# 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" ] # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "clean-cc-by": { "audio_tar": "", "manifest": "", }, "dirty-cc-by": { "audio_tar": "", "manifest": "", }, "clean-cc-by-sa": { "audio_tar": "", "manifest": "", }, "dirty-cc-by-sa": { "audio_tar": "", "manifest": "", }, "microset": { "audio_tar": "", "manifest": "", }, } _BASE_URL = "https://huggingface.co/datasets/MLCommons/peoples_speech/resolve/main/" # relative path to data inside dataset's repo _DATA_URL = _BASE_URL + "{config}/{config}_00000{archive_id}.tar" # relative path to metadata inside dataset's repo _MANIFEST_URL = _BASE_URL + "{config}.json" class PeoplesSpeech(datasets.GeneratorBasedBuilder): """The People's Speech dataset.""" VERSION = datasets.Version("1.1.0") BUILDER_CONFIGS = [ datasets.BuilderConfig(name="clean", version=VERSION, description="Clean, CC-BY licensed subset."), datasets.BuilderConfig(name="dirty", version=VERSION, description="Dirty, CC-BY licensed subset."), datasets.BuilderConfig(name="clean_sa", version=VERSION, description="Clean, CC-BY-SA licensed subset."), datasets.BuilderConfig(name="dirty_sa", version=VERSION, description="Dirty, CC-BY-SA licensed subset."), ] DEFAULT_CONFIG_NAME = "clean" DEFAULT_WRITER_BATCH_SIZE = 1 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()], supervised_keys=("file", "text"), homepage=_HOMEPAGE, license="/".join(_LICENSE), # license must be a string citation=_CITATION, ) def _split_generators(self, dl_manager): # TODO: for demo purposes I use just first 5 archives # TODO: this should be changed to the actual number of archives further urls = [_DATA_URL.format(config=self.config.name, archive_id=i) for i in range(5)] archive_paths = [dl_manager.download(url) for url in urls] local_extracted_archive_paths = [dl_manager.extract(path) for path in archive_paths] \ if not dl_manager.is_streaming else [None] * len(archive_paths) manifest_url = _MANIFEST_URL.format(config=self.config.name) 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_paths, "archives": [dl_manager.iter_archive(path) for path in archive_paths], "manifest_path": manifest_path }, ), ] 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): meta[audio_filename] = { "audio_document_id": _id, "text": text, "duration_ms": duration } print("generating examples") for local_extracted_archive_path, archive in zip(local_extracted_archive_paths, archives): for audio_filename, audio_file in archive: 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"] }