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"""Simple sentences Dataset - contains 90 mins of speech data""" |
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import csv |
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import json |
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import os |
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import datasets |
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_CITATION = """\ |
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@misc{simpledata_1, |
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title = {Whisper model for tamil-to-eng translation}, |
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publisher = {Achitha}, |
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year = {2022}, |
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} |
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@misc{simpledata_2, |
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title = {Fine-tuning whisper model}, |
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publisher = {Achitha}, |
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year = {2022}, |
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} |
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""" |
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_DESCRIPTION = """\ |
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The data contains roughly one and half hours of audio and transcripts in Tamil language. |
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""" |
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_HOMEPAGE = "" |
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_LICENSE = "MIT" |
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_METADATA_URLS = { |
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"train": "data/train.jsonl", |
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"test": "data/test.jsonl" |
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} |
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_URLS = { |
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"train": "data/train.tar.gz", |
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"test": "data/test.tar.gz", |
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} |
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class simple_data(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.1.0") |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"path": datasets.Value("string"), |
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"sentence": datasets.Value("string"), |
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"length": datasets.Value("float") |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=("sentence", "label"), |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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metadata_paths = dl_manager.download(_METADATA_URLS) |
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train_archive = dl_manager.download(_URLS["train"]) |
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test_archive = dl_manager.download(_URLS["test"]) |
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local_extracted_train_archive = dl_manager.extract(train_archive) if not dl_manager.is_streaming else None |
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local_extracted_test_archive = dl_manager.extract(test_archive) if not dl_manager.is_streaming else None |
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test_archive = dl_manager.download(_URLS["test"]) |
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train_dir = "train" |
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test_dir = "test" |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"metadata_path": metadata_paths["train"], |
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"local_extracted_archive": local_extracted_train_archive, |
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"path_to_clips": train_dir, |
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"audio_files": dl_manager.iter_archive(train_archive), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"metadata_path": metadata_paths["test"], |
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"local_extracted_archive": local_extracted_test_archive, |
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"path_to_clips": test_dir, |
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"audio_files": dl_manager.iter_archive(test_archive), |
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}, |
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), |
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] |
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def _generate_examples(self, metadata_path, local_extracted_archive, path_to_clips, audio_files): |
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"""Yields examples as (key, example) tuples.""" |
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examples = {} |
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with open(metadata_path, encoding="utf-8") as f: |
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for key, row in enumerate(f): |
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data = json.loads(row) |
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examples[data["path"]] = data |
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inside_clips_dir = False |
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id_ = 0 |
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for path, f in audio_files: |
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if path.startswith(path_to_clips): |
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inside_clips_dir = True |
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if path in examples: |
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result = examples[path] |
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path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path |
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result["audio"] = {"path": path, "bytes": f.read()} |
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result["path"] = path |
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yield id_, result |
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id_ += 1 |
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elif inside_clips_dir: |
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break |
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