY3333HVlZWUycOJGAAPP7jx07lp49e/L66687/j6GhYUxduxYPDw8aNSoEddffz3z5s0rVpCZN28eGzZsYPfu3Y6/nxMnTqRp06asWLGCtm3bsm/fPv7zn//QqFEjAOrXr+/4/L59++jbty/NmzcHoE6dOkWuoSjcemvp+++/59tvv+W7775j9erVfPXVV7z11lt89dVXxW7Tx8eH4ODgAi9X8/XyIC7MvHeq20siIu7VqFEjOnXqxJdffgnAjh07WLhwIffeey9gPmjy8ssv07x5c8LDwwkMDGT27Nns27evUO1v3ryZuLg4R4gB6Nix41n7TZkyhc6dOxMdHU1gYCDPPvtsoY9x5rHi4+MdIQagc+fO2O12tm7d6ljXtGlTPDw8HO9jYmI4dOhQkY515jHj4uIKXARo0qQJoaGhbN68GYDHHnuM++67j65du/Laa6+xc+dOx74PP/ww//3vf+ncuTPPP/98sTpXF4Vbr8j85z//4amnnnLcImrevDl79+5l9OjRDBo0yJFSk5OTiYmJcXwuOTn5nD293aleZCD7jmWy83A6HetGuLscERHn8/I3r4y469hFcO+99zJ8+HA+/PBDxo8fT926dbniiisAePPNN3nvvfcYM2YMzZs3JyAggBEjRpCTk+O0cpcsWcKAAQN48cUX6datGyEhIUyePJm3337bacc406nbOqdYLBbsdrtLjgXmE1e33347M2bMYNasWTz//PNMnjyZm266ifvuu49u3boxY8YMfv/9d0aPHs3bb7/N8OHDXVKLW6/IZGZmYrUWLMHDw8Nx8mvXrk10dDTz5s1zbE9NTWXZsmXnTL/upCeXRKTCs1jM2zvueBWx72G/fv2wWq189913TJw4kXvuucfRX2bRokX06tWLO+64g/j4eOrUqcO2bdsK3Xbjxo1JSEjg4MGDjnVLly4tsM/ixYupWbMmzzzzDG3atKF+/frs3bu3wD7e3t7YbLaLHmvdunVkZGQ41i1atAir1UrDhg0LXXNRnPp+CQkJjnWbNm3ixIkTNGnSxLGuQYMGPProo/z+++/06dOH8ePHO7bFxcXx4IMP8uOPP/L444/z2WefuaRWcHOQ6dmzJ6+88gozZsxgz549TJs2jXfeeYebbroJMBPliBEj+O9//8v06dPZsGEDd955J7GxsfTu3dudpZ/l1CzYGktGRMT9AgMD6d+/P08//TQHDx7krrvucmyrX78+c+bMYfHixWzevJkHHnigwBM5F9O1a1caNGjAoEGDWLduHQsXLuSZZ54psE/9+vXZt28fkydPZufOnbz//vtMmzatwD61atVi9+7drF27liNHjpCdnX3WsQYMGICvry+DBg1i48aNzJ8/n+HDhzNw4MCz+o8Wlc1mY+3atQVemzdvpmvXrjRv3pwBAwawevVqli9fzp133skVV1xBmzZtOHnyJMOGDePPP/9k7969LFq0iBUrVtC4cWMARowYwezZs9m9ezerV69m/vz5jm2u4NYg88EHH3DzzTfz0EMP0bhxY0aOHMkDDzzAyy+/7NjniSeeYPjw4QwePJi2bduSnp7Ob7/9hq+vrxsrP5smjxQRKVvuvfdejh8/Trdu3Qr0Z3n22We55JJL6NatG1deeSXR0dFF+j/HVquVadOmcfLkSdq1a8d9993HK6+8UmCfG2+8kUcffZRhw4bRsmVLFi9ezHPPPVdgn759+9K9e3euuuoqqlates5HwP39/Zk9ezbHjh2jbdu23HzzzXTp0oWxY8cW7WScQ3p6Oq1atSrw6tmzJxaLhZ9//pmwsDAuv/xyunbtSp06dZgyZQpg3jk5evQod955Jw0aNKBfv3706NHD8cSwzWZj6NChNG7cmO7du9OgQQM++uijEtd7PhbjzGF0K6DU1FRCQkJISUlxacfflMxc4l/6HYB/XuxGgI9bux+JiJRIVlYWu3fvpnbt2mXu/zhKxXGh37PC/v3WXEtOEuLvRZVA87Fr3V4SEREpHQoyTlS3qiaPFBERKU0KMk6kySNFRERKl4KME+kRbBERkdKlIONEmjxSRCqaCv48iLiZM36/FGSc6NQVmb1HM8m1uW5ERRERVzs1UmxmppsmiZRK4dTv179HJi4KPSPsRDEhvvh7e5CZY2Pv0UxHsBERKW88PDwIDQ11zNfj7+/vGBlXpKQMwyAzM5NDhw4RGhpaYJ6oolKQcSKLxULdqoFsOJDCjkPpCjIiUq6dmu+uuJMPilxMaGjoBWf/LgwFGSerF2kGGT25JCLlncViISYmhsjISHJzc91djlQwXl5eJboSc4qCjJM5HsFWh18RqSA8PDyc8gdHxBXU2dfJHIPi6YqMiIiIyynIONmZV2T02KKIiIhrKcg4Wc2IADytFjJybCSlZrm7HBERkQpNQcbJvDys1IjwBzQwnoiIiKspyLhAvarq8CsiIlIaFGRcwDHnkjr8ioiIuJSCjAtoziUREZHSoSDjAqdnwc5wcyUiIiIVm4KMC9TNDzJH0rNJydRomCIiIq6iIOMCgT6eRAf7AuonIyIi4koKMi6iqQpERERcT0HGRRxBRldkREREXEZBxkXqRurJJREREVdTkHERTR4pIiLiegoyLnLq1lLCsUyycm1urkZERKRiUpBxkaqBPgT7emI3YM9RjScjIiLiCgoyLmKxWNRPRkRExMUUZFzo9OSRuiIjIiLiCgoyLnSqn8yahONurkRERKRiUpBxoasbRWK1wJ9bD7NyzzF3lyMiIlLhKMi4UP2oIPq3jQPg5RmbsdsNN1ckIiJSsSjIuNij1zQgwNuDdQkn+GV9orvLERERqVAUZFwsMsiXIVfWBeCN37ZqTBkREREnUpApBfdeWoeYEF8OnDjJl4t2u7scERGRCkNBpiQS14Bx8X4vft4ePNG9IQAfzd/JkfRsV1cmIiJSKSjIFNevj8GnV8KGHwq1e6/4arSoHkJ6dh7vztnm2tpEREQqCQWZ4gqOMX/+9iRkHL3o7larhWeuawzApOX72J6c5srqREREKgUFmeLq9AhENoXMozD76UJ9pH2dCLo1jcJuwKszN7u4QBERkYpPQaa4PL3hxg8AC6yfAtvnFupjT/VojJeHhflbD7Nw+2HX1igiIlLBKciURPXW0GGIufzro5B98ckha1cJYGCHWgC8MmMzNg2SJyIiUmwKMiV11TMQUgNS9sH8Vwr1kYe71CPEz4stSWlMXZng4gJFREQqLgWZkvIJhBveNZeXfgz7V170I6H+3jzcpT4Ab8/ZRnp2nisrFBERqbAUZJyhfldo0R8wYPrDkJdz0Y8M7FCTWhH+HE7L5pMFO11fo4iISAWkIOMs3UaDfwQc+gcWv3fR3b09rTzVw3wc+7OFu0g8cdLVFYqIiFQ4CjLOEhAB3V8zlxe8AYcvPuhdt6ZRtKsVTlaundd/2+LiAkVERCoeBRlnan4L1LsGbDnwyyNgt19wd4vFwrM3NMZigZ/XJvK/VftLqVAREZGKQUHGmSwWuOEd8AqAfYth9YSLfqRF9VBGdGkAwDM/bWBLUqqLixQREak4FGScLbQGdBllLs95HlITL/qR4VfX44oGVcnKtTPkm9WkZuW6uEgREZGKQUHGFdrdD9XaQHYqzBh50RmyrVYLY/q3pFqoH7uPZPDE1PUYhZhVW0REpLJTkHEFq4c5fYHVE7bOgE0/X/QjYQHefDjgErw8LPz2TxJf/L27FAoVEREp3xRkXCWqCVz6mLk843HIOHLRj7SMC2XUDU0AGD1rCyv2HHNlhSIiIuWegowrXT4SIptA5hGY8dhFbzEB3NGhJjfGx2KzGwz9djWH07JLoVAREZHySUHGlTx9oPfH5i2mTT/Dxv9d9CMWi4XRfZpTLzKQQ2nZPDxpjSaWFBEROQ8FGVeLbQmXjTSXZ46EtOSLfiTAx5Nxd1yCv7cHS3Yd5Z05W11bo4iISDmlIFMaLh8J0S3g5HFzoLxC3GKqFxnE631bAPDh/J3M23zxACQiIlLZKMiUBg8vuGkcWL1g2yxYN7lQH+sZH8tdnWoB8OiUtSQcy3RhkSIiIuWPgkxpiWoKVz5lLs96ElIOFOpj/3ddYy6r7sFTeePY+MndpGRkubBIERGR8kVBpjR1HgHVWkN2CkwfXqhbTN6JKxif/Ri3e/5Bj+zfePejDzmSrieZREREQEGmdHl4mk8xefjAznmw+qvz72u3w8J3YHwPPNMOYFg9Aeic+iv9PlnCwZSTpVS0iIhI2aUgU9qqNoQuz5nLs5+B43vP3if9EHzbF+a9CIYNmvfDcu/vAFztsZaMwwnc/PES9hzJKMXCRUREyh4FGXfo8BDEdYCcdPh5qHn15ZRdf8K4S2HnH+DpB70+hD6fmrekanbGAzsPBC/mwImT3PLJErYmpbnta4iIiLibgow7WD2g90fg5Q97FsKKz8GWB/Nehom9IT3ZHBF48J/Q6g6wWMzPtb4LgEE+f9Ekyp/Dadn0/3QJaxNOuOmLiIiIuJeCjLtE1IWuL5rLc5+H8T1g4VuAYQaW++ZBZKOCn2l8I/iG4pG2n6nXZNGqRignMnMZ8NlSluw8WtrfQERExO0UZNyp7X1Q6zLIzYT9y8E7CG7+Enq+B97+Z+/v5QstbwcgYOM3fHNvezrXiyAjx8ag8cs1aJ6IiFQ6CjLuZLWafWBCa5h9Zh78C5r1vfBnLhlk/tw6i4Dsw3wxqC1dG0eRk2fnga9X8cu6RNfXLSIiUkYoyLhbWE14ZD3cOxvC61x8/8hGUKOj+TTT2m/w9fLg4zsuoXfLWPLsBg9PXsM7v28lz2a/eFsiIiLlnIJMWXCqM29h5Xf6ZdVEsNvx8rDyTr+WDOpYE8OA9//Ywe2fLyMpRaMAi4hIxaYgUx416QW+IZCyD3b9AYDVauHFXs1479aWBHh7sHz3MXq89xd/bFG/GRERqbgUZMojLz9ocau5vGpCgU29WlZjxsOX0axaMMczc7lnwkpembGJnDzdahIRkYpHQaa8an260y9pBa+61KoSwP+GdHLMnP3Zwt3c8skSzZ4tIiIVjtuDzIEDB7jjjjuIiIjAz8+P5s2bs3LlSsd2wzAYNWoUMTEx+Pn50bVrV7Zv3+7GisuIqKZQvR3Y82Dtt2dt9vH04IUbm/LJwNaE+HmxLuEE172/kJkbDrqhWBEREddwa5A5fvw4nTt3xsvLi1mzZrFp0ybefvttwsLCHPu88cYbvP/++4wbN45ly5YREBBAt27dyMpSR1ZHp9/VXxWc5uAM3ZpGM/ORy2hdM4y0rDwe+nY1z0zbQFaurfTqFBERcRGLYRiGuw7+1FNPsWjRIhYuXHjO7YZhEBsby+OPP87IkSMBSElJISoqigkTJnDrrbee9Zns7Gyys7Md71NTU4mLiyMlJYXg4GDXfBF3ycmEtxtBdgoM/AnqXnXeXXNtdt6ds42P/twJQN2qAbzbvyUtqoeWTq0iIiJFkJqaSkhIyEX/frv1isz06dNp06YNt9xyC5GRkbRq1YrPPvvMsX337t0kJSXRtWtXx7qQkBDat2/PkiVLztnm6NGjCQkJcbzi4uJc/j3cxtsfWvQzl//V6fffvDysPNG9ERPvaUfVIB92Hs6gz0eLeW/udnI15oyIiJRTbg0yu3bt4uOPP6Z+/frMnj2bIUOG8PDDD/PVV18BkJSUBEBUVFSBz0VFRTm2/dvTTz9NSkqK45WQkODaL+Fupzr9bvkV0g9ddPfLG1Tl9xGXc33zGPLsBu/O3cbNHy9m5+F0FxcqIiLifG4NMna7nUsuuYRXX32VVq1aMXjwYO6//37GjRtX7DZ9fHwIDg4u8KrQoptDtTb5nX6/K9RHwgK8GXt7K967tSXBvp6s25/Cde8tZMKi3djtbrvTKCIiUmRuDTIxMTE0adKkwLrGjRuzb98+AKKjowFITi74eHFycrJjm1CoTr//ZrFY6NWyGrMfvZzL6lchO8/OC79sYuCXy0g8cdJ1tYqIiDiRW4NM586d2bp1a4F127Zto2bNmgDUrl2b6Oho5s2b59iemprKsmXL6NixY6nWWqY162POnH1sF+w5d8fp84kJ8WPiPe14uVdTfL2sLNpxlG5j/mLamv24sR+4iIhIobg1yDz66KMsXbqUV199lR07dvDdd9/x6aefMnToUMC8ajBixAj++9//Mn36dDZs2MCdd95JbGwsvXv3dmfpZYt3QKE7/Z6LxWJhYMdazHz4MlrGhZKWlcejU9bx0q+bnFuniIiIk7k1yLRt25Zp06YxadIkmjVrxssvv8yYMWMYMGCAY58nnniC4cOHM3jwYNq2bUt6ejq//fYbvr6+bqy8DDp1e2nzL5B27o7QF1OnaiA/PNiRx69pgMUC4xftYdLyfc6rUURExMncOo5MaSjsc+gVwmdd4MBKs/PvoF/Mx7OLaewf23nr9214eViYdH8H2tQKd2KhIiIiF1YuxpERJ+v9MfiFmWHmh3vAllfspoZeVY/rm8eQazN48JvV6gAsIiJlkoJMRVK1Adw2GTx9YdssmPk4FPOCm8Vi4c1bWtAoOogj6dk88PUqTWsgIiJljoJMRVOjA/T9HLCYHX//eqvYTfl7e/LZnW0I8/diw4EUnvrfej3JJCIiZYqCTEXUuCdc96a5PP+/sObs2bELKy7cnw8HXIKH1cJPaxP5fOFuJxUpIiJScgoyFVW7+6HzCHN5+nDYPrfYTXWqW4Xnrm8MwOhZm1mw7bATChQRESk5BZmKrMvz0KI/GDb4/k5IXFPspgZ1qkW/NtWxGzD8u9XsPpLhxEJFRESKR0GmIrNa4caxUOdKyM2Ab/vB8T3FaspisfBy72a0qhFKalYe909cSVpWrlPLFRERKSoFmYrO0xv6fQ1RzSHjEHzTFzKOFqspH08PPrmjNVHBPuw4lM6jU9ZpkkkREXErBZnKwDcYBkyFkDg4ugMm9Yf9qyA7vchNRQb78snANnh7Wpm7OZkxc7e5oGAREZHC0ci+lcnhrfDFtZB14vS60JoQ2Tj/1QSqNoIqDcDrwlNA/G/Vfh6fug6A8Xe15apGkS4sXEREKpvC/v1WkKlsDqyCP/4LSRvNW03nYrFCRD3o9irUv+a8TT3300a+XrqXUH8vZjx8GdVC/VxUtIiIVDYKMvkUZC4g4ygc3gyHNsOhTad/ZqWY2/3C4ZF15q2pc8jOs3HLuCWs359Cy7hQvn+gI96eulspIiIlpyCTT0GmiAwD0g7CVz3N/jRXPg1XPnXe3ROOZXL9+wtJzcrjrk61eOHGpqVYrIiIVFSaNFKKx2KB4Fi46hnz/eKxF3zKKS7cn3f6tQRgwuI9zFh/sBSKFBERMSnIyLk16Q3RzSEnDRa9e8FduzaJ4oEr6gDw5P/Ws+tw0Z+GEhERKQ4FGTk3qxWuHmUuL/8MUi98peU/1zakXa1w0rPzeOjb1ZopW0RESoWCjJxf/WsgrgPkZcFfb15wV08PKx/c3ooqgd5sSUpj1M8bS6lIERGpzBRk5PwsFuiSf1Vm9Vdw7MIzX0cF+/Lera2wWOD7lfuZujKhFIoUEZHKTEFGLqxWZ6jbBex58OdrF929c70qPNa1AQDP/byRLUmprq5QREQqMQUZubguz5k/108xx5q5iKFX1eOKBlXJyrXz0DerNbmkiIi4jIKMXFxsK2jcEzDMUYEvwmq18G7/lsSE+LLrSAZP/m89Nk0uKSIiLqAgI4Vz1bOABbb8ak5zcBHhAd6Mvf0SPK0WZm5I4p4JK0jVlRkREXEyBRkpnMhGEH+ruTzv5UJ9pHXNMD64rRW+XlYWbDvMTR8uYs+RDBcWKSIilY2CjBTelU+B1Qt2zYfdfxXqIz2axzD1gU5EB/uy83AGvT9axOKdR1xcqIiIVBYKMlJ4YbWg9SBzed7L5rxMhdC8egjTh3UmPi6UE5m53PnFcr5dttd1dYqISKWhICNFc/l/wNMP9i+HbbML/bHIYF+mDO5Ar5ax5NkNnpm2kRem/0Oeze7CYkVEpKJTkJGiCYqG9oPN5T9eBnvhg4ivlwdj+rfkP90aAuYkk3eNX0FKpjoBi4hI8SjISNF1HgE+wZC8ETZNK9JHLRYLQ6+qx7g7WuPv7cHfO45w00eLNNGkiIgUi4KMFJ1/OHQabi7PfgaO7ChyE92bRfPDg52IzR9rpveHi1iw7bCTCxURkYpOQUaKp8MQqNIQ0g7C+O6QtKHITTSJDebnYZfSumYYqVl53D1+OR//uROjkJ2IRUREFGSkeHyC4O6ZEN0CMg7DhOshYXmRm6ka5MN397fn1rZx2A14/bctDJu0hsycPBcULSIiFY2CjBRfQBUY9AvEdYCsFJjYG3bOL3IzPp4ejO7TnP/2boan1cKM9Qfp89FiEo5lOr9mERGpUBRkpGT8QmHgj1D3asjNgO/6wZYZRW7GYrFwR4eaTBrcgSqBPmxJSqPn2L/5e7sGzxMRkfNTkJGS8w6A2yZDoxvAlgNTBsL674vVVNta4fwyvDPx1UPMwfO+XMbnC3ep34yIiJyTgow4h6cP3PIVxN8Ghg1+HAwrPi9WUzEhfkx5oCM3t66O3YD/ztjMo1PWcjLH5uSiRUSkvFOQEefx8IReH0G7wYABMx6Hhe8UqylfLw/evLkFL97YFA+rhZ/WJnLzuMUknjjp3JpFRKRcU5AR57JaoccbcNlI8/28F2Hui4Wel+lMFouFQZ1q8e197YkI8OafxFRu+mgRmw+mOrloEREprxRkxPksFujyHFzzkvn+73fMgfOK2c+lQ50Ipg+/lAZRgSSnZnPLuCUs2qFOwCIioiAjrtT5Ebj+bXN56Ycw68lih5lqoX5MfbAT7WuHk56dx6AvlzNtzX4nFisiIuWRgoy4Vtv7oOd7gAWWfwIzHivSRJNnCvHzYuK97egZb86g/eiUdXw4f4eeaBIRqcQUZMT1Wt8FvT4ELLDyS/j1kWKHGR9PD97r35LBl9cB4M3ZW3nu543Y7AozIiKVkYKMlI5WA+CmT8BihdUT4eehYC/e49RWq4X/u64xz/dsgsUC3yzdxwNfr9Lj2SIilZCCjJSe+P7Q5zOweMC672Dag2Ar/pxKd3euzUe3X4K3p5W5m5O57bOlHE3PdmLBIiJS1inISOlqfjPc/CVYPWHD9/Dj/WDLLXZzPZrH8N197Qn192Jtwgn6fryYvUcznFiwiIiUZQoyUvqa9jZHAbZ6wT8/wg/3QF5OsZtrUyucHx7sRPUwP/YczeSOL5bpyoyISCWhICPu0fgG6P8NeHjD5ukwdRBkFX+gu3qRgfw4pBM1wv1JOHaSwV+vIitXfWZERCo6BRlxn4bd4dZJ4OEDW2fC2Law4YdijzUTGezLl3e1JdjXk1V7j/PED+v1aLaISAVXrCCTkJDA/v2nByNbvnw5I0aM4NNPP3VaYVJJ1O8Kd/4E4XUgPQn+dy9MvBEObytWc/UiAxl3R2s8rRamr0vk3bnbnVuviIiUKcUKMrfffjvz588HICkpiWuuuYbly5fzzDPP8NJLLzm1QKkEanaCIUvgqmfA0xd2/wUfd4K5L0BO0TvudqpXhVduagbA+/O2awRgEZEKrFhBZuPGjbRr1w6A77//nmbNmrF48WK+/fZbJkyY4Mz6pLLw8oUrnoCHlkL9bmDPhb/fhbHtYNP0It9u6t+2Bg9eUReAJ3/YwPLdx1xRtYiIuFmxgkxubi4+Pj4AzJ07lxtvvBGARo0acfDgQedVJ5VPeG24fQrc+h2E1IDU/fD9QPj2Zji6s0hNPdGtId2bRpNjs/PA1yvZc0SPZYuIVDTFCjJNmzZl3LhxLFy4kDlz5tC9e3cAEhMTiYiIcGqBUglZLNDoehi6DC573HxMe8dc+KgjbPu90M1YrRbe7d+SFtVDOJ6Zyz0TVpCSWfwxa0REpOwpVpB5/fXX+eSTT7jyyiu57bbbiI+PB2D69OmOW04iJebtD11Gmbebal8Otmz4dQRkpxe6CT9vDz6/sw2xIb7sOpLBg9+sIievePM8iYhI2WMxivl8qs1mIzU1lbCwMMe6PXv24O/vT2RkpNMKLKnU1FRCQkJISUkhODjY3eVIceWehA/bw4m90PkRuKZonco3H0zllnFLSM/O45bW1Xnj5hZYLBYXFSsiIiVV2L/fxboic/LkSbKzsx0hZu/evYwZM4atW7eWqRAjFYiXH/R4w1xe8iEc2lKkjzeOCeaD21thtcDUVft5f94OjTEjIlIBFCvI9OrVi4kTJwJw4sQJ2rdvz9tvv03v3r35+OOPnVqgiEPD7tCgB9jzYObIIj/JdFXDSF64sSkA787dxv9N26DbTCIi5Vyxgszq1au57LLLAPjhhx+Iiopi7969TJw4kffff9+pBYoU0OM1c6yZPQth4/+K/PE7O9bimesaY7HApOUJmpdJRKScK1aQyczMJCgoCIDff/+dPn36YLVa6dChA3v37nVqgSIFhNWCy0aay7OfKdb8TPdfXocvB7UlyMeT5buP0evDRWxJKv48TyIi4j7FCjL16tXjp59+IiEhgdmzZ3PttdcCcOjQIXWoFdfrNPz0lAZ/ji5WE1c1iuTHhzpRM8Kf/cdP0vejxczZlOzkQkVExNWKFWRGjRrFyJEjqVWrFu3ataNjx46AeXWmVatWTi1Q5CxevnDdm+bysk8gaWOxmqkfFcRPD3WmU90IMnJsDP56JR/OVydgEZHypNiPXyclJXHw4EHi4+OxWs08tHz5coKDg2nUqJFTiywJPX5dgU0ZCJunQ42OcPcscyC9Ysi12Xnpl018vdS8LdqrZSyv922Br5eHM6sVEZEiKOzf72IHmVNOzYJdvXr1kjTjMgoyFVjKfhjbFnIzoffH0PL2EjX39dK9vDD9H2x2g/jqIXx6Zxuign2dVKyIiBSFS8eRsdvtvPTSS4SEhFCzZk1q1qxJaGgoL7/8Mna7HmeVUhJS3ZxoEuD35+DkiRI1N7BDTb6+tx2h/l6s259C7w8XcTDlZMnrFBERlylWkHnmmWcYO3Ysr732GmvWrGHNmjW8+uqrfPDBBzz33HPOrlHk/DoMhSoNIfMI/PHfEjfXqW4Vfh7amTpVAziYksX9E1eSmZPnhEJFRMQVinVrKTY2lnHjxjlmvT7l559/5qGHHuLAgQNOK7CkdGupEti1ACbeCBYr3D8fYluWuMmEY5n0+nARxzJyuK55NGNvuwSrVVMaiIiUFpfeWjp27Ng5O/Q2atSIY8eOFadJkeKrcwU06wuGHWY8Dk64vRkX7s8nA1vj5WFh5oYkxszd5oRCRUTE2YoVZOLj4xk7duxZ68eOHUuLFi1KXJRIkV37CngHwYGV5tgyttwSN9m2Vjiv3tQcgPf/2MH0dYklblNERJyrWLeWFixYwPXXX0+NGjUcY8gsWbKEhIQEZs6c6Zi+oCzQraVKZMlHMPtpczm8DnQZBU16F/ux7FNenbmZT//ahY+nlSkPdKRlXGiJSxURkQtz6a2lK664gm3btnHTTTdx4sQJTpw4QZ8+ffjnn3/4+uuvi120SIl0GALXvQX+VeDYLph6F3x2NexeWKJmn+zeiC6NIsnOszN44kqSUrKcU6+IiJRYiceROdO6deu45JJLsNlszmqyxHRFphLKToPFY2HxB5CbYa6rdw10fQGimxWrybSsXPp+vJhtyek0rxbC9w90xM9bA+aJiLiKS6/IuMJrr72GxWJhxIgRjnVZWVkMHTqUiIgIAgMD6du3L8nJmg9HLsInCK56Gh5eA23vA6sn7JgD4y6FaUPgREKRmwzy9eKLQW0JD/Bmw4EURk5dh92uqQxERNytTASZFStW8Mknn5zVUfjRRx/ll19+YerUqSxYsIDExET69Onjpiql3AmKguvfhqHLzb4yGLDuO/igNSx6v8jNxYX7M+4O80mmGRsO8t687U4vWUREisbtQSY9PZ0BAwbw2WefERYW5lifkpLCF198wTvvvMPVV19N69atGT9+PIsXL2bp0qXnbS87O5vU1NQCL6nkIupCv6/gvj+g5qVgy4Y5z8GeRUVuql3tcF7pbT7J9N687fy6Xk8yiYi4k2dRdr7Y1ZATJ04UuYChQ4dy/fXX07VrV/7739Mjs65atYrc3Fy6du3qWNeoUSNq1KjBkiVL6NChwznbGz16NC+++GKR65BKoHpruOtXmD4c1nwNvzwCD/5tzqZdBP3axrH9UBqfLdzN49+vI8Dbk6saRbqoaBERuZAiXZEJCQm54KtmzZrceeedhW5v8uTJrF69mtGjR5+1LSkpCW9vb0JDQwusj4qKIikp6bxtPv3006SkpDheCQlF7w8hFZjFAte+DAGRcHQ7/P1OsZp5qkdjujY2n2S656sVfPbXLpzYb15ERAqpSFdkxo8f77QDJyQk8MgjjzBnzhx8fZ03w7CPjw8+Pj5Oa08qIL8wuO4N8/Hshe9A05sgsnGRmvCwWvhoQGtG/byRySsSeGXmZrYmp/HKTc3w8dTTTCIipcVtfWRWrVrFoUOHuOSSS/D09MTT05MFCxbw/vvv4+npSVRUFDk5OWfdrkpOTiY6Oto9RUvF0aQ3NOgB9lzzFlMxpjXw9rQyuk9znu/ZBKsFfli1n9s/W8aR9Gzn1ysiIufktiDTpUsXNmzYwNq1ax2vNm3aMGDAAMeyl5cX8+bNc3xm69at7Nu3zzGasEixWSxw/VvgHQgJy2DlF8VsxsLdnWsz4e52BPl6smrvcXqNXcSmRHUyFxEpDU4dEK+krrzySlq2bMmYMWMAGDJkCDNnzmTChAkEBwczfPhwABYvXlzoNjUgnlzQsk9h1n/MeZqGLoOQasVuaufhdO77aiW7j2Tg7+3Bu/1b0q2prh6KiBRHuRsQ71zeffddbrjhBvr27cvll19OdHQ0P/74o7vLkoqk7b1QvS3kpMHM/0AJcn3dqoFMe6gTnetFkJlj44GvV/Hh/B3qBCwi4kJl6oqMK+iKjFxU8ib45HKzv0y/idCkV4may7XZ+e+vm/hqyV4AboyP5Y2bW+DrpU7AIiKFVSGuyIiUiqgmcOkIc3nmE3DyRIma8/Kw8mKvZvy3dzM8rRamr0tk0JfLScvKLXGpIiJSkIKMCMBlIyGiHqQnwdwXnNLkHR1qMvHedgT5eLJs9zFu+2wpR/VEk4iIUynIiIA5um/P98zlVeNhb+E7lF9Ip7pVmDS4AxEB3mw8kMotnywh8cRJp7QtIiIKMiKn1boULskfmfqXRyDPOVdPmlULYeqDHYkN8WXX4Qxu/ngxuw6nO6VtEZHKTkFG5EzXvGROX3BkGyx822nN1qkayA9DOlGnagCJKVncMm4JGw+kOK19EZHKSkFG5Eynpi8A+OstM8wUY9Tfc4kN9WPqAx1pVi2Yoxk53PbpUpbvPuaUtkVEKisFGZF/a9IbWt8Nhg3mvQTf9oX0Q05pOiLQh0n3d6Bd7XDSsvMY+MUy5m9xTtsiIpWRgozIv1kscMO70OtD8PSDnX/AuEth159OaT7I14uJ97SjSyNz9uz7J67k57UHnNK2iEhloyAjci4WC7S6Awb/CVUbQ3oyTOwNf7wCtrwSN+/r5cG4ga3p3TKWMPtxPP93N3/98nWJ2xURqWwUZEQuJLIR3P8HXDIIMOCvN2DijZCaWOKmvTysvNOvJZ/FTOd6j2VErxjNjPUHS16ziEgloiAjcjHe/nDj+9D3C3O27L2LzFtN234vcdPWQxuJPz4bgAbWA7w2ZS4Ltx8ucbsiIpWFgoxIYTW/GR74C2LiIfMofHcL/P4c2G3Fb3Pui1g4Pd1ZJ9bxwNerWLPvuBMKFhGp+BRkRIoioi7cOwfaP2i+X/w+LBpTvLZ2/wU75oDVE1r0B6Bv8GYyc2zcPWEF25PTnFOziEgFpiAjUlSePtDjdfPJJoA/XzNn0C4Kwzg9p1Pru6HdYADa2tdzSfUgTmTmMvCL5ew/num8ukVEKiAFGZHian03NOgOthz4aQjYijC79ebpcGAVeAXAFU9AbCvwC8OSncqEayzUjwwkKTWLgV8s54gmmhQROS8FGZHisljghjHgGwoH1xb+FpMt1xxoD6DTMAiMBKsH1LkKgOADf/H1ve2pFurH7iMZDPpyOWlZRQhJIiKViIKMSEkEx0CP/CkN/nwdkv+5+GfWfA1Hd4B/Feg47PT6el3MnzvmEh3iy9f3tiMiwJt/ElO576uVZOWWoFOxiEgFpSAjUlIt+kHD68Cee/FbTDkZZp8aMG8p+Qaf3lY3P8gkroGMo9SpGshX97Qj0MeTZbuPMXzSGvJszpn3SUSkolCQESmpU1Ma+IbCwXXw97vn33fpR+YowaE1zT42ZwqOgcimgAG75gPQrFoIn93ZBm9PK3M2JfPUjxuw242z2xURqaQUZEScISgarnvLXF7wBiRtPHufjKPw93vmcpdR4Ol99j6O20vzHKs61o1g7G2t8LBa+GHVfl6ZuRnDUJgREQEFGRHnaX4zNLoh/xbTg2ffYlr4NuSkQXQLaNrn3G2cCjI755mPaOe7tmk0r/dtAcAXf+9m7B87XPENRETKHQUZEWexWOD6d8AvDJI2mMHllON7YcVn5vI1L4L1PP/p1egIXv7m7afkgld1bm5dnVE3NAHg7TnbmLhkjwu+hIhI+aIgI+JMQVGnbzH99SYcXG8uz3/VHG+mzpVQ9+rzf97TB2pdZi7vmHvW5nsurc3DXeoDMOrnf/hpzQEnFi8iUv4oyIg4W7O+0Lgn2PPMp5gOrIb1U8xtXV+4+OfrdTV/ntFP5kyPdq3PXZ1qAfD41HXM3ZRc8ppFRMopBRkRZ3PcYgo3bw9N7A0YZr+Y2FYX//ypfjL7lkL22fMtWSwWRt3QhD6tqmGzGwz