Delete peoples_speech.py

peoples_speech.py

@@ -1,225 +0,0 @@
-# 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 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(),
- "duration_ms": datasets.Value("int32"),
- "text": datasets.Value("string"),
- }
- ),
- 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][config])
- ] 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[split]) 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],
- }
- )
- )
-
- return split_generators
-
- 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"]
- }