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import json |
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import os |
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import datasets |
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from datasets.tasks import AutomaticSpeechRecognition |
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from tqdm.auto import tqdm |
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_CITATION = """\ |
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@article{DBLP:journals/corr/abs-2111-09344, |
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author = {Daniel Galvez and |
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Greg Diamos and |
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Juan Ciro and |
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Juan Felipe Ceron and |
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Keith Achorn and |
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Anjali Gopi and |
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David Kanter and |
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Maximilian Lam and |
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Mark Mazumder and |
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Vijay Janapa Reddi}, |
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title = {The People's Speech: A Large-Scale Diverse English Speech Recognition |
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Dataset for Commercial Usage}, |
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journal = {CoRR}, |
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volume = {abs/2111.09344}, |
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year = {2021}, |
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url = {https://arxiv.org/abs/2111.09344}, |
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eprinttype = {arXiv}, |
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eprint = {2111.09344}, |
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timestamp = {Mon, 22 Nov 2021 16:44:07 +0100}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2111-09344.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The People's Speech is a free-to-download 30,000-hour and growing supervised |
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conversational English speech recognition dataset licensed for academic and |
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commercial usage under CC-BY-SA (with a CC-BY subset). |
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""" |
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_HOMEPAGE = "https://mlcommons.org/en/peoples-speech/" |
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_LICENSE = [ |
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"cc-by-2.0", "cc-by-2.5", "cc-by-3.0", "cc-by-4.0", "cc-by-sa-2.5", |
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"cc-by-sa-3.0", "cc-by-sa-4.0" |
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] |
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_BASE_URL = "https://huggingface.co/datasets/MLCommons/peoples_speech/resolve/main/" |
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_DATA_URL = _BASE_URL + "{split}/{config}/{config}_{archive_id:06d}.tar" |
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_N_FILES_URL = _BASE_URL + "{split}/{config}/n_files.txt" |
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_MANIFEST_URL = _BASE_URL + "{split}/{config}.json" |
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class PeoplesSpeech(datasets.GeneratorBasedBuilder): |
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"""The People's Speech dataset.""" |
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VERSION = datasets.Version("1.1.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="microset", version=VERSION, description="Small subset of clean data for example pusposes."), |
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datasets.BuilderConfig(name="clean", version=VERSION, description="Clean, CC-BY licensed subset."), |
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datasets.BuilderConfig(name="dirty", version=VERSION, description="Dirty, CC-BY licensed subset."), |
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datasets.BuilderConfig(name="clean_sa", version=VERSION, description="Clean, CC-BY-SA licensed subset."), |
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datasets.BuilderConfig(name="dirty_sa", version=VERSION, description="Dirty, CC-BY-SA licensed subset."), |
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] |
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DEFAULT_CONFIG_NAME = "clean" |
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DEFAULT_WRITER_BATCH_SIZE = 1 |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=16_000), |
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"duration_ms": datasets.Value("int32"), |
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"text": datasets.Value("string"), |
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} |
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), |
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task_templates=[AutomaticSpeechRecognition()], |
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supervised_keys=("file", "text"), |
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homepage=_HOMEPAGE, |
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license="/".join(_LICENSE), |
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citation=_CITATION, |
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) |
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def _get_n_files(self, dl_manager, split, config): |
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n_files_url = _N_FILES_URL.format(split=split, config=config) |
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n_files_path = dl_manager.download_and_extract(n_files_url) |
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with open(n_files_path, encoding="utf-8") as f: |
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return int(f.read().strip()) |
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def _split_generators(self, dl_manager): |
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if self.config.name == "microset": |
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url = [_DATA_URL.format(split="train", config="clean", archive_id=0)] |
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archive_path = dl_manager.download(url) |
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local_extracted_archive_path = dl_manager.extract(archive_path) if not dl_manager.is_streaming else [None] |
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manifest_url = _MANIFEST_URL.format(split="train", config="clean_000000") |
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manifest_path = dl_manager.download_and_extract(manifest_url) |
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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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"local_extracted_archive_paths": local_extracted_archive_path, |
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"archives": [dl_manager.iter_archive(path) for path in archive_path], |
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"manifest_path": manifest_path, |
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}, |
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), |
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] |
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n_files_train = self._get_n_files(dl_manager, split="train", config=self.config.name) |
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n_files_dev = self._get_n_files(dl_manager, split="dev", config="dev") |
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n_files_test = self._get_n_files(dl_manager, split="test", config="test") |
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urls = { |
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"train": [_DATA_URL.format(split="train", config=self.config.name, archive_id=i) for i in range(n_files_train)], |
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"dev": [_DATA_URL.format(split="dev", config="dev", archive_id=i) for i in range(n_files_dev)], |
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"test": [_DATA_URL.format(split="test", config="test", archive_id=i) for i in range(n_files_test)], |
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} |
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archive_paths = dl_manager.download(urls) |
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local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else \ |
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{ |
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"train": [None] * len(archive_paths), |
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"dev": [None] * len(archive_paths), |
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"test": [None] * len(archive_paths), |
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} |
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manifest_urls = { |
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"train": _MANIFEST_URL.format(split="train", config=self.config.name), |
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"dev": _MANIFEST_URL.format(split="dev", config="dev"), |
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"test": _MANIFEST_URL.format(split="test", config="test"), |
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} |
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manifest_paths = dl_manager.download_and_extract(manifest_urls) |
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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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"local_extracted_archive_paths": local_extracted_archive_paths["train"], |
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"archives": [dl_manager.iter_archive(path) for path in archive_paths["train"]], |
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"manifest_path": manifest_paths["train"], |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"local_extracted_archive_paths": local_extracted_archive_paths["dev"], |
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"archives": [dl_manager.iter_archive(path) for path in archive_paths["dev"]], |
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"manifest_path": manifest_paths["dev"], |
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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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"local_extracted_archive_paths": local_extracted_archive_paths["dev"], |
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"archives": [dl_manager.iter_archive(path) for path in archive_paths["test"]], |
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"manifest_path": manifest_paths["test"], |
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}, |
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), |
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] |
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def _generate_examples(self, local_extracted_archive_paths, archives, manifest_path): |
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meta = dict() |
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with open(manifest_path, "r", encoding="utf-8") as f: |
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for line in tqdm(f, desc="reading metadata file"): |
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sample_meta = json.loads(line) |
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_id = sample_meta["audio_document_id"] |
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texts = sample_meta["training_data"]["label"] |
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audio_filenames = sample_meta["training_data"]["name"] |
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durations = sample_meta["training_data"]["duration_ms"] |
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for audio_filename, text, duration in zip(audio_filenames, texts, durations): |
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audio_filename = audio_filename.lstrip("./") |
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meta[audio_filename] = { |
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"audio_document_id": _id, |
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"text": text, |
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"duration_ms": duration |
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} |
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for local_extracted_archive_path, archive in zip(local_extracted_archive_paths, archives): |
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for audio_filename, audio_file in archive: |
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audio_filename = audio_filename.lstrip("./") |
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path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path \ |
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else audio_filename |
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yield audio_filename, { |
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"id": audio_filename, |
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"audio": {"path": path, "bytes": audio_file.read()}, |
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"text": meta[audio_filename]["text"], |
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"duration_ms": meta[audio_filename]["duration_ms"] |
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} |
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