9bjVLdx116lcQESkvFGREXCEwEq7P7yOTnWJODHn1s4X7bHgdCKtldhrevfCcu1itFl6/uQVdG0eRnWfnvq9WsmF/inNqFxEpRxRkRFylWR9o0ttcbnOPOXN2YVgspwfH23nu20sAXh5Wxt7eig51wknPzmPQ+OXsOJResppFRMoZBRkRV7rpE7h1Elz7StE+5+gnc3aH3zP5ennw2Z1taFE9hGMZOQz8YplmzBaRSkVBRsSVvHyh0XXnHvzuQmpfBlYvOL4Hju684K5Bvl5MuLsddasGcDDFnDH7UGpW8WsWESlHFGREyiKfIKjRwVw+z9NLZwoP8Oab+07PmH3rZ0sVZkSkUlCQESmr6l28n8yZYkL8mHR/B6qF+rHrsMKMiFQOCjIiZdWpDr+7/4K87EJ9pEaEP5Pu70BsiC+7Dmdwm8KMiFRwCjIiZVV0cwiMgtxMc0yZQqoR4c/kwR2JDfFlp8KMiFRwCjIiZZXFcno6g4s8vfRv5wwzaQozIlLxKMiIlGWnHsPe+UeRP1ojwp9JgzucDjOfKsyISMWjICNSltW5CrCYUx2kHizyx2tGBDBpcAdiFGZEpIJSkBEpywIiTs/PVIyrMmCGmclnhJnbP1umMCMiFYaCjEhZd+ox7CL2kznTmWFmx6F0hRkRqTAUZETKulP9ZHbNB7ut2M3UjAhg0v2nw8xtn+ppJhEp/xRkRMq6am3AJwROHofENSVqqlaVgreZbv10KckKMyJSjinIiJR1Hp5Q5wpzuRDTFVxMzYgApgzuaI4AfMQMM0kpCjMiUj4pyIiUB6f6yWz8H6QcKHFz5jgzHU7PzfTpEg6mnCxxuyIipU1BRqQ8aNAdvAPhyFYY2wb+egtyS3YVJS7cDDPVw/zYczSTWz9dSuIJhRkRKV8UZETKg6BouHsW1OhoTlnwx8vwUQfYOgsMo9jNngozceF+7M0PMwcUZkSkHFGQESkvYlqYYabPZxAYDcd3w6Rb4dtb4MiOYjdbPcyczqBGuD/7jmVy66dL2H8804mFi4i4joKMSHlisUCLfjB8JXQeAVYv2DHHvDozZxRkpxWr2Wqhfkwe3IGaEf4kHDtJ/0+WknBMYUZEyj4FGZHyyCcIrnkRHloK9a4Bey4seg8+aAP/TCtWk7H5YaZ2lQAOnDjJrZ8uZc+RDCcXLiLiXAoyIuVZlXowYCrcNgXCakN6Eky9C7bPKVZzMSF+TLq/A3Xyw0yvDxfx9/Yjzq1ZRMSJFGREyjuLBRp2N6/OtLzDXPfzUMg4WqzmokN8mfxAB1rGhZJyMpc7v1zGF3/vxihBp2IREVdRkBGpKLx84fq3oGojSE+GXx8p9hNNkUG+TB7cgb6XVMduwMu/buI/P6wnO6/4UySIiLiCgoxIReLlB30+NTsBb/4F1n5X7KZ8vTx465YWPHt9Y6wW+GHVfm7V/EwiUsYoyIhUNDHxcNXT5vKsJ+H4nmI3ZbFYuO+yOky4ux3Bvp6s2XeCG8cuYl3CCaeUKiJSUgoyIhVR5xHm4Hk5afDjAyWaNRvg8gZV+XnYpdSLDCQpNYtbPlnCtDX7nVOriEgJKMiIVERWD7hpHHgHQcJSWDSmxE3WrhLAtIc60bVxJDl5dh6dso5XZ27GZlcnYBFxHwUZkYoqrBb0eN1cnv8qJK4tcZNBvl58OrANQ6+qC8Cnf+3ivq9WkJGdV+K2RUSKQ0FGpCJreTs07gn2PPhxMOSWfB4lq9XCf7o14oPbWuHjaWX+1sPcMm4JSSnqBCwipU9BRqQis1jghvcgMMqcOXvuC05rumd8LJMHdyAiwJtNB1O56aNFbEpMdVr7IiKFoSAjUtEFRECvj8zlZeNg5x9Oa7pVjTB+GtqZulUDOJiSxS3jFvPn1kNOa19E5GIUZEQqg/pdoe195vJPD0HmMac1HRfuz49DOtOhTjgZOTbu/Wol3y3b57T2RUQuREFGpLK45mWIqA9pB+HXR4s96u+5hPh7MfGe9vRpVQ2b3eD/pm1g9KzN2PVEk4i4mIKMSGXh7Z8/6q8nbPoJVnzu3OY9rbzdL54RXesD8MmCXQyftIasXE1rICKuoyAjUplUuwS6jDKXZz0B2+c6tXmLxcKIrg14p188Xh4WZmw4yO2fLeVoerZTjyMicoqCjEhl0+lhaDkADDtMvQuS/3H6IfpcUp2J97Qn2NeT1fnTGqza67x+OSIipyjIiFQ2FgvcMAZqXmpOYfBdf0hLdvphOtaN4MeHOlMzwp8DJ07S75OlvD9vu0YCFhGnUpARqYw8vaH/1xBeF1ISYPJtkJPp9MPUiwzk1+GXclN+J+B35mzjtk+XcuBEyQfmExEBBRmRyss/HAZMBb8wOLAKfnoQ7HanHybI14t3+7fknX7xBHh7sHzPMXqM+YtZGw46/VgiUvkoyIhUZhF14dbvwOoFm36GP1522aH6XFKdmY9cRnxcKKlZeQz5djVP/7iekzl6qklEik9BRqSyq9kJeo01l/9+B9Z847pDRQTww4MdGXJlXSwWmLQ8gZ5j/9bUBiJSbAoyIgLxt8LlT5jLvzwCu/9y2aG8PKw82b0R39zbnsggH3YcSqf3h4v48u/dGkBPRIpMQUZETFf9HzTra86UPWUgHNnu0sN1rleF30ZcTtfGkeTY7Lz06yYGfrlMHYFFpEjcGmRGjx5N27ZtCQoKIjIykt69e7N169YC+2RlZTF06FAiIiIIDAykb9++JCc7/1FRkUrPYjEnl6zeDrJOwLe3QMYRlx4yPMCbz+5sw8u9muLrZWXRjqN0f/cvpq5MwHDiFAoiUnG5NcgsWLCAoUOHsnTpUubMmUNubi7XXnstGRkZjn0effRRfvnlF6ZOncqCBQtITEykT58+bqxapALz8jU7/4bWgOO74fOucHibSw9psVgY2LEWsx65nEtqhJKWncd/fljP/RNXcThNIwKLyIVZjDL0f3sOHz5MZGQkCxYs4PLLLyclJYWqVavy3XffcfPNNwOwZcsWGjduzJIlS+jQocNZbWRnZ5Odffofv9TUVOLi4khJSSE4OLjUvotIuXZkO3zTB07sA58QuGU81Ovi8sPa7Aaf/rWLd+dsI8dmJzzAm1d6N6NH8xiXH1tEypbU1FRCQkIu+ve7TPWRSUlJASA8PByAVatWkZubS9euXR37NGrUiBo1arBkyZJztjF69GhCQkIcr7i4ONcXLlLRVKkP98+HGh0hO8W8zbTsU6fOmH0uHlYLQ66sy/ThnWkcE8yxjByGfLuaRyavISUz16XHFpHyqcwEGbvdzogRI+jcuTPNmjUDICkpCW9vb0JDQwvsGxUVRVJS0jnbefrpp0lJSXG8EhISXF26SMUUUAXu/Dl/XiYbzPoPzHgcbK4PFI2ig/l5aGeGXVUPqwV+XpvItWMW8OfWQy4/toiUL2UmyAwdOpSNGzcyefLkErXj4+NDcHBwgZeIFJOnD/T6EK55CbDAyi/gm76Q6foJIL09rYzs1pD/DelEnaoBJKdmc9f4FTz943rSsnR1RkRMZSLIDBs2jF9//ZX58+dTvXp1x/ro6GhycnI4ceJEgf2Tk5OJjo4u5SpFKimLBTo/YnYC9g6E3QvMTsBHdpTK4VvVCGPG8Mu4u3MtwBxEr/uYhSza4donqkSkfHBrkDEMg2HDhjFt2jT++OMPateuXWB769at8fLyYt68eY51W7duZd++fXTs2LG0yxWp3BpdB/fMhpA4OLYTPr8adv1ZKof28/bg+Z5NmTy4A3Hhfhw4cZIBny/j2Z82kJGdVyo1iEjZ5Nanlh566CG+++47fv75Zxo2bOhYHxISgp+fHwBDhgxh5syZTJgwgeDgYIYPHw7A4sWLC3WMwvZ6FpFCSj8MUwZAwjKweMDl/4GOQ8G3dP77ysjO47VZW/h66V4A4sL9ePPmeDrUiSiV44tI6Sjs32+3BhmLxXLO9ePHj+euu+4CzAHxHn/8cSZNmkR2djbdunXjo48+KvStJQUZERfIzTKnMlif36fNLww6DoP2D4BPUKmUsGjHEZ74Yb1jJOC7OtXiie4N8ff2LJXji4hrlYsgUxoUZERcxDDgnx/hz9fgSP6geX7h0PlhaHs/+AS6vIS0rFxenbmFScv3AVArwp83b4mnba1wlx9bRFxLQSafgoyIi9ltsPFHWPAaHM3vAOwfAZ1HQNt7wTvA5SUs2HaYJ39YT1JqFhYL3No2jkevaUBkkK/Ljy0irqEgk09BRqSU2PJg4w/mFZrju811AVXNQNPmHvD2d+nhU07m8vKvm/hh1X7z0N4eDLmyLvdeWgc/bw+XHltEnE9BJp+CjEgps+XB+imw4HU4YXbIJbwO3DoJIhu5/PAr9hzjvzM2sy7hBAAxIb78p1tDereshtV67n55IlL2KMjkU5ARcRNbLqybBPNHQ1oieAdB38+hYXeXH9puN/hlfSJv/LbV0Rm4ebUQnrm+sZ5uEiknFGTyKciIuFnGEfj+Tti7CLBA1+fN203neWrRmbJybXy5aDcfzd9Jev54M9c2ieKpHo2oU9X1nZFFpPgUZPIpyIiUAXk5MOsJWDXefN+8H9z4Pnj5lcrhj6RnM2buNiYtT8BmN/C0Wrj3sto82rUBvl7qPyNSFinI5FOQESkjDANWfA6znjQnoYy9xJz2IDim1ErYnpzG6Flb+GOLOflkvchA3rolnpZxoaVWg4gUjoJMPgUZkTJm1wKYOghOHofAaDPMVG9dqiXM2ZTM/03bwOG0bKwWeOCKuozoWh8fT12dESkrCvv3u0xMGikilUidK+D+P6BqI0hPgvE9YP33pVrCNU2imPPo5fRuGYvdgI//3EnPD/5m/f4TpVqHiJScrsiIiHtkpcKP98O238z3rQZCjQ4QVgtCa0JwLFhdf4Vk9j9JPDNtA0fSc/CwWhhyRV2Gd6mnqzMibqZbS/kUZETKMLsN/ngZ/n737G1WLwitAWE1zXATVgti4qH2FU5/4ulYRg7PT/+HX9YlAtAwKoi3+8XTrFqIU48jIoWnIJNPQUakHNj2O2ydaQ6gd3wPnNgH9rxz73vl03DlUy4pY9aGgzz700aOZphXZ+7qVIu7OtUiLty1oxKLyNkUZPIpyIiUQ3YbpCbmh5r8cHN4C2z+xdze53NocYtLDn00PZtR0/9hxvqDgHnx5+qGkdzRsSZX1K+q0YFFSomCTD4FGZEKZM4oWPQeeHjDoF+hRnuXHWr+lkN8uWg3C7cfcayrEe7PHR1qcEvrOMICvF12bBFRkHFQkBGpQOx2+H4gbPkV/KvAfXMhvLZLD7nrcDrfLN3H1FUJpGWZt7t8PK30jI9lYIeaxGsMGhGXUJDJpyAjUsHkZMD46+DgWqjSEO79HfxCXX7YzJw8flmXyMQle/knMdWxvnm1EG5tF8eN8bEE+Xq5vA6RykJBJp+CjEgFlHoQPu8CqQegzpUw4AfwKJ0QYRgGaxJO8PWSvcxYf5Acmx0APy8Prm8Rw61t42hdMwxLKcwlJVKRKcjkU5ARqaAOrocvu0NuBrS+G254t1QmojzT0fRspq05wOQVCew4lO5YX6dqALe2jaPPJdWpEuhTqjWJVBQKMvkUZEQqsK2zYNJtgAHXvgKdhrmlDMMwWL3vOJOXJ/Dr+oOczLUB4Gm1cE2TKPq1jePSelXw8tBg6iKFpSCTT0FGpIJb8hHMfhqwmPM2NbrOreWkZeXy6/qDTF6RwLqEE4714QHe9GgWTc/4WNrWCsdDj3GLXJCCTD4FGZEKzjBgxmOw8kvw8oe7Z0FsS3dXBcDmg6lMWZHA9HWJHMvIcayPCvbhuuYx9IyPpVVcqPrTiJyDgkw+BRmRSsCWC9/1g51/QFCMGWZc/Fh2UeTZ7CzeeZRf1ycya2OS4zFugOphftzQIpae8TE0iQlWqBHJpyCTT0FGpJLISoEvrjVHAPbyN6cy6DCk1J5mKqzsPBsLtx3hl/WJzNmUTGaOzbGtU90InuzeSGPTiKAg46AgI1KJpOyHHwfD3kXm+6jm0HMMVG/j1rLO52SOjflbD/HLukTmbk4m12b+c9yjWTQjuzWkbtVAN1co4j4KMvkUZEQqGcOAtd/C78/CyeOABdreC11GgW/Znc064VgmY+Zu58c1+zEM8LBauKV1dR7pWp+YED93lydS6hRk8inIiFRSGUfMMLNukvk+MBp6vAZNepf6eDNFsTUpjTdnb2Xu5mTAnA7hrk61GHJlXUL9Nb+TVB4KMvkUZEQquV0L4NdH4dhO8339a+G6tyCspnvruoiVe47x+m9bWLHnOABBvp48eEVdBnWqRaCPp5urE3E9BZl8CjIiQm4W/P0u/P0O2HLMzsCt74bmN0NsqzJ7hcYwDOZvPcQbv21lS1IaAN6eVi6rV4VuTaPp2iSKcM3CLRWUgkw+BRkRcTi8zbw6s/fv0+vC65qBptnNULWB+2q7AJvdYPq6A3zwxw52Hc5wrLdaoF3tcLo3jebaptHEhqovjVQcCjL5FGREpADDgG2zYf0Uc4qDvJOnt0W3gOa3QLM+EFLdfTWeh2EYbD+Uzm8bk/htYxKbDqYW2B5fPYRuzaLp0iiKBlGBGpNGyjUFmXwKMiJyXtnpsHUmbPgBds4D++mB6qjZGVoNNIONR9nsk5JwLJPZ/yQx+58kVu49zpn/mlcJ9KZj3Sp0qhtBp7oR1Aj3V7CRckVBJp+CjIgUSsZR2PQTbPzf6XFoACLqmYPrNe0D1rI76eOhtCzmbjrE7H+SWLb7KFm59gLbq4X60TE/1HSsG6FHuqXMU5DJpyAjIkWWsh/WTYYlH8LJY+a6yKZw9TPQ8Loy2zn4lOw8G2v3nWDxzqMs2XmUNQnHHYPtnVIvMpAbWsRwY3wsdTTwnpRBCjL5FGREpNiyUmHZOFj8AWTn90eJbQVXPwt1uxQu0BiG+XLj1ZzMnDxW7jmeH2yOsOFACvYz/uVvXi2EG+NjuSE+RldqpMxQkMmnICMiJZZ5zAwzy8ZBbqa5rkYnM9DU6gy2PEjdD8d2w/E9cDz/56n39jy4ZBBcOgKCot33PfKlnMxl7qZkpq9L5O8dR7DlpxqLBdrWCufG+Fiuax6jR7vFrRRk8inIiIjTpB82x6NZ8TnYss11wdUgPblgR+Hz8fSFtvdB50cgMNK1tRbS0fRsZm5M4pe1iSzfc8yx3tNqoXO9KlxWvwqd6lahUXQQVmvZvqUmFYuCTD4FGRFxupQDsPAtWD3xdIDx8DFHCw6rDWG1ILz26eW0RPjzNUhYZu7r6Qft7jcDTUAVd32LsySeOMmv6xOZvi6RjQcKPtod5u9FhzqnOgtXoW7VAD0FJS6lIJNPQUZEXCblAJzYC6E1ISjmwv1gDMN8xHv+aDiw0lznFQDtB0Onh8E/vHRqLqSdh9OZuymZJbuOsmL3MTJybAW2Rwb50LFuBB3rRNCsWgj1IgPx9fJwU7VSESnI5FOQEZEyxTBg+xyY/wocXGuu8w6E9g9Aw+shqgl4la0Ot7k2O+v3p7Bk5xEW7zzKqr3Hyc4r+Hi31QK1qgTQKDqIBlFBjp81IwLw0C0pKQYFmXwKMiJSJhkGbPvNDDRJG06vt3hAZGOIiYeYlubP6GbgHeC2Uv8tK9fGmn0nWLLzCMv3HGNrUhrHM3PPua+Pp5X6UYE0iw2hefUQWlQLpUF0ID6eunojF6Ygk09BRkTKNMOALb/CqgmQuAYyj569j8UKVRqYoab+tdDo+jJ11cYwDA6nZbMlKY2tSWlsTTZ/bktOO+vKDYC3h5VGMUE0rxZCi+ohNK8WSv2oQLw8yu6Ag1L6FGTyKciISLlhGJB6AA6uM1+Ja82f6UkF9/MJNueDajkAqrctswP02ewGe49msCUpjY0HUtiQ/zpxjqs33p5W6lYNpHqYX/7Lv8ByiJ+XG76BuJOCTD4FGREp99KSzECTsAw2TIUT+05vi6gHLW+HFrdCSDX31VhIhmGw//hJ1u9PYf2BE2zYn8KG/SmkZV/48fUgX0+qh/lTM9yfBlGB1I8y++DUrhKAt6eu5FRECjL5FGREpEKx2825oNZ+Z84NdWqAPixQ9yrzKk2DbuAT5M4qi8RuN9h7LJM9RzLYfzyT/cdP5r/M5aMZOef9rKfVQq0qATSMCqJ+VCANzgg46mRcvinI5FOQEZEKKzsNNk03Q83evwtuC4w2r9ZE1DF/htfN/1kbPH3cU28xZebkceD4SRKOZ7L7SCbbktLYdiiN7cnppJ/nSo6flweNYoJoGhtM09gQmsYG0yAqSI+IlyMKMvkUZESkUji225zocv1kc1qE87FYIaS62XG45QCodw14eJZamc5kGAYHU7LYlmyGmq3JaWxPTmNbcjonc21n7e9htVCvaiBNY4NpEhtMtVA/IoN9iAzyJTLYR09SlTEKMvkUZESk0jl5Ao7thKOnXjvM17Fdpye/PCUwGloNgFZ3QHgdt5TrbDa7we4jGfyTmMKmxFQ2HUzln8RUjl3gFhVAqL8XkUE+RAX7UjX/Z0SAN+HnePl7l8/wV54oyORTkBERyWcYkHHYDDVbZ8LaSZB55PT22pebk1s2ugG8fN1XpwsYhkFSahb/HDBDzdbkVJJSsjiUls2h1GxybGc/Jn4hvl5Wwv29CQ/0JjrYlxrhAdSM8KdGhNkhuXqYvzohl5CCTD4FGRGR88jLgW2zzDmjdswD8v8c+IZC/K3QaqA5GF8FZxgGKSdzSU7N5lBaFodSs0nO/3ksI4djGTkczcjheP5yYUKP1QIxIX7UjPCnZoQZbE5d7YkM9iEqyJdQfy/NV3UBCjL5FGRERArhRAKs/RbWfAMpCafXRzWHFrdAs5vLxePdrmYYBhk5No6l53AsM4ej6dkknjjJ3qOZ7D2Wyb6jmew7lnnOPjr/5u1hpWqQT34/HR+qBPrg7+2Br9fpl5+XB75eVvOnt/n+1O2tMH/vCv1kloJMPgUZEZEisNtg13xY9RVsnQX2U4PXWaDWpdCiPzS5EXxD3FpmWXZqpOO9xzLZezSTfUczOHAii0NpWRxOyyY5Neu8UzoUhcWCeXsrwJuIQG8iAn2ICPAmIsDHDEhnXAGKCPDGs5yNnKwgk09BRkSkmDKPwaafYf33sG/x6fUePtCwOzTvB/WvMR/nzsuBk8fP/cpKgaBoiG5uziNVjsa4cZXsPBtH0nNITjVvYR1Oy+JIeg5ZeTaycmyczLWRlWvP/3nqZScjJ4/jGTmcOJlLUf56Wy0QEXhGuAnyIdjPC0+rBU8PK15WC16eVjytFrw8rHh6mD+9Paz4elnx8fTAJ/+n7xk/fb088PE0rxg5OygpyORTkBERcYIT+8xRhdd/D4e3nF7vFWBeGshJL3xbYbUhqqkZbKKamq/QWmAtX1cM3CnPZud4Zi5HM7I5mm724Tmafmo5O//KT7bjKpDdxX/pX+7VlIEdazm1zcL+/dbzYyIicnGhNeCyx+HSx8zZutdPgY3/g7SDZ+xkAb9Q8AszX775yz5BZr+bpI3mvFHHd5uvLb+e/qh3oDm2TVw7qN7O/BlQpZS/ZPnhmd+/pmrQxQc3tNkNjmaYT2c5OjOnZpORk0euzU6ezSDXZifXZpBnP/O9uS4r10Z2nr3Az1PLpyYFdecYPLoiIyIixWO3weGt5qPafmHgE3LxqyoZRyD5H0jeaP5M2mBe4bGdY4yX8LoQ194MNXHtoWojXbUpYwzDIDvPjtVicfrj5rq1lE9BRkSkjLPlmmPb7F9pToyZsByObD17P58QqNYKIpuafW0im0DVhuATWPo1i8spyORTkBERKYcyj50RbJbBgVVnTJD5L6E1zFBzKtycGqHYnmdeNbLnnb186nNV6oN3QOl8JykSBZl8CjIiIhWALQ+SN8DB9eatqEOb4NBmSE8uedshcWagqdKg4Csw0uzIbLebUztkpeS/TpyxnAKBUVD3avAPL8H3yzXDW1BUyb9PBaHOviIiUnF4eEJsK/N1poyjcHizGWpOvVISzMkxrR5g9cx/eYDljPeGzZxoM/OIuX9KAuz8o2DbPiFgAbJScYx6fD4WD7MfT4NroUF3sz/PhUbttdvNYLb7L9i1APYuhtwMM0A17gmNbzQ7P2vk34vSFRkREam8Mo/BkW3m6/BWOLLdXD6xF4x/TUXg6WsOBOgbmv8zxHwi69BmM0ydKaQGNOhmvmpdZo61c2wX7PoTdi+A3Qvh5LEL1xZSwww1TW40n+SqZB2ddWspn4KMiIgUWW4WHN9jXsnxDQGf4AtPpHl8L2z/HbbNNq+y2LJPb/P0M5/qSkss+BnvQKjZGepcAbWvgJDqsGOuOQjhjrkF+wQFRkGj66HhdeAXboYsw5b/0272/3Gsw7wtFl6nXHeEVpDJpyAjIiKlKifDDDPbfoNtv58OMB7e5pWVU8Gl2iXg4XWeNjLNW12bp8PW3yA7pXi1BMVARD0z1ETUy3/VhbBa5lWiMkxBJp+CjIiIuI1hmGPmnDwB1VqDt3/R28jLgT1/wabpZkCy28y+M6f6AVmsZh8di9V8AaQeuPCtK4sVAiLNDsqnBjB0LIefXvYJAqtXwb5GjuUz3vuFOf3qj4JMPgUZERGplDKPmf1yju40x+k5lv/z6M6iTSlRGDe8C23ucWqTempJRESkMvPPv7JSvU3B9YYB6YfM6SIyj+VP7nkMMo+fsZy/PjvN7HdzrrF4znxvPc8tslKgICMiIlKZWCzmeDUVZMyayvUsl4iIiFQoCjIiIiJSbinIiIiISLlVLoLMhx9+SK1atfD19aV9+/YsX77c3SWJiIhIGVDmg8yUKVN47LHHeP7551m9ejXx8fF069aNQ4cOubs0ERERcbMyH2Teeecd7r//fu6++26aNGnCuHHj8Pf358svv3R3aSIiIuJmZTrI5OTksGrVKrp27epYZ7Va6dq1K0uWLDnnZ7Kzs0lNTS3wEhERkYqpTAeZI0eOYLPZiIoq+Kx7VFQUSUlJ5/zM6NGjCQkJcbzi4uJKo1QRERFxgzIdZIrj6aefJiUlxfFKSEhwd0kiIiLiImV6ZN8qVarg4eFBcnJygfXJyclER0ef8zM+Pj74+JTtGT1FRETEOcr0FRlvb29at27NvHnzHOvsdjvz5s2jY8eObqxMREREyoIyfUUG4LHHHmPQoEG0adOGdu3aMWbMGDIyMrj77rvdXZqIiIi4WZkPMv379+fw4cOMGjWKpKQkWrZsyW+//XZWB2ARERGpfCyGYRjuLsKVUlNTCQkJISUlheDgYHeXIyIiIoVQ2L/fZf6KTEmdymkaT0ZERKT8OPV3+2LXWyp8kElLSwPQeDIiIiLlUFpaGiEhIefdXuFvLdntdhITEwkKCsJisTit3dTUVOLi4khISNAtq1Kg8126dL5Ln8556dL5Ll3FOd+GYZCWlkZsbCxW6/kfsq7wV2SsVivVq1d3WfvBwcH6j6AU6XyXLp3v0qdzXrp0vktXUc/3ha7EnFKmx5ERERERuRAFGRERESm3FGSKycfHh+eff17TIZQSne/SpfNd+nTOS5fOd+ly5fmu8J19RUREpOLSFRkREREptxRkREREpNxSkBEREZFyS0FGREREyi0FmWL68MMPqVWrFr6+vrRv357ly5e7u6QK4a+//qJnz57ExsZisVj46aefCmw3DINRo0YRExODn58fXbt2Zfv27e4ptgIYPXo0bdu2JSgoiMjISHr37s3WrVsL7JOVlcXQoUOJiIggMDCQvn37kpyc7KaKy7ePP/6YFi1aOAYF69ixI7NmzXJs17l2nddeew2LxcKIESMc63S+neuFF17AYrEUeDVq1Mix3VXnW0GmGKZMmcJjjz3G888/z+rVq4mPj6dbt24cOnTI3aWVexkZGcTHx/Phhx+ec/sbb7zB+++/z7hx41i2bBkBAQF069aNrKysUq60YliwYAFDhw5l6dKlzJkzh9zcXK699loyMjIc+zz66KP88ssvTJ06lQULFpCYmEifPn3cWHX5Vb16dV577TVWrVrFypUrufrqq+nVqxf//PMPoHPtKitWrOCTTz6hRYsWBdbrfDtf06ZNOXjwoOP1999/O7a57HwbUmTt2rUzhg4d6nhvs9mM2NhYY/To0W6squIBjGnTpjne2+12Izo62njzzTcd606cOGH4+PgYkyZNckOFFc+hQ4cMwFiwYIFhGOb59fLyMqZOnerYZ/PmzQZgLFmyxF1lVihhYWHG559/rnPtImlpaUb9+vWNOXPmGFdccYXxyCOPGIah321XeP755434+PhzbnPl+dYVmSLKyclh1apVdO3a1bHOarXStWtXlixZ4sbKKr7du3eTlJRU4NyHhITQvn17nXsnSUlJASA8PByAVatWkZubW+CcN2rUiBo1auicl5DNZmPy5MlkZGTQsWNHnWsXGTp0KNdff32B8wr63XaV7du3ExsbS506dRgwYAD79u0DXHu+K/ykkc525MgRbDYbUVFRBdZHRUWxZcsWN1VVOSQlJQGc89yf2ibFZ7fbGTFiBJ07d6ZZs2aAec69vb0JDQ0tsK/OefFt2LCBjh07kpWVRWBgINOmTaNJkyasXbtW59rJJk+ezOrVq1mxYsVZ2/S77Xzt27dnwoQJNGzYkIMHD/Liiy9y2WWXsXHjRpeebwUZEQHM/+e6cePGAve0xfkaNmzI2rVrSUlJ4YcffmDQoEEsWLDA3WVVOAkJCTzyyCPMmTMHX19fd5dTKfTo0cOx3KJFC9q3b0/NmjX5/vvv8fPzc9lxdWupiKpUqYKHh8dZPa2Tk5OJjo52U1WVw6nzq3PvfMOGDePXX39l/vz5VK9e3bE+OjqanJwcTpw4UWB/nfPi8/b2pl69erRu3ZrRo0cTHx/Pe++9p3PtZKtWreLQoUNccskleHp64unpyYIFC3j//ffx9PQkKipK59vFQkNDadCgATt27HDp77eCTBF5e3vTunVr5s2b51hnt9uZN28eHTt2dGNlFV/t2rWJjo4ucO5TU1NZtmyZzn0xGYbBsGHDmDZtGn/88Qe1a9cusL1169Z4eXkVOOdbt25l3759OudOYrfbyc7O1rl2si5durBhwwbWrl3reLVp04YBAwY4lnW+XSs9PZ2dO3cSExPj2t/vEnUVrqQmT55s+Pj4GBMmTDA2bdpkDB482AgNDTWSkpLcXVq5l5aWZqxZs8ZYs2aNARjvvPOOsWbNGmPv3r2GYRjGa6+9ZoSGhho///yzsX79eqNXr15G7dq1jZMnT7q58vJpyJAhRkhIiPHnn38aBw8edLwyMzMd+zz44INGjRo1jD/++MNYuXKl0bFjR6Njx45urLr8euqpp4wFCxYYu3fvNtavX2889dRThsViMX7//XfDMHSuXe3Mp5YMQ+fb2R5//HHjzz//NHbv3m0sWrTI6Nq1q1GlShXj0KFDhmG47nwryBTTBx98YNSoUcPw9vY22rVrZyxdutTdJVUI8+fPN4CzXoMGDTIMw3wE+7nnnjOioqIMHx8fo0uXLsbWrVvdW3Q5dq5zDRjjx4937HPy5EnjoYceMsLCwgx/f3/jpptuMg4ePOi+osuxe+65x6hZs6bh7e1tVK1a1ejSpYsjxBiGzrWr/TvI6Hw7V//+/Y2YmBjD29vbqFatmtG/f39jx44dju2uOt8WwzCMkl3TEREREXEP9ZERERGRcktBRkRERMotBRkREREptxRkREREpNxSkBEREZFyS0FGREREyi0FGRERESm3FGRERESk3FKQEZFKx2Kx8NNPP7m7DBFxAgUZESlVd911FxaL5axX9+7d3V2aiJRDnu4uQEQqn+7duzN+/PgC63x8fNxUjYiUZ7oiIyKlzsfHh+jo6AKvsLAwwLzt8/HHH9OjRw/8/PyoU6cOP/zwQ4HPb9iwgauvvho/Pz8iIiIYPHgw6enpBfb58ssvadq0KT4+PsTExDBs2LAC248cOcJNN92Ev78/9evXZ/r06a790iLiEgoyIlLmPPfcc/Tt25d169YxYMAAbr31VjZv3gxARkYG3bp1IywsjBUrVjB16lTmzp1bIKh8/PHHDB06lMGDB7NhwwamT59OvXr1ChzjxRdfpF+/fqxfv57rrruOAQMGcOzYsVL9niLiBCWeP1tEpAgGDRpkeHh4GAEBAQVer7zyimEYhgEYDz74YIHPtG/f3hgyZIhhGIbx6aefGmFhYUZ6erpj+4wZMwyr1WokJSUZhmEYsbGxxjPPPHPeGgDj2WefdbxPT083AGPWrFlO+54iUjrUR0ZESt1VV13Fxx9/XGBdeHi4Y7ljx44FtnXs2JG1a9cCsHnzZuLj4wkICHBs79y5M3a7na1bt2KxWEhMTKRLly4XrKFFixaO5YCAAIKDgzl06FBxv5KIuImCjIiUuoCAgLNu9TiLn59fofbz8vIq8N5isWC3211Rkoi4kPrIiEiZs3Tp0rPeN27cGIDGjRuzbt06MjIyHNsXLVqE1WqlYcOGBAUFUatWLebNm1eqNYuIe+iKjIiUuuzsbJKSkgqs8/T0pEqVKgBMnTqVNm3acOmll/Ltt9+yfPlyvvjiCwAGDBjA888/z6BBg3jhhRc4fPgww4cPZ+DAgURFRQHwwgsv8OCDDxIZGUmPHj1IS0tj0aJFDB8+vHS/qIi4nIKMiJS63377jZiYmALrGjZsyJYtWwDziaLJkyfz0EMPERMTw6RJk2jSpAkA/v7+zJ49m0ceeYS2bdvi7+9P3759eeeddxxtDRo0iKysLN59911GjhxJlSpVuPnmm0vvC4pIqbEYhmG4uwgRkVMsFgvTpk2jd+/e7i5FRMoB9ZERERGRcktBRkRERMot9ZERkTJFd7tFpCh0RUZERETKLQUZERERKbcUZERERKTcUpARERGRcktBRkRERMotBRkREREptxRkREREpNxSkBEREZFy6/8BosA8ecyE09kAAAAASUVORK5CYII="},"metadata":{}}]},{"cell_type":"markdown","source":"# 