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} | Fix some model names (MatchboxNet -> MarbleNet) in VAD tutorials.
(Thought I've fixed all of them before but it's still there hmm) | {
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"Seems theres some formatting errors, could you run `python setup.py style --scope nemo --fix`",
"This PR will be merged into another PR (https://github.com/NVIDIA/NeMo/pull/2240) due to inactivity. One difference is that it will not raise the error, but instead simply log that the file was not loaded properly.",... | 2021-05-15T23:13:34 | 2021-05-20T01:46:17 | 2021-05-19T23:41:38 | CONTRIBUTOR | {
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It would be helpful to know offending audio files that fail to load if they are corrupt and/or not supported. | {
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"We generally don't prefer to have other file formats for efficiency reasons (wav takes storage but at least it prevents on the fly resampling to 16khz monochannel audio). \r\n\r\nTarred datasets are meant for distributed training on clusters, where network I/O can have a significant impact on training speed. Likew... | 2021-05-15T21:33:30 | 2021-05-28T08:37:25 | 2021-05-28T08:37:25 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
`TarredAudioToTextDataset` assumes audio is `wav`. However I was able to train a model with `mp3` files and would like `TarredAudioToTextDataset` to support other file types.
**Steps/Code to reproduce bug**
Use any dataset with non-wav files.
**Expected behavior**
Model should be trainable with non-wav tarred datasets
**Environment overview (please complete the following information)**
- Environment location: Bare-metal
- Method of NeMo install: pip install nemo['all']==1.0.0rc1
**Environment details**
Otherwise, please provide:
- OS version: Ubuntu 20.04
- PyTorch version: 1.7.1
- Python version: 3.8
**Additional context**
My suggestion is to simply add another field to the `*_ds` config called `file_format` or `file_extension` which can be one of a list of supported files by `soundfile`.
Can submit a PR if this is fine, but some guidance/feedback on the approach first would be good.
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"These docs are quite outdated, could you point me to where they were referenced so we can update them ?\r\n\r\nThese are the latest docs - https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/\r\n\r\nThis section (https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/asr/asr_language_model... | 2021-05-15T21:28:17 | 2021-06-09T12:33:26 | 2021-05-15T23:06:14 | NONE | {
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} | null | null | null | Hello, thank you again for the amazing project. I am trying to modify the code in https://colab.research.google.com/github/NVIDIA/NeMo/blob/main/tutorials/asr/Offline_ASR.ipynb#scrollTo=I9eIxAyKHREB.
Am I correct, based on (https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/zh/latest/asr/tutorial.html#using-kenlm) that this does not actually build the KenLM binary?
When I try to use the train_kenlm.py script, it says I don't have /content/NeMo/decoders/kenlm/build/bin/lmplz, but reading through the KenLM README and the various setup.sh and setup.py scripts in NeMo and OpenSeq2Seq, I cannot seem to get it to build this /bin folder. I am sure this is my user error :-)
Thanks! | {
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"Just a note - The manifest file can (and generally it should) have absolute paths to the dataset files when on a local machine, which the tarfile would then use. ",
"> Just a note - The manifest file can (and generally it should) have absolute paths to the dataset files when on a local machine, which the tarfile... | 2021-05-15T21:17:24 | 2021-05-17T22:55:28 | 2021-05-17T22:55:28 | CONTRIBUTOR | {
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} | To preserve disk space on my machine, I generally use symlinks to create different splits of my data.
The tarred audio dataset script doesn't follow symlinks, so simply pass `dereference=True` to the `tarfile.open()` method.
See more info in the python docs: https://docs.python.org/3/library/tarfile.html#tarfile.TarFile | {
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"Hi, again! I just tried with the `hi-mia` dataset and received the same error ",
"Hi, I just ran `speaker_reco_finetune.py` and everything looks good to me. \r\nCan you make sure you have set the following params in conf/SpeakerNet_verification_3x2x512.yaml:\r\ntrain_set\r\ndev_set\r\ndecoder.emb_sizes: 256\r\nd... | 2021-05-15T11:59:48 | 2021-05-23T11:42:04 | 2021-05-23T11:42:04 | NONE | {
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} | null | null | null | Hi!
As an experiment, I was fine-tuning the pre-trained `SpeakerNet` model with the `an4` dataset plus my own voice recordings. I used the example script `speaker_reco_finetune.py` and changed the configuration file according to the documentation and the colab notebook tutorial. But it appears something is always wrong with setting up the optimizer:
```
Traceback (most recent call last):
File "/Users/xujinghua/speaker-recognition-nemo/scripts/experimental/fine_tune_an4.py", line 119, in <module>
main()
File "/Users/xujinghua/speaker-recognition-nemo/scripts/experimental/fine_tune_an4.py", line 108, in main
speaker_model.setup_optimization(speaker_model._cfg.optim)
File "/Users/xujinghua/miniconda3/lib/python3.7/site-packages/nemo/core/classes/modelPT.py", line 603, in setup_optimization
optimizer=self._optimizer, scheduler_config=scheduler_config, train_dataloader=self._train_dl
File "/Users/xujinghua/miniconda3/lib/python3.7/site-packages/nemo/core/optim/lr_scheduler.py", line 539, in prepare_lr_scheduler
max_epochs, accumulate_grad_batches, num_workers, num_samples, batch_size, drop_last,
File "/Users/xujinghua/miniconda3/lib/python3.7/site-packages/nemo/core/optim/lr_scheduler.py", line 587, in compute_max_steps
sampler_num_samples = math.ceil(num_samples / num_workers)
ZeroDivisionError: division by zero
```
I tried a few ways to debug but nothing helped I have no clue what is going on wrong. Has anyone got an idea or run into such problems before? Help would be super appreciated!
Cheers,
JX
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"I couldn't reproduce any training issues with [this notebook](https://colab.research.google.com/github/NVIDIA/NeMo/blob/r1.0.0rc1/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb) running on Colab. A few warnings do appear during installation but they can be disregarded. \r\nCould you please specify the exact c... | 2021-05-15T01:45:26 | 2021-10-26T23:44:59 | 2021-10-26T23:44:59 | NONE | {
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} | null | null | null | **Describe the bug**
A clear and concise description of what the bug is.
I am trying to use a pretrained model on the relation extraction task and chem-rot dataset. As suggested by the authors, I'm using the notebook provided: Relation-Extraction-BioMegatron.ipynb
**Steps/Code to reproduce bug**
When I run the codes in the notebook, I get different errors. The first one is inconsistency between torch, torchtext and coda version. I was able to solve this problem by using this command:
```
!pip install torch==1.7.1+cu110 torchvision==0.8.2+cu110 torchaudio==0.7.2 torchtext==0.8 -f https://download.pytorch.org/whl/torch_stable.html
```
Then I had problems with installing apex and I was able to solve by using this code:
```
%%writefile setup.sh
# git clone https://github.com/qywu/apex
# cd apex
# pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
!sh setup.sh
```
Now I have another issue, I can use this command:
```
import apex
```
but this command returns error:
```
from apex.normalization.fused_layer_norm import FusedLayerNorm
ModuleNotFoundError: No module named 'apex.normalization'
```
What do you suggest?
**Expected behavior**
I expect to be able to run the code without any errors.
**Environment overview (please complete the following information)**
- Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
- Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
- If method of install is [Docker], provide `docker pull` & `docker run` commands used
I used everything mentioned in this notebook:
`Relation-Extraction-BioMegatron.ipynb`
**Environment details**
If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version
**Additional context**
Add any other context about the problem here.
Example: GPU model
I used GPU version of the Google Colab | {
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} | # Bugfix
- Fix a memory leak in the WER calculation loop in the notebook
- (NOTE: The notebook content has not changed, the diff is just large cause colab version updated)
# Update
- Update the model notebook to use the new artifact system + log the loss properly
Closes https://github.com/NVIDIA/NeMo/issues/2208 | {
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"which pip version do you have? Could be a problem with your environment. Are you able to pip install other packages? Like this: ```pip install pytorch-lightning==1.1.4``` ? I literally copy-pasted this: ```pip install nemo_toolkit[all]==1.0.0rc1``` and it worked."
] | 2021-05-14T23:11:58 | 2021-05-17T17:59:49 | 2021-05-17T17:59:49 | MEMBER | {
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} | null | null | null | Hi,
I have a problem installing NeMo from pip. I'm using the instructions [here](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/starthere/intro.html#pip)
Following them, I'm getting:
```
(venv) ✘ mszolucha@szal-workstation ~ sudo apt-get update && sudo apt-get install -y libsndfile1 ffmpeg
[...]
Reading package lists... Done
Reading package lists... Done
Building dependency tree
Reading state information... Done
libsndfile1 is already the newest version (1.0.28-7).
ffmpeg is already the newest version (7:4.2.4-1ubuntu0.1).
[...]
0 upgraded, 0 newly installed, 0 to remove and 298 not upgraded.
(venv) mszolucha@szal-workstation ~ pip install Cython
Requirement already satisfied: Cython in ./virtualenv/venv/lib/python3.8/site-packages (0.29.23)
(venv) mszolucha@szal-workstation ~ pip install nemo_toolkit['all']==1.0.0rc1
zsh: no matches found: nemo_toolkit[all]==1.0.0rc1
(venv) ✘ mszolucha@szal-workstation ~ pip install nemo_toolkit[all]==1.0.0rc1
zsh: no matches found: nemo_toolkit[all]==1.0.0rc1
```
What should I do to install NeMo?
My environment:
```
(venv) ✘ mszolucha@szal-workstation ~ python --version
Python 3.8.5
(venv) mszolucha@szal-workstation ~ nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 TITAN RTX Off | 00000000:01:00.0 Off | N/A |
| 23% 44C P0 33W / 280W | 0MiB / 24220MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
```
Ubuntu 20
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"This pull request **introduces 1 alert** when merging 85b00da1038709cc1b145f28c8e8dd043efb2932 into d1b37ed30a70bff00df4778b55b020d8bab2e6fb - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-df3f0c730d38a856855a25d9a48542d309857460)\n\n**new alerts:**\n\n* 1 for Variable defined multiple times",
... | 2021-05-14T20:48:05 | 2021-05-18T18:56:24 | 2021-05-18T18:56:22 | COLLABORATOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2210 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2210/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2210/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2210/events | https://github.com/NVIDIA/NeMo/pull/2210 | 891,991,299 | MDExOlB1bGxSZXF1ZXN0NjQ0NzcwNjE5 | 2,210 | Small updates in TalkNet and FastPitch | {
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} | * update `_loader` method in TalkNet and FastPitch for easy creating model from config for non-training/validating purposes (e.g. just check inference speed).
