# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # 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. import json import os import datasets from datasets.tasks import AutomaticSpeechRecognition from tqdm.auto import tqdm # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @article{DBLP:journals/corr/abs-2111-09344, author = {Daniel Galvez and Greg Diamos and Juan Ciro and Juan Felipe Ceron and Keith Achorn and Anjali Gopi and David Kanter and Maximilian Lam and Mark Mazumder and Vijay Janapa Reddi}, title = {The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage}, journal = {CoRR}, volume = {abs/2111.09344}, year = {2021}, url = {https://arxiv.org/abs/2111.09344}, eprinttype = {arXiv}, eprint = {2111.09344}, timestamp = {Mon, 22 Nov 2021 16:44:07 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2111-09344.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ # You can copy an official description _DESCRIPTION = """\ The People's Speech is a free-to-download 30,000-hour and growing supervised conversational English speech recognition dataset licensed for academic and commercial usage under CC-BY-SA (with a CC-BY subset). """ _HOMEPAGE = "https://mlcommons.org/en/peoples-speech/" _LICENSE = [ "cc-by-2.0", "cc-by-2.5", "cc-by-3.0", "cc-by-4.0", "cc-by-sa-2.5", "cc-by-sa-3.0", "cc-by-sa-4.0" ] _BASE_URL = "https://huggingface.co/datasets/MLCommons/peoples_speech/resolve/main/" # relative path to data inside dataset's repo _DATA_URL = _BASE_URL + "{split}/{config}/{config}_{archive_id:06d}.tar" # relative path to file containing number of audio archives inside dataset's repo _N_SHARDS_URL = _BASE_URL + "n_shards.json" # relative path to metadata inside dataset's repo _MANIFEST_URL = _BASE_URL + "{split}/{config}.json" class PeoplesSpeechConfig(datasets.BuilderConfig): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) class PeoplesSpeech(datasets.GeneratorBasedBuilder): """The People's Speech dataset.""" VERSION = datasets.Version("1.1.0") BUILDER_CONFIGS = [ PeoplesSpeechConfig(name="microset", version=VERSION, description="Small subset of clean data for example pusposes."), PeoplesSpeechConfig(name="clean", version=VERSION, description="Clean, CC-BY licensed subset."), PeoplesSpeechConfig(name="dirty", version=VERSION, description="Dirty, CC-BY licensed subset."), PeoplesSpeechConfig(name="clean_sa", version=VERSION, description="Clean, CC-BY-SA licensed subset."), PeoplesSpeechConfig(name="dirty_sa", version=VERSION, description="Dirty, CC-BY-SA licensed subset."), PeoplesSpeechConfig(name="test", version=VERSION, description="Only test data."), PeoplesSpeechConfig(name="validation", version=VERSION, description="Only validation data."), ] DEFAULT_CONFIG_NAME = "clean" DEFAULT_WRITER_BATCH_SIZE = 512 def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "id": datasets.Value("string"), "audio": datasets.Audio(sampling_rate=16_000), "duration_ms": datasets.Value("int32"), "text": datasets.Value("string"), } ), task_templates=[AutomaticSpeechRecognition()], homepage=_HOMEPAGE, license="/".join(_LICENSE), # license must be a string citation=_CITATION, ) def _split_generators(self, dl_manager): if self.config.name == "microset": # take only first data archive for demo purposes url = [_DATA_URL.format(split="train", config="clean", archive_id=0)] archive_path = dl_manager.download(url) local_extracted_archive_path = dl_manager.extract(archive_path) if not dl_manager.is_streaming else [None] manifest_url = _MANIFEST_URL.format(split="train", config="clean_000000") # train/clean_000000.json manifest_path = dl_manager.download_and_extract(manifest_url) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "local_extracted_archive_paths": local_extracted_archive_path, # use iter_archive here to access the files in the TAR archives: "archives": [dl_manager.iter_archive(path) for path in archive_path], "manifest_path": manifest_path, }, ), ] n_shards_path = dl_manager.download_and_extract(_N_SHARDS_URL) with open(n_shards_path, encoding="utf-8") as f: n_shards = json.load(f) if self.config.name in ["validation", "test"]: splits_to_configs = {self.config.name: self.config.name} else: splits_to_configs = { "train": self.config.name, "validation": "validation", "test": "test" } audio_urls = { split: [ _DATA_URL.format(split=split, config=config, archive_id=i) for i in range(n_shards[split]) ] for split, config in splits_to_configs.items() } audio_archive_paths = dl_manager.download(audio_urls) # In non-streaming mode, we extract the archives to have the data locally: local_extracted_archive_paths = dl_manager.extract(audio_archive_paths) \ if not dl_manager.is_streaming else \ {split: [None] * len(audio_archive_paths) for split in splits_to_configs} manifest_urls = { split: _MANIFEST_URL.format(split=split, config=config) for split, config in splits_to_configs.items() } manifest_paths = dl_manager.download_and_extract(manifest_urls) # To access the audio data from the TAR archives using the download manager, # we have to use the dl_manager.iter_archive method # # This is because dl_manager.download_and_extract # doesn't work to stream TAR archives in streaming mode. # (we have to stream the files of a TAR archive one by one) # # The iter_archive method returns an iterable of (path_within_archive, file_obj) for every # file in a TAR archive. splits_to_names = { "train": datasets.Split.TRAIN, "validation": datasets.Split.VALIDATION, "test": datasets.Split.TEST, } split_generators = [] for split in splits_to_configs: split_generators.append( datasets.SplitGenerator( name=splits_to_names[split], gen_kwargs={ "local_extracted_archive_paths": local_extracted_archive_paths[split], # use iter_archive here to access the files in the TAR archives: "archives": [dl_manager.iter_archive(path) for path in audio_archive_paths[split]], "manifest_path": manifest_paths[split], } ) ) def _generate_examples(self, local_extracted_archive_paths, archives, manifest_path): meta = dict() with open(manifest_path, "r", encoding="utf-8") as f: for line in tqdm(f, desc="reading metadata file"): sample_meta = json.loads(line) _id = sample_meta["audio_document_id"] texts = sample_meta["training_data"]["label"] audio_filenames = sample_meta["training_data"]["name"] durations = sample_meta["training_data"]["duration_ms"] for audio_filename, text, duration in zip(audio_filenames, texts, durations): audio_filename = audio_filename.lstrip("./") meta[audio_filename] = { "audio_document_id": _id, "text": text, "duration_ms": duration } for local_extracted_archive_path, archive in zip(local_extracted_archive_paths, archives): # Here we iterate over all the files within the TAR archive: for audio_filename, audio_file in archive: audio_filename = audio_filename.lstrip("./") # if an audio file exists locally (i.e. in default, non-streaming mode) set the full path to it # joining path to directory that the archive was extracted to and audio filename. path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path \ else audio_filename yield audio_filename, { "id": audio_filename, "audio": {"path": path, "bytes": audio_file.read()}, "text": meta[audio_filename]["text"], "duration_ms": meta[audio_filename]["duration_ms"] }