5. Make a Prediction ","metadata":{"tags":[]}},{"cell_type":"code","source":"# url = 'https://drive.google.com/uc?id=1vWscXs4Vt0a_1IH1-ct2TCgXAZT-N3_Y'\n# output = 'checkpoints.zip'\n# gdown.download(url, output, quiet=False)\n# gdown.extractall('checkpoints.zip', 'models')","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-05T03:35:01.750124Z","iopub.status.idle":"2023-11-05T03:35:01.750612Z","shell.execute_reply.started":"2023-11-05T03:35:01.750366Z","shell.execute_reply":"2023-11-05T03:35:01.750390Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# model.load_weights('./models/checkpoint')","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-05T03:35:01.752219Z","iopub.status.idle":"2023-11-05T03:35:01.752694Z","shell.execute_reply.started":"2023-11-05T03:35:01.752455Z","shell.execute_reply":"2023-11-05T03:35:01.752477Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_data = test.as_numpy_iterator()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-05T04:34:44.757516Z","iopub.execute_input":"2023-11-05T04:34:44.757900Z","iopub.status.idle":"2023-11-05T04:34:44.771487Z","shell.execute_reply.started":"2023-11-05T04:34:44.757872Z","shell.execute_reply":"2023-11-05T04:34:44.770617Z"},"trusted":true},"execution_count":80,"outputs":[]},{"cell_type":"code","source":"sample = test_data.next()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-05T04:34:44.806437Z","iopub.execute_input":"2023-11-05T04:34:44.807305Z","iopub.status.idle":"2023-11-05T04:37:56.157931Z","shell.execute_reply.started":"2023-11-05T04:34:44.807238Z","shell.execute_reply":"2023-11-05T04:37:56.156911Z"},"trusted":true},"execution_count":81,"outputs":[{"name":"stderr","text":"[mpeg1video @ 0x7d7690060740] ac-tex damaged at 22 17\n[mpeg1video @ 0x7d7690060740] Warning MVs not available\n","output_type":"stream"}]},{"cell_type":"code","source":"sample[0].shape","metadata":{"execution":{"iopub.status.busy":"2023-11-05T04:37:56.161158Z","iopub.execute_input":"2023-11-05T04:37:56.161474Z","iopub.status.idle":"2023-11-05T04:37:56.170595Z","shell.execute_reply.started":"2023-11-05T04:37:56.161443Z","shell.execute_reply":"2023-11-05T04:37:56.169473Z"},"trusted":true},"execution_count":82,"outputs":[{"execution_count":82,"output_type":"execute_result","data":{"text/plain":"(2, 75, 46, 140, 1)"},"metadata":{}}]},{"cell_type":"code","source":"sample[1].shape","metadata":{"execution":{"iopub.status.busy":"2023-11-05T04:37:56.172444Z","iopub.execute_input":"2023-11-05T04:37:56.172707Z","iopub.status.idle":"2023-11-05T04:37:56.184295Z","shell.execute_reply.started":"2023-11-05T04:37:56.172684Z","shell.execute_reply":"2023-11-05T04:37:56.183192Z"},"trusted":true},"execution_count":83,"outputs":[{"execution_count":83,"output_type":"execute_result","data":{"text/plain":"(2, 40)"},"metadata":{}}]},{"cell_type":"code","source":"sample[1]","metadata":{"execution":{"iopub.status.busy":"2023-11-05T04:37:56.186788Z","iopub.execute_input":"2023-11-05T04:37:56.187062Z","iopub.status.idle":"2023-11-05T04:37:56.196114Z","shell.execute_reply.started":"2023-11-05T04:37:56.187039Z","shell.execute_reply":"2023-11-05T04:37:56.194342Z"},"trusted":true},"execution_count":84,"outputs":[{"execution_count":84,"output_type":"execute_result","data":{"text/plain":"array([[16, 12,  1,  3,  5, 39,  7, 18,  5,  5, 14, 39,  9, 14, 39, 17,\n        39,  6, 15, 21, 18, 39, 16, 12,  5,  1, 19,  5,  0,  0,  0,  0,\n         0,  0,  0,  0,  0,  0,  0,  0],\n       [19,  5, 20, 39,  2, 12, 21,  5, 39,  2, 25, 39,  8, 39, 20, 23,\n        15, 39, 14, 15, 23,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n         0,  0,  0,  0,  0,  0,  0,  0]])"},"metadata":{}}]},{"cell_type":"code","source":"yhat = model.predict(sample[0])","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-05T04:37:56.202107Z","iopub.execute_input":"2023-11-05T04:37:56.202372Z","iopub.status.idle":"2023-11-05T04:37:56.394706Z","shell.execute_reply.started":"2023-11-05T04:37:56.202349Z","shell.execute_reply":"2023-11-05T04:37:56.392818Z"},"trusted":true},"execution_count":85,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 0s 123ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"print('-'*100, 'REAL TEXT')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in sample[1]]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-05T04:37:56.397095Z","iopub.execute_input":"2023-11-05T04:37:56.397944Z","iopub.status.idle":"2023-11-05T04:37:56.528511Z","shell.execute_reply.started":"2023-11-05T04:37:56.397905Z","shell.execute_reply":"2023-11-05T04:37:56.526286Z"},"trusted":true},"execution_count":86,"outputs":[{"name":"stdout","text":"---------------------------------------------------------------------------------------------------- REAL TEXT\n","output_type":"stream"},{"execution_count":86,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'place green in q four please'>,\n <tf.Tensor: shape=(), dtype=string, numpy=b'set blue by h two now'>]"},"metadata":{}}]},{"cell_type":"code","source":"decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75,75], greedy=True)[0][0].numpy()","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-05T04:37:56.530430Z","iopub.execute_input":"2023-11-05T04:37:56.530719Z","iopub.status.idle":"2023-11-05T04:37:56.545610Z","shell.execute_reply.started":"2023-11-05T04:37:56.530695Z","shell.execute_reply":"2023-11-05T04:37:56.544443Z"},"trusted":true},"execution_count":87,"outputs":[]},{"cell_type":"code","source":"print('-'*100, 'PREDICTIONS')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]","metadata":{"tags":[],"execution":{"iopub.status.busy":"2023-11-05T04:37:56.547326Z","iopub.execute_input":"2023-11-05T04:37:56.547597Z","iopub.status.idle":"2023-11-05T04:37:56.778273Z","shell.execute_reply.started":"2023-11-05T04:37:56.547573Z","shell.execute_reply":"2023-11-05T04:37:56.777117Z"},"trusted":true},"execution_count":88,"outputs":[{"name":"stdout","text":"---------------------------------------------------------------------------------------------------- PREDICTIONS\n","output_type":"stream"},{"execution_count":88,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'place green in four please'>,\n <tf.Tensor: shape=(), dtype=string, numpy=b'set blue by h two now'>]"},"metadata":{}}]},{"cell_type":"markdown","source":"# Test on a Video","metadata":{}},{"cell_type":"code","source":"sample = load_data(tf.convert_to_tensor('./data/s1/bras9a.mpg'))","metadata":{"execution":{"iopub.status.busy":"2023-11-05T04:37:56.780835Z","iopub.execute_input":"2023-11-05T04:37:56.781544Z","iopub.status.idle":"2023-11-05T04:37:57.185771Z","shell.execute_reply.started":"2023-11-05T04:37:56.781494Z","shell.execute_reply":"2023-11-05T04:37:57.184304Z"},"trusted":true},"execution_count":89,"outputs":[]},{"cell_type":"code","source":"print('-'*100, 'REAL TEXT')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in [sample[1]]]","metadata":{"execution":{"iopub.status.busy":"2023-11-05T04:37:57.187989Z","iopub.execute_input":"2023-11-05T04:37:57.188297Z","iopub.status.idle":"2023-11-05T04:37:57.245672Z","shell.execute_reply.started":"2023-11-05T04:37:57.188264Z","shell.execute_reply":"2023-11-05T04:37:57.243360Z"},"trusted":true},"execution_count":90,"outputs":[{"name":"stdout","text":"---------------------------------------------------------------------------------------------------- REAL TEXT\n","output_type":"stream"},{"execution_count":90,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'bin red at s nine again'>]"},"metadata":{}}]},{"cell_type":"code","source":"yhat = model.predict(tf.expand_dims(sample[0], axis=0))","metadata":{"execution":{"iopub.status.busy":"2023-11-05T04:37:57.247885Z","iopub.execute_input":"2023-11-05T04:37:57.248193Z","iopub.status.idle":"2023-11-05T04:37:57.399858Z","shell.execute_reply.started":"2023-11-05T04:37:57.248169Z","shell.execute_reply":"2023-11-05T04:37:57.398706Z"},"trusted":true},"execution_count":91,"outputs":[{"name":"stdout","text":"1/1 [==============================] - 0s 83ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"decoded = tf.keras.backend.ctc_decode(yhat, input_length=[75], greedy=True)[0][0].numpy()","metadata":{"execution":{"iopub.status.busy":"2023-11-05T04:37:57.402856Z","iopub.execute_input":"2023-11-05T04:37:57.403150Z","iopub.status.idle":"2023-11-05T04:37:57.418343Z","shell.execute_reply.started":"2023-11-05T04:37:57.403125Z","shell.execute_reply":"2023-11-05T04:37:57.417325Z"},"trusted":true},"execution_count":92,"outputs":[]},{"cell_type":"code","source":"print('-'*100, 'PREDICTIONS')\n[tf.strings.reduce_join([num_to_char(word) for word in sentence]) for sentence in decoded]","metadata":{"execution":{"iopub.status.busy":"2023-11-05T04:37:57.420095Z","iopub.execute_input":"2023-11-05T04:37:57.420796Z","iopub.status.idle":"2023-11-05T04:37:57.540800Z","shell.execute_reply.started":"2023-11-05T04:37:57.420760Z","shell.execute_reply":"2023-11-05T04:37:57.539188Z"},"trusted":true},"execution_count":93,"outputs":[{"name":"stdout","text":"---------------------------------------------------------------------------------------------------- PREDICTIONS\n","output_type":"stream"},{"execution_count":93,"output_type":"execute_result","data":{"text/plain":"[<tf.Tensor: shape=(), dtype=string, numpy=b'bin red at s nine again'>]"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/README.md b/README.md
index f833b4a6cf2dc86736125b4e7fd8b82098b91a58..e377af96b45691c3c5b7b707c48b853300891021 100644
--- a/README.md
+++ b/README.md
@@ -1,19 +1,205 @@
+# CS550: Machine Learning - Final Project
+
+**Indian Institute of Technology, Bhilai**
+
+## Advance Lipreading (Sentence-Level)
+
+- **Project GitHub Link:** [https://github.com/Antoniocolapso/Lip-Reader](https://github.com/Antoniocolapso/Lip-Reader)
+
+### AUTHOR: **Omm Prakash Sahoo (12141190)**
+
 ---
-title: LipReader
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
+
+## Table of Contents
+
+1. [Introduction](#introduction)
+2. [Dataset](#dataset)
+3. [Data Augmentation](#data-augmentation)
+4. [Data Pipeline](#data-pipeline)
+5. [Deep Neural Network Architecture](#deep-neural-network-architecture)
+6. [Model Training](#model-training)
+7. [Model Performance](#model-performance)
+8. [Accuracy Analysis](#accuracy-analysis)
+9. [Cherry on Top](#cherry-on-top)
+10. [App Development](#app-development)
+    - [Video Selection Bar](#video-selection-bar)
+    - [Left Vertical Drawer](#left-vertical-drawer)
+    - [Video Preview](#video-preview)
+    - [What Your Model Actually Takes as Input](#what-your-model-actually-takes-as-input)
+    - [Output / Prediction](#output--prediction)
+    - [App UI](#app-ui)
+11. [Tasks Done In Final Submission](#tasks-done-in-final-submission)
+12. [Conclusion](#conclusion)
+13. [Individual Contribution to the Project](#individual-contribution-to-the-project)
+
 ---
 
-# Welcome to Streamlit!
+## Introduction
+
+Lip Reading is a fascinating project aiming to predict sentences from input video data without using audio information. In this Final Submission report, we provide an overview of the project, the dataset used, data preprocessing steps, and all the steps in designing and training the deep neural network. Additionally, we present a brief overview of our developed app.
+
+## Dataset
+
+The project uses the Grid corpus, which includes 1000 short videos and corresponding alignment files for 34 individuals, totaling 34000 videos and alignments, comprising 18 men and 16 women.
+
+**Links:**
+- Research Paper: [https://arxiv.org/abs/1611.01599](https://arxiv.org/abs/1611.01599)
+- Dataset: [http://spandh.dcs.shef.ac.uk/gridcorpus](http://spandh.dcs.shef.ac.uk/gridcorpus)
+
+## Data Augmentation
+
+### Video Preprocessing
+- Extract 75 uniform frames from every video.
+- Convert each frame to grayscale.
+- Crop only the mouth region from each frame manually.
+- Standardize the data.
+
+### Unique Character to Integer Encoding
+- Map each character to an integer.
+- Encode vocabularies to their respective indices.
+- Replace characters not in the vocabulary with an empty string.
+
+### Decoding
+- Decoding is done by returning the corresponding character for each integer value.
+
+### Alignment Preprocessing
+- Alignments contain words corresponding to time stamps.
+- Words are encoded in the same way as unique character to integer encoding, considering silence as a space in between.
+
+## Data Pipeline
+- Select 500 random videos from each of the 34 folders.
+- Use 450 videos for training and 50 videos for validation.
+- Perform the specified preprocessing.
+- Add a prefetching step to the dataset for optimized performance.
+
+## Deep Neural Network Architecture
+
+After researching and training several models, we have selected the current best model for phase one of submission and for the final submission.
+
+![Model Architecture](app/photos/model.png)
+
+## Model Training
+- Utilizing the CTC loss function.
+- Implementing a learning rate scheduler.
+- Periodically saving the model.
+
+## Model Performance
+
+On epoch 1:
+- Original: bin white at t two now
+- Prediction: le e e e e eo
+
+On epoch 50:
+- Original: bin blue by s six please
+- Prediction: bin blue by six please
+
+On epoch 96:
+- Original: place green in d five soon
+- Prediction: place green in d five soon
+
+## Accuracy Analysis
+
+We used the standard levenshtein-distance algorithm to evaluate word and sentence accuracy of both of our models.
+
+**Accuracy of Model submitted for phase 1:**
+- Mean Word Error Rate: 14.40%
+- Mean Sentence Error Rate: 6.06%
+- Word-level Accuracy: 85.6%
+- Sentence-level Accuracy: 93.94%
+
+**Accuracy of Model submitted for Final Submission:**
+- Mean Word Error Rate: 1.77%
+- Mean Sentence Error Rate: 0.67%
+- Word-level Accuracy: 98.23%
+- Sentence-level Accuracy: 99.33%
+
+**Cherry on Top:**
+Our model is capable enough to predict sentences from given Video (i.e., without any audio) almost in real-time while combined with our app.
+
+## How Did We Achieve Such Accuracy
+
+We trained our model on 34 individual people with different skin tones, different accents, different genders, and with sentences comprising most random words possible, making our model robust enough to predict on any video. Our optimization at different levels not only decreased the training time from 12 hours for 500 videos to 9 hours for 1000 videos, which is almost 2.7x faster training speed, but also our unique encoding and decoding technique made predictions fast enough to predict over a given video almost in real time.
+
+## App Development
+
+**NeuroSync Lipscape:** An app that synchronizes sentence predictions with your lip movement by harnessing the power of neural networks.