* update FastPitch config | {
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"> \r\n> \r\n> Doesn't this just dump the files into the project dir? @blisc any thoughts if this is correct ?\r\n\r\nActually I made this from \"\" to None last time to hope it fixes the w&b but it made the issue that now we have a folder name \"None\" and then there is another folder under that named \"checkpoint... | 2021-05-14T02:58:08 | 2021-08-06T08:01:13 | 2021-05-17T13:30:09 | COLLABORATOR | {
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} | Fix the versioning name to avoid creating the extra folder name "None" when training is supposed to continue. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2208 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2208/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2208/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2208/events | https://github.com/NVIDIA/NeMo/issues/2208 | 891,533,211 | MDU6SXNzdWU4OTE1MzMyMTE= | 2,208 | Memory leak in 01_ASR_with_NeMo.ipynb | {
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"Thanks for finding this, I'll send a PR tomorrow patching this. If you only need inference, you can freeze the model with model.freeze() and this will prevent any leak. \r\n\r\nIt's the notebook environment which has the potential for leaking cuda tensors, and scripts don't have this leakage.\r\n\r\nThe notebooks ... | 2021-05-14T02:07:58 | 2021-05-15T08:41:11 | 2021-05-15T08:41:11 | NONE | {
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} | null | null | null | Running 01_ASR_with_NeMo.ipynb WER cells appears to be leaking data in CUDA. With batch_size 32, using print(torch.cuda.memory_allocated()) prints 91,033,600 prior to running, then 129,805,824 after. There's a hard delete in https://github.com/NVIDIA/NeMo/blob/8e239ecc7d7bc1206c72daa207b0f122e85551f1/examples/asr/speech_to_text_infer.py that's missing in the notebook.
Additionally, a few more questions:
1) Is there some other way to run WER?
2) Why does batch size change the WER? Isn't it averaged across batches?
3) Is there any way to run this without Cuda, e.g., so that it can be used on Colab TPU's
Thanks! I have really appreciated getting to use this!
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"@okuchaiev fyi regarding the dockerfile ignore and correctly updating the self.trainer inside set_trainer."
] | 2021-05-14T01:51:43 | 2021-05-14T07:03:56 | 2021-05-14T06:55:42 | COLLABORATOR | {
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} | # Bugfixes
- Correct WER calculation for RNNT by resetting the metric after each computation.
- Prevent numba from performing a sync across PT cuda stream simply to initialize a cost vector.
- Correctly update the trainer value using `set_trainer`
- Adds .nemo and .ckpt files to dockerignore
# Additions
- Adds RNNT global flags to enable detached gradient normalization of encoder, decoder and joint models
- Adds `weights_init_scale` and `hidden_hidden_bias_scale` values (default to original values) to attempt reduction in variance between runs
- Add `tds_uniform` and `tds_normal` initialization modes for Jasper based models.
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- cherry pick the bugfix from main for SpecCutout into v1.0.0
Refer to https://github.com/NVIDIA/NeMo/pull/2201 | {
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"Closing. re-open if issue is still relevant."
] | 2021-05-13T01:12:36 | 2022-04-05T23:35:56 | 2022-04-05T23:35:56 | CONTRIBUTOR | {
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} | null | null | null | **Is your feature request related to a problem? Please describe.**
I would like to store my checkpoints/models using AWS/GCP so that when I run training on ephemeral instances, I don't lose my progress.
**Describe the solution you'd like**
Register a new callback on epoch end to upload the ckpt/Nemo file to AWS/GCP if the option is passed in the config file via the trainer (using the checkpoint callback field)
**Describe alternatives you've considered**
None, open to discussion though.
**Additional context**
I have a working implementation for a local repo, but would like to gauge interest for upstream support in NeMo before submitting a PR. | {
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"Thanks Robert for catching the bug! It looks like it has been around for a long time.\r\nWhy do you believe that \"it causes the placement of the masks to be limited to the lowest frequency bins\"?\r\nThe starting point for the frequency masking looks correct to me and can be any frequency. The only problem is tha... | 2021-05-12T15:39:02 | 2021-05-13T17:54:12 | 2021-05-13T17:54:12 | CONTRIBUTOR | {
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} | [Colab notebook demonstrating bug and fix](https://colab.research.google.com/drive/1Tksb-RXAeLSkBW72KDgm1DYUaPAnHXBx?usp=sharing)
There is a bug in the NeMo implementation of SpecCutout that causes the dimensions of masks to be transposed so that `rect_freq` is the size of the mask in the time dimension and `rect_time` is the size of the mask in the frequency dimension. This also causes the placement of the masks to be limited to the lowest frequency bins.
Output after fix:
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"You generally should not train with such long sequences, since it would use far too much memory + enormous amounts of training data + compute to train a model on long segments. Generally durations of 15-20 second segments are used for training. \r\n\r\nIt would be best to segment the audio in the second dataset. I... | 2021-05-12T07:13:23 | 2021-07-13T15:26:34 | 2021-07-13T15:26:34 | NONE | {
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I used QuartNet15*5 and fine-tuned it with the Persian Common Voice dataset. the WER was 30%.
Everything worked well and I saved it as a checkpoint.
After a while, I restored the checkpoint. I fine-tuned it with another Persian data set that has a longer duration than 16.7 seconds (i.e., 2 minutes, 3minutes, and so on ).
When I test the model with the long duration audio, the vocabulary disarranged and the model could not transcript any audio correctly.
What's your idea for fine-tuning QuartNet15*5 with a long duration?
Thanks | {
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"Thanks for the PR, could you sign your commit according to the instructions on the dco bot ?(https://github.com/NVIDIA/NeMo/pull/2199/checks?check_run_id=2563046850)\r\n",
"@titu1994 done with the signoff",
"Thanks for the fix !"
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"wouldn't this break all the checkpoints trained with pre_ln=True?",
"This pull request **introduces 1 alert** when merging 06d1bb03d4144ea1d95b1656083db015711489c2 into d1b37ed30a70bff00df4778b55b020d8bab2e6fb - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-72c6d188f1fb006ed4703c0de0401a0abd5... | 2021-05-11T21:16:59 | 2021-05-18T17:07:28 | 2021-05-18T17:07:23 | COLLABORATOR | {
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Signed-off-by: Jocelyn Huang <jocelynh@nvidia.com> | {
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"+ please run tests in an env that does not have pynini installed to ensure pytest tests/ --cpu is runnable "
] | 2021-05-11T18:25:09 | 2021-05-12T21:23:01 | 2021-05-12T21:22:58 | CONTRIBUTOR | {
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} | * Allow models such as facebook/bart-large in NMT encoder
* Fixes in nmt webapp | {
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} | # Changelog
- Adds the support to change the SqueezeExcite context window post instantiation inside a given ConvASREncoder module.
- Adds tests to ensure the model updates the configs and changes the modules SE behaviour
# Usage
```python
model = ASRModel.from_pretrained("stt_en_citrinet_256")
model.change_conv_asr_se_context_window(context_window=128, update_config=True) # equivalent to 128 * 0.01s of context
``` | {
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"What are you trying to accomplish? a) just to extract .pt from .nemo or b) export the model to Torchscript ? \r\nIf a): then simply use 'tar xvf model.nemo'\r\nIf b): Megatron model currently is not exportable to Torchscript. You can export it to ONNX though by supplying name with .onnx extension to export()\r\n",... | 2021-05-10T23:34:11 | 2021-05-18T01:15:04 | 2021-05-18T01:15:04 | NONE | {
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} | null | null | null | **Describe the bug**
When trying to do a `model.export()` of `qa_squadv2.0_megatron_uncased` nemo model I get `RuntimeError: Could not export Python function call '_ReduceFromModelParallelRegion'`
**Steps/Code to reproduce bug**
```
wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/nemo/qa_squadv2_0_megatron_uncased/versions/1.0.0rc1/zip -O qa_squadv2_0_megatron_uncased_1.0.0rc1.zip
import nemo
import nemo.collections.nlp as nemo_nlp
model = nemo_nlp.models.question_answering.qa_model.QAModel.from_pretrained(model_name="qa_squadv2.0_megatron_uncased")
model.export('/QA.pt')
```
**Expected behavior**
The nemo model should be exported as a PyTorch .pt model.
**Environment overview (please complete the following information)**
- Environment location: AWS
Running in docker container `nvcr.io/nvidia/pytorch:21.04-py3` with nemo installed via `python -m pip install git+https://github.com/NVIDIA/NeMo.git@main#egg=nemo_toolkit[all]`
The last section of output is:
```
[NeMo W 2021-05-10 23:12:08 megatron_bert:185] Megatron-lm checkpoint version not found. Setting checkpoint_version to 0.
[NeMo I 2021-05-10 23:12:08 megatron_bert:193] Checkpoint loaded from from /root/.cache/torch/megatron/megatron-bert-345m-uncased
[NeMo I 2021-05-10 23:12:11 modelPT:434] Model QAModel was successfully restored from /root/.cache/torch/NeMo/NeMo_1.0.0/qa_squadv2.0_megatron_uncased/747e4a01f09deb5e4269b1b36cfa4d8e/qa_squadv2.0_megatron_uncased.nemo.
>>> model.export('/foo.pt')
[NeMo W 2021-05-10 23:12:48 export_utils:198] Swapped 49 modules
> initializing torch distributed ...
> initializing tensor model parallel with size 1
> initializing pipeline model parallel with size 1
> setting random seeds to 1234 ...
