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

import os

import datasets


_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_url = _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=None,
        )

    def _split_generators(self, dl_manager):
        
        transcripts = dl_manager.download({
            "train": self.config.data_root_url + "/train/transcripts.txt",
            "dev": self.config.data_root_url + "/dev/transcripts.txt",
            "test": self.config.data_root_url + "/test/transcripts.txt",
        })

        # Download handles.txt files containing ids for limited supervision train sets
        limited_supervision_9h = dl_manager.download(
            [self.config.data_root_url + "/train/limited_supervision/9hr/handles.txt"],
        )
        # in our 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_supervision_1h = dl_manager.download([
            self.config.data_root_url + f"/train/limited_supervision/1hr/{i}/handles.txt" for i in range(6)
        ])
        
        # each split contains many .tar.gz archives with its audio files
        # audio_filenames.txt contains the names of these archives
        audio_filenames_paths = dl_manager.download({
            "train": self.config.data_root_url + "/train/audio_filenames.txt",
            "dev": self.config.data_root_url + "/dev/audio_filenames.txt",
            "test": self.config.data_root_url + "/test/audio_filenames.txt",
        })

        audio_archives = {}
        for split in audio_filenames_paths:
            with open(audio_filenames_paths[split], encoding="utf-8") as f:
                audio_filenames = [line.strip() for line in f.readlines()]
                audio_archives[split] = dl_manager.download([
                    self.config.data_root_url + "/" + split + "/audio/" + filename
                    for filename in audio_filenames
                ])

        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
        local_extracted_archives = dl_manager.extract(audio_archives) if not dl_manager.is_streaming else {}

        train_splits = [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "transcript_path": transcripts["train"],
                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]],
                    "local_extracted_archive": local_extracted_archives.get("train"),
                }
            ),
            datasets.SplitGenerator(
                name="train.9h",
                gen_kwargs={
                    "transcript_path": transcripts["train"],
                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]],
                    "local_extracted_archive": local_extracted_archives.get("train"),
                    "limited_ids_paths": tuple(limited_supervision_9h),
                },
            ),
            datasets.SplitGenerator(
                name="train.1h",
                gen_kwargs={
                    "transcript_path": transcripts["train"],
                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["train"]],
                    "local_extracted_archive": local_extracted_archives.get("train"),
                    "limited_ids_paths": tuple(limited_supervision_1h),
                },
            ),
        ]

        return train_splits + [
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION, gen_kwargs={
                    "transcript_path": transcripts["dev"],
                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["dev"]],
                    "local_extracted_archive": local_extracted_archives.get("dev"),
                }
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST, gen_kwargs={
                    "transcript_path": transcripts["test"],
                    "audio_archives": [dl_manager.iter_archive(archive) for archive in audio_archives["test"]],
                    "local_extracted_archive": local_extracted_archives.get("test"),
                }
            ),
        ]

    def _generate_examples(self, transcript_path, audio_archives, local_extracted_archive, 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 archive_idx, audio_archive in enumerate(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

                local_audio_file_path = os.path.join(
                    local_extracted_archive[archive_idx], audio_filename
                ) if local_extracted_archive else None

                yield audio_filename, {
                    "file": local_audio_file_path,
                    "audio": {
                        "path": local_audio_file_path if local_audio_file_path else audio_filename,
                        "bytes": file.read()
                    },
                    "text": audio_transcript,
                    "speaker_id": speaker_id,
                    "chapter_id": chapter_id,
                    "id": audio_id
                }