+
+### App Contents
+
+#### Video Selection Bar
+![Video Selection Bar](app/photos/bar.png)
+
+By this bar, you can select to predict a sentence on an existing video or upload* your own video on which you want to predict a sentence.
+
+#### Left Vertical Drawer
+For now, it is just showcasing the app and its description, but later we will make it more functional, giving it more functionality like choosing your model to predict or it will be open for integration with other apps and also act as a nav-bar for our app. You can find this at the top left part of the app.
+
+#### Video Preview
+In this tab, there will be a preview of the video that you have selected/uploaded*. In this section, to make sure we can process videos with any extensions, we are converting that to MP4 format. You can find this at the middle left part of the app.
+
+![Original Video](app/photos/ogvid.png)
+
+#### What Your Model Actually Takes as Input
+In this section, we have made users visualize the model input by animating the 75 preprocessed images to a GIF with 10 frames per second. You can find this at the middle right part of the app.
+
+![Model Input Visualization](app/photos/gif.png)
+
+#### Output / Prediction
+In this section, we have shown raw model output as a tensor and along with decoded sentence corresponding to that tensor. You can find this at the bottom right part of the app.
+
+![Model Output](app/photos/op.png)
+
+### App UI
+This is the complete UI of our current developed app:
+
+![App UI](app/photos/app.png)
+
+## Deployment
+
+### Local (Streamlit)
+1. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+2. Run the app:
+   ```bash
+   streamlit run app/streamlitapp.py
+   ```
+
+### Docker
+1. Build the image:
+   ```bash
+   docker build -t lip-reader .
+   ```
+2. Run the container:
+   ```bash
+   docker run --rm -p 8501:8501 lip-reader
+   ```
+
+### Hugging Face Spaces (Streamlit)
+1. Create a new Space and select **Streamlit** as the SDK.
+2. Add the repository files (or connect the repo).
+3. Ensure `packages.txt` includes system packages (e.g., `ffmpeg`) and `requirements.txt` is present.
+4. Set the Space to use `app.py` as the entrypoint (default for Streamlit Spaces).
+5. Push/commit and the Space will build and deploy automatically.
+
+## Tasks Done In Final Submission
+- Training model after fine-tuning it on 34000 videos (before it was only trained on 1000).
+- Did accuracy analysis to know actual sreangth of model.
+- Developed an full stack app to utilize the model in real life for hearing impared or deaf people.
+- We did exlore possibility of predidcting on any length (possibly very big i.e. in Hours) and came with solution to
+just classify each neighbour frame to one of 44 unique phonemes but unavalibility of proper dataset was major issue
+for us and we did try to make our own dataset after which we did end up making a 10 minutes long video with
+manual transcripting but due to our accent and low quality of datasets ( like touching or licking lips in between and
+many more reasons) we could not achieve the accuracy near this model (47%).
+- And also many phonemes in english are similar when pronouncing so that was also measure issue also we couldnot
+completely decode the predicted sequence also.
+- Due to above reasons and many more we decided to fine-tune and make it perfect previous model only which we
+successfully did and planning to deploy app after giving it some more finishing touches.
+
+## Conclusion
+
+Our lip reading project successfully achieved advanced accuracy levels, reducing mean word and sentence error rates to 1.77% and 0.67%, respectively. The model, trained on a diverse dataset, demonstrated robustness and adaptability. Real-time sentence predictions were realized through the innovative "NeuroSync Lipscape" app, offering accessibility for the hearing-impaired. The unique character-to-integer encoding and data preprocessing techniques significantly contributed to model performance. The proposed Bi-GRU LSTM feature extractor, manual crop for mouth region extraction, and learning rate scheduler enhanced the model's capabilities. The app's user-friendly interface integrates video preview, model input visualization, and detailed predictions. Our collaborative efforts, including individual contributions and constant model monitoring, ensured project success. Overall, our work not only advances lip reading accuracy but also translates research into a practical, real-time solution for improved accessibility.
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+[Project GitHub Link](https://github.com/Antoniocolapso/Lip-Reader)
diff --git a/SECURITY.md b/SECURITY.md
new file mode 100644
index 0000000000000000000000000000000000000000..034e848032092eaf8ef96eac731b6ed5961987f3
--- /dev/null
+++ b/SECURITY.md
@@ -0,0 +1,21 @@
+# Security Policy
+
+## Supported Versions
+
+Use this section to tell people about which versions of your project are
+currently being supported with security updates.
+
+| Version | Supported          |
+| ------- | ------------------ |
+| 5.1.x   | :white_check_mark: |
+| 5.0.x   | :x:                |
+| 4.0.x   | :white_check_mark: |
+| < 4.0   | :x:                |
+
+## Reporting a Vulnerability
+
+Use this section to tell people how to report a vulnerability.
+
+Tell them where to go, how often they can expect to get an update on a
+reported vulnerability, what to expect if the vulnerability is accepted or
+declined, etc.
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..80cfeb27f573ae04244826fcb8b5fc189954e17c
--- /dev/null
+++ b/app.py
@@ -0,0 +1,9 @@
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+APP_DIR = Path(__file__).resolve().parent / "app"
+sys.path.insert(0, str(APP_DIR))
+
+import streamlitapp  # noqa: F401  # Streamlit runs the module top-level code.
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index 0000000000000000000000000000000000000000..e12d14399f20e0fdc8997b2f4bbe8ef0a771b1ad
--- /dev/null
+++ b/app/data/alignments/s1/bbbz8n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbbz9s.align b/app/data/alignments/s1/bbbz9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..af6438ea7a263b4f6ba8338bdc6a3fcc3c0fbdf8
--- /dev/null
+++ b/app/data/alignments/s1/bbbz9s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbie8n.align b/app/data/alignments/s1/bbie8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..d952a0b0c3dadec6ba96f45d96dd17808524a1de
--- /dev/null
+++ b/app/data/alignments/s1/bbie8n.align
@@ -0,0 +1,8 @@
+0 16000 sil
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diff --git a/app/data/alignments/s1/bbie9s.align b/app/data/alignments/s1/bbie9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..3f37ebb82a0226d237376f5f6508f7d38712ec55
--- /dev/null
+++ b/app/data/alignments/s1/bbie9s.align
@@ -0,0 +1,8 @@
+0 15500 sil
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diff --git a/app/data/alignments/s1/bbif1a.align b/app/data/alignments/s1/bbif1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..08933bc7ad32764904f6e8cc6b20c4851c2a38bc
--- /dev/null
+++ b/app/data/alignments/s1/bbif1a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbifzp.align b/app/data/alignments/s1/bbifzp.align
new file mode 100644
index 0000000000000000000000000000000000000000..71d8629bd1549a3e748a6b8bb87372f870f510a0
--- /dev/null
+++ b/app/data/alignments/s1/bbifzp.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbil2n.align b/app/data/alignments/s1/bbil2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..bd1923e92b3cfacb73a4d8c589226837e31f1a0a
--- /dev/null
+++ b/app/data/alignments/s1/bbil2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbil3s.align b/app/data/alignments/s1/bbil3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..21a1e37bc47e3d0cfdedc34a7d9affc7efad7ccc
--- /dev/null
+++ b/app/data/alignments/s1/bbil3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbil4p.align b/app/data/alignments/s1/bbil4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..912586e51b994fade69e19075dd95802667321d0
--- /dev/null
+++ b/app/data/alignments/s1/bbil4p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbil5a.align b/app/data/alignments/s1/bbil5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..3cb8af13b935bd0d3b7ca42aa557cd51efacb4af
--- /dev/null
+++ b/app/data/alignments/s1/bbil5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbir6n.align b/app/data/alignments/s1/bbir6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..003427e159126377a00c30963df003a17b4892cf
--- /dev/null
+++ b/app/data/alignments/s1/bbir6n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbir7s.align b/app/data/alignments/s1/bbir7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..024665f1dc8a2c7ff6ef208c1c51ff2d714471cd
--- /dev/null
+++ b/app/data/alignments/s1/bbir7s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbir8p.align b/app/data/alignments/s1/bbir8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..2bde99ca5feca5c83ef4e38326042b491548a644
--- /dev/null
+++ b/app/data/alignments/s1/bbir8p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbir9a.align b/app/data/alignments/s1/bbir9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..6f258a2de087c367db024b3429222907287d19ea
--- /dev/null
+++ b/app/data/alignments/s1/bbir9a.align
@@ -0,0 +1,8 @@
+0 13250 sil
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diff --git a/app/data/alignments/s1/bbiz1s.align b/app/data/alignments/s1/bbiz1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..11c4f654025459b8257fa9d9cd657791da479af1
--- /dev/null
+++ b/app/data/alignments/s1/bbiz1s.align
@@ -0,0 +1,8 @@
+0 30750 sil
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diff --git a/app/data/alignments/s1/bbiz2p.align b/app/data/alignments/s1/bbiz2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..a05b2dfe0c608e47bb7c29c5f468d13ef94c5b33
--- /dev/null
+++ b/app/data/alignments/s1/bbiz2p.align
@@ -0,0 +1,9 @@
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diff --git a/app/data/alignments/s1/bbiz3a.align b/app/data/alignments/s1/bbiz3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..a827cd18845c174504e32be37ae032d7f55354ca
--- /dev/null
+++ b/app/data/alignments/s1/bbiz3a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbizzn.align b/app/data/alignments/s1/bbizzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..7ebe8b042a75c87328b382bbdd9f58f4517350ff
--- /dev/null
+++ b/app/data/alignments/s1/bbizzn.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbwg1s.align b/app/data/alignments/s1/bbwg1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..5c22935bc4a9436fc1e016679d7f0221a89b8763
--- /dev/null
+++ b/app/data/alignments/s1/bbwg1s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbwg2p.align b/app/data/alignments/s1/bbwg2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..624aa97de54e6d80acf4c6599149dc6459515cd5
--- /dev/null
+++ b/app/data/alignments/s1/bbwg2p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbwg3a.align b/app/data/alignments/s1/bbwg3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..1cee10a76785d36c3225df2026037e88eccaf9ea
--- /dev/null
+++ b/app/data/alignments/s1/bbwg3a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbwgzn.align b/app/data/alignments/s1/bbwgzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..feb6ec30a441a0fad7bbc11428a7ce6c4323b3ee
--- /dev/null
+++ b/app/data/alignments/s1/bbwgzn.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbwm4n.align b/app/data/alignments/s1/bbwm4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..7936f2e0e0670a51e08153544172bc778de76688
--- /dev/null
+++ b/app/data/alignments/s1/bbwm4n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbwm5s.align b/app/data/alignments/s1/bbwm5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..83fce8cacf804bc872b22d2651a82c997a2ff332
--- /dev/null
+++ b/app/data/alignments/s1/bbwm5s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbwm6p.align b/app/data/alignments/s1/bbwm6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..42667198ee507df6cae9b6069f2e2c6c0e4aaf8d
--- /dev/null
+++ b/app/data/alignments/s1/bbwm6p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbwm7a.align b/app/data/alignments/s1/bbwm7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..bb491d8fab74a601c7d69834027c8855901e3421
--- /dev/null
+++ b/app/data/alignments/s1/bbwm7a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbws8n.align b/app/data/alignments/s1/bbws8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..efcbcd67ba113229f494e92cc11c089a454c99a7
--- /dev/null
+++ b/app/data/alignments/s1/bbws8n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbws9s.align b/app/data/alignments/s1/bbws9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..4be52df4e34f6617f3f1d0a30ed896ca7ea846d6
--- /dev/null
+++ b/app/data/alignments/s1/bbws9s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbwt1a.align b/app/data/alignments/s1/bbwt1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..eeebcf090f0d359a237e67b56818f7974f2507ec
--- /dev/null
+++ b/app/data/alignments/s1/bbwt1a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bbwtzp.align b/app/data/alignments/s1/bbwtzp.align
new file mode 100644
index 0000000000000000000000000000000000000000..e091cc64151973fd5dda8e3ea3c2f7d7704278c9
--- /dev/null
+++ b/app/data/alignments/s1/bbwtzp.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgaa6n.align b/app/data/alignments/s1/bgaa6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..93eaed20b00e80633a18f9ac70426a20be59cb6c
--- /dev/null
+++ b/app/data/alignments/s1/bgaa6n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgaa7s.align b/app/data/alignments/s1/bgaa7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..f2fa6d02ae13996a1a3be8da731ef1406909516c
--- /dev/null
+++ b/app/data/alignments/s1/bgaa7s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgaa8p.align b/app/data/alignments/s1/bgaa8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..e7f0dd9547bb0d2dec9845dd6224622cfc35bd8f
--- /dev/null
+++ b/app/data/alignments/s1/bgaa8p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgaa9a.align b/app/data/alignments/s1/bgaa9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..9b038083c68f0c2b050e753f3b2078f6affc5cb0
--- /dev/null
+++ b/app/data/alignments/s1/bgaa9a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgah1s.align b/app/data/alignments/s1/bgah1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..a808901e5fb391d44b96b74d2e277540e2fa276a
--- /dev/null
+++ b/app/data/alignments/s1/bgah1s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgah2p.align b/app/data/alignments/s1/bgah2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..136ccb07b7d14e5173d818c5f65e7c8515923f08
--- /dev/null
+++ b/app/data/alignments/s1/bgah2p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgah3a.align b/app/data/alignments/s1/bgah3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..4b55203928f445f2e242a6c2cb8fa4d85999e3de
--- /dev/null
+++ b/app/data/alignments/s1/bgah3a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgahzn.align b/app/data/alignments/s1/bgahzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..dd3419c3d4682f256181e4014df84ad9d622685d
--- /dev/null
+++ b/app/data/alignments/s1/bgahzn.align
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diff --git a/app/data/alignments/s1/bgan4n.align b/app/data/alignments/s1/bgan4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..d32260cb47b71246a60df62169471ff203b481c9
--- /dev/null
+++ b/app/data/alignments/s1/bgan4n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgan5s.align b/app/data/alignments/s1/bgan5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..9f93efade8be0add47f5ea4bec43a8a9dedcb1d1
--- /dev/null
+++ b/app/data/alignments/s1/bgan5s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgan6p.align b/app/data/alignments/s1/bgan6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..47fb3cc47adb48419a78542f3a130d40fccfcfe4
--- /dev/null
+++ b/app/data/alignments/s1/bgan6p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgan7a.align b/app/data/alignments/s1/bgan7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..d617b7cfcc71c90f48f7c0040e83caa725bdccb8
--- /dev/null
+++ b/app/data/alignments/s1/bgan7a.align
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diff --git a/app/data/alignments/s1/bgat8n.align b/app/data/alignments/s1/bgat8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..cb03df8d11245990ed6531e95a9ca6a6a6d621a0
--- /dev/null
+++ b/app/data/alignments/s1/bgat8n.align
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diff --git a/app/data/alignments/s1/bgat9s.align b/app/data/alignments/s1/bgat9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..45eb0f3e4c5c5bc75d4ddbcee3e564e591dca63d
--- /dev/null
+++ b/app/data/alignments/s1/bgat9s.align
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diff --git a/app/data/alignments/s1/bgau1a.align b/app/data/alignments/s1/bgau1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..ff6547d8a5fcf041f897c5b4ed20107c88136871
--- /dev/null
+++ b/app/data/alignments/s1/bgau1a.align
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diff --git a/app/data/alignments/s1/bgauzp.align b/app/data/alignments/s1/bgauzp.align
new file mode 100644
index 0000000000000000000000000000000000000000..bc9169349837de14cd0dc176a25dca4f389edc78
--- /dev/null
+++ b/app/data/alignments/s1/bgauzp.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbb1s.align b/app/data/alignments/s1/bgbb1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..82b1bf450c95f8c413ae75a8a2efe35240c12c97
--- /dev/null
+++ b/app/data/alignments/s1/bgbb1s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbb2p.align b/app/data/alignments/s1/bgbb2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..f721914d8f767a976a539b997d8e42f919bcab8f
--- /dev/null
+++ b/app/data/alignments/s1/bgbb2p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbb3a.align b/app/data/alignments/s1/bgbb3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..7c3e6edbb44da03dcc3fb2ee4ab0e14a387d66a5
--- /dev/null
+++ b/app/data/alignments/s1/bgbb3a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbbzn.align b/app/data/alignments/s1/bgbbzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..d24d0c944e11a66d8f30f58f89b21c3cc6e33990
--- /dev/null
+++ b/app/data/alignments/s1/bgbbzn.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbh4n.align b/app/data/alignments/s1/bgbh4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..1e95c8a884bef9fb94e026173e5e5c40305386ef
--- /dev/null
+++ b/app/data/alignments/s1/bgbh4n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbh5s.align b/app/data/alignments/s1/bgbh5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..587cae29c8926b671dd6ed0482ce5b41104b2f75
--- /dev/null
+++ b/app/data/alignments/s1/bgbh5s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbh6p.align b/app/data/alignments/s1/bgbh6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..369c4a9833e88980ebc2d6b2ebcdad69c4c3fc95
--- /dev/null
+++ b/app/data/alignments/s1/bgbh6p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbh7a.align b/app/data/alignments/s1/bgbh7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..dd3c1cb5662b2eb5b185b3d3fb63face4c7d6ed8
--- /dev/null
+++ b/app/data/alignments/s1/bgbh7a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbn8n.align b/app/data/alignments/s1/bgbn8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..cd3ec2806ef272719d175f42095de616420d1b01
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+++ b/app/data/alignments/s1/bgbn8n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbn9s.align b/app/data/alignments/s1/bgbn9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..542257440b44b4f2fcf54ac9cd4e214eb4162129
--- /dev/null
+++ b/app/data/alignments/s1/bgbn9s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbo1a.align b/app/data/alignments/s1/bgbo1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..0bd18083fa9b6a3d6bcf32e9a4e3b74c818adc77
--- /dev/null
+++ b/app/data/alignments/s1/bgbo1a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbozp.align b/app/data/alignments/s1/bgbozp.align
new file mode 100644
index 0000000000000000000000000000000000000000..193a5fdfd8875206c35b1c31332bf8094cb59566
--- /dev/null
+++ b/app/data/alignments/s1/bgbozp.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbu2n.align b/app/data/alignments/s1/bgbu2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..a684fc797b58da9d713dd3386bfebb260565addf
--- /dev/null
+++ b/app/data/alignments/s1/bgbu2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbu3s.align b/app/data/alignments/s1/bgbu3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..77f30537691aba4c84ac8022953dc093773414dc
--- /dev/null
+++ b/app/data/alignments/s1/bgbu3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbu4p.align b/app/data/alignments/s1/bgbu4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..baf1a7557eb862b1fed6d83a76962e69c8946b37
--- /dev/null
+++ b/app/data/alignments/s1/bgbu4p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgbu5a.align b/app/data/alignments/s1/bgbu5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..e458274c11180dd5ccf84efe7e810395a7db04b0
--- /dev/null
+++ b/app/data/alignments/s1/bgbu5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgia2n.align b/app/data/alignments/s1/bgia2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..130009bef61a9ad04a185480ed706a0626e17a51
--- /dev/null
+++ b/app/data/alignments/s1/bgia2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgia3s.align b/app/data/alignments/s1/bgia3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..81b0b9b28b133a9cea80e896cf98bac8fd52c932
--- /dev/null
+++ b/app/data/alignments/s1/bgia3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgia4p.align b/app/data/alignments/s1/bgia4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..ed167aac5004453eed95376577d105f0a3c69b31
--- /dev/null
+++ b/app/data/alignments/s1/bgia4p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgia5a.align b/app/data/alignments/s1/bgia5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..fd434556a7f79675e458d90c6120b1677e1c7298
--- /dev/null
+++ b/app/data/alignments/s1/bgia5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgig6n.align b/app/data/alignments/s1/bgig6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..1efe860d72ae63c8c44ce735fb205192baf9fa7f
--- /dev/null
+++ b/app/data/alignments/s1/bgig6n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgig7s.align b/app/data/alignments/s1/bgig7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..6d1499e446edd331cca056022435c1276ea1217c
--- /dev/null
+++ b/app/data/alignments/s1/bgig7s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgig8p.align b/app/data/alignments/s1/bgig8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..fa2884a012300dc2b1a24c918e573c866a503c8d
--- /dev/null
+++ b/app/data/alignments/s1/bgig8p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgig9a.align b/app/data/alignments/s1/bgig9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..efb3ff33a9fa85cabdae8d2b09cf09bec764105b
--- /dev/null
+++ b/app/data/alignments/s1/bgig9a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgin1s.align b/app/data/alignments/s1/bgin1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..aa294ee776df44eb4fd9e622b9d2ed0ec045c387
--- /dev/null
+++ b/app/data/alignments/s1/bgin1s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgin2p.align b/app/data/alignments/s1/bgin2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..31e7db568d7c5f3c05a81a1b1387fcb8d7b89f59
--- /dev/null
+++ b/app/data/alignments/s1/bgin2p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgin3a.align b/app/data/alignments/s1/bgin3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..7a47522e316ea4c23cf6ead828ded5a4bcce5d94
--- /dev/null
+++ b/app/data/alignments/s1/bgin3a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bginzn.align b/app/data/alignments/s1/bginzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..93c79f06191d0a9f0988845788d8768ba7044130
--- /dev/null
+++ b/app/data/alignments/s1/bginzn.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgit4n.align b/app/data/alignments/s1/bgit4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..862cffa5f82a7bb4534f2511fdb98b5f1f2549da
--- /dev/null
+++ b/app/data/alignments/s1/bgit4n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgit5s.align b/app/data/alignments/s1/bgit5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..369a633373ce81e4a679c0d53b05542e9370bb45
--- /dev/null
+++ b/app/data/alignments/s1/bgit5s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgit6p.align b/app/data/alignments/s1/bgit6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..186d0dd53e4c5ddcaaf937a9f4eb9484edbbe69c
--- /dev/null
+++ b/app/data/alignments/s1/bgit6p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgit7a.align b/app/data/alignments/s1/bgit7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..b0eb304774d08fba90cd4201b710c596ee2dbf3d
--- /dev/null
+++ b/app/data/alignments/s1/bgit7a.align
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diff --git a/app/data/alignments/s1/bgwb4n.align b/app/data/alignments/s1/bgwb4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..9c1656d9bd57b40dc60c1e322d2a106cab6f0319
--- /dev/null
+++ b/app/data/alignments/s1/bgwb4n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgwb5s.align b/app/data/alignments/s1/bgwb5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..347a6c0fcb8782ca39869666b4cfb010243c4bce
--- /dev/null
+++ b/app/data/alignments/s1/bgwb5s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgwb6p.align b/app/data/alignments/s1/bgwb6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..0f7721ef17052f0ba19aa79b9db8add9bfa77958
--- /dev/null
+++ b/app/data/alignments/s1/bgwb6p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgwb7a.align b/app/data/alignments/s1/bgwb7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..3ebc2029fd44f479b78a18a3693e88fbcb75ff45
--- /dev/null
+++ b/app/data/alignments/s1/bgwb7a.align
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diff --git a/app/data/alignments/s1/bgwh8n.align b/app/data/alignments/s1/bgwh8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..e235d36d81ad421104ed066f509a1c0e086005ad
--- /dev/null
+++ b/app/data/alignments/s1/bgwh8n.align
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diff --git a/app/data/alignments/s1/bgwh9s.align b/app/data/alignments/s1/bgwh9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..3ca6fd1f84d98da4bfdf0bf50bcb3ede7ade04d9
--- /dev/null
+++ b/app/data/alignments/s1/bgwh9s.align
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diff --git a/app/data/alignments/s1/bgwi1a.align b/app/data/alignments/s1/bgwi1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..32af67e09d49d8650bea013cbd7008778f2d9e95
--- /dev/null
+++ b/app/data/alignments/s1/bgwi1a.align
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diff --git a/app/data/alignments/s1/bgwizp.align b/app/data/alignments/s1/bgwizp.align
new file mode 100644
index 0000000000000000000000000000000000000000..3d7aab044d71821fd6b0b24a9f1e5bacb7164020
--- /dev/null
+++ b/app/data/alignments/s1/bgwizp.align
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diff --git a/app/data/alignments/s1/bgwo2n.align b/app/data/alignments/s1/bgwo2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..08a09a84fcb50d5e33bbe843940ec453f0a94128
--- /dev/null
+++ b/app/data/alignments/s1/bgwo2n.align
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diff --git a/app/data/alignments/s1/bgwo3s.align b/app/data/alignments/s1/bgwo3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..35dc963f82d5166ec07039917abbd36a1ba68468
--- /dev/null
+++ b/app/data/alignments/s1/bgwo3s.align
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diff --git a/app/data/alignments/s1/bgwo4p.align b/app/data/alignments/s1/bgwo4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..efc12974456b2d75772daab312dd595faaae2097
--- /dev/null
+++ b/app/data/alignments/s1/bgwo4p.align
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diff --git a/app/data/alignments/s1/bgwo5a.align b/app/data/alignments/s1/bgwo5a.align
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index 0000000000000000000000000000000000000000..a2df948b6287003a91a238a52fae4e519789601c
--- /dev/null
+++ b/app/data/alignments/s1/bgwo5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bgwu6n.align b/app/data/alignments/s1/bgwu6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..18a1016428ff643ceab1f5d3a3b678651cf04477
--- /dev/null
+++ b/app/data/alignments/s1/bgwu6n.align
@@ -0,0 +1,8 @@
+0 16500 sil
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diff --git a/app/data/alignments/s1/bgwu7s.align b/app/data/alignments/s1/bgwu7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..33575d3f9148d7fce2d81f9d0e7876ceff703ba6
--- /dev/null
+++ b/app/data/alignments/s1/bgwu7s.align
@@ -0,0 +1,8 @@
+0 12000 sil
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diff --git a/app/data/alignments/s1/bgwu8p.align b/app/data/alignments/s1/bgwu8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..ca00f9090417c00903afdaa5487c3451698a2368
--- /dev/null
+++ b/app/data/alignments/s1/bgwu8p.align
@@ -0,0 +1,8 @@
+0 15250 sil
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diff --git a/app/data/alignments/s1/bgwu9a.align b/app/data/alignments/s1/bgwu9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..c5196bf8cc4e96dc3530e0e23d0a0f5cc151b548
--- /dev/null
+++ b/app/data/alignments/s1/bgwu9a.align
@@ -0,0 +1,8 @@
+0 19750 sil
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diff --git a/app/data/alignments/s1/braf8n.align b/app/data/alignments/s1/braf8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..a64dbe01f6002cb26914f951e82f7bec34e9b33a
--- /dev/null
+++ b/app/data/alignments/s1/braf8n.align
@@ -0,0 +1,8 @@
+0 16750 sil
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diff --git a/app/data/alignments/s1/braf9s.align b/app/data/alignments/s1/braf9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..30b07cff71eb978aa6a620cce126e9e1f6ec6041
--- /dev/null
+++ b/app/data/alignments/s1/braf9s.align
@@ -0,0 +1,8 @@
+0 17250 sil
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diff --git a/app/data/alignments/s1/brag1a.align b/app/data/alignments/s1/brag1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..ae37bda48d627c97dc4d41eb6cbdbfecb9bea8c9
--- /dev/null
+++ b/app/data/alignments/s1/brag1a.align
@@ -0,0 +1,8 @@
+0 15000 sil
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diff --git a/app/data/alignments/s1/bragzp.align b/app/data/alignments/s1/bragzp.align
new file mode 100644
index 0000000000000000000000000000000000000000..55e011e9d2f470f172484e2e948c2bb1ed3ee4be
--- /dev/null
+++ b/app/data/alignments/s1/bragzp.align
@@ -0,0 +1,8 @@
+0 18250 sil
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diff --git a/app/data/alignments/s1/bram2n.align b/app/data/alignments/s1/bram2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..aac27b3b88070d262a9f46e9e4c40ec2eeced364
--- /dev/null
+++ b/app/data/alignments/s1/bram2n.align
@@ -0,0 +1,8 @@
+0 13000 sil
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diff --git a/app/data/alignments/s1/bram3s.align b/app/data/alignments/s1/bram3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..73b7cda2e6538469a8c8cba5430f1cc900629916
--- /dev/null
+++ b/app/data/alignments/s1/bram3s.align
@@ -0,0 +1,8 @@
+0 13000 sil
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diff --git a/app/data/alignments/s1/bram4p.align b/app/data/alignments/s1/bram4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..5268a3f18b1b22f7ea13b53a3df291a4abb017c9
--- /dev/null
+++ b/app/data/alignments/s1/bram4p.align
@@ -0,0 +1,8 @@
+0 21500 sil
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diff --git a/app/data/alignments/s1/bram5a.align b/app/data/alignments/s1/bram5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..d5a07f91b9526dee5e3c34cf58ed1a7a3b4782b9
--- /dev/null
+++ b/app/data/alignments/s1/bram5a.align
@@ -0,0 +1,8 @@
+0 17500 sil
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diff --git a/app/data/alignments/s1/bras6n.align b/app/data/alignments/s1/bras6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..35e1859715b1e4f9ab67b9592036b73c0538f9f7
--- /dev/null
+++ b/app/data/alignments/s1/bras6n.align
@@ -0,0 +1,8 @@
+0 9750 sil
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diff --git a/app/data/alignments/s1/bras7s.align b/app/data/alignments/s1/bras7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..4f23de801d2508377f589696c178fcb9933efa87
--- /dev/null
+++ b/app/data/alignments/s1/bras7s.align
@@ -0,0 +1,8 @@
+0 19250 sil
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diff --git a/app/data/alignments/s1/bras8p.align b/app/data/alignments/s1/bras8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..f0f666d6bf81c7ca81040fe3cde66389a2bf2a26
--- /dev/null
+++ b/app/data/alignments/s1/bras8p.align
@@ -0,0 +1,9 @@
+0 23250 sil
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diff --git a/app/data/alignments/s1/bras9a.align b/app/data/alignments/s1/bras9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..0cdf9a9a59468a0aa6d9cfa9879bcbb5e99d9202
--- /dev/null
+++ b/app/data/alignments/s1/bras9a.align
@@ -0,0 +1,8 @@
+0 15750 sil
