Datasets:
Tasks:
Automatic Speech Recognition
Languages:
English
Multilinguality:
monolingual
ArXiv:
Tags:
License:
# 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. | |
""" | |
GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality | |
labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised | |
and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts | |
and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, | |
sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable | |
for speech recognition training, and to filter out segments with low-quality transcription. For system training, | |
GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h. | |
For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage, | |
and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand, | |
are re-processed by professional human transcribers to ensure high transcription quality. | |
""" | |
import csv | |
import os | |
import datasets | |
_CITATION = """\ | |
@article{DBLP:journals/corr/abs-2106-06909, | |
author = {Guoguo Chen and | |
Shuzhou Chai and | |
Guanbo Wang and | |
Jiayu Du and | |
Wei{-}Qiang Zhang and | |
Chao Weng and | |
Dan Su and | |
Daniel Povey and | |
Jan Trmal and | |
Junbo Zhang and | |
Mingjie Jin and | |
Sanjeev Khudanpur and | |
Shinji Watanabe and | |
Shuaijiang Zhao and | |
Wei Zou and | |
Xiangang Li and | |
Xuchen Yao and | |
Yongqing Wang and | |
Yujun Wang and | |
Zhao You and | |
Zhiyong Yan}, | |
title = {GigaSpeech: An Evolving, Multi-domain {ASR} Corpus with 10, 000 Hours | |
of Transcribed Audio}, | |
journal = {CoRR}, | |
volume = {abs/2106.06909}, | |
year = {2021}, | |
url = {https://arxiv.org/abs/2106.06909}, | |
eprinttype = {arXiv}, | |
eprint = {2106.06909}, | |
timestamp = {Wed, 29 Dec 2021 14:29:26 +0100}, | |
biburl = {https://dblp.org/rec/journals/corr/abs-2106-06909.bib}, | |
bibsource = {dblp computer science bibliography, https://dblp.org} | |
} | |
""" | |
_DESCRIPTION = """\ | |
GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality | |
labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised | |
and unsupervised training. Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts | |
and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, | |
sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable | |
for speech recognition training, and to filter out segments with low-quality transcription. For system training, | |
GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h. | |
For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage, | |
and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand, | |
are re-processed by professional human transcribers to ensure high transcription quality. | |
""" | |
_HOMEPAGE = "https://groups.inf.ed.ac.uk/ami/corpus/" | |
_LICENSE = "CC BY 4.0" | |
_TRAIN_SAMPLE_IDS = [ | |
"EN2001a", | |
"EN2001b", | |
"EN2001d", | |
"EN2001e", | |
"EN2003a", | |
"EN2004a", | |
"EN2005a", | |
"EN2006a", | |
"EN2006b", | |
"EN2009b", | |
"EN2009c", | |
"EN2009d", | |
"ES2002a", | |
"ES2002b", | |
"ES2002c", | |
"ES2002d", | |
"ES2003a", | |
"ES2003b", | |
"ES2003c", | |
"ES2003d", | |
"ES2005a", | |
"ES2005b", | |
"ES2005c", | |
"ES2005d", | |
"ES2006a", | |
"ES2006b", | |
"ES2006c", | |
"ES2006d", | |
"ES2007a", | |
"ES2007b", | |
"ES2007c", | |
"ES2007d", | |
"ES2008a", | |
"ES2008b", | |
