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# 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/speech-seq2seq/ami/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
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