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diff --git a/app/data/alignments/s1/brba1a.align b/app/data/alignments/s1/brba1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..c67913b3e9ae3a7b3691b2408ef50f09701a27e4
--- /dev/null
+++ b/app/data/alignments/s1/brba1a.align
@@ -0,0 +1,8 @@
+0 18000 sil
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diff --git a/app/data/alignments/s1/brbazp.align b/app/data/alignments/s1/brbazp.align
new file mode 100644
index 0000000000000000000000000000000000000000..90c00c7d764d0315e3204c54c082f5036aee942a
--- /dev/null
+++ b/app/data/alignments/s1/brbazp.align
@@ -0,0 +1,8 @@
+0 17500 sil
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diff --git a/app/data/alignments/s1/brbg2n.align b/app/data/alignments/s1/brbg2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..28e0b7e9b2226728c704165f807535cdace5dae7
--- /dev/null
+++ b/app/data/alignments/s1/brbg2n.align
@@ -0,0 +1,9 @@
+0 26000 sil
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diff --git a/app/data/alignments/s1/brbg3s.align b/app/data/alignments/s1/brbg3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..197a71dde0d6207910cfa31beb16c0063ae24e5b
--- /dev/null
+++ b/app/data/alignments/s1/brbg3s.align
@@ -0,0 +1,8 @@
+0 16500 sil
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diff --git a/app/data/alignments/s1/brbg4p.align b/app/data/alignments/s1/brbg4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..38944a4a1b332f3dd677832a17bf705d7ade6732
--- /dev/null
+++ b/app/data/alignments/s1/brbg4p.align
@@ -0,0 +1,8 @@
+0 15500 sil
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diff --git a/app/data/alignments/s1/brbg5a.align b/app/data/alignments/s1/brbg5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..1518c6178224678341fb97f0749bdccc5e6193a9
--- /dev/null
+++ b/app/data/alignments/s1/brbg5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brbm6n.align b/app/data/alignments/s1/brbm6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..0840778a610acfd2f7b4706306821983f30f5178
--- /dev/null
+++ b/app/data/alignments/s1/brbm6n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brbm7s.align b/app/data/alignments/s1/brbm7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..0f971df49614104975093a436af44ef968d99188
--- /dev/null
+++ b/app/data/alignments/s1/brbm7s.align
@@ -0,0 +1,8 @@
+0 13500 sil
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diff --git a/app/data/alignments/s1/brbm8p.align b/app/data/alignments/s1/brbm8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..2198408cfc4236e7b159873be1362c5c090c761a
--- /dev/null
+++ b/app/data/alignments/s1/brbm8p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brbm9a.align b/app/data/alignments/s1/brbm9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..647850b76e1dc9d87f829347a7fa7e5af5fd9200
--- /dev/null
+++ b/app/data/alignments/s1/brbm9a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brbt1s.align b/app/data/alignments/s1/brbt1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..2830d661f15554cb8e033cf0b41016d365689568
--- /dev/null
+++ b/app/data/alignments/s1/brbt1s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brbt2p.align b/app/data/alignments/s1/brbt2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..aababc98f7c0616586948920261fcf785d6bf582
--- /dev/null
+++ b/app/data/alignments/s1/brbt2p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brbt3a.align b/app/data/alignments/s1/brbt3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..8e50ff2214d9bb821fd01fd32b2d3b04b64f3fcc
--- /dev/null
+++ b/app/data/alignments/s1/brbt3a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brbtzn.align b/app/data/alignments/s1/brbtzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..508ab1daccdb9010432c7ef6d92625d61a6a62bb
--- /dev/null
+++ b/app/data/alignments/s1/brbtzn.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brif4n.align b/app/data/alignments/s1/brif4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..362c1f2f5cf41389124ec74f4a94abcb98665647
--- /dev/null
+++ b/app/data/alignments/s1/brif4n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brif5s.align b/app/data/alignments/s1/brif5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..509ac25e14902ca7b2bc6ed96df70cb932de6295
--- /dev/null
+++ b/app/data/alignments/s1/brif5s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brif6p.align b/app/data/alignments/s1/brif6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..f676f4329a2958728829a55200034c8de604acf1
--- /dev/null
+++ b/app/data/alignments/s1/brif6p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brif7a.align b/app/data/alignments/s1/brif7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..b7fc6fb4c095b2d2c5d99b5ed3fb32f7b57e5d8d
--- /dev/null
+++ b/app/data/alignments/s1/brif7a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bril8n.align b/app/data/alignments/s1/bril8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..6ffbe60abc79db4cf33215bb4131bfb0c39ad49b
--- /dev/null
+++ b/app/data/alignments/s1/bril8n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bril9s.align b/app/data/alignments/s1/bril9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..faba49da5cac72893ae33d7b92c4a253d06d05b7
--- /dev/null
+++ b/app/data/alignments/s1/bril9s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brim1a.align b/app/data/alignments/s1/brim1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..20d1b8dad62521b9b4b9d9a9bf1f4c9dbeabb9df
--- /dev/null
+++ b/app/data/alignments/s1/brim1a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brimzp.align b/app/data/alignments/s1/brimzp.align
new file mode 100644
index 0000000000000000000000000000000000000000..af7d4b9c800cb7ce8d3c55c819d142133c3b85ec
--- /dev/null
+++ b/app/data/alignments/s1/brimzp.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bris2n.align b/app/data/alignments/s1/bris2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..2afb3f7341e739541fbe0cf4c54ef70ba2068fc0
--- /dev/null
+++ b/app/data/alignments/s1/bris2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bris3s.align b/app/data/alignments/s1/bris3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..60f93086e803f8df99bcf92a3969e8959105621b
--- /dev/null
+++ b/app/data/alignments/s1/bris3s.align
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diff --git a/app/data/alignments/s1/bris4p.align b/app/data/alignments/s1/bris4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..0681c9a87424ad35471e47c906ffa7462819da13
--- /dev/null
+++ b/app/data/alignments/s1/bris4p.align
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diff --git a/app/data/alignments/s1/bris5a.align b/app/data/alignments/s1/bris5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..3de3665eaa3facb2047eff40b9360368de92e228
--- /dev/null
+++ b/app/data/alignments/s1/bris5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/briz6n.align b/app/data/alignments/s1/briz6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..61e0787293c408e9e82844e5dc0af2365fd91606
--- /dev/null
+++ b/app/data/alignments/s1/briz6n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/briz7s.align b/app/data/alignments/s1/briz7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..830c3628a985ebb93249dd023710f28d7d9160ce
--- /dev/null
+++ b/app/data/alignments/s1/briz7s.align
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diff --git a/app/data/alignments/s1/briz8p.align b/app/data/alignments/s1/briz8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..cce9472e57f3f63b402f8866158f29caa9a0c66e
--- /dev/null
+++ b/app/data/alignments/s1/briz8p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/briz9a.align b/app/data/alignments/s1/briz9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..41a86b1a5389a071e1efdc608fe43266db0e8d5e
--- /dev/null
+++ b/app/data/alignments/s1/briz9a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwa2n.align b/app/data/alignments/s1/brwa2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..6a8f788b9d682cfe1a3a362e2f01e24259bc2a04
--- /dev/null
+++ b/app/data/alignments/s1/brwa2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwa3s.align b/app/data/alignments/s1/brwa3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..acdd80d16690d666e7dd0aa465e808530ac0a92e
--- /dev/null
+++ b/app/data/alignments/s1/brwa3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwa4p.align b/app/data/alignments/s1/brwa4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..59a2beda801b9dca274fc8a7186e01d71badd693
--- /dev/null
+++ b/app/data/alignments/s1/brwa4p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwa5a.align b/app/data/alignments/s1/brwa5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..50436528d23bce5d33ce6e5fb7e5933827cd5e4b
--- /dev/null
+++ b/app/data/alignments/s1/brwa5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwg6n.align b/app/data/alignments/s1/brwg6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..eede88d392906ea62ef811735c016c003b902d07
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+++ b/app/data/alignments/s1/brwg6n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwg7s.align b/app/data/alignments/s1/brwg7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..f5095b7be6eab5363f2a34b8c286a558072daf71
--- /dev/null
+++ b/app/data/alignments/s1/brwg7s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwg8p.align b/app/data/alignments/s1/brwg8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..aae82faba2b246020a24e82f1d1f5646d7b62cb5
--- /dev/null
+++ b/app/data/alignments/s1/brwg8p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwg9a.align b/app/data/alignments/s1/brwg9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..2428814030507c23fd68d217244cb7908d3afc56
--- /dev/null
+++ b/app/data/alignments/s1/brwg9a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwn1s.align b/app/data/alignments/s1/brwn1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..46ade07f045d6d7d9b0846d5406ccf57c068890e
--- /dev/null
+++ b/app/data/alignments/s1/brwn1s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwn2p.align b/app/data/alignments/s1/brwn2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..b123c815e83aabf346e4978a02fbbf4a99004959
--- /dev/null
+++ b/app/data/alignments/s1/brwn2p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwn3a.align b/app/data/alignments/s1/brwn3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..b04b97500db80821d526a64558c563501aa9b6db
--- /dev/null
+++ b/app/data/alignments/s1/brwn3a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwnzn.align b/app/data/alignments/s1/brwnzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..d85d2466a3f7da5400530d3a27e16460574687db
--- /dev/null
+++ b/app/data/alignments/s1/brwnzn.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwt4n.align b/app/data/alignments/s1/brwt4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..6f6137339a78db678c3c2ed2556ed9b0f677a0bd
--- /dev/null
+++ b/app/data/alignments/s1/brwt4n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwt5s.align b/app/data/alignments/s1/brwt5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..b72bd7fc97263bdbb6536ea35f7b8dc01d3e273a
--- /dev/null
+++ b/app/data/alignments/s1/brwt5s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwt6p.align b/app/data/alignments/s1/brwt6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..1d455104bd03e20399bd0afbd999380ff608f0a8
--- /dev/null
+++ b/app/data/alignments/s1/brwt6p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/brwt7a.align b/app/data/alignments/s1/brwt7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..8d647ba9aa16c52140169d1036fa06bb77332fe5
--- /dev/null
+++ b/app/data/alignments/s1/brwt7a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwaa1s.align b/app/data/alignments/s1/bwaa1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..f1f640ddfdedf1e9c712a17f0a563792ed26e80c
--- /dev/null
+++ b/app/data/alignments/s1/bwaa1s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwaa2p.align b/app/data/alignments/s1/bwaa2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..6426920f44fc603e129fe53e131066cb573dced6
--- /dev/null
+++ b/app/data/alignments/s1/bwaa2p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwaa3a.align b/app/data/alignments/s1/bwaa3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..93b4cf6bc84a4d3eab3b25887ce8156e8f293e9b
--- /dev/null
+++ b/app/data/alignments/s1/bwaa3a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwaazn.align b/app/data/alignments/s1/bwaazn.align
new file mode 100644
index 0000000000000000000000000000000000000000..669d84fdf8d27c26484929bb84c4b5f39c1da1c5
--- /dev/null
+++ b/app/data/alignments/s1/bwaazn.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwag4n.align b/app/data/alignments/s1/bwag4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..4b9482295d592df34d166a5809ca273c4da08f90
--- /dev/null
+++ b/app/data/alignments/s1/bwag4n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwag5s.align b/app/data/alignments/s1/bwag5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..8cefef930301d91bc26c63c7cb1909aa9a375fce
--- /dev/null
+++ b/app/data/alignments/s1/bwag5s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwag6p.align b/app/data/alignments/s1/bwag6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..cd04f115530aa710a89ef2520932e2042f3c749e
--- /dev/null
+++ b/app/data/alignments/s1/bwag6p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwag7a.align b/app/data/alignments/s1/bwag7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..c83f9c02e6287a4f9db84822eada14273fe0288f
--- /dev/null
+++ b/app/data/alignments/s1/bwag7a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwam8n.align b/app/data/alignments/s1/bwam8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..ba7240f78b9aa120575a91978044f71fd90d715d
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+++ b/app/data/alignments/s1/bwam8n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwam9s.align b/app/data/alignments/s1/bwam9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..80c25ef72ac5910f354fa2d87f5bcd669d83f427
--- /dev/null
+++ b/app/data/alignments/s1/bwam9s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwan1a.align b/app/data/alignments/s1/bwan1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..a4517d8e9fbb1f43032e87878925ecc0e6e3b968
--- /dev/null
+++ b/app/data/alignments/s1/bwan1a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwanzp.align b/app/data/alignments/s1/bwanzp.align
new file mode 100644
index 0000000000000000000000000000000000000000..5ecb71d66012a1e4715e21b325293261e937c8be
--- /dev/null
+++ b/app/data/alignments/s1/bwanzp.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwat2n.align b/app/data/alignments/s1/bwat2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..796c16b45bf1c96d89e0f916aa9908cfc4f45ab5
--- /dev/null
+++ b/app/data/alignments/s1/bwat2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwat3s.align b/app/data/alignments/s1/bwat3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..651b81dfbfa915bc3107fa9fc188c963a9f8487d
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+++ b/app/data/alignments/s1/bwat3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/bwat4p.align b/app/data/alignments/s1/bwat4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..cfd05163ad9167e071e8c24665b57a5195973411
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+++ b/app/data/alignments/s1/bwat4p.align
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diff --git a/app/data/alignments/s1/bwat5a.align b/app/data/alignments/s1/bwat5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..7c4fe07820dbc88ef2c24ad7fb274ee5b039e792
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+++ b/app/data/alignments/s1/bwat5a.align
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diff --git a/app/data/alignments/s1/bwba4n.align b/app/data/alignments/s1/bwba4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..98de57480972179e094de8765b02b56a758425c2
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+++ b/app/data/alignments/s1/bwba4n.align
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diff --git a/app/data/alignments/s1/bwba5s.align b/app/data/alignments/s1/bwba5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..14004269ab82e7ba85d29c448cdf13c052ea5999
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+++ b/app/data/alignments/s1/bwba5s.align
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diff --git a/app/data/alignments/s1/bwba6p.align b/app/data/alignments/s1/bwba6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..0ac58984717892798da3d55f3470d53afd26b46d
--- /dev/null
+++ b/app/data/alignments/s1/bwba6p.align
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diff --git a/app/data/alignments/s1/bwba7a.align b/app/data/alignments/s1/bwba7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..c46cd12e00e4df90c02ed31da5f37ed0c03cd306
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new file mode 100644
index 0000000000000000000000000000000000000000..50efa2ffd4f37adbcff74b0f0c816104e86f5c20
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new file mode 100644
index 0000000000000000000000000000000000000000..e525ad70e86707fd030d99c7d4426d037bf43bc6
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diff --git a/app/data/alignments/s1/bwbh1a.align b/app/data/alignments/s1/bwbh1a.align
new file mode 100644
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+++ b/app/data/alignments/s1/bwbh1a.align
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diff --git a/app/data/alignments/s1/bwbhzp.align b/app/data/alignments/s1/bwbhzp.align
new file mode 100644
index 0000000000000000000000000000000000000000..37e640d6697c2b9564def1444b8c192251c65429
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+++ b/app/data/alignments/s1/bwbhzp.align
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diff --git a/app/data/alignments/s1/bwbn2n.align b/app/data/alignments/s1/bwbn2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..38f8fcdbd37653e74478dc2840a5dd59e0758762
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+++ b/app/data/alignments/s1/bwbn2n.align
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diff --git a/app/data/alignments/s1/bwbn3s.align b/app/data/alignments/s1/bwbn3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..c240c41e48280199cb60cea68aa3281489c7ef60
--- /dev/null
+++ b/app/data/alignments/s1/bwbn3s.align
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diff --git a/app/data/alignments/s1/bwbn4p.align b/app/data/alignments/s1/bwbn4p.align
new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..8bad623d4aa234964fd9a3abc98017950b6995a0
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+++ b/app/data/alignments/s1/bwbn5a.align
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+++ b/app/data/alignments/s1/bwbt7s.align
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+++ b/app/data/alignments/s1/bwbt8p.align
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new file mode 100644
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+++ b/app/data/alignments/s1/bwbt9a.align
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index 0000000000000000000000000000000000000000..a8d58d550dd3e70cf7e1124610cda2ea0daf562f
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+++ b/app/data/alignments/s1/bwig2p.align
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index 0000000000000000000000000000000000000000..deda158fb6f8de0d13ee1c62fc677c0359ec042e
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diff --git a/app/data/alignments/s1/bwitzp.align b/app/data/alignments/s1/bwitzp.align
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index 0000000000000000000000000000000000000000..1b630fda3f464478aee4ca14b26a38943617f13e
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+++ b/app/data/alignments/s1/bwitzp.align
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diff --git a/app/data/alignments/s1/bwwa8n.align b/app/data/alignments/s1/bwwa8n.align
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index 0000000000000000000000000000000000000000..b9e6eb785bc664b89f4b520bc7b791663462dec3
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+++ b/app/data/alignments/s1/bwwa9s.align
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+++ b/app/data/alignments/s1/bwwh2n.align
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diff --git a/app/data/alignments/s1/lbaq7a.align b/app/data/alignments/s1/lbaq7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..fa16f2fc310c496f62d356f591fdc2cb4242b41b
--- /dev/null
+++ b/app/data/alignments/s1/lbaq7a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbax8n.align b/app/data/alignments/s1/lbax8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..cbb6d4203485552a5304a51e211107565079b87e
--- /dev/null
+++ b/app/data/alignments/s1/lbax8n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbax9s.align b/app/data/alignments/s1/lbax9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..fa101fd979fd60e6b816db81b80149829ea80699
--- /dev/null
+++ b/app/data/alignments/s1/lbax9s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbay1a.align b/app/data/alignments/s1/lbay1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..03d2860eddb0bde5991e163be80810b83f29dbe9
--- /dev/null
+++ b/app/data/alignments/s1/lbay1a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbayzp.align b/app/data/alignments/s1/lbayzp.align
new file mode 100644
index 0000000000000000000000000000000000000000..8ea08785619918a26cf0991a53d1724aadb608cf
--- /dev/null
+++ b/app/data/alignments/s1/lbayzp.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbbe1s.align b/app/data/alignments/s1/lbbe1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..a9f30fb2e02be393b88bb906fd790c7f25953e06
--- /dev/null
+++ b/app/data/alignments/s1/lbbe1s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbbe2p.align b/app/data/alignments/s1/lbbe2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..71c34cb2363cde0ca361b6d12acbdd4354844753
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+++ b/app/data/alignments/s1/lbbe2p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbbe3a.align b/app/data/alignments/s1/lbbe3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..af083864a03f6cbff6f6d2f172af05f5e510dcfd
--- /dev/null
+++ b/app/data/alignments/s1/lbbe3a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbbezn.align b/app/data/alignments/s1/lbbezn.align
new file mode 100644
index 0000000000000000000000000000000000000000..3a89f0eddbf929b46c5d78de150f47013b7bf361
--- /dev/null
+++ b/app/data/alignments/s1/lbbezn.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbbk4n.align b/app/data/alignments/s1/lbbk4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..0299f6190d116721e04b14ff5e9eafc118aa7405
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+++ b/app/data/alignments/s1/lbbk4n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbbk5s.align b/app/data/alignments/s1/lbbk5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..1dedf47caf5d6ef0ae283754dbe6548c4ea034be
--- /dev/null
+++ b/app/data/alignments/s1/lbbk5s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbbk6p.align b/app/data/alignments/s1/lbbk6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..5197d55c6c399dd258c8d1b910dac4f7246c403e
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+++ b/app/data/alignments/s1/lbbk6p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbbk7a.align b/app/data/alignments/s1/lbbk7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..d7cfe65ab79f8919b61be98a20c07c577a97d753
--- /dev/null
+++ b/app/data/alignments/s1/lbbk7a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbbq8n.align b/app/data/alignments/s1/lbbq8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..b7d45055bd27a829075f5c5dc19a6eb877c4d86d
--- /dev/null
+++ b/app/data/alignments/s1/lbbq8n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbbq9s.align b/app/data/alignments/s1/lbbq9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..d245d4aabc8b02a5115658624354b85a5cfb8c13
--- /dev/null
+++ b/app/data/alignments/s1/lbbq9s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbbr1a.align b/app/data/alignments/s1/lbbr1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..f159ad51c4b4ce81a75ca80696eb787030d0437a
--- /dev/null
+++ b/app/data/alignments/s1/lbbr1a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbbrzp.align b/app/data/alignments/s1/lbbrzp.align
new file mode 100644
index 0000000000000000000000000000000000000000..fc45ca79df86a1f3922a135e913c4079e16d526e
--- /dev/null
+++ b/app/data/alignments/s1/lbbrzp.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbby2n.align b/app/data/alignments/s1/lbby2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..665f712b6ddb51643f00f9d88739b4b89d4036c9
--- /dev/null
+++ b/app/data/alignments/s1/lbby2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbby3s.align b/app/data/alignments/s1/lbby3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..b35c61336d6a818eb906672555f1c885904345a6
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+++ b/app/data/alignments/s1/lbby3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbby4p.align b/app/data/alignments/s1/lbby4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..7a9039e01c77b340fe5014161e13636766de5c03
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+++ b/app/data/alignments/s1/lbby4p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbby5a.align b/app/data/alignments/s1/lbby5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..ebe791badc21c37b42fa68949490449c81510f70
--- /dev/null
+++ b/app/data/alignments/s1/lbby5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbid2n.align b/app/data/alignments/s1/lbid2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..e4a99dcd101edf90af839a92fc5d4c7ba55a3735
--- /dev/null
+++ b/app/data/alignments/s1/lbid2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbid3s.align b/app/data/alignments/s1/lbid3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..30e2c27a0542b81a0cbd945abebba75c2a97d3e4
--- /dev/null
+++ b/app/data/alignments/s1/lbid3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbid4p.align b/app/data/alignments/s1/lbid4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..6f100cbe3efad20e77a0a1f7def98d1ab1016ab0
--- /dev/null
+++ b/app/data/alignments/s1/lbid4p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbid5a.align b/app/data/alignments/s1/lbid5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..0db1c54a28f86372ce12aa57e7ab7da424b88fdd
--- /dev/null
+++ b/app/data/alignments/s1/lbid5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbij6n.align b/app/data/alignments/s1/lbij6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..46fd915bdab46071a6f786e1af449c97edfa2dda
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+++ b/app/data/alignments/s1/lbij6n.align
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diff --git a/app/data/alignments/s1/lbij7s.align b/app/data/alignments/s1/lbij7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..d3ae7e219cf803438b838a736f474483ccba733a
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+++ b/app/data/alignments/s1/lbij7s.align
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diff --git a/app/data/alignments/s1/lbij8p.align b/app/data/alignments/s1/lbij8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..378de3ded7bf9ecce7e03fc5e36e229d09d0c09e
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+++ b/app/data/alignments/s1/lbij8p.align
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diff --git a/app/data/alignments/s1/lbij9a.align b/app/data/alignments/s1/lbij9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..b06e77bc42cbc5cf6f8700e5311de74ad2819592
--- /dev/null
+++ b/app/data/alignments/s1/lbij9a.align
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diff --git a/app/data/alignments/s1/lbiq1s.align b/app/data/alignments/s1/lbiq1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..02bb3631f39be66ae281b3ce2b5f5fc719ec21de
--- /dev/null
+++ b/app/data/alignments/s1/lbiq1s.align
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diff --git a/app/data/alignments/s1/lbiq2p.align b/app/data/alignments/s1/lbiq2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..b92c842c20e9a1a52c5dee7d061bce6592b5dc9c
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+++ b/app/data/alignments/s1/lbiq2p.align
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diff --git a/app/data/alignments/s1/lbiq3a.align b/app/data/alignments/s1/lbiq3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..a86b45de3b1ce8a87e63c94af25871e7db755c98
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+++ b/app/data/alignments/s1/lbiq3a.align
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diff --git a/app/data/alignments/s1/lbiqzn.align b/app/data/alignments/s1/lbiqzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..b473d3b13eadf427628b5643f1cea61f6495eec9
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+++ b/app/data/alignments/s1/lbiqzn.align
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diff --git a/app/data/alignments/s1/lbix4n.align b/app/data/alignments/s1/lbix4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..da5d6f75f8cfb3f5fe3cc45f1400aa801291a0a8
--- /dev/null
+++ b/app/data/alignments/s1/lbix4n.align
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diff --git a/app/data/alignments/s1/lbix5s.align b/app/data/alignments/s1/lbix5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..70779dcea629143ab67193e35654ef1cb9f0203e
--- /dev/null
+++ b/app/data/alignments/s1/lbix5s.align
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diff --git a/app/data/alignments/s1/lbix6p.align b/app/data/alignments/s1/lbix6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..6556e527456497357decd80109d56495194c0402
--- /dev/null
+++ b/app/data/alignments/s1/lbix6p.align
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diff --git a/app/data/alignments/s1/lbix7a.align b/app/data/alignments/s1/lbix7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..314cec86c9e32551ee2198c3d120e5d22d461102
--- /dev/null
+++ b/app/data/alignments/s1/lbix7a.align
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diff --git a/app/data/alignments/s1/lbwe4n.align b/app/data/alignments/s1/lbwe4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..9094e99b8613d85fdd9f19b276b99da50cca3af3
--- /dev/null
+++ b/app/data/alignments/s1/lbwe4n.align
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diff --git a/app/data/alignments/s1/lbwe5s.align b/app/data/alignments/s1/lbwe5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..fb07c00003c1f009b4ec1971c5dff232fe2364f0
--- /dev/null
+++ b/app/data/alignments/s1/lbwe5s.align
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diff --git a/app/data/alignments/s1/lbwe6p.align b/app/data/alignments/s1/lbwe6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..a6efb8d4973dbdf68be6e3f16c40e888395f0e31
--- /dev/null
+++ b/app/data/alignments/s1/lbwe6p.align
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diff --git a/app/data/alignments/s1/lbwe7a.align b/app/data/alignments/s1/lbwe7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..5e9d47c242a12604dfb88fa4bc2e1f2c2ef09a59
--- /dev/null
+++ b/app/data/alignments/s1/lbwe7a.align
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diff --git a/app/data/alignments/s1/lbwk8n.align b/app/data/alignments/s1/lbwk8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..39f0e625f79162d6063e782146fcafa172cb0bed
--- /dev/null
+++ b/app/data/alignments/s1/lbwk8n.align
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diff --git a/app/data/alignments/s1/lbwk9s.align b/app/data/alignments/s1/lbwk9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..73bf6214f7b4436277d2c549fce21b0fe66f518a
--- /dev/null
+++ b/app/data/alignments/s1/lbwk9s.align
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diff --git a/app/data/alignments/s1/lbwl1a.align b/app/data/alignments/s1/lbwl1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..cd566dd705b6a6591002a74361840fd84c2eb200
--- /dev/null
+++ b/app/data/alignments/s1/lbwl1a.align
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diff --git a/app/data/alignments/s1/lbwlzp.align b/app/data/alignments/s1/lbwlzp.align
new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..55bb374331ae3395c52ff7264832068e9d3230e8
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new file mode 100644
index 0000000000000000000000000000000000000000..7d1f670bcbf1929bbf192cd98e97e7ae064bcc17
--- /dev/null
+++ b/app/data/alignments/s1/lbwr3s.align
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new file mode 100644
index 0000000000000000000000000000000000000000..0a6d909781f0698e5dd2bcc32148522a9c62cd4e
--- /dev/null
+++ b/app/data/alignments/s1/lbwr4p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbwr5a.align b/app/data/alignments/s1/lbwr5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..021f650c90abf1d9963f074b0fb0c4df361e91ec
--- /dev/null
+++ b/app/data/alignments/s1/lbwr5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lbwy6n.align b/app/data/alignments/s1/lbwy6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..5a9a1768181696e64581f631b9c4542017d2c4e6
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+++ b/app/data/alignments/s1/lbwy6n.align