> initializing model parallel cuda seeds on global rank 0, model parallel rank 0, and data parallel rank 0 with model parallel seed: 3952 and data parallel seed: 1234
[NeMo W 2021-05-10 23:12:49 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/jit/_trace.py:720: UserWarning: `optimize` is deprecated and has no effect. Use `with torch.jit.optimized_execution() instead
warnings.warn(
[NeMo W 2021-05-10 23:12:53 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/megatron/mpu/utils.py:22: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
assert numerator % denominator == 0, '{} is not divisible by {}'.format(
[NeMo W 2021-05-10 23:12:53 nemo_logging:349] /opt/conda/lib/python3.8/site-packages/torch/_tensor.py:565: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.
To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at ../aten/src/ATen/native/BinaryOps.cpp:335.)
return torch.floor_divide(self, other)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/conda/lib/python3.8/site-packages/nemo/core/classes/exportable.py", line 194, in export
jitted_model.save(output)
File "/opt/conda/lib/python3.8/site-packages/torch/jit/_script.py", line 495, in save
return self._c.save(str(f), **kwargs)
RuntimeError:
Could not export Python function call '_ReduceFromModelParallelRegion'. Remove calls to Python functions before export. Did you forget to add @script or @script_method annotation? If this is a nn.ModuleList, add it to __constants__:
/opt/conda/lib/python3.8/site-packages/megatron/mpu/mappings.py(149): reduce_from_tensor_model_parallel_region
/opt/conda/lib/python3.8/site-packages/megatron/mpu/layers.py(191): forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1003): _slow_forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1015): _call_impl
/opt/conda/lib/python3.8/site-packages/megatron/model/language_model.py(183): forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1003): _slow_forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1015): _call_impl
/opt/conda/lib/python3.8/site-packages/megatron/model/language_model.py(321): forward
/opt/conda/lib/python3.8/site-packages/megatron/model/language_model.py(414): forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1003): _slow_forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1015): _call_impl
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/modules/common/megatron/megatron_bert.py(163): forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1003): _slow_forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1015): _call_impl
/opt/conda/lib/python3.8/site-packages/nemo/collections/nlp/models/question_answering/qa_model.py(81): forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1003): _slow_forward
/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py(1015): _call_impl
/opt/conda/lib/python3.8/site-packages/torch/jit/_trace.py(950): trace_module
/opt/conda/lib/python3.8/site-packages/torch/jit/_trace.py(733): trace
/opt/conda/lib/python3.8/site-packages/nemo/core/classes/exportable.py(184): export
<stdin>(1): <module>
``` | {
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"LGTM."
] | 2021-05-10T21:42:16 | 2021-08-06T08:01:24 | 2021-05-11T00:28:10 | COLLABORATOR | {
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"This pull request **introduces 1 alert** when merging a5b2e9be03ccadfd608d829ab10809af788c0a2f into b44a1077df4579ecdba9a5386fbfe2c0bcd20667 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-2d1e71128b7ca228053fb661556cd8f6fede1c7f)\n\n**new alerts:**\n\n* 1 for Unused import"
] | 2021-05-10T13:46:46 | 2021-05-11T06:17:14 | 2021-05-10T22:15:10 | CONTRIBUTOR | {
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"I'm not too familiar with the internals of PTL, but I'm guessing the solution is to determine whether there are GPUs _or_ TPUs and not _just_ GPUs.",
"We don't support TPU training, and it's not tested. If you get it working, please let us know. ",
"looks like you can try disabling learning rate scheduler. but... | 2021-05-08T18:13:51 | 2021-05-10T22:18:39 | 2021-05-10T22:18:39 | CONTRIBUTOR | {
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} | null | null | null | **Describe the bug**
Training any model does not work with TPUs due to an error with the way `modelPT.py` calculates `optim_config['sched']['t_num_workers']` [here](https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/modelPT.py#L649).
When you have 0 GPUs, but are still using TPUs, `t_num_workers` is 0.
This causes a division by zero error [here](https://github.com/NVIDIA/NeMo/blob/main/nemo/core/optim/lr_scheduler.py#L646).
```
Exception in device=TPU:0: division by zero
Traceback (most recent call last):
File "/usr/local/lib/python3.7/dist-packages/torch_xla/distributed/xla_multiprocessing.py", line 330, in _mp_start_fn
_start_fn(index, pf_cfg, fn, args)
File "/usr/local/lib/python3.7/dist-packages/torch_xla/distributed/xla_multiprocessing.py", line 324, in _start_fn
fn(gindex, *args)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/accelerators/tpu_accelerator.py", line 135, in tpu_train_in_process
self.__setup_tpu_training(model, trainer)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/accelerators/tpu_accelerator.py", line 222, in __setup_tpu_training
self.setup_optimizers(model)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/accelerators/accelerator.py", line 150, in setup_optimizers
optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model)
File "/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/optimizers.py", line 31, in init_optimizers
optim_conf = model.configure_optimizers()
File "/usr/local/lib/python3.7/dist-packages/nemo/core/classes/modelPT.py", line 699, in configure_optimizers
self.setup_optimization()
File "/usr/local/lib/python3.7/dist-packages/nemo/core/classes/modelPT.py", line 691, in setup_optimization
optimizer=self._optimizer, scheduler_config=scheduler_config, train_dataloader=self._train_dl
File "/usr/local/lib/python3.7/dist-packages/nemo/core/optim/lr_scheduler.py", line 596, in prepare_lr_scheduler
drop_last=drop_last,
File "/usr/local/lib/python3.7/dist-packages/nemo/core/optim/lr_scheduler.py", line 646, in compute_max_steps
sampler_num_samples = math.ceil(num_samples / num_workers)
ZeroDivisionError: division by zero
```
**Steps/Code to reproduce bug**
Follow any colab with TPU support and set `trainer.tpu_cores=8` in the config.
```
from hydra.experimental import compose, initialize
import pytorch_lightning as pl
from omegaconf import OmegaConf
from nemo.collections.asr.models import EncDecCTCModel
from nemo.core.config import hydra_runner
from nemo.utils import logging
from nemo.utils.exp_manager import exp_manager
with initialize(config_path="configs"):
cfg = compose(config_name="config")
print(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
trainer = pl.Trainer(**cfg.trainer)
exp_manager(trainer, cfg.get("exp_manager", None))
asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer)
trainer.fit(asr_model)
```
**Expected behavior**
Training works.
**Environment overview (please complete the following information)**
- Environment location: Colab
- Method of NeMo install: `pip install nemo_toolkit['all']==1.0.0rc1`
**Environment details**
Otherwise, please provide:
- OS version: Debian ? Not sure what colab runs
- PyTorch version: 1.7
- Python version: 3.7
**Additional context**
Installed PyTorch XLA using:
```
pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.7-cp37-cp37m-linux_x86_64.whl
pip install pytorch-lightning
```
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- UI improvements
- refactored code | {
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"It's better to have higher number_of_samples/speaker with shorter durations (of about 4 sec) than vice versa. ",
"Hi, @nithinraok Thank you!"
] | 2021-05-08T08:36:40 | 2021-05-28T08:39:41 | 2021-05-28T08:39:41 | NONE | {
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} | null | null | null | Hey there,
I was trying to fine-tune the pre-trained speaker recognition model [`Speakernet`](https://ngc.nvidia.com/catalog/models/nvidia:nemo:speakerrecognition_speakernet) in order for it to recognize our registered speakers. I noticed in the tutorials the dataset `an4` was only used for illustration purposes, I wonder in real cases, how much data would be considered reasonable for fine-tuning? For example, in our system, we have about 15 registered users, whose voice would need to be recognized through about 4 seconds of speech for inference. Would it be enough to fine-tune with 20 recordings of about 5 seconds of each user's speech? Or might it be a better idea to try to gather the long speech of the users (10 recordings of 30 seconds speech for instance)? Thank you for your help in advance!
Cheers,
Xu | {
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"This should be fixed via https://github.com/NVIDIA/NeMo/pull/2178"
] | 2021-05-08T03:44:37 | 2021-05-10T17:38:26 | 2021-05-10T17:38:26 | NONE | {
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} | null | null | null | Some interface changes in 1.3.0 (at least around pl.ModelCheckpoint), breaks some expectation in the current code base.
`requirements/requirements.txt` has `pytorch-lightning>=1.2.8` which could lead the installation of pytorch-lightning 1.3.0.
Or aybe you are already planning on supporting 1.3.0? | {
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"Librispeech KenLM should be built on concatenated datasets of entire Librispeech lm text corpus (after normalization to lower case) + the 3 train transcripts. Overall it should be close to 41 M lines of text rather than the 300k lines of text here. \r\n\r\nSecondly, this sentence is confusing - \"However I got hig... | 2021-05-07T20:12:08 | 2021-05-10T22:16:19 | 2021-05-10T22:16:18 | NONE | {
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} | null | null | null | Hi,
I trained a Language Model with [train_kenlm.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_language_modeling/ngram_lm/train_kenlm.py) (**6-gram** with **286967 l**ines of text collected from LibriSpeech's train_clean_100, train_clean_360, train_other_500 and some other standard English text lines).
Results of counting:
`
=== 1/5 Counting and sorting n-grams ===
Reading /home/lang_model/model.tmp.txt
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
Unigram tokens 16718614 types 937
=== 2/5 Calculating and sorting adjusted counts ===
Chain sizes: 1:11244 2:822115520 3:1541466624 4:2466346752 5:3596755712 6:4932693504
Substituting fallback discounts for order 0: D1=0.5 D2=1 D3+=1.5
Statistics:
1 937 D1=0.5 D2=1 D3+=1.5
2 287721 D1=0.506285 D2=1.04345 D3+=1.59677
3 3215127 D1=0.697273 D2=1.12271 D3+=1.48872
4 8173916 D1=0.82992 D2=1.20302 D3+=1.43574
5 11728534 D1=0.908197 D2=1.24826 D3+=1.3993
6 13666388 D1=0.918745 D2=1.31875 D3+=1.46753
Memory estimate for binary LM:
type MB
probing 770 assuming -p 1.5
probing 904 assuming -r models -p 1.5
trie 337 without quantization
trie 168 assuming -q 8 -b 8 quantization
trie 291 assuming -a 22 array pointer compression
trie 122 assuming -a 22 -q 8 -b 8 array pointer compression and quantization
=== 3/5 Calculating and sorting initial probabilities ===
Chain sizes: 1:11244 2:4603536 3:64302540 4:196173984 5:328398952 6:437324416
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
####################################################################################################
=== 4/5 Calculating and writing order-interpolated probabilities ===
Chain sizes: 1:11244 2:4603536 3:64302540 4:196173984 5:328398952 6:437324416
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
####################################################################################################
=== 5/5 Writing ARPA model ===
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
Name:lmplz VmPeak:13261380 kB VmRSS:11264 kB RSSMax:2523692 kB user:25.2489 sys:5.2742 CPU:30.5231 real:19.9458
[NeMo I 2021-05-07 01:48:58 train_kenlm:135] Running binary_build command
/home/lang_model/kenlm/build/bin/lmplz -o 6 --text /home/lang_model/model.tmp.txt --arpa /home/lang_model/model.tmp.arpa --discount_fallback
Reading /home/lang_model/model.tmp.arpa
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
Identifying n-grams omitted by SRI
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
Writing trie
----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100
vin
SUCCESS
`
Language model is trained without any error and in evaluation using [eval_beamsearch_ngram.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py), I got better WER i.e. **2.61% on Dev_clean and 6.27% on Dev_other by alpha=1.0 and beta=1.0.**
However I got higher WER using LM i.e. approx 16.4% as compared to 3.4% WER using only CitriNet without LM on my audios.
