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# coding=utf-8
# Copyright 2022 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
"""Multilingual Librispeech automatic speech recognition dataset."""
from functools import partial
import os
import datasets
from datasets.tasks import AutomaticSpeechRecognition
_CITATION = """\
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
"""
_DESCRIPTION = """\
This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94)
to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research.
The dataset is derived from read audiobooks from LibriVox and consists of 8 languages:
English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
"""
_URL = "http://www.openslr.org/94"
_DL_URL_FORMAT = "data/mls_{name}"
class MultilingualLibrispeechConfig(datasets.BuilderConfig):
"""BuilderConfig for MultilingualLibrispeech."""
def __init__(self, name, **kwargs):
"""
Args:
name: `string`, name of dataset config (=language)
**kwargs: keyword arguments forwarded to super.
"""
super(MultilingualLibrispeechConfig, self).__init__(
version=datasets.Version("2.1.0", ""), name=name, **kwargs
)
# relative path to full data inside a repo (for example `data/mls_german`)
self.data_root_dir = _DL_URL_FORMAT.format(name=name)
class MultilingualLibrispeech(datasets.GeneratorBasedBuilder):
"""Multilingual Librispeech dataset."""
BUILDER_CONFIGS = [
MultilingualLibrispeechConfig(name="german", description="German LibriSpeech dataset"),
MultilingualLibrispeechConfig(name="dutch", description="Dutch LibriSpeech dataset"),
MultilingualLibrispeechConfig(name="french", description="French LibriSpeech dataset"),
MultilingualLibrispeechConfig(name="spanish", description="Spanish LibriSpeech dataset"),
MultilingualLibrispeechConfig(name="italian", description="Italian LibriSpeech dataset"),
MultilingualLibrispeechConfig(name="portuguese", description="Portuguese LibriSpeech dataset"),
MultilingualLibrispeechConfig(name="polish", description="Polish LibriSpeech dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.features.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_file_path_column="file", transcription_column="text")],
)
def _split_generators(self, dl_manager):
download_transcript = partial(
download_extract_transcript, dl_manager=dl_manager, root_dir=self.config.data_root_dir
)
download_audio = partial(
download_audio_archives, dl_manager=dl_manager, root_dir=self.config.data_root_dir
)
download_limited_ids = partial(
download_extract_limited_ids, dl_manager=dl_manager, root_dir=self.config.data_root_dir
)
train_kwargs = {
"transcript_path": download_transcript(split="train"),
"audio_archives": download_audio(split="train")
}
train_splits = [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs=train_kwargs
),
datasets.SplitGenerator(
name="train.9h",
gen_kwargs={
**train_kwargs,
"limited_ids_paths": download_limited_ids(sub_folder="limited_supervision/9hr"),
},
),
datasets.SplitGenerator(
name="train.1h",
gen_kwargs={
**train_kwargs,
"limited_ids_paths": download_limited_ids(sub_folder="limited_supervision/1hr"),
},
),
]
return train_splits + [
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={
"transcript_path": download_transcript(split="dev"),
"audio_archives": download_audio(split="dev"),
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={
"transcript_path": download_transcript(split="test"),
"audio_archives": download_audio(split="test"),
}
),
]
def _generate_examples(self, transcript_path, audio_archives, limited_ids_paths=None):
"""Generate examples from a Multilingual LibriSpeech data dir."""
transcripts = dict()
with open(transcript_path, "r", encoding="utf-8") as file:
for line in file:
audio_id, transcript = line.split("\t")
transcripts[audio_id] = transcript
limited_ids, limited_ids_archives_names = [], []
if limited_ids_paths:
for path in limited_ids_paths:
with open(path, "r", encoding="utf-8") as file:
limited_ids.extend([line.strip() for line in file.readlines()])
limited_ids = set(limited_ids)
for audio_archive in audio_archives:
# TODO: check that archive doesn't contain needed ids
# if limited_ids and audio_archive not in limited_ids_archives_names:
# continue
for audio_filename, file in audio_archive:
speaker_id, chapter_id = audio_filename.split("_")[:2]
speaker_id, chapter_id = int(speaker_id), int(chapter_id)
audio_id = audio_filename.split(".flac")[0]
audio_transcript = transcripts[audio_id]
if limited_ids and audio_id not in limited_ids:
# this only can be true in limited supervision sets ("train.9h" and "train.1h")
continue
yield audio_filename, {
"file": audio_filename,
"audio": {"path": audio_filename, "bytes": file.read()},
"text": audio_transcript,
"speaker_id": speaker_id,
"chapter_id": chapter_id,
"id": audio_id
}
def download_extract_limited_ids(dl_manager, root_dir, sub_folder):
"""Download and extract all handles.txt files containing ids for limited supervision train sets. """
sub_path = os.path.join(root_dir, "train", sub_folder)
if sub_folder.endswith("9hr"):
limited_ids_paths = [os.path.join(sub_path, "handles.txt")]
else: # => sub_folder.endswith("1hr")
# in case of 1 hour limited supervision ("train.1h") there are always 6 subfolders like:
# "limited_supervision/1h/0/handles.txt", "limited_supervision/1h/1/handles.txt", ...
limited_ids_paths = [os.path.join(sub_path, str(i), "handles.txt") for i in range(6)]
limited_ids_paths = dl_manager.download_and_extract(limited_ids_paths)
return limited_ids_paths
def download_extract_transcript(dl_manager, root_dir, split):
"""Downloading and extracting file with audio transcriptions. """
transcript_path = os.path.join(root_dir, split, "transcripts.txt")
return dl_manager.download_and_extract(transcript_path)
def download_audio_archives(dl_manager, root_dir, split):
"""Prepare archives with audio files for iterating over them.
Return:
audio_archives (List `Generator`): list of generators to iterate over files in each audio archive.
"""
# each split contains many .tar.gz archives with its audio files
# audio_filenames.txt contains the names of these archives
split_dir = os.path.join(root_dir, split)
audio_filenames_path = dl_manager.download_and_extract(os.path.join(split_dir, "audio_filenames.txt"))
with open(audio_filenames_path, "r", encoding="utf-8") as file:
audio_filenames = [line.strip() for line in file.readlines()]
archive_paths = dl_manager.download([os.path.join(split_dir, "audio", filename) for filename in audio_filenames])
audio_archives = [dl_manager.iter_archive(archive_path) for archive_path in archive_paths]
return audio_archives
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