"ES2008c", | |
"ES2008d", | |
"ES2009a", | |
"ES2009b", | |
"ES2009c", | |
"ES2009d", | |
"ES2010a", | |
"ES2010b", | |
"ES2010c", | |
"ES2010d", | |
"ES2012a", | |
"ES2012b", | |
"ES2012c", | |
"ES2012d", | |
"ES2013a", | |
"ES2013b", | |
"ES2013c", | |
"ES2013d", | |
"ES2014a", | |
"ES2014b", | |
"ES2014c", | |
"ES2014d", | |
"ES2015a", | |
"ES2015b", | |
"ES2015c", | |
"ES2015d", | |
"ES2016a", | |
"ES2016b", | |
"ES2016c", | |
"ES2016d", | |
"IB4005", | |
"IN1001", | |
"IN1002", | |
"IN1005", | |
"IN1007", | |
"IN1008", | |
"IN1009", | |
"IN1012", | |
"IN1013", | |
"IN1014", | |
"IN1016", | |
"IS1000a", | |
"IS1000b", | |
"IS1000c", | |
"IS1000d", | |
"IS1001a", | |
"IS1001b", | |
"IS1001c", | |
"IS1001d", | |
"IS1002b", | |
"IS1002c", | |
"IS1002d", | |
"IS1003a", | |
"IS1003b", | |
"IS1003c", | |
"IS1003d", | |
"IS1004a", | |
"IS1004b", | |
"IS1004c", | |
"IS1004d", | |
"IS1005a", | |
"IS1005b", | |
"IS1005c", | |
"IS1006a", | |
"IS1006b", | |
"IS1006c", | |
"IS1006d", | |
"IS1007a", | |
"IS1007b", | |
"IS1007c", | |
"IS1007d", | |
"TS3005a", | |
"TS3005b", | |
"TS3005c", | |
"TS3005d", | |
"TS3006a", | |
"TS3006b", | |
"TS3006c", | |
"TS3006d", | |
"TS3007a", | |
"TS3007b", | |
"TS3007c", | |
"TS3007d", | |
"TS3008a", | |
"TS3008b", | |
"TS3008c", | |
"TS3008d", | |
"TS3009a", | |
"TS3009b", | |
"TS3009c", | |
"TS3009d", | |
"TS3010a", | |
"TS3010b", | |
"TS3010c", | |
"TS3010d", | |
"TS3011a", | |
"TS3011b", | |
"TS3011c", | |
"TS3011d", | |
"TS3012a", | |
"TS3012b", | |
"TS3012c", | |
"TS3012d", | |
] | |
_VALIDATION_SAMPLE_IDS = [ | |
"ES2011a", | |
"ES2011c", | |
"IB4001", | |
"IB4003", | |
"IB4010", | |
"IS1008a", | |
"IS1008c", | |
"TS3004a", | |
"TS3004c", | |
"ES2011b", | |
"ES2011d", | |
"IB4002", | |
"IB4004", | |
"IB4011", | |
"IS1008b", | |
"IS1008d", | |
"TS3004b", | |
"TS3004d", | |
] | |
_EVAL_SAMPLE_IDS = [ | |
"EN2002a", | |
"EN2002b", | |
"EN2002c", | |
"EN2002d", | |
"ES2004a", | |
"ES2004b", | |
"ES2004c", | |
"ES2004d", | |
"IS1009a", | |
"IS1009b", | |
"IS1009c", | |
"IS1009d", | |
"TS3003a", | |
"TS3003b", | |
"TS3003c", | |
"TS3003d", | |
] | |
_SUBSETS = ("ihm",) | |
_BASE_DATA_URL = "https://huggingface.co/datasets/patrickvonplaten/ami-ihm-kaldi-chunked/resolve/main/" | |
_AUDIO_ARCHIVE_URL = _BASE_DATA_URL + "audio/{subset}/{split}/{_id}.tar.gz" | |
_ANNOTATIONS_ARCHIVE_URL = _BASE_DATA_URL + "annotations/{split}/text" | |
logger = datasets.utils.logging.get_logger(__name__) | |
class AMIConfig(datasets.BuilderConfig): | |
"""BuilderConfig for AMI.""" | |
def __init__(self, name, *args, **kwargs): | |
"""BuilderConfig for AMI""" | |
super().__init__(name=name, *args, **kwargs) | |
class AMI(datasets.GeneratorBasedBuilder): | |
""" | |
GigaSpeech is an evolving, multi-domain English speech recognition corpus with 10,000 hours of high quality | |
labeled audio suitable for supervised training, and 40,000 hours of total audio suitable for semi-supervised | |
and unsupervised training (this implementation contains only labelled data for now). | |
Around 40,000 hours of transcribed audio is first collected from audiobooks, podcasts | |
and YouTube, covering both read and spontaneous speaking styles, and a variety of topics, such as arts, science, | |
sports, etc. A new forced alignment and segmentation pipeline is proposed to create sentence segments suitable | |