When I saw the predicted output I found that some consecutive words are missing, hence WER is high.
One of the examples is given below:
`
**Ground truth:** " hello welcome how can i help you today eh what kind of **products you** guys make sorry i **couldn't this can please help me with more** i recently here purchase reflects three dot o from one of the stores and this product is not behaving"
**Predicted output:** "hello welcome how can i help you today what kind of guys make sorry i recently purchase reflects three doo from one of the stores and this product is not behaving"
`
In the above example you can see bold text is missing in the predicted output, this is the output on choosing beam_alpha=1.0 and beam_beta=1.0.
And there is lot of variations in output for missing words by just changing alpha and beta values.
Why the words are missing in the prediction and how to decide the right alpha and beta values?
@EmreOzkose did you face the same? | {
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"This pull request **introduces 1 alert** when merging bc2cdec3c9e9767af3ef755a8160db979f572d59 into d9aab777c4954d13ae95d8b71ef08de65c947f5c - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-30686a7b465ee52e9ed5f4017a818b2b8d5eb153)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2021-05-07T19:04:37 | 2021-05-07T21:50:39 | 2021-05-07T21:50:36 | CONTRIBUTOR | {
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} | improved cardinal in textnormalization in terms of perf | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2178 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2178/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2178/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2178/events | https://github.com/NVIDIA/NeMo/pull/2178 | 879,303,348 | MDExOlB1bGxSZXF1ZXN0NjMzMDMxOTcw | 2,178 | Update ptl to 1.3 on main branch | {
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"This pull request **introduces 1 alert** when merging 1c2ead52bc3b9c67f46f46636b31a0fc01a39935 into b44a1077df4579ecdba9a5386fbfe2c0bcd20667 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-49f72b46b780b693b22b9ea54e2714adcf6aa8e4)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2021-05-07T16:22:37 | 2021-05-10T17:37:47 | 2021-05-10T17:37:26 | COLLABORATOR | {
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} | # Changelog
- Update PTL to 1.3
- Fix related broken tests in NeMo | {
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"Thanks for noticing this bug. I'll fix this in #2166 ",
"Oh, it looks like someone already fixed it. We will fix this in the upcoming 1.0 release."
] | 2021-05-07T13:36:28 | 2021-05-07T14:35:54 | 2021-05-07T14:35:08 | NONE | {
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} | variable name `spec_gen` was used for parsing text and creating spectogram, which should be `spec_generator` | {
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"If it's the KenLM binary causing the memory issue, then a smaller N-gram can be used. However we don't have much control over the c++ swig decoders so from our side there isn't a sufficient solution to reduce memory. Maybe @vsl9 has some ideas?",
"If the memory issue is from the beam search decoder, then you may... | 2021-05-07T12:50:17 | 2021-05-28T08:37:02 | 2021-05-28T08:37:02 | NONE | {
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} | null | null | null | Hello, I am using an n-gram KenLM (my language model is only 67 MBs) with BeamSearchDecoderWithLM. Scorer class inside BeamSearchDecoderWithLM which is imported from swig_decoders is using 2.3 gb of ram. I was wondering if there is anything I can do to reduce this memory requirement? I am trying to run this on a device with low memory and I am running out of memory during the initialization of BeamSearchDecoderWithLM.
Is there any alternatives to Scorer that I can swap? I'm okay with it performing worse as long as memory requirements are reduced.
Thanks. | {
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"> exortable changes look good to me. @blisc what about tts changes?\r\n\r\ntts changes are to conform to new extended syntax accepted by onnx.export (optional Dict as the last input tuple element, representing kwargs) instead of former non-standard extended syntax I briefly introduced in previous revision (accepti... | 2021-05-07T04:16:52 | 2021-05-17T13:30:17 | 2021-05-17T13:30:17 | COLLABORATOR | {
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} | Also, removed previous (non–functional) extension for dicts in export - this schema matches what onnx.export expects.
Signed-off-by: Boris Fomitchev <bfomitchev@nvidia.com> | {
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} | # Bugfix
- Make all ITN tests optional (will run only if installed)
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https://github.com/NVIDIA/NeMo/issues/2161 | {
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} | # Changelog
- Upper bounds PTL version to 1.2.10 for main branch
Signed-off-by: smajumdar <titu1994@gmail.com> | {
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"@okuchaiev @ericharper we need to enable the full CI before merging this",
"> @okuchaiev @ericharper we need to enable the full CI before merging this\r\n\r\nLooks like the Jenkinsfile reverted to main. I replaced with v1.0.0 again."
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"@titu1994 Could you review the register_artifact docs?\r\n\r\n@MaximumEntropy Could you review NLP/NMT docs?\r\n\r\n"
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"thanks for feedback, will add."
] | 2021-05-06T08:31:24 | 2022-12-19T21:50:18 | 2021-05-17T16:24:47 | NONE | {
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} | null | null | null | ITN fails to normalize telephone numbers like this `five four two one two nine seven zero six five`
It works fine after replacing `zero` with `o`
I think this is because `zero` is in `zero.tsv` not `digit.tsv`, and you are not including `zero.tsv` in the digit FST.
https://github.com/NVIDIA/NeMo/blob/74f1264e049c10b012a1ea483fcd468f4d89ecf8/nemo_text_processing/inverse_text_normalization/taggers/telephone.py#L38
Also, it's quite common for ASR to output `oh` instead of `o`, like this `five oh seven` (507).
So do you think we can add two more rules like this?
```python
digit = ( pynini.invert(pynini.string_file(get_abs_path("data/numbers/digit.tsv"))).optimize()
| pynini.cross("0", "o")
| pynini.cross("0", "zero")
| pynini.cross("0", "oh")
)
```
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"This pull request **introduces 3 alerts** when merging d75335ceb01cff0fcd27dbfdc171669389f6cc15 into 74f1264e049c10b012a1ea483fcd468f4d89ecf8 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-6b9dd3e2f2dbe721fa62dd144bab1d74313eb4c8)\n\n**new alerts:**\n\n* 1 for Unused local variable\n* 1 for Un... | 2021-05-06T02:38:41 | 2022-01-11T16:37:00 | 2021-05-14T22:03:39 | COLLABORATOR | {
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} | This PR adds support for multilingual training for NMT.
### Usage ###
To train a model in multilingual mode, simply set the following:
* `model.multilingual=True`
* `model.encoder_tokenizer.library=sentencepiece` and `model.decoder_tokenizer.library=sentencepiece`.
Currently we only support sentencepiece for multilingual NMT.
* `model.src_language=['lang1','lang2']` or `model.tgt_language=['lang1','lang2']`
This PR only supports `many-to-one` or `one-to-many` model, as a result, only either of the above can be a list.
* `model.train_ds.metadata_file = [a,b]` if using tarred dataset
or
`model.train_ds.src_file_name = [a.src,b.src]` and `model.train_ds.tgt_file_name = [a.ref,b.ref]`
* MultiValidation from #2113 can be used to provide val sets from different languages
NOTE: All the lists in the configuration, including src/tgt_languages, train_set, val_set need to be in the same order.
That is,
if `model.tgt_language=['de','fr']`, the train data should also be provided as `['de_train_data','fr_train_data']`.
### Additional Info ###
Under the hood, the model automatically adds language identifier tokens which are created as `control_symbols` in sentencepiece.
So for instance, if `model.tgt_language=['de','fr']`, `<de>` is prepended to all German source sentences and `<fr>` is prepended to all French source sentences.
### Edit ###
The PR also does the following changes to the current `TarredTranslationDataset`:
* Adjust `__len__` of the dataset based on the world size when shard strategy is set to `scatter`.
* Change naming convention of fragment pkl files (during tar file creation in `preproc_mt_data`) to make them globally unique in the dataset.
* Remove `load_from_cached_dataset`.
* Small fix for a variable name and file ptr when only tar metadata is passed.
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} | This PR makes changes to the HifiGan module that should make it jittable/torchscriptable. nemo export should work as well.
Verified compatible with checkpoint on NGC.
```python
from nemo.collections.tts.models import HifiGanModel
model = HifiGanModel.from_pretrained(model_name="tts_hifigan")
model.export("test.onnx")
```
Signed-off-by: Ryan Leary <rleary@nvidia.com> | {
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} | This PR :
* Adds new NMSE spectral clustering method
* Updates Docs to include dataset creation for any dataset, with special mentions for AMI and CH109.
* Adds ability to diarize for unknown number of speakers/session with very good accuracies
* Replace SpectralCluster from requirements with pyamg (math computing repo)
Signed-off-by: nithinraok <nithinrao.koluguri@gmail.com> | {
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- All references of r1.0.0rc1 -> v1.0.0
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"This pull request **introduces 1 alert** when merging ed015efee22a8d532065f533db44694352a8023a into 7a62084946e93c4ce4fc885c1af8634e8e160937 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-17e3c52503f2d4b8a1bb0c8816a7aa08885956f1)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2021-05-05T16:03:01 | 2021-05-05T18:31:35 | 2021-05-05T18:31:35 | CONTRIBUTOR | {
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"Hello,\r\nIt looks like your nemo file does not have the model weights checkpoint that should be saved. Can you untar the nemo file and make sure it has a model_weights.ckpt and model_config.yaml? If it doesn't, please let me know what the contents are.",
"> Hello,\r\n> It looks like your nemo file does not have... | 2021-05-05T07:58:49 | 2021-05-12T07:06:54 | 2021-05-12T07:06:54 | NONE | {
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} | null | null | null | I trained Tacotron2 in Thai language using NeMo and it failed to build jmir format.
