multilingual_librispeech.py

# 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
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# 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.
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# Lint as: python3
"""Multilingual Librispeech automatic speech recognition dataset."""


from functools import partial
import os

import datasets
from datasets.tasks import AutomaticSpeechRecognition
from datasets.utils.streaming_download_manager import xopen


_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.strip().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(os.path.join(split_dir, "audio_filenames.txt"))

    with xopen(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