demo init

.gitattributes

@@ -52,3 +52,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text

Demo-dataset.py

@@ -0,0 +1,134 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""SQUAD: The Stanford Question Answering Dataset."""
+
+
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@article{2016arXiv160605250R,
+       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
+                 Konstantin and {Liang}, Percy},
+        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
+      journal = {arXiv e-prints},
+         year = 2016,
+          eid = {arXiv:1606.05250},
+        pages = {arXiv:1606.05250},
+archivePrefix = {arXiv},
+       eprint = {1606.05250},
+}
+"""
+
+_DESCRIPTION = """\
+Stanford Question Answering Dataset (SQuAD) is a reading comprehension \
+dataset, consisting of questions posed by crowdworkers on a set of Wikipedia \
+articles, where the answer to every question is a segment of text, or span, \
+from the corresponding reading passage, or the question might be unanswerable.
+"""
+
+_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
+_URLS = {
+    "train": _URL + "train-v1.1.json",
+    "dev": _URL + "dev-v1.1.json",
+}
+
+
+class SquadConfig(datasets.BuilderConfig):
+    """BuilderConfig for SQUAD."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SQUAD.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SquadConfig, self).__init__(**kwargs)
+
+
+class Squad(datasets.GeneratorBasedBuilder):
+    """SQUAD: The Stanford Question Answering Dataset. Version 1.1."""
+
+    BUILDER_CONFIGS = [
+        SquadConfig(
+            name="tibetan_voice",
+            version=datasets.Version("1.0.0", ""),
+            description="The dataset comprises 6.5 hours of validated transcribed speech data from 9 audio book ",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "path": datasets.Value("string"),
+                    "sentence": datasets.Value("string"),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://rajpurkar.github.io/SQuAD-explorer/",
+            citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        key = 0
+        with open(filepath, encoding="utf-8") as f:
+            squad = json.load(f)
+            for article in squad["data"]:
+                title = article.get("title", "")
+                for paragraph in article["paragraphs"]:
+                    context = paragraph["context"]  # do not strip leading blank spaces GH-2585
+                    for qa in paragraph["qas"]:
+                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+                        answers = [answer["text"] for answer in qa["answers"]]
+                        # Features currently used are "context", "question", and "answers".
+                        # Others are extracted here for the ease of future expansions.
+                        yield key, {
+                            "title": title,
+                            "context": context,
+                            "question": qa["question"],
+                            "id": qa["id"],
+                            "answers": {
+                                "answer_start": answer_starts,
+                                "text": answers,
+                            },
+                        }
+                        key += 1

init.py

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+
+# ziping wav file to wav.tar.gz
+
+
+import tarfile
+import os.path
+from datasets import DownloadManager
+
+source_dir = '/Users/tenzingayche/Downloads/wav 2'
+output_filename = 'wav.tar'
+def make_tarfile(output_filename, source_dir):
+    with tarfile.open(output_filename, "w:gz") as tar:
+        tar.add(source_dir, arcname=os.path.basename(source_dir))
+
+#extracting wav.tar.gz to wav file
+def extract_tarfile(output_filename, source_dir):
+    with tarfile.open(output_filename, "r:gz") as tar:
+        tar.extractall(source_dir)
+_url='https://github.com/TenzinGayche/Demo-dataset/raw/master/wav.tar.gz'
+# dl_manager = DownloadManager()
+# a=dl_manager.download_and_extract(_url)
+# print(a)
+# os.listdir(a)
+make_tarfile(output_filename, source_dir)

splits/test.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6da4477b62c374b78e9c99a6a061966773d83a33eb6afec17b71f9c99b7c0374
+size 1261

splits/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c18b17fa11796fef513610c895539f81e1badf043f52033bbb6772e70fadbae
+size 2721

splits/valid.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6da4477b62c374b78e9c99a6a061966773d83a33eb6afec17b71f9c99b7c0374
+size 1261