I use command for build jmir like in https://docs.nvidia.com/deeplearning/jarvis/user-guide/docs/service-tts.html#pipeline-configuration .
I tried to use Tacotron2 that provides in https://ngc.nvidia.com/catalog/models/nvidia:tlt-jarvis:speechsynthesis_english_tacotron2/files and its works.
What is different between these two nemo files? | {
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"@ekmb note that v1.0.0 has been cut. If this is a bugfix, send it to v1.0.0\r\n"
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} | We instantiate a TorchMetric for each dataloader. This needs to happen after the dataloaders are created. Moved the logic for instantiating TorchMetrics to after the dataloaders are created. This is necessary when restoring from .nemo. | {
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"This pull request **introduces 6 alerts** when merging 1677f4448ac8536a58f2d6a533e7234cabdfa369 into 855ce265b80c0dc40f4f06ece76d2c9d6ca1be8d - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-796aa3ea4cf0df26414f426444817cfc0c976ee7)\n\n**new alerts:**\n\n* 5 for Unused import\n* 1 for Missing ca... | 2021-05-03T21:20:20 | 2021-05-04T16:44:54 | 2021-05-04T16:44:47 | CONTRIBUTOR | {
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"@VahidooX fyi",
"The transcribe method used to return Tensors, but now it returns numpy arrays. So softmax does not need the conversion. I think you are using an older version of NeMo with the old transcribe method.",
"@EmreOzkose \r\nHi!\r\n\r\nDid you try to run eval_beamsearch_ngram.py from inside of Docke... | 2021-05-03T09:18:08 | 2021-06-25T10:32:09 | 2021-05-03T09:18:14 | NONE | {
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} | null | null | null | Hi! I encountered a problem during inference of Citrinet-1024 with a language model. When I run [eval_beamsearch_ngram.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py), output is :
```
Traceback (most recent call last):
File "eval_beamsearch_ngram.py", line 347, in <module>
main()
File "eval_beamsearch_ngram.py", line 256, in main
all_probs = [kenlm_utils.softmax(logits) for logits in all_logits]
File "eval_beamsearch_ngram.py", line 256, in <listcomp>
all_probs = [kenlm_utils.softmax(logits) for logits in all_logits]
File "/home/username/workspace/nemo_asr/scripts/kenlm_utils.py", line 39, in softmax
print(x - np.max(x))
File "<__array_function__ internals>", line 6, in amax
File "/home/username/anaconda3/envs/nemo/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 2706, in amax
keepdims=keepdims, initial=initial, where=where)
File "/home/username/anaconda3/envs/nemo/lib/python3.7/site-packages/numpy/core/fromnumeric.py", line 85, in _wrapreduction
return reduction(axis=axis, out=out, **passkwargs)
TypeError: max() received an invalid combination of arguments - got (out=NoneType, axis=NoneType, ), but expected one of:
* ()
* (name dim, bool keepdim)
didn't match because some of the keywords were incorrect: out, axis
* (Tensor other)
* (int dim, bool keepdim)
didn't match because some of the keywords were incorrect: out, axis
```
So I modified kenlm_utils/softmax function as
```
def softmax(x, to_cpu=False):
if to_cpu:
x = x.cpu().numpy()
e = np.exp(x - np.max(x))
return e / e.sum(axis=-1).reshape([x.shape[0], 1])
```
and call with to_cpu=True in [eval_beamsearch_ngram.py](https://github.com/NVIDIA/NeMo/blob/main/scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py). The problem is solved. I just want to inform people who faced the same problem. | {
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"Thanks for the PR ! It was an oversight since for papers we use batch size 1 with transcribe() method, and it disables dithering inside."
] | 2021-05-01T22:42:49 | 2021-05-02T20:02:09 | 2021-05-02T20:00:14 | CONTRIBUTOR | {
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} | In the eval mode the model should be deterministic (return the same value for the same input).
I find it strange that this was not fixed before - this makes exports and evaluation non-deterministic. | {
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"Could you try quartzner with batch size 1?",
"Issue solved, SpecAugment and SpecCutout weren't getting the seed, setting the seed manually on their rng with `self._rng.seed(8)` in the `__init__()` of each solved the issue. Thank you. "
] | 2021-04-30T22:24:59 | 2021-05-01T14:01:45 | 2021-05-01T14:01:45 | CONTRIBUTOR | {
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} | null | null | null | I'm working on implementing something new but I'm having trouble getting the model to output the same thing each time. The output from the featurizer is consistent, but the output from Quartznet 15x5 (encoder + decoder) is slightly different each time. I am aware of #1030 and have taken the following steps.
- Set pytorch lightning seed with `pl.utilities.seed.seed_everything(8)`
- Set num_workers = 0 in the dataloader (This is the default NeMo value anyway, right?)
- Setting shuffle=False in the train dataloader
- Pass the flag `deterministic=True` to the lightning trainer.
- Turn off dithering (this shouldn't really matter since I'm setting a seed)
- Setting the environment variable `CUBLAS_WORKSPACE_CONFIG=:16:8`
Any idea what could be causing this? If I load the model weights in to a quartznet like model in pytorch lightning and set the seed, the output is the same every time. Thank you! | {
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"This pull request **introduces 1 alert** when merging 3afd577d38d6304614bfc59b57beae7f58e2679f into b6bdccd4effef234b7cb44917d57f1368b9b23d9 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-cc87bcbdf4417483b27b2f378f45917a0f3961e2)\n\n**new alerts:**\n\n* 1 for Unused import"
] | 2021-04-30T21:54:24 | 2021-05-01T03:11:38 | 2021-05-01T00:31:23 | COLLABORATOR | {
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} | …orting
I did not remove graph surgeon install from Dockerfile - as it's nice to have anyway and maybe some users already use it.
But I did remove the extra GS step on export. | {
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} | [] | closed | false | null | [] | null | [] | 2021-04-30T21:00:33 | 2021-05-05T18:06:47 | 2021-05-05T18:06:44 | COLLABORATOR | {
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} | Adding:
- Data loading test for the `FastSpeech2Dataset`
- FS2 data format documentation
- Some TTS class APIs | {
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"@EmreOzkose change. you have changed “Citrinet” in place of “quartznet”. you should change the class name which should be “nemo.asr.models.EncDecCTCModelBPE” at place of “nemo.asr.models.EncDecCTCModel”. ",
"I changed the class name, but it didn't work.",
"The kenlm model being used is meant for Character enc... | 2021-04-30T08:04:17 | 2024-01-28T09:24:12 | 2021-05-05T08:40:29 | NONE | {
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} | null | null | null | Hi, I am using Citrinet models to transcribe an audio (taken from Mini-Librispeech) and it transcribes good. However when I use beam search decoding [in this tutorial](https://github.com/NVIDIA/NeMo/blob/acbd88257f20e776c09f5015b8a793e1bcfa584d/tutorials/asr/Offline_ASR.ipynb), I am getting nonrelevant results. What can be the problem? Is there anyone to face with that problem?
Target sentence: THE MANAGER TOOK MY ADVICE BUT MARIA'S STILL MISSING
```
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name='stt_en_citrinet_1024', strict=False)
transcript = asr_model.transcribe(paths2audio_files=[wav_path])[0]
print(f'Transcript: "{transcript}"')
```
> the manager took my advice but maria still missing
```
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name='stt_en_citrinet_1024', strict=False)
lm_path = 'lowercase_3-gram.pruned.1e-7.arpa'
beam_search_lm = nemo_asr.modules.BeamSearchDecoderWithLM(
vocab=list(asr_model.decoder.vocabulary),
beam_width=16,
alpha=2, beta=1.5,
lm_path=lm_path,
num_cpus=max(os.cpu_count(), 1),
input_tensor=False)
result_lm = beam_search_lm.forward(log_probs = np.expand_dims(probs, axis=0), log_probs_length=None)
print(result_lm)
```
> [[(-163.07594299316406, 'a'),
(-164.35794067382812, 'an'),
(-171.96630859375, 'aa'),
(-173.6032257080078, 'aan'),
(-2158.844482421875, 'aavn'),
(-2158.845458984375, 'aai'),
(-2158.8759765625, 'aaan'),
(-2159.080078125, 'aabn'),
(-2159.123779296875, 'aani'),
(-2159.14208984375, 'avn'),
(-2159.169677734375, 'aav'),
(-2159.1845703125, 'aaa'),
(-2159.33349609375, 'aab'),
(-2159.3623046875, 'abn'),
(-2159.367431640625, 'aain'),
(-2159.513427734375, 'aavni')]]
In adition to that, when I use QuartzNet15x5Base, I got good results.
```
asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name='QuartzNet15x5Base-En', strict=False)
lm_path = 'lowercase_3-gram.pruned.1e-7.arpa'
beam_search_lm = nemo_asr.modules.BeamSearchDecoderWithLM(
vocab=list(asr_model.decoder.vocabulary),
beam_width=16,
alpha=2, beta=1.5,
lm_path=lm_path,
num_cpus=max(os.cpu_count(), 1),
input_tensor=False)
result_lm = beam_search_lm.forward(log_probs = np.expand_dims(probs, axis=0), log_probs_length=None)
print(result_lm)
```
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] | 2021-04-30T00:41:51 | 2021-04-30T01:58:57 | 2021-04-30T01:58:55 | CONTRIBUTOR | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2140 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2140/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2140/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2140/events | https://github.com/NVIDIA/NeMo/pull/2140 | 871,255,313 | MDExOlB1bGxSZXF1ZXN0NjI2MzAzNDYx | 2,140 | Update bert | {
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"This error happens usually in cases you train for limited steps and no validation has happend at all. Therefore there would be no validation loss logged. You may train for more steps, or drop the validation section, or increase the frequency of validation.",
"@VahidooX Thank you for your response! I had to set `... | 2021-04-29T12:16:36 | 2021-05-06T13:43:24 | 2021-05-06T13:43:24 | NONE | {
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} | null | null | null | Hello, I've been trying to pretrain a BERT model with the [`[examples/nlp/language_modeling/bert_pretraining.py]`](https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/language_modeling/bert_pretraining.py) script and the training hangs at the first epoch. Previously I used to train with the script that was there before [using PyTorch Lightning's DDPPlugin](https://github.com/NVIDIA/NeMo/commit/7d4ad85dc38d41eae4369f2d1b6ec8f3de5cf1b7#diff-388430d4055e0020d401768aa02e821c948d18afe24e37c10e9d443c88de0477) (i.e with older dependencies, using **nemo_toolkit[all]==1.0.0b1**) which worked just fine.