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new file mode 100644
index 0000000000000000000000000000000000000000..4ff2dbd4b9a5def1903d96ab5ba652ba8678569c
--- /dev/null
+++ b/app/data/alignments/s1/lbwy7s.align
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+++ b/app/data/alignments/s1/lbwy8p.align
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diff --git a/app/data/alignments/s1/lgaf4n.align b/app/data/alignments/s1/lgaf4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..b4e8cc37387d9875dd85fa87d08a2241c1442abe
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diff --git a/app/data/alignments/s1/lgaf5s.align b/app/data/alignments/s1/lgaf5s.align
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index 0000000000000000000000000000000000000000..9fc036feb857a0dccf8b47d73139332193032235
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+++ b/app/data/alignments/s1/lgaf5s.align
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diff --git a/app/data/alignments/s1/lgaf7a.align b/app/data/alignments/s1/lgaf7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..50441256c5d6a87cdb48deb23a3191737d1f5d07
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diff --git a/app/data/alignments/s1/lgal8n.align b/app/data/alignments/s1/lgal8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..8f3db7de13ec8809e7863aacd0530cb5fee705a3
--- /dev/null
+++ b/app/data/alignments/s1/lgal8n.align
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diff --git a/app/data/alignments/s1/lgal9s.align b/app/data/alignments/s1/lgal9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..320f903ef17d60d4eb2f67c8318d88d4a838c2dd
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diff --git a/app/data/alignments/s1/lgam1a.align b/app/data/alignments/s1/lgam1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..c6356bbebfd4ea80c8aabdcb4b47ada807f2f747
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diff --git a/app/data/alignments/s1/lgas2n.align b/app/data/alignments/s1/lgas2n.align
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diff --git a/app/data/alignments/s1/lgas3s.align b/app/data/alignments/s1/lgas3s.align
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new file mode 100644
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diff --git a/app/data/alignments/s1/lgas5a.align b/app/data/alignments/s1/lgas5a.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lgaz6n.align b/app/data/alignments/s1/lgaz6n.align
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+++ b/app/data/alignments/s1/lgaz6n.align
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diff --git a/app/data/alignments/s1/lgaz7s.align b/app/data/alignments/s1/lgaz7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..3d26d54b98f0a124635c2a8aec87722ff3935df1
--- /dev/null
+++ b/app/data/alignments/s1/lgaz7s.align
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diff --git a/app/data/alignments/s1/lgaz8p.align b/app/data/alignments/s1/lgaz8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..9c786bb74a727075111ffc5736e59ba72e505934
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+++ b/app/data/alignments/s1/lgaz8p.align
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diff --git a/app/data/alignments/s1/lgaz9a.align b/app/data/alignments/s1/lgaz9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..18e08d703f6d30484cd939e2b4a0884b37146b38
--- /dev/null
+++ b/app/data/alignments/s1/lgaz9a.align
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diff --git a/app/data/alignments/s1/lgbf8n.align b/app/data/alignments/s1/lgbf8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..ddf6c9859d2f53d9a8bbdb2a9b89bc514e2aa38b
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+++ b/app/data/alignments/s1/lgbf8n.align
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diff --git a/app/data/alignments/s1/lgbf9s.align b/app/data/alignments/s1/lgbf9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..b89f782884a72e5e9a301c2095b797578297e574
--- /dev/null
+++ b/app/data/alignments/s1/lgbf9s.align
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diff --git a/app/data/alignments/s1/lgbg1a.align b/app/data/alignments/s1/lgbg1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..e6075e591edb07d7356b2932699e3ab585914983
--- /dev/null
+++ b/app/data/alignments/s1/lgbg1a.align
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diff --git a/app/data/alignments/s1/lgbgzp.align b/app/data/alignments/s1/lgbgzp.align
new file mode 100644
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+++ b/app/data/alignments/s1/lgbgzp.align
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diff --git a/app/data/alignments/s1/lgbm2n.align b/app/data/alignments/s1/lgbm2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..8b5240cb1f01ed90b82f28dd7f4587d7300c8fe0
--- /dev/null
+++ b/app/data/alignments/s1/lgbm2n.align
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diff --git a/app/data/alignments/s1/lgbm3s.align b/app/data/alignments/s1/lgbm3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..13e633aca4f30761efcf1687a281d7cfeaf51499
--- /dev/null
+++ b/app/data/alignments/s1/lgbm3s.align
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diff --git a/app/data/alignments/s1/lgbm4p.align b/app/data/alignments/s1/lgbm4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..3882679b52bbe262e978ee39115c39e5b3265568
--- /dev/null
+++ b/app/data/alignments/s1/lgbm4p.align
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diff --git a/app/data/alignments/s1/lgbm5a.align b/app/data/alignments/s1/lgbm5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..3aca5aa06913b7daaeff201a574232a8c0754e9c
--- /dev/null
+++ b/app/data/alignments/s1/lgbm5a.align
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diff --git a/app/data/alignments/s1/lgbs6n.align b/app/data/alignments/s1/lgbs6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..752cace4f54ebbaa622c381f2d4394d01d33183b
--- /dev/null
+++ b/app/data/alignments/s1/lgbs6n.align
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diff --git a/app/data/alignments/s1/lgbs7s.align b/app/data/alignments/s1/lgbs7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..4f1c9415b8805733de638cc158dbe25a2e979bbf
--- /dev/null
+++ b/app/data/alignments/s1/lgbs7s.align
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diff --git a/app/data/alignments/s1/lgbs8p.align b/app/data/alignments/s1/lgbs8p.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lgbs9a.align b/app/data/alignments/s1/lgbs9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..caffeb987ade5a908402a2f1746ec7920441a82c
--- /dev/null
+++ b/app/data/alignments/s1/lgbs9a.align
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diff --git a/app/data/alignments/s1/lgif1s.align b/app/data/alignments/s1/lgif1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..9bd21f1b70d25b5ff2afc2d72a30722d1904e7e2
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diff --git a/app/data/alignments/s1/lgif2p.align b/app/data/alignments/s1/lgif2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..1ce47acd084a34eea5d589f9eee13940e60526d5
--- /dev/null
+++ b/app/data/alignments/s1/lgif2p.align
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diff --git a/app/data/alignments/s1/lgif3a.align b/app/data/alignments/s1/lgif3a.align
new file mode 100644
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--- /dev/null
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diff --git a/app/data/alignments/s1/lgifzn.align b/app/data/alignments/s1/lgifzn.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lgil4n.align b/app/data/alignments/s1/lgil4n.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lgil5s.align b/app/data/alignments/s1/lgil5s.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lgil6p.align b/app/data/alignments/s1/lgil6p.align
new file mode 100644
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--- /dev/null
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diff --git a/app/data/alignments/s1/lgil7a.align b/app/data/alignments/s1/lgil7a.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lgir8n.align b/app/data/alignments/s1/lgir8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..57326a64496457deb11063ad8a1ef803e49865ec
--- /dev/null
+++ b/app/data/alignments/s1/lgir8n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgir9s.align b/app/data/alignments/s1/lgir9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..e387b3062d45a7746244655162ed4353ef3d8508
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+++ b/app/data/alignments/s1/lgir9s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgis1a.align b/app/data/alignments/s1/lgis1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..0ba3bca8883c860e26740a9581550acfbec38b7d
--- /dev/null
+++ b/app/data/alignments/s1/lgis1a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgiszp.align b/app/data/alignments/s1/lgiszp.align
new file mode 100644
index 0000000000000000000000000000000000000000..c3e905c586bcc3c8c3e828c2d66033c551b291e4
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+++ b/app/data/alignments/s1/lgiszp.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgiz2n.align b/app/data/alignments/s1/lgiz2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..788e9440d7836ab05fd432d9bbf734cf098a3fcd
--- /dev/null
+++ b/app/data/alignments/s1/lgiz2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgiz3s.align b/app/data/alignments/s1/lgiz3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..a2013d4ab97617581635fe538fb0eb913e58d337
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+++ b/app/data/alignments/s1/lgiz3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgiz4p.align b/app/data/alignments/s1/lgiz4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..474ce77e38b8d6ebf6ce000b78bd17c4c20452c5
--- /dev/null
+++ b/app/data/alignments/s1/lgiz4p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgiz5a.align b/app/data/alignments/s1/lgiz5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..e5b25ca89f180c7b651ecf672ece6e6c12866989
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+++ b/app/data/alignments/s1/lgiz5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgwa1a.align b/app/data/alignments/s1/lgwa1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..b460fa3b61bae7bfb75188f3233d80259a9699e3
--- /dev/null
+++ b/app/data/alignments/s1/lgwa1a.align
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diff --git a/app/data/alignments/s1/lgwazp.align b/app/data/alignments/s1/lgwazp.align
new file mode 100644
index 0000000000000000000000000000000000000000..ad7d7789c509c7f9282cdd0e20d12893ab82f7a5
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+++ b/app/data/alignments/s1/lgwazp.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgwg2n.align b/app/data/alignments/s1/lgwg2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..2e06aee81a9a98210e56d440b80a8a311f0c3101
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+++ b/app/data/alignments/s1/lgwg2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgwg3s.align b/app/data/alignments/s1/lgwg3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..4cd5d20155e672f7bd96cfe364b34e7e3224ebca
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+++ b/app/data/alignments/s1/lgwg3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgwg4p.align b/app/data/alignments/s1/lgwg4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..0b88c1c61fc7746e856f3b10b41a5cc06ffe4ad4
--- /dev/null
+++ b/app/data/alignments/s1/lgwg4p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgwg5a.align b/app/data/alignments/s1/lgwg5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..38848ef04949bc282daf554efbc8935328366019
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+++ b/app/data/alignments/s1/lgwg5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgwm6n.align b/app/data/alignments/s1/lgwm6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..0df640ca4af213354c365a4f9c3278b05204dd02
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+++ b/app/data/alignments/s1/lgwm6n.align
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diff --git a/app/data/alignments/s1/lgwm7s.align b/app/data/alignments/s1/lgwm7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..5978af659eedf4733a0cc3a4e8959b2010dc7a88
--- /dev/null
+++ b/app/data/alignments/s1/lgwm7s.align
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diff --git a/app/data/alignments/s1/lgwm8p.align b/app/data/alignments/s1/lgwm8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..cad47291f55468035f27eaf511ec48dbf7559924
--- /dev/null
+++ b/app/data/alignments/s1/lgwm8p.align
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diff --git a/app/data/alignments/s1/lgwm9a.align b/app/data/alignments/s1/lgwm9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..85c2f74f7f55d922ddb8fa769e13e9115317e6d9
--- /dev/null
+++ b/app/data/alignments/s1/lgwm9a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgwt1s.align b/app/data/alignments/s1/lgwt1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..38828fb593f74b161687f7d33ed4f7dec255a676
--- /dev/null
+++ b/app/data/alignments/s1/lgwt1s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgwt2p.align b/app/data/alignments/s1/lgwt2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..6dae6c27299457cf97dbce10629a02681ff1f68f
--- /dev/null
+++ b/app/data/alignments/s1/lgwt2p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgwt3a.align b/app/data/alignments/s1/lgwt3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..da322c0f31b24e8c044f67aa51800b332a78d47c
--- /dev/null
+++ b/app/data/alignments/s1/lgwt3a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lgwtzn.align b/app/data/alignments/s1/lgwtzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..2e902e565a0029e1232539cd21bfbc3e714f8d82
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+++ b/app/data/alignments/s1/lgwtzn.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lrae2n.align b/app/data/alignments/s1/lrae2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..949c64d1c7f9545906089b684142f730aa6abe64
--- /dev/null
+++ b/app/data/alignments/s1/lrae2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lrae3s.align b/app/data/alignments/s1/lrae3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..08101ec81c076429a319504e263a8d4cecd9865c
--- /dev/null
+++ b/app/data/alignments/s1/lrae3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lrae4p.align b/app/data/alignments/s1/lrae4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..518a8d50c1b7825404d69d9c707559e79228341b
--- /dev/null
+++ b/app/data/alignments/s1/lrae4p.align
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diff --git a/app/data/alignments/s1/lrae5a.align b/app/data/alignments/s1/lrae5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..8bfd996ccbe2df8d1b465f896160e0a6b4569a9a
--- /dev/null
+++ b/app/data/alignments/s1/lrae5a.align
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diff --git a/app/data/alignments/s1/lrak6n.align b/app/data/alignments/s1/lrak6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..ddc52313f2abc1dbcc1ae46455f2a0c8e474a8fd
--- /dev/null
+++ b/app/data/alignments/s1/lrak6n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lrak7s.align b/app/data/alignments/s1/lrak7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..686960801d59d7441b4122ae6350c22b0c046581
--- /dev/null
+++ b/app/data/alignments/s1/lrak7s.align
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diff --git a/app/data/alignments/s1/lrak8p.align b/app/data/alignments/s1/lrak8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..23a7a26f6bc088ac7020ea047a5ff3bdf9a2c55d
--- /dev/null
+++ b/app/data/alignments/s1/lrak8p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lrak9a.align b/app/data/alignments/s1/lrak9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..3197865603cc223fd37d54b7a9a44e9a5355a9b6
--- /dev/null
+++ b/app/data/alignments/s1/lrak9a.align
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diff --git a/app/data/alignments/s1/lrar1s.align b/app/data/alignments/s1/lrar1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..3a867a87d035fc85efa1951c310ba91164f133cd
--- /dev/null
+++ b/app/data/alignments/s1/lrar1s.align
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diff --git a/app/data/alignments/s1/lrar2p.align b/app/data/alignments/s1/lrar2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..aba293c9fbd3cd21fddbafad6db258360d0bc66a
--- /dev/null
+++ b/app/data/alignments/s1/lrar2p.align
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diff --git a/app/data/alignments/s1/lrar3a.align b/app/data/alignments/s1/lrar3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..4a9a77ecdf0e2aea07e7350cde2022434be2ed67
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+++ b/app/data/alignments/s1/lrar3a.align
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diff --git a/app/data/alignments/s1/lrarzn.align b/app/data/alignments/s1/lrarzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..30a824d9d77b8e005c3d2766779dbb2c24a63fc8
--- /dev/null
+++ b/app/data/alignments/s1/lrarzn.align
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diff --git a/app/data/alignments/s1/lray4n.align b/app/data/alignments/s1/lray4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..738c173ffecff94855a4c35d273dfe2f5b60390a
--- /dev/null
+++ b/app/data/alignments/s1/lray4n.align
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diff --git a/app/data/alignments/s1/lray5s.align b/app/data/alignments/s1/lray5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..8aba427aa23a2a3c418476c1862ce421dae4a5fe
--- /dev/null
+++ b/app/data/alignments/s1/lray5s.align
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diff --git a/app/data/alignments/s1/lray6p.align b/app/data/alignments/s1/lray6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..2c957f7da1dd0cf27bdfc2eaa16483b2451e0aff
--- /dev/null
+++ b/app/data/alignments/s1/lray6p.align
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diff --git a/app/data/alignments/s1/lray7a.align b/app/data/alignments/s1/lray7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..a82e778a60463dbee3d6400fea419c0cec88e5a3
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+++ b/app/data/alignments/s1/lray7a.align
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diff --git a/app/data/alignments/s1/lrbe6n.align b/app/data/alignments/s1/lrbe6n.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lrbe7s.align b/app/data/alignments/s1/lrbe7s.align
new file mode 100644
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new file mode 100644
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diff --git a/app/data/alignments/s1/lrbe9a.align b/app/data/alignments/s1/lrbe9a.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lrbl1s.align b/app/data/alignments/s1/lrbl1s.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lrbl2p.align b/app/data/alignments/s1/lrbl2p.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lrbl3a.align b/app/data/alignments/s1/lrbl3a.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lrblzn.align b/app/data/alignments/s1/lrblzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..f563c467b3e3d1f968d587ead0ecbf10571de1fd
--- /dev/null
+++ b/app/data/alignments/s1/lrblzn.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lrbr4n.align b/app/data/alignments/s1/lrbr4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..9cee9cce6f191a80a7de0e253c1e2a355163c385
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+++ b/app/data/alignments/s1/lrbr4n.align
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index 0000000000000000000000000000000000000000..c9c1e460f7eb7f9d778df5bb849d860af1d4ef97
--- /dev/null
+++ b/app/data/alignments/s1/lrbr5s.align
@@ -0,0 +1,8 @@
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new file mode 100644
index 0000000000000000000000000000000000000000..8921390b59be38cb83d395a2683530b4273bbaf2
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+++ b/app/data/alignments/s1/lrbr6p.align
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diff --git a/app/data/alignments/s1/lrbr7a.align b/app/data/alignments/s1/lrbr7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..e4e98b55f45a87211dd775e65e728de83ea3b8ba
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+++ b/app/data/alignments/s1/lrbr7a.align
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diff --git a/app/data/alignments/s1/lrby8n.align b/app/data/alignments/s1/lrby8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..4d7cea21ea3229f3035168fa5f6f3ad331c6283e
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new file mode 100644
index 0000000000000000000000000000000000000000..8d9eb552c382138bf9753ecb6b669ecdc7590cda
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+++ b/app/data/alignments/s1/lrby9s.align
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diff --git a/app/data/alignments/s1/lrbz1a.align b/app/data/alignments/s1/lrbz1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..412619442dd6aff101f153ad82a52f5507d1b875
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+++ b/app/data/alignments/s1/lrbz1a.align
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new file mode 100644
index 0000000000000000000000000000000000000000..925acfab155a8700655bc8397bf2490e859ab2cd
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+++ b/app/data/alignments/s1/lrbzzp.align
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diff --git a/app/data/alignments/s1/lrid8n.align b/app/data/alignments/s1/lrid8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..7814bee850af162a3e82c2c42b7d693c605cd995
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+++ b/app/data/alignments/s1/lrid8n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lrid9s.align b/app/data/alignments/s1/lrid9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..0a33f26f8575ba0860410758d7a4f652dbc79119
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+++ b/app/data/alignments/s1/lrid9s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lrie1a.align b/app/data/alignments/s1/lrie1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..43dcf7973dc4521f1a3b90f38bcd421b3b094cd7
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+++ b/app/data/alignments/s1/lrie1a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lriezp.align b/app/data/alignments/s1/lriezp.align
new file mode 100644
index 0000000000000000000000000000000000000000..be640e2fe4ac246f30dfa4eaf6688a1a5e05753c
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+++ b/app/data/alignments/s1/lriezp.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lrik2n.align b/app/data/alignments/s1/lrik2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..0761cd1f4143f5e9263206b37a80d60e3202dbe4
--- /dev/null
+++ b/app/data/alignments/s1/lrik2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lrik3s.align b/app/data/alignments/s1/lrik3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..3ba2540a2b5e55f4f97ea3f970a1bc3b82b144a6
--- /dev/null
+++ b/app/data/alignments/s1/lrik3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lrik4p.align b/app/data/alignments/s1/lrik4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..0355f4097cfb35ae9ca2b460c4f757a9defbfe44
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+++ b/app/data/alignments/s1/lrik4p.align
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diff --git a/app/data/alignments/s1/lrik5a.align b/app/data/alignments/s1/lrik5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..1f4ab6b854410a7d0692c2f07aedf40b375470e1
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+++ b/app/data/alignments/s1/lrik5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lriq6n.align b/app/data/alignments/s1/lriq6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..014e09eb1d3061ce88906ac5f2e2263124604f5b
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+++ b/app/data/alignments/s1/lriq6n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lriq7s.align b/app/data/alignments/s1/lriq7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..140330fedd18f66c9660bee2733aaf138c7f88a3
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+++ b/app/data/alignments/s1/lriq7s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lriq8p.align b/app/data/alignments/s1/lriq8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..892eda55eea76f2159b3a4643e0393ce65630571
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+++ b/app/data/alignments/s1/lriq8p.align
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diff --git a/app/data/alignments/s1/lriq9a.align b/app/data/alignments/s1/lriq9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..cb9316dd052f64323623d8479f932d4878c28c8d
--- /dev/null
+++ b/app/data/alignments/s1/lriq9a.align
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diff --git a/app/data/alignments/s1/lriy1s.align b/app/data/alignments/s1/lriy1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..b6ce1bc5202fb0245c6738a131a0cf72368e2c53
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+++ b/app/data/alignments/s1/lriy1s.align
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diff --git a/app/data/alignments/s1/lriy2p.align b/app/data/alignments/s1/lriy2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..c83653bb5a6a821aacf4fde509505298f806835a
--- /dev/null
+++ b/app/data/alignments/s1/lriy2p.align
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diff --git a/app/data/alignments/s1/lriy3a.align b/app/data/alignments/s1/lriy3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..db802a947679a484a4864b24bea6fc04a8ecb629
--- /dev/null
+++ b/app/data/alignments/s1/lriy3a.align
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diff --git a/app/data/alignments/s1/lriyzn.align b/app/data/alignments/s1/lriyzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..21d76be5b3ed578abf96ae0d601b3ea8e1225677
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+++ b/app/data/alignments/s1/lriyzn.align
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diff --git a/app/data/alignments/s1/lrwf1s.align b/app/data/alignments/s1/lrwf1s.align
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index 0000000000000000000000000000000000000000..e1ed98f64026ab66a074910945fa0a7a7b98d63d
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diff --git a/app/data/alignments/s1/lrwf2p.align b/app/data/alignments/s1/lrwf2p.align
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index 0000000000000000000000000000000000000000..5ed227cff4c2b851c39ad69f96b2b79c07bea0b8
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diff --git a/app/data/alignments/s1/lrwf3a.align b/app/data/alignments/s1/lrwf3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..e0800e24e39b4e2700f859a67a4995f5c7936e7b
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+++ b/app/data/alignments/s1/lrwf3a.align
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diff --git a/app/data/alignments/s1/lrwfzn.align b/app/data/alignments/s1/lrwfzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..665d513b38df3543825f4964ae1634806f5fd0c6
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+++ b/app/data/alignments/s1/lrwfzn.align
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diff --git a/app/data/alignments/s1/lrwl4n.align b/app/data/alignments/s1/lrwl4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..0b5d9e940f62d27760a74512057b875f2f3d9231
--- /dev/null
+++ b/app/data/alignments/s1/lrwl4n.align
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diff --git a/app/data/alignments/s1/lrwl5s.align b/app/data/alignments/s1/lrwl5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..83bd0dc78d740106cad7879051ee6fd0ce2ebe5c
--- /dev/null
+++ b/app/data/alignments/s1/lrwl5s.align
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diff --git a/app/data/alignments/s1/lrwl6p.align b/app/data/alignments/s1/lrwl6p.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lrwl7a.align b/app/data/alignments/s1/lrwl7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..3d4b9dba56901e44c67c485e0326184d70a684b3
--- /dev/null
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+++ b/app/data/alignments/s1/lrwr8n.align
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new file mode 100644
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+++ b/app/data/alignments/s1/lrws1a.align
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new file mode 100644
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+++ b/app/data/alignments/s1/lrwszp.align
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diff --git a/app/data/alignments/s1/lrwz2n.align b/app/data/alignments/s1/lrwz2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..3e307f74569479d29b72397c64f57eb90806d286
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diff --git a/app/data/alignments/s1/lrwz3s.align b/app/data/alignments/s1/lrwz3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..312080114358779d98237bbd5bdd2102f9ff46c7
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+++ b/app/data/alignments/s1/lrwz3s.align
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diff --git a/app/data/alignments/s1/lrwz4p.align b/app/data/alignments/s1/lrwz4p.align
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+++ b/app/data/alignments/s1/lrwz4p.align
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diff --git a/app/data/alignments/s1/lrwz5a.align b/app/data/alignments/s1/lrwz5a.align
new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..9787751d8189b30f2b09510ab6edaa2ebea9e60a
--- /dev/null
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new file mode 100644
index 0000000000000000000000000000000000000000..250e9cbf9ae9262ca29dc544bb121036cfc7491a
--- /dev/null
+++ b/app/data/alignments/s1/lwae9s.align
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diff --git a/app/data/alignments/s1/lwaf1a.align b/app/data/alignments/s1/lwaf1a.align
new file mode 100644
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diff --git a/app/data/alignments/s1/lwafzp.align b/app/data/alignments/s1/lwafzp.align
new file mode 100644
index 0000000000000000000000000000000000000000..8dbb027681d30cf28f48754b387abd0e7eab5be2
--- /dev/null
+++ b/app/data/alignments/s1/lwafzp.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lwal2n.align b/app/data/alignments/s1/lwal2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..9531f13c288bc4d6f8773e4e925bde39186767ba
--- /dev/null
+++ b/app/data/alignments/s1/lwal2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lwal3s.align b/app/data/alignments/s1/lwal3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..5d5d982174eddd8707375c0c7a368ca8d76fe130
--- /dev/null
+++ b/app/data/alignments/s1/lwal3s.align
@@ -0,0 +1,8 @@
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new file mode 100644
index 0000000000000000000000000000000000000000..95a5c73fe335ea996ed88deeb6b2969278949409
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+++ b/app/data/alignments/s1/lwal4p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lwal5a.align b/app/data/alignments/s1/lwal5a.align
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index 0000000000000000000000000000000000000000..45b3e220fcaeb94aae6d70c9edecd6044b6582d8
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+++ b/app/data/alignments/s1/lwal5a.align
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index 0000000000000000000000000000000000000000..b72f0479541cb33d47a6ae1a5c0011595262286b
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index 0000000000000000000000000000000000000000..3c69951e7bf0db626823ddb9e000ac5370de900b
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index 0000000000000000000000000000000000000000..fc82f14b6dbdd6f4fe40500b82007325efa1e6a2
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diff --git a/app/data/alignments/s1/lwaz1s.align b/app/data/alignments/s1/lwaz1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..a39f40646d41b87b19b51ac090b65a997e66c8c9
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+++ b/app/data/alignments/s1/lwaz1s.align
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diff --git a/app/data/alignments/s1/lwaz2p.align b/app/data/alignments/s1/lwaz2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..67cfd58056538431b5c5b858d93d2d23a10eb985
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+++ b/app/data/alignments/s1/lwaz2p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lwaz3a.align b/app/data/alignments/s1/lwaz3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..51cf4fadc9f5cc3519f83a99e05dcd8ebfdaa29e
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+++ b/app/data/alignments/s1/lwaz3a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lwazzn.align b/app/data/alignments/s1/lwazzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..439dcc96b77fe5ac9f08601e1d0ec0254f73abdb
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+++ b/app/data/alignments/s1/lwazzn.align
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diff --git a/app/data/alignments/s1/lwbf2n.align b/app/data/alignments/s1/lwbf2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..c7f715c3ade90dcd057ea2896d26f3473d6f1bd7
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+++ b/app/data/alignments/s1/lwbf2n.align