for speech recognition training, and to filter out segments with low-quality transcription. For system training, | |
GigaSpeech provides five subsets of different sizes, 10h, 250h, 1000h, 2500h, and 10000h. | |
For our 10,000-hour XL training subset, we cap the word error rate at 4% during the filtering/validation stage, | |
and for all our other smaller training subsets, we cap it at 0%. The DEV and TEST evaluation sets, on the other hand, | |
are re-processed by professional human transcribers to ensure high transcription quality. | |
""" | |
VERSION = datasets.Version("1.0.0") | |
BUILDER_CONFIGS = [ | |
AMIConfig(name=subset) for subset in _SUBSETS | |
] | |
DEFAULT_WRITER_BATCH_SIZE = 128 | |
def _info(self): | |
features = datasets.Features( | |
{ | |
"segment_id": datasets.Value("string"), | |
"audio_id": datasets.Value("string"), | |
"text": datasets.Value("string"), | |
"audio": datasets.Audio(sampling_rate=16_000), | |
"begin_time": datasets.Value("float32"), | |
"end_time": datasets.Value("float32"), | |
"microphone_id": datasets.Value("string"), | |
"speaker_id": datasets.Value("string"), | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
train_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="train", _id=m) for m in _TRAIN_SAMPLE_IDS} | |
dev_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="dev", _id=m) for m in _VALIDATION_SAMPLE_IDS} | |
eval_audio_files = {m: _AUDIO_ARCHIVE_URL.format(subset=self.config.name, split="eval", _id=m) for m in _EVAL_SAMPLE_IDS} | |
train_audio_archives = dl_manager.download_and_extract(train_audio_files) | |
dev_audio_archives = dl_manager.download_and_extract(dev_audio_files) | |
eval_audio_archives = dl_manager.download_and_extract(eval_audio_files) | |
train_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="train")) | |
dev_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="dev")) | |
eval_annotation = dl_manager.download_and_extract(_ANNOTATIONS_ARCHIVE_URL.format(split="eval")) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={"audio": train_audio_archives, "annotation": train_annotation, "split": "train"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={"audio": dev_audio_archives, "annotation": dev_annotation, "split": "dev"}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={"audio": eval_audio_archives, "annotation": eval_annotation, "split": "eval"}, | |
), | |
] | |
def _generate_examples(self, audio, annotation, split): | |
# open annotation file | |
with open(annotation, "r", encoding="utf-8") as f: | |
transcriptions = {} | |
for line in f.readlines(): | |
line_items = line.strip().split() | |
_id = line_items[0] | |
text = " ".join(line_items[1:]) | |
_, segment_id, microphone_id, speaker_id, begin_time, end_time = _id.split("_") | |
transcriptions[_id] = { | |
"audio_id": _id, | |
"segment_id": segment_id, | |
"text": text, | |
"begin_time": int(begin_time) / 100, | |
"end_time": int(end_time) / 100, | |
"microphone_id": microphone_id, | |
"speaker_id": speaker_id, | |
} | |
for _audio_id, (transcription_id, result) in enumerate(transcriptions.items()): | |
folder_id = result["segment_id"] | |
file_name = "_".join([split, transcription_id.lower()]) + ".wav" | |
audio_file = os.path.join(audio[folder_id], folder_id, file_name) | |
result["audio"] = audio_file | |
yield _audio_id, result | |