This is the raised exception now: `MisconfigurationException: ModelCheckpoint(monitor='val_loss') not found in the returned metrics: ['train_loss']. HINT: Did you call self.log('val_loss', value) in the LightningModule?`
**_P.S:_** I need to use this version of NeMo (1.0.0rc1) because I'm going to integrate another PyTorch Lightning plugin once this works, the 1.0.0rc1 is only compatible with an older Lightning version that doesn't support plugins.
- Environment location: personal laptop
- Method of NeMo install:
apt-get update && apt-get install -y libsndfile1 ffmpeg
pip install Cython
python -m pip install git+https://github.com/NVIDIA/NeMo.git@main
**Environment details**
- Ubuntu 20.4
- PyTorch version==1.8.1+cu111
- Python version==Python 3.8.5
- PyTorch Lightning version==1.2.10
- NeMo toolkit version==1.0.0rc4
Stack trace:
```
[NeMo W 2021-04-29 13:10:38 nemo_logging:349] /home/mallonus/tunbert-pip/lib/python3.8/site-packages/omegaconf/basecontainer.py:225: UserWarning: cfg.pretty() is deprecated and will be removed in a future version.
Use OmegaConf.to_yaml(cfg)
warnings.warn(
[NeMo I 2021-04-29 13:10:38 bert_pretraining:29] Config:
name: PretrainingBERTFromText
trainer:
gpus: 1
num_nodes: 1
max_epochs: 10
max_steps: null
accumulate_grad_batches: 1
precision: 16
amp_level: O1
accelerator: ddp
gradient_clip_val: 0.0
log_every_n_steps: 1
val_check_interval: 1.0
checkpoint_callback: false
logger: false
model:
nemo_path: null
only_mlm_loss: false
num_tok_classification_layers: 1
num_seq_classification_layers: 2
max_seq_length: 128
mask_prob: 0.15
short_seq_prob: 0.1
language_model:
pretrained_model_name: bert-base-uncased
lm_checkpoint: null
config:
attention_probs_dropout_prob: 0.1
hidden_act: gelu
hidden_dropout_prob: 0.1
hidden_size: 768
initializer_range: 0.02
intermediate_size: 3072
max_position_embeddings: 512
num_attention_heads: 12
num_hidden_layers: 12
type_vocab_size: 2
vocab_size: 30522
config_file: null
tokenizer:
tokenizer_name: ${model.language_model.pretrained_model_name}
vocab_file: null
tokenizer_model: null
special_tokens:
unk_token: '[UNK]'
sep_token: '[SEP]'
pad_token: '[PAD]'
bos_token: '[CLS]'
mask_token: '[MASK]'
eos_token: '[SEP]'
cls_token: '[CLS]'
train_ds:
data_file: dev-data/pretraining_data/train.txt
max_seq_length: ${model.max_seq_length}
mask_prob: ${model.mask_prob}
short_seq_prob: ${model.short_seq_prob}
batch_size: 2
shuffle: true
num_samples: -1
num_workers: 2
drop_last: false
pin_memory: false
validation_ds:
data_file: dev-data/pretraining_data/valid.txt
max_seq_length: ${model.max_seq_length}
mask_prob: ${model.mask_prob}
short_seq_prob: ${model.short_seq_prob}
batch_size: 2
shuffle: false
num_samples: -1
num_workers: 2
drop_last: false
pin_memory: false
optim:
name: adamw
lr: 3.0e-05
weight_decay: 0.0
sched:
name: CosineAnnealing
warmup_steps: null
warmup_ratio: 0.1
min_lr: 0.0
last_epoch: -1
exp_manager:
exp_dir: null
name: PretrainingBERTFromText
create_tensorboard_logger: true
create_checkpoint_callback: true
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
Using native 16bit precision.
[NeMo I 2021-04-29 13:10:38 exp_manager:216] Experiments will be logged at /home/mallonus/tunbert-master/nemo_experiments/PretrainingBERTFromText/2021-04-29_13-10-38
[NeMo I 2021-04-29 13:10:38 exp_manager:563] TensorboardLogger has been set up
[NeMo I 2021-04-29 13:10:47 modelPT:695] Optimizer config = AdamW (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
eps: 1e-08
lr: 3e-05
weight_decay: 0.0
)
[NeMo I 2021-04-29 13:10:47 lr_scheduler:617] Scheduler "<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7f44269322e0>"
will be used during training (effective maximum steps = 80) -
Parameters :
(warmup_steps: null
warmup_ratio: 0.1
min_lr: 0.0
last_epoch: -1
max_steps: 80
)
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
[NeMo I 2021-04-29 13:10:47 modelPT:695] Optimizer config = AdamW (
Parameter Group 0
amsgrad: False
betas: (0.9, 0.999)
eps: 1e-08
lr: 3e-05
weight_decay: 0.0
)
[NeMo I 2021-04-29 13:10:47 lr_scheduler:617] Scheduler "<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7f4426757b20>"
will be used during training (effective maximum steps = 80) -
Parameters :
(warmup_steps: null
warmup_ratio: 0.1
min_lr: 0.0
last_epoch: -1
max_steps: 80
)
initializing ddp: GLOBAL_RANK: 0, MEMBER: 1/1
INFO:root:Added key: store_based_barrier_key:1 to store for rank: 0
| Name | Type | Params
-------------------------------------------------------------------------
0 | bert_model | BertEncoder | 109 M
1 | mlm_classifier | BertPretrainingTokenClassifier | 24.1 M
2 | mlm_loss | SmoothedCrossEntropyLoss | 0
3 | nsp_classifier | SequenceClassifier | 592 K
4 | nsp_loss | CrossEntropyLoss | 0
5 | agg_loss | AggregatorLoss | 0
6 | validation_perplexity | Perplexity | 0
-------------------------------------------------------------------------
110 M Trainable params
0 Non-trainable params
110 M Total params
442.788 Total estimated model params size (MB)
[NeMo W 2021-04-29 13:10:49 nemo_logging:349] /home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The dataloader, val dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
warnings.warn(*args, **kwargs)
Validation sanity check: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 5.42it/s][NeMo I 2021-04-29 13:10:49 bert_lm_model:184] evaluation perplexity 26155.0595703125
[NeMo W 2021-04-29 13:10:49 nemo_logging:349] /home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The validation_epoch_end should not return anything as of 9.1. To log, use self.log(...) or self.write(...) directly in the LightningModule
warnings.warn(*args, **kwargs)
[NeMo W 2021-04-29 13:10:49 nemo_logging:349] /home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
warnings.warn(*args, **kwargs)
Epoch 0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:03<00:00, 2.73it/s, loss=10.7, v_num=0-38][NeMo I 2021-04-29 13:10:53 bert_lm_model:184] evaluation perplexity 26092.4775390625██████████████ | 1/2 [00:00<00:00, 6.30it/s]
[NeMo W 2021-04-29 13:10:53 nemo_logging:349] /home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/utilities/distributed.py:68: UserWarning: The {log:dict keyword} was deprecated in 0.9.1 and will be removed in 1.0.0
Please use self.log(...) inside the lightningModule instead.
# log on a step or aggregate epoch metric to the logger and/or progress bar (inside LightningModule)
self.log('train_loss', loss, on_step=True, on_epoch=True, prog_bar=True)
warnings.warn(*args, **kwargs)
Epoch 0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:03<00:00, 2.63it/s, loss=10.7, v_num=0-38Saving latest checkpoint...
Traceback (most recent call last):
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 637, in run_train
self.train_loop.run_training_epoch()
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 577, in run_training_epoch
self.trainer.run_evaluation(on_epoch=True)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 752, in run_evaluation
self.evaluation_loop.on_evaluation_end()
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/evaluation_loop.py", line 100, in on_evaluation_end
self.trainer.call_hook('on_validation_end', *args, **kwargs)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1095, in call_hook
trainer_hook(*args, **kwargs)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/callback_hook.py", line 185, in on_validation_end
callback.on_validation_end(self, self.lightning_module)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 212, in on_validation_end
self.save_checkpoint(trainer, pl_module)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 247, in save_checkpoint
self._validate_monitor_key(trainer)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 495, in _validate_monitor_key
raise MisconfigurationException(m)
pytorch_lightning.utilities.exceptions.MisconfigurationException: ModelCheckpoint(monitor='val_loss') not found in the returned metrics: ['train_loss']. HINT: Did you call self.log('val_loss', value) in the LightningModule?
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "models/bert-nvidia/bert_pretraining.py", line 33, in main
trainer.fit(bert_model)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 499, in fit
self.dispatch()
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 546, in dispatch
self.accelerator.start_training(self)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/accelerators/accelerator.py", line 73, in start_training
self.training_type_plugin.start_training(trainer)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py", line 114, in start_training
self._results = trainer.run_train()
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 670, in run_train
self.train_loop.on_train_end()
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 134, in on_train_end
self.check_checkpoint_callback(should_update=True, is_last=True)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/trainer/training_loop.py", line 164, in check_checkpoint_callback
cb.on_validation_end(self.trainer, model)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 212, in on_validation_end
self.save_checkpoint(trainer, pl_module)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 247, in save_checkpoint
self._validate_monitor_key(trainer)
File "/home/mallonus/tunbert-pip/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 495, in _validate_monitor_key
raise MisconfigurationException(m)
pytorch_lightning.utilities.exceptions.MisconfigurationException: ModelCheckpoint(monitor='val_loss') not found in the returned metrics: ['train_loss']. HINT: Did you call self.log('val_loss', value) in the LightningModule?