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diff --git a/app/data/alignments/s1/lwbf3s.align b/app/data/alignments/s1/lwbf3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..e162d93348050841c2055f2da026203c59024285
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+++ b/app/data/alignments/s1/lwbf3s.align
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diff --git a/app/data/alignments/s1/lwbf4p.align b/app/data/alignments/s1/lwbf4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..3ddad353357634b2fb3c2a7449dbdf813080c81f
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+++ b/app/data/alignments/s1/lwbf4p.align
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diff --git a/app/data/alignments/s1/lwbf5a.align b/app/data/alignments/s1/lwbf5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..a40119c40b70e31ee6d58f5826ef4f155dca1453
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+++ b/app/data/alignments/s1/lwbf5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lwbl6n.align b/app/data/alignments/s1/lwbl6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..8f11fda4a27ef4545f19eabf2cedff1ccbe93e6f
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diff --git a/app/data/alignments/s1/lwbl7s.align b/app/data/alignments/s1/lwbl7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..2c7fda9d21f251818b2e4c5a67b08aa40733f1ca
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+++ b/app/data/alignments/s1/lwbl7s.align
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diff --git a/app/data/alignments/s1/lwbl8p.align b/app/data/alignments/s1/lwbl8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..9271438a2ffaf7eb27f7668af300fd55d015b60a
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+++ b/app/data/alignments/s1/lwbl8p.align
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diff --git a/app/data/alignments/s1/lwbl9a.align b/app/data/alignments/s1/lwbl9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..4ab4bc9b27ffce9e5cf2652b0051365bb338ddfb
--- /dev/null
+++ b/app/data/alignments/s1/lwbl9a.align
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diff --git a/app/data/alignments/s1/lwbs1s.align b/app/data/alignments/s1/lwbs1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..2b4e83940d2e9605cab897e6a06a0adda8a7f50d
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+++ b/app/data/alignments/s1/lwbs1s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/lwbs2p.align b/app/data/alignments/s1/lwbs2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..af9d1adc156c0b5aa8db01366eeb3dc6bfa052e7
--- /dev/null
+++ b/app/data/alignments/s1/lwbs2p.align
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diff --git a/app/data/alignments/s1/lwbs3a.align b/app/data/alignments/s1/lwbs3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..ac2209ade026ba411b37052427aba55e9f12e123
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+++ b/app/data/alignments/s1/lwbs3a.align
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diff --git a/app/data/alignments/s1/lwbszn.align b/app/data/alignments/s1/lwbszn.align
new file mode 100644
index 0000000000000000000000000000000000000000..0d9d01787abe130f9078af952ec1766d18afcdb2
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+++ b/app/data/alignments/s1/lwbszn.align
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diff --git a/app/data/alignments/s1/lwbz4n.align b/app/data/alignments/s1/lwbz4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..b8126129653423d34281567d6856334dc53eb865
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+++ b/app/data/alignments/s1/lwbz4n.align
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diff --git a/app/data/alignments/s1/lwbz5s.align b/app/data/alignments/s1/lwbz5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..fbb68f4280946f003cf043037e2476314482df45
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+++ b/app/data/alignments/s1/lwbz5s.align
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diff --git a/app/data/alignments/s1/lwbz6p.align b/app/data/alignments/s1/lwbz6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..e79c41f10bb2817ba0bc7fec63d98f5456ebe31a
--- /dev/null
+++ b/app/data/alignments/s1/lwbz6p.align
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diff --git a/app/data/alignments/s1/lwbz7a.align b/app/data/alignments/s1/lwbz7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..ff81f33ac1bff49b55bad57e89bdd3b90b5491cb
--- /dev/null
+++ b/app/data/alignments/s1/lwbz7a.align
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diff --git a/app/data/alignments/s1/lwie4n.align b/app/data/alignments/s1/lwie4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..9f7fe799e5ca6bd4aaa1874a709eb62010960a4a
--- /dev/null
+++ b/app/data/alignments/s1/lwie4n.align
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diff --git a/app/data/alignments/s1/lwie5s.align b/app/data/alignments/s1/lwie5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..998e8ce1929ebbaca6988516d76b641e4867dafe
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+++ b/app/data/alignments/s1/lwie5s.align
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diff --git a/app/data/alignments/s1/lwie6p.align b/app/data/alignments/s1/lwie6p.align
new file mode 100644
index 0000000000000000000000000000000000000000..f360900b3a90ad2a1ae2653cd0f08f8b8e9d062b
--- /dev/null
+++ b/app/data/alignments/s1/lwie6p.align
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diff --git a/app/data/alignments/s1/lwie7a.align b/app/data/alignments/s1/lwie7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..48c9bf459decf5d85f52c9084983b7ed0d951f1d
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+++ b/app/data/alignments/s1/lwie7a.align
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diff --git a/app/data/alignments/s1/lwik8n.align b/app/data/alignments/s1/lwik8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..aeecfcec01eca0178bf0afa4a1cd58b13d08113f
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+++ b/app/data/alignments/s1/lwik8n.align
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diff --git a/app/data/alignments/s1/lwik9s.align b/app/data/alignments/s1/lwik9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..aabfc56f561c59db48bb99d45a695fb077ac1a3b
--- /dev/null
+++ b/app/data/alignments/s1/lwik9s.align
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diff --git a/app/data/alignments/s1/lwil1a.align b/app/data/alignments/s1/lwil1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..f1cd887ded887968f554466a05ca53a1da7dbdb7
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+++ b/app/data/alignments/s1/lwil1a.align
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new file mode 100644
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+++ b/app/data/alignments/s1/lwilzp.align
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diff --git a/app/data/alignments/s1/lwir2n.align b/app/data/alignments/s1/lwir2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..b821b15a4b50b9d31fdfe7836a9c7c642356ec1b
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+++ b/app/data/alignments/s1/lwir2n.align
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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index 0000000000000000000000000000000000000000..157eb90c8fcf5e0913a2b9efa8f800e6b454e4d5
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index 0000000000000000000000000000000000000000..e56bf19e0df8cd67be96ecf1dc21e01b57de5094
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diff --git a/app/data/alignments/s1/pbac2p.align b/app/data/alignments/s1/pbac2p.align
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diff --git a/app/data/alignments/s1/pbac3a.align b/app/data/alignments/s1/pbac3a.align
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diff --git a/app/data/alignments/s1/pbai4n.align b/app/data/alignments/s1/pbai4n.align
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diff --git a/app/data/alignments/s1/pbai5s.align b/app/data/alignments/s1/pbai5s.align
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diff --git a/app/data/alignments/s1/pbai6p.align b/app/data/alignments/s1/pbai6p.align
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diff --git a/app/data/alignments/s1/pbai7a.align b/app/data/alignments/s1/pbai7a.align
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index 0000000000000000000000000000000000000000..8c5eb74027b5616fa536efc90cf5e4ef6bf38016
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diff --git a/app/data/alignments/s1/pbao8n.align b/app/data/alignments/s1/pbao8n.align
new file mode 100644
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diff --git a/app/data/alignments/s1/pbao9s.align b/app/data/alignments/s1/pbao9s.align
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diff --git a/app/data/alignments/s1/pbap1a.align b/app/data/alignments/s1/pbap1a.align
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diff --git a/app/data/alignments/s1/pbav2n.align b/app/data/alignments/s1/pbav2n.align
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diff --git a/app/data/alignments/s1/pbav3s.align b/app/data/alignments/s1/pbav3s.align
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diff --git a/app/data/alignments/s1/pbav5a.align b/app/data/alignments/s1/pbav5a.align
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diff --git a/app/data/alignments/s1/pbbc4n.align b/app/data/alignments/s1/pbbc4n.align
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index 0000000000000000000000000000000000000000..7680c232552579589dd7f0d5be6c3e814dbf1ab0
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diff --git a/app/data/alignments/s1/pgaq9a.align b/app/data/alignments/s1/pgaq9a.align
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index 0000000000000000000000000000000000000000..0ec3ceffdc07da874e8d8af3ccf13763d4dcf95d
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diff --git a/app/data/alignments/s1/pgay1s.align b/app/data/alignments/s1/pgay1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..0d6427dbf8113add8efcacb06fecc3fef17bef0b
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+++ b/app/data/alignments/s1/pgay1s.align
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diff --git a/app/data/alignments/s1/pgay2p.align b/app/data/alignments/s1/pgay2p.align
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index 0000000000000000000000000000000000000000..3b4a1cb40e0c66e497185974cc73df26363aba93
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+++ b/app/data/alignments/s1/pgay2p.align
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diff --git a/app/data/alignments/s1/pgay3a.align b/app/data/alignments/s1/pgay3a.align
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index 0000000000000000000000000000000000000000..f264b59082cd80413723d6a09cf5eae786466aec
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+++ b/app/data/alignments/s1/pgay3a.align
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new file mode 100644
index 0000000000000000000000000000000000000000..8fbb93bbb83a5cc235dd01a77ca2328d1dbf4872
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+++ b/app/data/alignments/s1/pgayzn.align
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diff --git a/app/data/alignments/s1/pgbe2n.align b/app/data/alignments/s1/pgbe2n.align
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index 0000000000000000000000000000000000000000..2d16eb569527b7a80f9fc466a443293439dafb0c
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+++ b/app/data/alignments/s1/pgbe2n.align
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index 0000000000000000000000000000000000000000..0b77771dd0c2d377542a89f01af67c75b6bde0eb
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index 0000000000000000000000000000000000000000..a82fcf61db7581cfa71a3dc09afad8fdbb6c8bf1
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new file mode 100644
index 0000000000000000000000000000000000000000..71559bd8ec22994a4ea0a8c167b5f40341f691e6
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diff --git a/app/data/alignments/s1/pgbk6n.align b/app/data/alignments/s1/pgbk6n.align
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index 0000000000000000000000000000000000000000..65696b91ab8fa548f54996eb1e5e4b4c0b0b26d4
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new file mode 100644
index 0000000000000000000000000000000000000000..83729375508a6ae50ca72317637a9650d158c2e4
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+++ b/app/data/alignments/s1/pgbk7s.align
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index 0000000000000000000000000000000000000000..a9b3baac4f58e40cad582471b26e2626ed6fea5f
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index 0000000000000000000000000000000000000000..f7e8ee440ffb9351f98a5c30a42002f6310601df
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+++ b/app/data/alignments/s1/pgbk9a.align
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diff --git a/app/data/alignments/s1/pgbr1s.align b/app/data/alignments/s1/pgbr1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..039f36b80be110baf52a2399642f7e071c946147
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diff --git a/app/data/alignments/s1/pgbr2p.align b/app/data/alignments/s1/pgbr2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..d3d4f1a4a46dfc41463c0428535d006cc2539fb9
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+++ b/app/data/alignments/s1/pgbr2p.align
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diff --git a/app/data/alignments/s1/pgbr3a.align b/app/data/alignments/s1/pgbr3a.align
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+++ b/app/data/alignments/s1/pgbr3a.align
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diff --git a/app/data/alignments/s1/pgbrzn.align b/app/data/alignments/s1/pgbrzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..2f8501d44a36b4fb34604d1eef1249e01a9d7c71
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diff --git a/app/data/alignments/s1/pgby4n.align b/app/data/alignments/s1/pgby4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..ed8f3991e80a8e860e9f23ee237414732a399237
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+++ b/app/data/alignments/s1/pgby4n.align
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diff --git a/app/data/alignments/s1/pgby5s.align b/app/data/alignments/s1/pgby5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..45dc3e28c42d0dbcc0e35945291a78243c923e26
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+++ b/app/data/alignments/s1/pgby5s.align
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+++ b/app/data/alignments/s1/pgby6p.align
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diff --git a/app/data/alignments/s1/pgby7a.align b/app/data/alignments/s1/pgby7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..215a4f4346eaea8cfbd6fa840eab88c4b9c31f97
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+++ b/app/data/alignments/s1/pgby7a.align
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diff --git a/app/data/alignments/s1/pgid4n.align b/app/data/alignments/s1/pgid4n.align
new file mode 100644
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+++ b/app/data/alignments/s1/pgid4n.align
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new file mode 100644
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+++ b/app/data/alignments/s1/pgid5s.align
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new file mode 100644
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diff --git a/app/data/alignments/s1/pgij8n.align b/app/data/alignments/s1/pgij8n.align
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diff --git a/app/data/alignments/s1/pgik1a.align b/app/data/alignments/s1/pgik1a.align
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new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..485fe856c682f6e8ab6dbc785d71d9943e79be4c
--- /dev/null
+++ b/app/data/alignments/s1/pgwl3a.align
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diff --git a/app/data/alignments/s1/pgwlzn.align b/app/data/alignments/s1/pgwlzn.align
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index 0000000000000000000000000000000000000000..58844886ec3074d167f61d08f3f6bdeefa365814
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diff --git a/app/data/alignments/s1/pgwr4n.align b/app/data/alignments/s1/pgwr4n.align
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index 0000000000000000000000000000000000000000..f147b2d144cb2977618118f3a7a2e811815ff060
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diff --git a/app/data/alignments/s1/pgwz1a.align b/app/data/alignments/s1/pgwz1a.align
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index 0000000000000000000000000000000000000000..a807f128cc9d3c447b36de0db0ec2cf5ad6d1078
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diff --git a/app/data/alignments/s1/prac6n.align b/app/data/alignments/s1/prac6n.align
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index 0000000000000000000000000000000000000000..670702a0521c75e9a45558289e977f74119a8e19
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+++ b/app/data/alignments/s1/prac6n.align
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diff --git a/app/data/alignments/s1/prac7s.align b/app/data/alignments/s1/prac7s.align
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index 0000000000000000000000000000000000000000..8554e34cdb0190358daa0d403dd6f7e76cc9c473
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diff --git a/app/data/alignments/s1/prac8p.align b/app/data/alignments/s1/prac8p.align
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diff --git a/app/data/alignments/s1/prac9a.align b/app/data/alignments/s1/prac9a.align
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diff --git a/app/data/alignments/s1/praj1s.align b/app/data/alignments/s1/praj1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..4d05caf62e2a1df9df119a8038cebc485bfe74c0
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+++ b/app/data/alignments/s1/praj1s.align
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diff --git a/app/data/alignments/s1/praj2p.align b/app/data/alignments/s1/praj2p.align
new file mode 100644
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diff --git a/app/data/alignments/s1/praj3a.align b/app/data/alignments/s1/praj3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..6cb052a79727c9fd9f35f47f330c2292da855415
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diff --git a/app/data/alignments/s1/prajzn.align b/app/data/alignments/s1/prajzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..2c84242591b10d7833d54574963c3a561fc37b01
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diff --git a/app/data/alignments/s1/prap4n.align b/app/data/alignments/s1/prap4n.align
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diff --git a/app/data/alignments/s1/prap5s.align b/app/data/alignments/s1/prap5s.align
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index 0000000000000000000000000000000000000000..cfc47c66f61d2fed5c4a9ecace7cc2d0453ee24d
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diff --git a/app/data/alignments/s1/prap6p.align b/app/data/alignments/s1/prap6p.align
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index 0000000000000000000000000000000000000000..597b54e906ea862884df316f4bf8dee972bbe151
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new file mode 100644
index 0000000000000000000000000000000000000000..627c1083c72b5f06a143ed0915b965d0119131e3
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diff --git a/app/data/alignments/s1/prav8n.align b/app/data/alignments/s1/prav8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..58ce0321f6aa07b678e6f57a9def9c1de4e9ecee
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new file mode 100644
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+++ b/app/data/alignments/s1/prav9s.align
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diff --git a/app/data/alignments/s1/prax1a.align b/app/data/alignments/s1/prax1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..02ab9d9de6a143ed186bf9d12d975b57a73f0152
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diff --git a/app/data/alignments/s1/praxzp.align b/app/data/alignments/s1/praxzp.align
new file mode 100644
index 0000000000000000000000000000000000000000..b6122fcb51de4da33d1474e7fc1cbfc37488347f
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+++ b/app/data/alignments/s1/praxzp.align
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diff --git a/app/data/alignments/s1/prbd1s.align b/app/data/alignments/s1/prbd1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..999be3b1641229b65d01061cae23b0ee41628c62
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diff --git a/app/data/alignments/s1/prbd2p.align b/app/data/alignments/s1/prbd2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..9349b778cbf5cd06f677c979ae16b15b8a0ba9aa
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diff --git a/app/data/alignments/s1/prbd3a.align b/app/data/alignments/s1/prbd3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..75b347b10c7514e9ce7e56ea1abbac6ba61553ab
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diff --git a/app/data/alignments/s1/prbdzn.align b/app/data/alignments/s1/prbdzn.align
new file mode 100644
index 0000000000000000000000000000000000000000..0840b71eb8bd4011cc0a883a3df8420a60a79e7a
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diff --git a/app/data/alignments/s1/prbj4n.align b/app/data/alignments/s1/prbj4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..35e7328eb39f21633beababba6642c518fd2eb52
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diff --git a/app/data/alignments/s1/prbj5s.align b/app/data/alignments/s1/prbj5s.align
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diff --git a/app/data/alignments/s1/prbj7a.align b/app/data/alignments/s1/prbj7a.align
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diff --git a/app/data/alignments/s1/prbq1a.align b/app/data/alignments/s1/prbq1a.align
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diff --git a/app/data/alignments/s1/prbx2n.align b/app/data/alignments/s1/prbx2n.align
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diff --git a/app/data/alignments/s1/prwq3s.align b/app/data/alignments/s1/prwq3s.align
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new file mode 100644
index 0000000000000000000000000000000000000000..ebade49646fe65d469b56c217d2f968aa026620a
--- /dev/null
+++ b/app/data/alignments/s1/pwwy2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/pwwy3s.align b/app/data/alignments/s1/pwwy3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..e84921ace325b7538507930d562ecffdc66c0d50
--- /dev/null
+++ b/app/data/alignments/s1/pwwy3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/pwwy4p.align b/app/data/alignments/s1/pwwy4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..fca359b1298dd950606d8685522a96f0ecd9b175
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+++ b/app/data/alignments/s1/pwwy4p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/pwwy5a.align b/app/data/alignments/s1/pwwy5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..a177edbd316ee83e9f11b128363b2d7c4210fcc2
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+++ b/app/data/alignments/s1/pwwy5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/sbaa4n.align b/app/data/alignments/s1/sbaa4n.align
new file mode 100644
index 0000000000000000000000000000000000000000..bbf318f244853eb50a840ee1df951fc3a09eea0f
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+++ b/app/data/alignments/s1/sbaa4n.align
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diff --git a/app/data/alignments/s1/sbaa5s.align b/app/data/alignments/s1/sbaa5s.align
new file mode 100644
index 0000000000000000000000000000000000000000..94dddb9b3e60b0d26e7458a43f2020c853b2b689
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+++ b/app/data/alignments/s1/sbaa5s.align
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index 0000000000000000000000000000000000000000..ee31589b0967e9b47b0a40f709f7004d21dfa3ae
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+++ b/app/data/alignments/s1/sbaa6p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/sbaa7a.align b/app/data/alignments/s1/sbaa7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..dfda14d99810b9b57d68e74041672a8774a7964a
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+++ b/app/data/alignments/s1/sbaa7a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/sbag8n.align b/app/data/alignments/s1/sbag8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..dd8e7a0ee0c4172823595650b2349fda46a5b89f
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+++ b/app/data/alignments/s1/sbag8n.align
@@ -0,0 +1,8 @@
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new file mode 100644
index 0000000000000000000000000000000000000000..0652f772871eadaf27b87532265425dbe609a292
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+++ b/app/data/alignments/s1/sbag9s.align
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diff --git a/app/data/alignments/s1/sbah1a.align b/app/data/alignments/s1/sbah1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..0d5599135274f446f63c68d3b78b143d01246166
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+++ b/app/data/alignments/s1/sbah1a.align
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diff --git a/app/data/alignments/s1/sbahzp.align b/app/data/alignments/s1/sbahzp.align
new file mode 100644
index 0000000000000000000000000000000000000000..e977ec251b6f40254e40622550d2fea7ae598482
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+++ b/app/data/alignments/s1/sbahzp.align
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diff --git a/app/data/alignments/s1/sban2n.align b/app/data/alignments/s1/sban2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..95474f58827bdbd37b8aef47d073ebfe7ad91610
--- /dev/null
+++ b/app/data/alignments/s1/sban2n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/sban3s.align b/app/data/alignments/s1/sban3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..8467be12b067c8cb5960dca292d94c07f2015d63
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+++ b/app/data/alignments/s1/sban3s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/sban4p.align b/app/data/alignments/s1/sban4p.align
new file mode 100644
index 0000000000000000000000000000000000000000..d076496f58c5b3d06f1e33f4c7040d965c94cf88
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+++ b/app/data/alignments/s1/sban4p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/sban5a.align b/app/data/alignments/s1/sban5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..10f01e5343ba22cccafb33a2e4318779f634a0b1
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+++ b/app/data/alignments/s1/sban5a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/sbat6n.align b/app/data/alignments/s1/sbat6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..ecd2fc783900329411da8202830f0624a6fecfeb
--- /dev/null
+++ b/app/data/alignments/s1/sbat6n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/sbat7s.align b/app/data/alignments/s1/sbat7s.align
new file mode 100644
index 0000000000000000000000000000000000000000..5292c94ef7e6757399096abe53bdcfb5f05de115
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+++ b/app/data/alignments/s1/sbat7s.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/sbat8p.align b/app/data/alignments/s1/sbat8p.align
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index 0000000000000000000000000000000000000000..cf7644151dd6746f8cd6b921ec918d7e7bc8b4f8
--- /dev/null
+++ b/app/data/alignments/s1/sbat8p.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/sbat9a.align b/app/data/alignments/s1/sbat9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..c6a1c25dbcbbe0b0e056377e0b43a33aebe778a6
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+++ b/app/data/alignments/s1/sbat9a.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/sbba8n.align b/app/data/alignments/s1/sbba8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..e53f49a2b7024d8926cc852bb81001725f8b6c28
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+++ b/app/data/alignments/s1/sbba8n.align
@@ -0,0 +1,8 @@
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diff --git a/app/data/alignments/s1/sbba9s.align b/app/data/alignments/s1/sbba9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..dea9fb9aeb52bae91eb49ae813508c0965966bb7
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+++ b/app/data/alignments/s1/sbba9s.align
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diff --git a/app/data/alignments/s1/sbbb1a.align b/app/data/alignments/s1/sbbb1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..4ae91ecb55c1d000744959c8b74ac226f5b66a01
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+++ b/app/data/alignments/s1/sbbb1a.align
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new file mode 100644
index 0000000000000000000000000000000000000000..2eb9a812b34af7c56790cb970f6c0b01d1a6a80d
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+++ b/app/data/alignments/s1/sbbbzp.align
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diff --git a/app/data/alignments/s1/sbbh2n.align b/app/data/alignments/s1/sbbh2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..83dcca4b63e4fbf94ac868a55df63dab572ff096
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+++ b/app/data/alignments/s1/sbbh2n.align
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diff --git a/app/data/alignments/s1/sbbh3s.align b/app/data/alignments/s1/sbbh3s.align
new file mode 100644
index 0000000000000000000000000000000000000000..d71ae25d6890205b9f912f84742e4f9c1816259a
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+++ b/app/data/alignments/s1/sbbh3s.align
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diff --git a/app/data/alignments/s1/sbbh4p.align b/app/data/alignments/s1/sbbh4p.align
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index 0000000000000000000000000000000000000000..db263329a52e56745421c7a3da71ec6e86b3675c
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+++ b/app/data/alignments/s1/sbbh4p.align
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index 0000000000000000000000000000000000000000..700d78ae5645f905e718ff590d031d92450d52c7
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+++ b/app/data/alignments/s1/sbbh5a.align
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index 0000000000000000000000000000000000000000..ca99f658877f77e4d41f6004e4e0b19c9d9536b1
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+++ b/app/data/alignments/s1/sbbn6n.align
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index 0000000000000000000000000000000000000000..33620f7de9d51cab844e673e7fa3372f6c8afcb6
--- /dev/null
+++ b/app/data/alignments/s1/sbbn7s.align
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+++ b/app/data/alignments/s1/sbbn8p.align
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index 0000000000000000000000000000000000000000..847059c4224a0f1ca545bca3c4c6dd15625e44ad
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+++ b/app/data/alignments/s1/sbbn9a.align
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new file mode 100644
index 0000000000000000000000000000000000000000..ed30ed59c1813cac2df08b7ed23a0948134b46fa
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+++ b/app/data/alignments/s1/sbbu1s.align
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diff --git a/app/data/alignments/s1/sbbu2p.align b/app/data/alignments/s1/sbbu2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..e6dd80d094e1060b66a974be708657fb82c97d9c
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+++ b/app/data/alignments/s1/sbbu2p.align
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new file mode 100644
index 0000000000000000000000000000000000000000..d3d264ec595437ad025e0d38e6933cddb95bb8ba
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+++ b/app/data/alignments/s1/sbbu3a.align
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new file mode 100644
index 0000000000000000000000000000000000000000..8f06886d3428bbddae5dc874598221b6a4df3505
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+++ b/app/data/alignments/s1/sbbuzn.align
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new file mode 100644
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new file mode 100644
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diff --git a/app/data/alignments/s1/sbim8n.align b/app/data/alignments/s1/sbim8n.align
new file mode 100644
index 0000000000000000000000000000000000000000..e723f3b5ea853e46a3f83164cf942c17f86e799e
--- /dev/null
+++ b/app/data/alignments/s1/sbim8n.align
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index 0000000000000000000000000000000000000000..0ac226f106285f1d70ccb0cbd3c1a6ea2965da0b
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+++ b/app/data/alignments/s1/sbim9s.align
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+++ b/app/data/alignments/s1/sbin1a.align
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index 0000000000000000000000000000000000000000..b4b3ed09b849c64a77dbdb18498aac87b785c646
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+++ b/app/data/alignments/s1/sbinzp.align
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index 0000000000000000000000000000000000000000..1f98930abe0e7da7d110ecfd6cf93b9654cd6602
