Datasets:
albertvillanova
HF staff
Revert "Convert dataset to Parquet (part 00002-of-00003) (#9)"
276333a
verified
# coding=utf-8 | |
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
# | |
# 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. | |
# Lint as: python3 | |
"""Librispeech automatic speech recognition dataset.""" | |
import os | |
import datasets | |
from datasets.tasks import AutomaticSpeechRecognition | |
_CITATION = """\ | |
@inproceedings{panayotov2015librispeech, | |
title={Librispeech: an ASR corpus based on public domain audio books}, | |
author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev}, | |
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on}, | |
pages={5206--5210}, | |
year={2015}, | |
organization={IEEE} | |
} | |
""" | |
_DESCRIPTION = """\ | |
LibriSpeech is a corpus of approximately 1000 hours of read English speech with sampling rate of 16 kHz, | |
prepared by Vassil Panayotov with the assistance of Daniel Povey. The data is derived from read | |
audiobooks from the LibriVox project, and has been carefully segmented and aligned.87 | |
""" | |
_URL = "http://www.openslr.org/12" | |
_DL_URL = "http://www.openslr.org/resources/12/" | |
_DL_URLS = { | |
"clean": { | |
"dev": _DL_URL + "dev-clean.tar.gz", | |
"test": _DL_URL + "test-clean.tar.gz", | |
"train.100": _DL_URL + "train-clean-100.tar.gz", | |
"train.360": _DL_URL + "train-clean-360.tar.gz", | |
}, | |
"other": { | |
"test": _DL_URL + "test-other.tar.gz", | |
"dev": _DL_URL + "dev-other.tar.gz", | |
"train.500": _DL_URL + "train-other-500.tar.gz", | |
}, | |
"all": { | |
"dev.clean": _DL_URL + "dev-clean.tar.gz", | |
"dev.other": _DL_URL + "dev-other.tar.gz", | |
"test.clean": _DL_URL + "test-clean.tar.gz", | |
"test.other": _DL_URL + "test-other.tar.gz", | |
"train.clean.100": _DL_URL + "train-clean-100.tar.gz", | |
"train.clean.360": _DL_URL + "train-clean-360.tar.gz", | |
"train.other.500": _DL_URL + "train-other-500.tar.gz", | |
}, | |
} | |
class LibrispeechASRConfig(datasets.BuilderConfig): | |
"""BuilderConfig for LibriSpeechASR.""" | |
def __init__(self, **kwargs): | |
""" | |
Args: | |
data_dir: `string`, the path to the folder containing the files in the | |
downloaded .tar | |
citation: `string`, citation for the data set | |
url: `string`, url for information about the data set | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
super(LibrispeechASRConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs) | |
class LibrispeechASR(datasets.GeneratorBasedBuilder): | |
"""Librispeech dataset.""" | |
DEFAULT_WRITER_BATCH_SIZE = 256 | |
DEFAULT_CONFIG_NAME = "all" | |
BUILDER_CONFIGS = [ | |
LibrispeechASRConfig(name="clean", description="'Clean' speech."), | |
LibrispeechASRConfig(name="other", description="'Other', more challenging, speech."), | |
LibrispeechASRConfig(name="all", description="Combined clean and other dataset."), | |
] | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"file": datasets.Value("string"), | |
"audio": datasets.Audio(sampling_rate=16_000), | |
"text": datasets.Value("string"), | |
"speaker_id": datasets.Value("int64"), | |
"chapter_id": datasets.Value("int64"), | |
"id": datasets.Value("string"), | |
} | |
), | |
supervised_keys=("file", "text"), | |
homepage=_URL, | |
citation=_CITATION, | |
task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")], | |
) | |
def _split_generators(self, dl_manager): | |
archive_path = dl_manager.download(_DL_URLS[self.config.name]) | |
# (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files: | |
local_extracted_archive = dl_manager.extract(archive_path) if not dl_manager.is_streaming else {} | |
if self.config.name == "clean": | |
train_splits = [ | |
datasets.SplitGenerator( | |
name="train.100", | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("train.100"), | |
"files": dl_manager.iter_archive(archive_path["train.100"]), | |