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Epoch 0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:03<00:00, 2.54it/s, loss=10.7, v_num=0-38]
``` | {
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"This pull request **introduces 1 alert** when merging cd8a56e9ff9ad495a6c805e6b703e668423dae02 into 6ce670b7ab3ab5c84ce388fa1968748cd859b8f8 - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-b1aeb4e49c3b999fff107cf21c3f4ba261ec6d4b)\n\n**new alerts:**\n\n* 1 for Unused import",
"This pull reque... | 2021-04-29T06:37:56 | 2021-05-04T20:55:41 | 2021-05-04T20:55:36 | MEMBER | {
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} | This PR changes the following:
* how register_artifacts work
* how save_to works
The usage of both register_artifacts and save_to method remains identical.
Key differences in behaiviour:
* register_artifacts always guarantees to return existing path to artifact at the time of call.
* save_to will replace and hash artifact names in config file before creating .nemo file. This is necessary to prevent name collisions
* when restoring artifacts from .nemo file it will create a temporary folder with artifacts for the lifetime of a model
* temporary folders associated with the model get deleted by destructor
TODOs:
* Some unittests fail
* Model parallel save_to (?)
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"Can you send this to v1.0.0 instead?",
"@blisc ready for review",
"link is fixed\r\nbranch choice is yet to be commented by @blisc ",
"> link is fixed\r\n> branch choice is yet to be commented by @blisc\r\n\r\nPlease use `v1.0.0` for all files and branches",
"Okay, now it's ready to be merged."
] | 2021-04-28T14:20:09 | 2021-05-17T17:21:25 | 2021-05-17T17:21:22 | CONTRIBUTOR | {
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- ASR BPE models did not correctly preserve the new tokenizer config after changing vocabulary
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"This pull request... | 2021-04-27T06:11:28 | 2021-04-27T18:01:22 | 2021-04-27T18:01:19 | CONTRIBUTOR | {
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* merge electronic (Evelina)
* updated docs for ITN and TN
* updated Tutorials
* unified deployment route
* unit tests for both | {
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"> The model is trained and I get a fina .nemo file at the end. Now, if I look at the config inside the final \"adapted\" model, I see\r\n\r\nQuite a silent bug there, sending a patch in https://github.com/NVIDIA/NeMo/pull/2126\r\n\r\nWe normally follow this procedure for finetuning our models as well, but didnt fa... | 2021-04-26T17:01:13 | 2021-05-28T08:36:21 | 2021-05-28T08:36:21 | NONE | {
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} | null | null | null | **Describe the bug**
It seems that while the vocabulary is updated and the model is trained, trying to restore the model from checkpoint still uses the old tokenizer path.
**Steps/Code to reproduce bug**
I did something very similar to what's in the 08_ASR_with_Subword_Tokenization.ipynb notebook:
```
model_cfg = ASRModel.restore_from(restore_path="stt_en_citrinet_1024.nemo", return_config=True)
classpath = model_cfg.target
imported_class = model_utils.import_class_by_path(classpath)
trainer = pl.Trainer(
logger=False, # Provided by exp_manager
checkpoint_callback=False,
accelerator="ddp", gpus=4, max_epochs=1)
citrinet = imported_class.restore_from(restore_path=model_path)
citrinet.set_trainer(trainer)
model_cfg["validation_ds"]["manifest_filepath"]= "/path/to/my/val.manifest"
model_cfg["test_ds"]["manifest_filepath"]= "/path/to/my/test.manifest"
model_cfg["train_ds"]["manifest_filepath"]= "/path/to/my/train.manifest"
new_opt = copy.deepcopy(model_cfg["optim"])
new_opt["lr"] = 0.0003
citrinet.change_vocabulary(
new_tokenizer_dir="/my/bpe_exp01/tokenizer_spe_unigram_v3000/",
new_tokenizer_type="bpe"
)
model_cfg["decoder"] = citrinet._cfg.decoder
citrinet.setup_training_data(train_data_config=model_cfg["train_ds"])
citrinet.setup_validation_data(val_data_config=model_cfg["validation_ds"])
citrinet.setup_test_data(test_data_config=model_cfg["test_ds"])
trainer.fit(citrinet)
citrinet.save_to(save_path="./citrinet_1024-adapted03.nemo")
```
The model is trained and I get a fina .nemo file at the end. Now, if I look at the config inside the final "adapted" model, I see:
```
'tokenizer': {'dir': '../../manifests/asrset_1.4/tokenizers/no_appen/tokenizer_spe_unigram_v1024/', 'type': 'bpe'}
```
**Expected behavior**
I would have expect the tokenizer path to be the right one. Also, for a model that I let the training happen for a longer number of epochs, I can't covert a checkpoint into a final nemo package because `load_from_checkpoint()` throws:
```
FileNotFoundError: Could not find ../../manifests/asrset_1.4/tokenizers/no_appen/tokenizer_spe_unigram_v1024/tokenizer.model
```
How can I fix that?
**Environment overview (please complete the following information)**
- Environment location: AWS
- Method of NeMo install: from source
**Environment details**
- OS version : ubuntu 16.04
- PyTorch version 1.7.1
- Python version : 3.8
- CUDA 10.2
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2. Perplexity metric refactored to take into account samples of different length in a batch. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2119 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2119/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2119/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2119/events | https://github.com/NVIDIA/NeMo/issues/2119 | 867,618,529 | MDU6SXNzdWU4Njc2MTg1Mjk= | 2,119 | ASR checkpoint saving error in 1.0.0rc1 | {
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"Please post your environment - torch version, pytorch lightning version, colab of local, etc. \r\n\r\nThere's no official b4 docker container, so I assume a local docker container was built. \r\n\r\nAnother thing is main branch codebase does not work with RC1 container, please build a container for main specifical... | 2021-04-26T11:50:46 | 2021-06-19T04:37:32 | 2021-06-19T04:37:32 | COLLABORATOR | {
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} | null | null | null | **Describe the bug**
Error during saving first checkpoint in 1.0.0rc1 and main branches
``` Epoch 0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 563/563 [02:55<00:00, 3.20it/s, loss=767, v_num=6-59Epoch 0, global step 6: val_wer reached 1.00113 (best 1.00113), saving model to "nemo_experiments/QuartzNet15x5/2021-04-26_11-06-59/checkpoints/QuartzNet15x5---val_wer=1.00-epoch=0.ckpt" as top 3
Saving latest checkpoint...
Epoch 0, global step 5: val_wer reached 1.00113 (best 1.00113), saving model to "nemo_experiments/QuartzNet15x5/2021-04-26_11-06-59/checkpoints/QuartzNet15x5---val_wer=1.00-epoch=0.ckpt" as top 3
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 560, in train
self.train_loop.run_training_epoch()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 562, in run_training_epoch
self.trainer.run_evaluation()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 667, in run_evaluation
self.evaluation_loop.on_evaluation_end()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/evaluation_loop.py", line 110, in on_evaluation_end
self.trainer.call_hook('on_validation_end', *args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 924, in call_hook
trainer_hook(*args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/callback_hook.py", line 177, in on_validation_end
callback.on_validation_end(self, self.get_model())
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 203, in on_validation_end
self.save_checkpoint(trainer, pl_module)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 250, in save_checkpoint
self._save_top_k_checkpoints(trainer, pl_module, monitor_candidates)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 592, in _save_top_k_checkpoints
self._update_best_and_save(current, epoch, step, trainer, pl_module, metrics)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 644, in _update_best_and_save
self._save_model(filepath, trainer, pl_module)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 361, in _save_model
self.save_function(filepath, self.save_weights_only)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/properties.py", line 257, in save_checkpoint
self.checkpoint_connector.save_checkpoint(filepath, weights_only)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py", line 392, in save_checkpoint
checkpoint = self.dump_checkpoint(weights_only)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py", line 277, in dump_checkpoint
callback_states = self.trainer.on_save_checkpoint()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/callback_hook.py", line 199, in on_save_checkpoint
state = callback.on_save_checkpoint(self, self.get_model())
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/utilities/distributed.py", line 39, in wrapped_fn
return fn(*args, **kwargs)
TypeError: on_save_checkpoint() missing 1 required positional argument: 'checkpoint'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "NeMo/examples/asr/speech_to_text.py", line 54, in main
trainer.fit(asr_model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 509, in fit
results = self.accelerator_backend.train()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/accelerators/ddp_accelerator.py", line 152, in train
results = self.ddp_train(process_idx=self.task_idx, model=model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/accelerators/ddp_accelerator.py", line 301, in ddp_train
results = self.train_or_test()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/accelerators/accelerator.py", line 74, in train_or_test
results = self.trainer.train()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 591, in train
self.train_loop.on_train_end()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 162, in on_train_end
self.check_checkpoint_callback(should_save=True, is_last=True)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py", line 196, in check_checkpoint_callback
callback.on_validation_end(self.trainer, model)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 203, in on_validation_end
self.save_checkpoint(trainer, pl_module)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 250, in save_checkpoint
self._save_top_k_checkpoints(trainer, pl_module, monitor_candidates)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 592, in _save_top_k_checkpoints
self._update_best_and_save(current, epoch, step, trainer, pl_module, metrics)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 644, in _update_best_and_save
self._save_model(filepath, trainer, pl_module)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/model_checkpoint.py", line 361, in _save_model
self.save_function(filepath, self.save_weights_only)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/properties.py", line 257, in save_checkpoint
self.checkpoint_connector.save_checkpoint(filepath, weights_only)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py", line 392, in save_checkpoint
checkpoint = self.dump_checkpoint(weights_only)
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/connectors/checkpoint_connector.py", line 277, in dump_checkpoint
callback_states = self.trainer.on_save_checkpoint()
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/trainer/callback_hook.py", line 199, in on_save_checkpoint
state = callback.on_save_checkpoint(self, self.get_model())
File "/opt/conda/lib/python3.6/site-packages/pytorch_lightning/utilities/distributed.py", line 39, in wrapped_fn
return fn(*args, **kwargs)
TypeError: on_save_checkpoint() missing 1 required positional argument: 'checkpoint'
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
Epoch 0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 563/563 [02:56<00:00, 3.19it/s, loss=767, v_num=6-59]
```
**Steps/Code to reproduce bug**
Code error in branches 1.0.0rc1 and main with docker container 1.0.0rc1:
python NeMo/examples/asr/speech_to_text.py
But it works in the branch r1.0.0b4 with docker container r1.0.0b4
**Expected behavior**
Expected to save checkpoint.