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+++ b/app/data/alignments/s1/sbit2n.align
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+++ b/app/data/alignments/s1/sbit4p.align
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diff --git a/app/data/alignments/s1/sbit5a.align b/app/data/alignments/s1/sbit5a.align
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index 0000000000000000000000000000000000000000..af954884a8ea848659a5a6a0e7b9503acd5f4c37
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index 0000000000000000000000000000000000000000..9aa19c47969d69da7e2a5197be9f82397cd404bc
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+++ b/app/data/alignments/s1/sbwb4p.align
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new file mode 100644
index 0000000000000000000000000000000000000000..75609af6105f08eedb8da9e7ff3bed5809b2bd51
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+++ b/app/data/alignments/s1/sbwb5a.align
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diff --git a/app/data/alignments/s1/sbwh6n.align b/app/data/alignments/s1/sbwh6n.align
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diff --git a/app/data/alignments/s1/sbwh7s.align b/app/data/alignments/s1/sbwh7s.align
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index 0000000000000000000000000000000000000000..da3ecb3c10cf4dca24d9d876f8902cc1cfccc997
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+++ b/app/data/alignments/s1/sbwh7s.align
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diff --git a/app/data/alignments/s1/sbwh8p.align b/app/data/alignments/s1/sbwh8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..12bbe7e95d3cfd94fc7ce7ec1901366ca34d5807
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diff --git a/app/data/alignments/s1/sbwh9a.align b/app/data/alignments/s1/sbwh9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..f6ebf3e9286b9ebfcf38e5068cf1f72cf269f686
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+++ b/app/data/alignments/s1/sbwh9a.align
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diff --git a/app/data/alignments/s1/sbwo1s.align b/app/data/alignments/s1/sbwo1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..200d78667bfd75aeeab6513b6b383e22c02f5e55
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+++ b/app/data/alignments/s1/sbwo1s.align
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diff --git a/app/data/alignments/s1/sbwo2p.align b/app/data/alignments/s1/sbwo2p.align
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+++ b/app/data/alignments/s1/sbwo2p.align
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diff --git a/app/data/alignments/s1/sbwo3a.align b/app/data/alignments/s1/sbwo3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..88ad2b5772cf0b98c499f90d52fac2de5078a8fa
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+++ b/app/data/alignments/s1/sbwo3a.align
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diff --git a/app/data/alignments/s1/sbwozn.align b/app/data/alignments/s1/sbwozn.align
new file mode 100644
index 0000000000000000000000000000000000000000..2cb1b3ba4d0e2dcf2f11e61e4a63ba88db7452f3
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+++ b/app/data/alignments/s1/sbwozn.align
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diff --git a/app/data/alignments/s1/sbwu4n.align b/app/data/alignments/s1/sbwu4n.align
new file mode 100644
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diff --git a/app/data/alignments/s1/sbwu5s.align b/app/data/alignments/s1/sbwu5s.align
new file mode 100644
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+++ b/app/data/alignments/s1/sbwu5s.align
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diff --git a/app/data/alignments/s1/sbwu6p.align b/app/data/alignments/s1/sbwu6p.align
new file mode 100644
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+++ b/app/data/alignments/s1/sbwu6p.align
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diff --git a/app/data/alignments/s1/sbwu7a.align b/app/data/alignments/s1/sbwu7a.align
new file mode 100644
index 0000000000000000000000000000000000000000..454654437d4666a35d14c417ada93de3a62dd7a7
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+++ b/app/data/alignments/s1/sbwu7a.align
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diff --git a/app/data/alignments/s1/sgac2n.align b/app/data/alignments/s1/sgac2n.align
new file mode 100644
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diff --git a/app/data/alignments/s1/sgac3s.align b/app/data/alignments/s1/sgac3s.align
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diff --git a/app/data/alignments/s1/sgac5a.align b/app/data/alignments/s1/sgac5a.align
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diff --git a/app/data/alignments/s1/sgai6n.align b/app/data/alignments/s1/sgai6n.align
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diff --git a/app/data/alignments/s1/sgai9a.align b/app/data/alignments/s1/sgai9a.align
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diff --git a/app/data/alignments/s1/sgap3a.align b/app/data/alignments/s1/sgap3a.align
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index 0000000000000000000000000000000000000000..224441940bb43e95c620554be13469dbd162fb35
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+++ b/app/data/alignments/s1/sgbjzn.align
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diff --git a/app/data/alignments/s1/sgbp4n.align b/app/data/alignments/s1/sgbp4n.align
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+++ b/app/data/alignments/s1/sgbp4n.align
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diff --git a/app/data/alignments/s1/sgbp5s.align b/app/data/alignments/s1/sgbp5s.align
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index 0000000000000000000000000000000000000000..4884ff3e5b469e62a569bee69a4ea673fffa18ba
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+++ b/app/data/alignments/s1/sgbp5s.align
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index 0000000000000000000000000000000000000000..63717723514d8cd9033c6ea9bff63f0e6535295e
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+++ b/app/data/alignments/s1/sgbp6p.align
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+++ b/app/data/alignments/s1/sgbv8n.align
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index 0000000000000000000000000000000000000000..4570ae4ad7c7d349d8e39175a977107979757271
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+++ b/app/data/alignments/s1/sgbv9s.align
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index 0000000000000000000000000000000000000000..dd25a77a320a68148e9d648490cc12edb01ec86c
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+++ b/app/data/alignments/s1/sgbx1a.align
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index 0000000000000000000000000000000000000000..6df10fc44c76d9cca568bcefbde14b2df8a9ecc1
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+++ b/app/data/alignments/s1/sgbxzp.align
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index 0000000000000000000000000000000000000000..0cca195c367bc7a29072472c8b611c2a8691840d
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diff --git a/app/data/alignments/s1/sgib9s.align b/app/data/alignments/s1/sgib9s.align
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diff --git a/app/data/alignments/s1/sgic1a.align b/app/data/alignments/s1/sgic1a.align
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index 0000000000000000000000000000000000000000..e994e318d074826b93e0ee145d4e783f263eadc4
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+++ b/app/data/alignments/s1/sgic1a.align
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diff --git a/app/data/alignments/s1/sgiczp.align b/app/data/alignments/s1/sgiczp.align
new file mode 100644
index 0000000000000000000000000000000000000000..6faaafd6c451c384f3924362ca52ba7bd444db37
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+++ b/app/data/alignments/s1/sgiczp.align
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diff --git a/app/data/alignments/s1/sgii2n.align b/app/data/alignments/s1/sgii2n.align
new file mode 100644
index 0000000000000000000000000000000000000000..ccc50a0a0a9e24b9c3f5848aaf9fd429c59df9fd
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diff --git a/app/data/alignments/s1/sgii3s.align b/app/data/alignments/s1/sgii3s.align
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+++ b/app/data/alignments/s1/sgii3s.align
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diff --git a/app/data/alignments/s1/sgii4p.align b/app/data/alignments/s1/sgii4p.align
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diff --git a/app/data/alignments/s1/sgii5a.align b/app/data/alignments/s1/sgii5a.align
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+++ b/app/data/alignments/s1/sgii5a.align
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diff --git a/app/data/alignments/s1/sgio6n.align b/app/data/alignments/s1/sgio6n.align
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diff --git a/app/data/alignments/s1/sgio7s.align b/app/data/alignments/s1/sgio7s.align
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+++ b/app/data/alignments/s1/sgio7s.align
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diff --git a/app/data/alignments/s1/sgio8p.align b/app/data/alignments/s1/sgio8p.align
new file mode 100644
index 0000000000000000000000000000000000000000..c7a7719836e1c6746a12dec01cf3ca8048ac7f90
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+++ b/app/data/alignments/s1/sgio8p.align
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diff --git a/app/data/alignments/s1/sgio9a.align b/app/data/alignments/s1/sgio9a.align
new file mode 100644
index 0000000000000000000000000000000000000000..c0a3dd31a7aef8d8231ef66014e95d08567caa36
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+++ b/app/data/alignments/s1/sgio9a.align
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diff --git a/app/data/alignments/s1/sgiv1s.align b/app/data/alignments/s1/sgiv1s.align
new file mode 100644
index 0000000000000000000000000000000000000000..efbcb6a2f1515add801a797011d73285804fb488
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diff --git a/app/data/alignments/s1/sgiv2p.align b/app/data/alignments/s1/sgiv2p.align
new file mode 100644
index 0000000000000000000000000000000000000000..c53d7e5739b6c121ecf6b743702459747b01693f
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diff --git a/app/data/alignments/s1/sgiv3a.align b/app/data/alignments/s1/sgiv3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..832f90122ce995b6c48c4c7abfd30cbdec410ec2
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+++ b/app/data/alignments/s1/sgiv3a.align
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diff --git a/app/data/alignments/s1/sgivzn.align b/app/data/alignments/s1/sgivzn.align
new file mode 100644
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+++ b/app/data/alignments/s1/sgivzn.align
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diff --git a/app/data/alignments/s1/sgwd1s.align b/app/data/alignments/s1/sgwd1s.align
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diff --git a/app/data/alignments/s1/sgwj4n.align b/app/data/alignments/s1/sgwj4n.align
new file mode 100644
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new file mode 100644
index 0000000000000000000000000000000000000000..530f429cd172964176d021e013631dc8527396d4
--- /dev/null
+++ b/app/data/alignments/s1/srab2p.align
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diff --git a/app/data/alignments/s1/srab3a.align b/app/data/alignments/s1/srab3a.align
new file mode 100644
index 0000000000000000000000000000000000000000..2aa1a3eee6c7528ee7501fe8e09bb2bcbdc90990
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+++ b/app/data/alignments/s1/srab3a.align
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index 0000000000000000000000000000000000000000..1b7a577e951be35cf183aae71efa13f2d8499262
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+++ b/app/data/alignments/s1/srabzn.align
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diff --git a/app/data/alignments/s1/srah4n.align b/app/data/alignments/s1/srah4n.align
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index 0000000000000000000000000000000000000000..15cbe56ff008090e8ab664a1e6ff71d4047c0f9e
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+++ b/app/data/alignments/s1/srah4n.align
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index 0000000000000000000000000000000000000000..35d2dfcf9c4bb6f86c5d11e51af40226aced0f9d
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+++ b/app/data/alignments/s1/srah5s.align
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+++ b/app/data/alignments/s1/srah6p.align
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diff --git a/app/data/alignments/s1/srah7a.align b/app/data/alignments/s1/srah7a.align
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index 0000000000000000000000000000000000000000..8b8eecc8cc0f03a10bd4b1de10c806c510b4b8a5
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diff --git a/app/data/alignments/s1/srao1a.align b/app/data/alignments/s1/srao1a.align
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index 0000000000000000000000000000000000000000..8e5a71c6d20d5c0ff0bf84161dfa85cd11e38e22
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diff --git a/app/data/alignments/s1/srau2n.align b/app/data/alignments/s1/srau2n.align
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index 0000000000000000000000000000000000000000..471452a42c4a6c9bbe369ba0068e033dc86a3a3c
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+++ b/app/data/alignments/s1/srau2n.align
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diff --git a/app/data/alignments/s1/srau3s.align b/app/data/alignments/s1/srau3s.align
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index 0000000000000000000000000000000000000000..b8c4deb103ef5e2d7761fbc2e0ed037cc36d1d13
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index 0000000000000000000000000000000000000000..7848fa32b9dda0faf3aa3c0569dd11652f749d76
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+++ b/app/data/alignments/s1/srau4p.align
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diff --git a/app/data/alignments/s1/srau5a.align b/app/data/alignments/s1/srau5a.align
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index 0000000000000000000000000000000000000000..e94b15b4849599f11d1b4a1ba2e5e7efaad27c00
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new file mode 100644
index 0000000000000000000000000000000000000000..bda27e5b93aaa4a29c84b09e5901002d8798a809
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+++ b/app/data/alignments/s1/srbb4n.align
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index 0000000000000000000000000000000000000000..78b1f8aedb42cce9b2aa4eecdabb6cb81d7714b3
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+++ b/app/data/alignments/s1/srbb5s.align
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new file mode 100644
index 0000000000000000000000000000000000000000..b1decbb75f036f5ffe15c58790db9fb68748997a
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+++ b/app/data/alignments/s1/srbb6p.align
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new file mode 100644
index 0000000000000000000000000000000000000000..072406a6a63dfe555d737bc8ff7bf8205a26779d
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+++ b/app/data/alignments/s1/srbb7a.align
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new file mode 100644
index 0000000000000000000000000000000000000000..87b416817eb31636cab298c042ee31140282fb7d
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+++ b/app/data/alignments/s1/srbh8n.align
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diff --git a/app/data/alignments/s1/srbh9s.align b/app/data/alignments/s1/srbh9s.align
new file mode 100644
index 0000000000000000000000000000000000000000..2e9c5c956d66d2303fac0026c17d0886997b7418
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+++ b/app/data/alignments/s1/srbh9s.align
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diff --git a/app/data/alignments/s1/srbi1a.align b/app/data/alignments/s1/srbi1a.align
new file mode 100644
index 0000000000000000000000000000000000000000..df7eedcad95b23c323b4c362ef55279388bf34f6
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+++ b/app/data/alignments/s1/srbi1a.align
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diff --git a/app/data/alignments/s1/srbizp.align b/app/data/alignments/s1/srbizp.align
new file mode 100644
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+++ b/app/data/alignments/s1/srbizp.align
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diff --git a/app/data/alignments/s1/srbo2n.align b/app/data/alignments/s1/srbo2n.align
new file mode 100644
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+++ b/app/data/alignments/s1/srbo2n.align
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diff --git a/app/data/alignments/s1/srbo3s.align b/app/data/alignments/s1/srbo3s.align
new file mode 100644
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+++ b/app/data/alignments/s1/srbo3s.align
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+++ b/app/data/alignments/s1/srbo4p.align
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diff --git a/app/data/alignments/s1/srbo5a.align b/app/data/alignments/s1/srbo5a.align
new file mode 100644
index 0000000000000000000000000000000000000000..8e6a5003a7ad3e8a76bb73d4f76022abb9ac0888
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+++ b/app/data/alignments/s1/srbo5a.align
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+++ b/app/data/alignments/s1/srbu6n.align
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+++ b/app/data/alignments/s1/srbu8p.align
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diff --git a/app/data/alignments/s1/srbu9a.align b/app/data/alignments/s1/srbu9a.align
new file mode 100644
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+++ b/app/data/alignments/s1/srbu9a.align
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diff --git a/app/data/alignments/s1/sria6n.align b/app/data/alignments/s1/sria6n.align
new file mode 100644
index 0000000000000000000000000000000000000000..fcf47ed3eb88f586c08a237fd4a8f977886d1868
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new file mode 100644
index 0000000000000000000000000000000000000000..5adfaebb72086d24ea78f6e04081bb1d512e9d52
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new file mode 100644
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diff --git a/app/data/alignments/s1/srih1s.align b/app/data/alignments/s1/srih1s.align
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new file mode 100644
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index 0000000000000000000000000000000000000000..650621582858fe6fadd7f85093e522df94e6b5d2
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index 0000000000000000000000000000000000000000..cbf063b628a729a8d9133baf341b977b7862ec58
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diff --git a/app/data/alignments/s1/srwo9a.align b/app/data/alignments/s1/srwo9a.align
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diff --git a/app/data/alignments/s1/srwv1s.align b/app/data/alignments/s1/srwv1s.align
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index 0000000000000000000000000000000000000000..c7d6b67140be2630fa9162c5b4265676232b94cc
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diff --git a/app/data/alignments/s1/srwv2p.align b/app/data/alignments/s1/srwv2p.align
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index 0000000000000000000000000000000000000000..9f3736f3d0ff05903641cee325e96d293a9696d8
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diff --git a/app/data/alignments/s1/srwv3a.align b/app/data/alignments/s1/srwv3a.align
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index 0000000000000000000000000000000000000000..15ede7cc85526a2fd637cf9508eefe733add9509
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diff --git a/app/data/alignments/s1/srwvzn.align b/app/data/alignments/s1/srwvzn.align
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diff --git a/app/data/alignments/s1/swab6n.align b/app/data/alignments/s1/swab6n.align
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diff --git a/app/data/alignments/s1/swab7s.align b/app/data/alignments/s1/swab7s.align
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diff --git a/app/data/alignments/s1/swab9a.align b/app/data/alignments/s1/swab9a.align
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diff --git a/app/data/alignments/s1/swai1s.align b/app/data/alignments/s1/swai1s.align
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diff --git a/app/data/alignments/s1/swai2p.align b/app/data/alignments/s1/swai2p.align
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diff --git a/app/data/alignments/s1/swai3a.align b/app/data/alignments/s1/swai3a.align
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diff --git a/app/data/alignments/s1/swaizn.align b/app/data/alignments/s1/swaizn.align
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diff --git a/app/data/alignments/s1/swao4n.align b/app/data/alignments/s1/swao4n.align
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diff --git a/app/data/alignments/s1/swao6p.align b/app/data/alignments/s1/swao6p.align
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diff --git a/app/data/alignments/s1/swao7a.align b/app/data/alignments/s1/swao7a.align
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diff --git a/app/modelutil.py b/app/modelutil.py
new file mode 100644
index 0000000000000000000000000000000000000000..125aca21899bc98c3963529760b13a6e515fd0ad
--- /dev/null
+++ b/app/modelutil.py
@@ -0,0 +1,46 @@
+from __future__ import annotations
+
+import os
+
+import tensorflow as tf
+from tensorflow.keras.layers import (Activation, Bidirectional, Conv3D, Dense,
+                                     Dropout, Flatten, LSTM, MaxPool3D,
+                                     TimeDistributed)
+from tensorflow.keras.models import Sequential 
+
+
+# Disable all GPUS
+tf.config.set_visible_devices([], 'GPU')
+
+def load_model() -> Sequential:
+    model = Sequential()
+
+    model.add(Conv3D(128, 3, input_shape=(75,46,140,1), padding='same'))
+    model.add(Activation('relu'))
+    model.add(MaxPool3D((1,2,2)))
+
+    model.add(Conv3D(256, 3, padding='same'))
+    model.add(Activation('relu'))
+    model.add(MaxPool3D((1,2,2)))
+
+    model.add(Conv3D(75, 3, padding='same'))
+    model.add(Activation('relu'))
+    model.add(MaxPool3D((1,2,2)))
+
+    model.add(TimeDistributed(Flatten()))
+
+    model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
+    model.add(Dropout(.5))
+
+    model.add(Bidirectional(LSTM(128, kernel_initializer='Orthogonal', return_sequences=True)))
+    model.add(Dropout(.5))
+
+    model.add(Dense(41, kernel_initializer='he_normal', activation='softmax'))
+
+    base_dir = os.path.dirname(os.path.abspath(__file__))
+    weights_path = os.path.abspath(os.path.join(base_dir, '..', 'models', 'checkpoint'))
+    if not os.path.exists(weights_path):
+        raise FileNotFoundError(f"Model weights not found at {weights_path}")
+    model.load_weights(weights_path)
+
+    return model
diff --git a/app/photos/app.png b/app/photos/app.png
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diff --git a/app/photos/ogvid.png b/app/photos/ogvid.png
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+version https://git-lfs.github.com/spec/v1
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diff --git a/app/photos/op.png b/app/photos/op.png
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diff --git a/app/streamlitapp.py b/app/streamlitapp.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd0ce65a673b04db0c2b2dd1034b566d1ba2fc5f
--- /dev/null
+++ b/app/streamlitapp.py
@@ -0,0 +1,97 @@
+from __future__ import annotations
+
+# Import all of the dependencies
+from pathlib import Path
+import subprocess
+import tempfile
+
+import imageio
+import streamlit as st
+import tensorflow as tf
+
+from modelutil import load_model
+from utils import load_data, num_to_char
+
+# Set the layout to the streamlit app as wide
+st.set_page_config(layout='wide')
+
+# Setup the sidebar
+with st.sidebar:
+    st.image('https://www.onepointltd.com/wp-content/uploads/2020/03/inno2.png')
+    st.title('NeuroSync Lipscape')
+    st.info('This application is originally developed from the Lip-Reader deep learning model.')
+
+st.title('NeuroSync Lipscape Full Stack App')
+
+BASE_DIR = Path(__file__).resolve().parent
+DATA_DIR = BASE_DIR / 'data' / 's1'
+
+# Generating a list of options or videos
+options = sorted([item.name for item in DATA_DIR.glob('*.mpg')])
+selected_video = st.selectbox('Choose video', options)
+
+# Generate two columns
+col1, col2 = st.columns(2)
+
+if options:
+    # Rendering the video
+    with col1:
+        st.info('The video below displays the converted video in mp4 format')
+        file_path = DATA_DIR / selected_video
+        if not file_path.exists():
+            st.error(f"Video not found: {file_path}")
+        else:
+            output_path = None
+            try:
+                with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as output_file:
+                    output_path = Path(output_file.name)
+                subprocess.run(
+                    [
+                        "ffmpeg",
+                        "-i",
+                        str(file_path),
+                        "-vcodec",
+                        "libx264",
+                        str(output_path),
+                        "-y",
+                    ],
+                    check=True,
+                    capture_output=True,
+                    text=True,
+                )
+                with output_path.open('rb') as video:
+                    video_bytes = video.read()
+                st.video(video_bytes)
+            except subprocess.CalledProcessError as exc:
+                st.error("ffmpeg failed to convert the selected video.")
+                st.code(exc.stderr or "No ffmpeg error output captured.")
+            finally:
+                if output_path and output_path.exists():
+                    output_path.unlink()
+
+    with col2:
+        if file_path.exists():
+            st.info('This is all the machine learning model sees when making a prediction')
+            video, _annotations = load_data(tf.convert_to_tensor(str(file_path)))
+            gif_path = None
+            try:
+                with tempfile.NamedTemporaryFile(suffix=".gif", delete=False) as gif_file:
+                    gif_path = Path(gif_file.name)
+                imageio.mimsave(str(gif_path), video, fps=10)
+                st.image(str(gif_path), width=400)
+            finally:
+                if gif_path and gif_path.exists():
+                    gif_path.unlink()
+
+            st.info('This is the output of the machine learning model as tokens')
+            model = load_model()
+            yhat = model.predict(tf.expand_dims(video, axis=0))
+            decoder = tf.keras.backend.ctc_decode(yhat, [75], greedy=True)[0][0].numpy()
+            st.text(decoder)
+
+            # Convert prediction to text
+            st.info('Decode the raw tokens into words')
+            converted_prediction = tf.strings.reduce_join(num_to_char(decoder)).numpy().decode('utf-8')
+            st.text(converted_prediction)
+else:
+    st.warning(f"No videos were found in {DATA_DIR}.")
diff --git a/app/test_video.mp4 b/app/test_video.mp4
new file mode 100644
index 0000000000000000000000000000000000000000..7cc007c593163ec369ec148cb1db895aef5a84eb
--- /dev/null
+++ b/app/test_video.mp4
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46dd660a865bc05075cd065769cc94f87fe8fb240ec06091d25bf7804ec6c301
+size 112050
diff --git a/app/utils.py b/app/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..4aa180cebae2fb9d123b949dddfbb20d56d4f739
--- /dev/null
+++ b/app/utils.py
@@ -0,0 +1,90 @@
+########################################################################################################################
+# ------------------------------------------- of AUTHOR: Omm AKA Antonio Colapso---------------------------------------#
+########################################################################################################################
+from __future__ import annotations
+
+from typing import List
+
+import cv2
+import os
+import tensorflow as tf
+
+
+# Disable all GPUS
+tf.config.set_visible_devices([], 'GPU')
+
+
+
+
+
+vocab = [x for x in "abcdefghijklmnopqrstuvwxyz'?!123456789 "]
+char_to_num = tf.keras.layers.StringLookup(vocabulary=vocab, oov_token="")
+# Mapping integers back to original characters
+num_to_char = tf.keras.layers.StringLookup(
+    vocabulary=char_to_num.get_vocabulary(), oov_token="", invert=True
+)
+
+def load_video(path: str) -> List[float]:
+    cap = cv2.VideoCapture(path)
+    frames = []
+    for _ in range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT))): 
+        ret, frame = cap.read()
+        if not ret or frame is None:
+            break
+        frame = tf.image.rgb_to_grayscale(frame)
+        frames.append(frame[190:236,80:220,:])
+    cap.release()
+    if not frames:
+        raise ValueError(f"No frames were read from video: {path}")
+
+    mean = tf.math.reduce_mean(frames)
+    std = tf.math.reduce_std(tf.cast(frames, tf.float32))
+    return tf.cast((frames - mean), tf.float32) / std
+    
+def load_alignments(path: str) -> List[str]:
+    with open(path, 'r') as f: 
+        lines = f.readlines() 
+    tokens = []
+    for line in lines:
+        line = line.split()
+        if len(line) < 3:
+            continue
+        if line[2] != 'sil': 
+            tokens = [*tokens,' ',line[2]]
+    return char_to_num(tf.reshape(tf.strings.unicode_split(tokens, input_encoding='UTF-8'), (-1)))[1:]
+
+def load_data(path: str):
+    path = bytes.decode(path.numpy())
+    file_name = os.path.splitext(os.path.basename(path))[0]
+    
+    # Define the base directories
+    BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+    data_dir = os.path.abspath(os.path.join(BASE_DIR, 'data', 's1'))
+    alignment_dir = os.path.abspath(os.path.join(BASE_DIR,'data', 'alignments', 's1'))
+
+    # Construct the full paths
+    video_path = os.path.join(data_dir, f'{file_name}.mpg')
+    alignment_path = os.path.join(alignment_dir, f'{file_name}.align')
+
+    # Check if the files exist
+    if not os.path.exists(video_path):
+        raise FileNotFoundError(f"Video file {video_path} does not exist.")
+    if not os.path.exists(alignment_path):
+        raise FileNotFoundError(f"Alignment file {alignment_path} does not exist.")
+    
+    frames = load_video(video_path)
+    alignments = load_alignments(alignment_path)
+
+    return frames, alignments
+
+# def load_data(path: str): 
+#     path = bytes.decode(path.numpy())
+#     file_name = path.split('/')[-1].split('.')[0]
+#     # File name splitting for windows
+#     file_name = path.split('\\')[-1].split('.')[0]
+#     video_path = os.path.join('..','data','s1',f'{file_name}.mpg')
+#     alignment_path = os.path.join('..','data','alignments','s1',f'{file_name}.align')
+#     frames = load_video(video_path) 
+#     alignments = load_alignments(alignment_path)
+    
+#     return frames, alignments
diff --git a/models/checkpoint b/models/checkpoint
new file mode 100644
index 0000000000000000000000000000000000000000..5be71fcccd7de38d938cc0eba59a43e4021ad153
--- /dev/null
+++ b/models/checkpoint
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint"
+all_model_checkpoint_paths: "checkpoint"
diff --git a/models/checkpoint.data-00000-of-00001 b/models/checkpoint.data-00000-of-00001
new file mode 100644
index 0000000000000000000000000000000000000000..e896401178929b0462c5d3b8705d2c7314e900d4
--- /dev/null
+++ b/models/checkpoint.data-00000-of-00001
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e34a286a2b08711556b6e2dbbf1f21c41ea9aacdd7405762d17bce4f911b9c63
+size 101674367
diff --git a/models/checkpoint.index b/models/checkpoint.index
new file mode 100644
index 0000000000000000000000000000000000000000..d2f0bd76ce8393aa34f2df010785133bad46b659
Binary files /dev/null and b/models/checkpoint.index differ
diff --git a/models/checkpoint.keras b/models/checkpoint.keras
new file mode 100644
index 0000000000000000000000000000000000000000..5be71fcccd7de38d938cc0eba59a43e4021ad153
--- /dev/null
+++ b/models/checkpoint.keras
@@ -0,0 +1,2 @@
+model_checkpoint_path: "checkpoint"
+all_model_checkpoint_paths: "checkpoint"
diff --git a/packages.txt b/packages.txt
new file mode 100644
index 0000000000000000000000000000000000000000..3712e46e5510b2c789bf65fe1736728bf79e512a
--- /dev/null
+++ b/packages.txt
@@ -0,0 +1,2 @@
+libgl1
+ffmpeg
diff --git a/requirements.txt b/requirements.txt
index 28d994e22f8dd432b51df193562052e315ad95f7..7e77acc49aa361d5dcf4ecad0c4d762289e4dbe5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
\ No newline at end of file
+streamlit==1.37.0
+imageio==2.34.1
+tensorflow==2.15.0
+numpy==1.26.4
+opencv-python-headless==4.10.0.84
+Pillow==10.4.0
+ffmpeg-python==0.2.0