}, | |
), | |
datasets.SplitGenerator( | |
name="train.360", | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("train.360"), | |
"files": dl_manager.iter_archive(archive_path["train.360"]), | |
}, | |
), | |
] | |
dev_splits = [ | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("dev"), | |
"files": dl_manager.iter_archive(archive_path["dev"]), | |
}, | |
) | |
] | |
test_splits = [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("test"), | |
"files": dl_manager.iter_archive(archive_path["test"]), | |
}, | |
) | |
] | |
elif self.config.name == "other": | |
train_splits = [ | |
datasets.SplitGenerator( | |
name="train.500", | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("train.500"), | |
"files": dl_manager.iter_archive(archive_path["train.500"]), | |
}, | |
) | |
] | |
dev_splits = [ | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("dev"), | |
"files": dl_manager.iter_archive(archive_path["dev"]), | |
}, | |
) | |
] | |
test_splits = [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("test"), | |
"files": dl_manager.iter_archive(archive_path["test"]), | |
}, | |
) | |
] | |
elif self.config.name == "all": | |
train_splits = [ | |
datasets.SplitGenerator( | |
name="train.clean.100", | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("train.clean.100"), | |
"files": dl_manager.iter_archive(archive_path["train.clean.100"]), | |
}, | |
), | |
datasets.SplitGenerator( | |
name="train.clean.360", | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("train.clean.360"), | |
"files": dl_manager.iter_archive(archive_path["train.clean.360"]), | |
}, | |
), | |
datasets.SplitGenerator( | |
name="train.other.500", | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("train.other.500"), | |
"files": dl_manager.iter_archive(archive_path["train.other.500"]), | |
}, | |
), | |
] | |
dev_splits = [ | |
datasets.SplitGenerator( | |
name="validation.clean", | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("dev.clean"), | |
"files": dl_manager.iter_archive(archive_path["dev.clean"]), | |
}, | |
), | |
datasets.SplitGenerator( | |
name="validation.other", | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("dev.other"), | |
"files": dl_manager.iter_archive(archive_path["dev.other"]), | |
}, | |
), | |
] | |
test_splits = [ | |
datasets.SplitGenerator( | |
name="test.clean", | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("test.clean"), | |
"files": dl_manager.iter_archive(archive_path["test.clean"]), | |
}, | |
), | |
datasets.SplitGenerator( | |
name="test.other", | |
gen_kwargs={ | |
"local_extracted_archive": local_extracted_archive.get("test.other"), | |
"files": dl_manager.iter_archive(archive_path["test.other"]), | |
}, | |
), | |
] | |
return train_splits + dev_splits + test_splits | |
def _generate_examples(self, files, local_extracted_archive): | |
"""Generate examples from a LibriSpeech archive_path.""" | |
key = 0 | |
audio_data = {} | |
transcripts = [] | |
for path, f in files: | |
if path.endswith(".flac"): | |
id_ = path.split("/")[-1][: -len(".flac")] | |
audio_data[id_] = f.read() | |
elif path.endswith(".trans.txt"): | |
for line in f: | |
if line: | |
line = line.decode("utf-8").strip() | |
id_, transcript = line.split(" ", 1) | |
audio_file = f"{id_}.flac" | |
speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]] | |
audio_file = ( | |
os.path.join(local_extracted_archive, audio_file) | |
if local_extracted_archive | |
else audio_file | |
) | |
transcripts.append( | |
{ | |
"id": id_, | |
"speaker_id": speaker_id, | |
"chapter_id": chapter_id, | |
"file": audio_file, | |
"text": transcript, | |
} | |
) | |
if audio_data and len(audio_data) == len(transcripts): | |
for transcript in transcripts: | |
audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]} | |
yield key, {"audio": audio, **transcript} | |
key += 1 | |
audio_data = {} | |
transcripts = [] | |