**Environment overview (please complete the following information)**
docker run --runtime=nvidia -itd --ipc=host -p 8888:8888 -p 6006:6006 --restart unless-stopped --name nemo_100rc1 nvcr.io/nvidia/nemo:1.0.0rc1
**Additional context**
GPU model: v100
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https://api.github.com/repos/NVIDIA/NeMo/issues/2118 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2118/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2118/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2118/events | https://github.com/NVIDIA/NeMo/issues/2118 | 867,447,755 | MDU6SXNzdWU4Njc0NDc3NTU= | 2,118 | Exporting nemo nlp models to onnx | {
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"UPDATE:\r\nI tried all the steps of inference in the [qa_model inference](https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/nlp/models/question_answering/qa_model.py#L183) script, here instead of forward pass I did the pass through the onnx inference session and everything worked properly with exact result... | 2021-04-26T08:54:06 | 2021-05-11T13:59:49 | 2021-04-28T12:49:22 | NONE | {
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} | null | null | null | **Describe the bug**
I am trying to export the nemo_nlp QAModel to onnx format in order to deploy it on triton. I am able to do so using this block of code:
`restored_model = nemo_nlp.models.QAModel.restore_from('finetuning_bert_base_uncased_squad.nemo')
restored_model.export("test_bert_onnx/model.onnx")`
And if it is a larger model (ex. albert-xlarge-v2), I get the protobuf 2GB limit error and to get around this I add _use_external_data_format=True_ to the torch.onnx.export in exportable.py.
This works fine and I get the onnx file and I am able to load it using _onnx.load_ and check it using _onnx.checker.check_model_.
Only after this while running single inference I do not get good results at all. My inference block:
```python
import numpy as np
from onnxruntime import InferenceSession, SessionOptions, get_all_providers
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def create_model_for_provider(model_path: str, provider: str) -> InferenceSession:
assert provider in get_all_providers(), f"provider {provider} not found, {get_all_providers()}"
options = SessionOptions()
options.intra_op_num_threads = 1
return InferenceSession(model_path, options, providers=[provider])
output_path = "test_bert_onnx/model.onnx"
cpu_model = create_model_for_provider(output_path, "CPUExecutionProvider")
question = "Which NFL team won Super Bowl 50?"
text = "Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi's Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the \"golden anniversary\" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as \"Super Bowl L\"), so that the logo could prominently feature the Arabic numerals 50."
inputs = tokenizer.encode_plus(question,text, add_special_tokens=True,return_tensors="np")
logits = cpu_model.run(output_names=None,input_feed={"attention_mask":inputs['attention_mask'],"input_ids":inputs["input_ids"],"token_type_ids":inputs["token_type_ids"]})
answer_start_scores = logits[0][0][:,0]
answer_end_scores = logits[0][0][:,1]
ans_tokens = inputs["input_ids"][0][np.argmax(answer_start_scores) : np.argmax(answer_end_scores) + 1 ]
answer_tokens = tokenizer.convert_ids_to_tokens(ans_tokens , skip_special_tokens=True)
answer_tokens_to_string = tokenizer.convert_tokens_to_string(answer_tokens)
print ("\nQuestion : ",question)
print ("\nAnswer : ",answer_tokens_to_string)
```
This works fine but the output answer (and logits) are not what I expect from trained model (The pre-trained bert-base-uncased model that I am using gives an evaluation score of 81.28 exact match and 88.58 f1 score on squad dev1.1 dataset). The output that I get for an example question-context pair taken from squad dataset is this.
Can someone point out if I'm missing some processing script of input or output or if the method to export to onnx format is different.
I am able to deploy this in triton but the inference result is same as above.
Lastly, will Triton support NeMo models directly in future as this would prevent a lot of hassle?
**Expected behavior**
Onnx inference working same as _model.inference_ in nemo_nlp
**Environment overview**
- Environment location: GCP
- Installed Docker container: docker pull nvcr.io/nvidia/nemo:1.0.0rc1
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https://api.github.com/repos/NVIDIA/NeMo/issues/2117 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2117/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2117/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2117/events | https://github.com/NVIDIA/NeMo/pull/2117 | 867,302,895 | MDExOlB1bGxSZXF1ZXN0NjIyOTk0MzYz | 2,117 | Modified convert_to_tarred_audio_dataset.py to accept another argument also as input | {
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"This pull request **introduces 2 alerts** when merging 1ce64a75065b53909e042395c27bf1a35f68309d into b4040fb37350ae86b64a5f53be911371d7a3879d - [view on LGTM.com](https://lgtm.com/projects/g/NVIDIA/NeMo/rev/pr-8c2c9c4c374e615c273837a54858c8611874091b)\n\n**new alerts:**\n\n* 1 for Unused local variable\n* 1 for Un... | 2021-04-26T06:13:54 | 2021-04-26T07:07:00 | 2021-04-26T07:05:54 | NONE | {
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} | The newly added argument(dataset_dir) is concatenated with the path obtained from the manifest file. This solves the problem of FileNotFoundError as an absolute path is given for the datasets. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2114 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2114/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2114/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2114/events | https://github.com/NVIDIA/NeMo/pull/2114 | 866,906,376 | MDExOlB1bGxSZXF1ZXN0NjIyNjkwNTEy | 2,114 | [docs] Reviewed/edited NeMo doc files | {
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"Hi Lynsey, could you resolve the merge conflicts with current main? I took a look and they seem to be from others' recent documentation updates.",
"looks like all these changes are already there."
] | 2021-04-25T04:07:31 | 2022-09-22T17:08:09 | 2021-07-19T23:04:54 | CONTRIBUTOR | {
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} | I've reviewed and edited the NeMo doc files. All documentation have been reviewed. Majority of them were edited/updated. | {
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https://api.github.com/repos/NVIDIA/NeMo/issues/2113 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2113/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2113/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2113/events | https://github.com/NVIDIA/NeMo/pull/2113 | 866,836,900 | MDExOlB1bGxSZXF1ZXN0NjIyNjQ4NTg5 | 2,113 | [NMT] Add support for multiple validation and test dataloaders | {
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} | Specify multiple validation (or test) dataloaders by inputting a list of src (tgt) files.
# Usage
Replace `src_file_name` and `tgt_file_name` with a list of file paths.
```
model.validation_ds.src_file_name=/data/wmt14-en-de.src \
model.validation_ds.tgt_file_name=/data/wmt14-en-de.ref \
```
Becomes:
```
model.validation_ds.src_file_name=[/data/wmt13-en-de.src,/data/wmt14-en-de.src] \
model.validation_ds.tgt_file_name=[/data/wmt13-en-de.ref,/data/wmt14-en-de.ref] \
```
When using `val_loss` or `val_sacreBLEU` for the `exp_manager.checkpoint_callback_params.monitor` then the 0th indexed dataloader will be used as the monitor. This ensures backwards compatibility.
Additionally, any validation dataloader can now be used as a monitor by appending the index as below:
```
exp_manager.checkpoint_callback_params.monitor=val_sacreBLEU_dl_index_1
```
Multiple test datasets works exactly the same way as validation datasets, simply replace `validation_ds` by `test_ds` in the above examples.
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https://api.github.com/repos/NVIDIA/NeMo/issues/2112 | https://api.github.com/repos/NVIDIA/NeMo | https://api.github.com/repos/NVIDIA/NeMo/issues/2112/labels{/name} | https://api.github.com/repos/NVIDIA/NeMo/issues/2112/comments | https://api.github.com/repos/NVIDIA/NeMo/issues/2112/events | https://github.com/NVIDIA/NeMo/issues/2112 | 866,761,597 | MDU6SXNzdWU4NjY3NjE1OTc= | 2,112 | I get an incompatible library version error | {
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"(1) Please try running it Google's Colab (which would have a clean environment and should work)\r\n(2) If you try running it locally, I'd recommend creating a new conda environment with Python 3.8 (and without Pytorch, Torchtext, etc.). This way when you do \"pip install ...\" correct versions of torch and torchte... | 2021-04-24T14:39:12 | 2021-06-19T04:37:12 | 2021-06-19T04:37:12 | NONE | {
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} | null | null | null | I run the code in https://github.com/NVIDIA/NeMo/blob/main/tutorials/asr/06_Voice_Activiy_Detection.ipynb, however I got again an error while runing below cell:
1 # NeMo's "core" package
----> 2 import nemo
3 # NeMo's ASR collection - this collections contains complete ASR models and
4 # building blocks (modules) for ASR
5 import nemo.collections.asr as nemo_asr
11 frames
/usr/local/lib/python3.7/dist-packages/pytorch_lightning/utilities/apply_func.py in ()
23 TORCHTEXT_AVAILABLE = importlib.util.find_spec("torchtext") is not None
24 if TORCHTEXT_AVAILABLE:
---> 25 from torchtext.data import Batch
26 else:
27 Batch = type(None)
ImportError: cannot import name 'Batch' from 'torchtext.data' (/usr/local/lib/python3.7/dist-packages/torchtext/data/init.py)
and I got another error when I run below cell:
AttributeError Traceback (most recent call last)
in ()
----> 1 exp_dir = exp_manager(trainer, config.get("exp_manager", None))
1 frames
/content/MyVADNeMo/nemo/utils/exp_manager.py in get_log_dir(trainer, exp_dir, name, version, explicit_log_dir, use_datetime_version)
428 if trainer.logger.save_dir:
429 if exp_dir:
--> 430 raise LoggerMisconfigurationError(
431 "The pytorch lightning trainer that was passed to exp_manager contained a logger, the logger's "
432 f"save_dir was not None, and exp_dir ({exp_dir}) was not None. If trainer.logger.save_dir "
AttributeError: 'Trainer' object has no attribute 'is_slurm_managing_tasks'.
Thanks in advance! | {
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