asr-sample.py

# coding=utf-8
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""Sample automatic speech recognition dataset by Dennis Owusu."""


import os

import datasets


_CITATION = """\
@inproceedings{panayotov2015librispeech,
  title={Librispeech: an ASR corpus based on public domain audio books},
  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
  booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on},
  pages={5206--5210},
  year={2015},
  organization={IEEE}
}
"""

_DESCRIPTION = """\
Sample automatic speech recognition dataset from Librispeech (clean) by Dennis Owusu.
"""

_URL = "http://www.openslr.org/12"
_DL_URL = "http://www.openslr.org/resources/12/"


_DL_URLS = {
    "clean": {
        "dev": _DL_URL + "dev-clean.tar.gz",
        "test": _DL_URL + "test-clean.tar.gz",
    }
}


class LibrispeechASRConfig(datasets.BuilderConfig):
    """BuilderConfig for LibriSpeechASR."""

    def __init__(self, **kwargs):
        """
        Args:
          data_dir: `string`, the path to the folder containing the files in the
            downloaded .tar
          citation: `string`, citation for the data set
          url: `string`, url for information about the data set
          **kwargs: keyword arguments forwarded to super.
        """
        super(LibrispeechASRConfig, self).__init__(version=datasets.Version("2.1.0", ""), **kwargs)


class LibrispeechASR(datasets.GeneratorBasedBuilder):
    """Librispeech dataset."""

    DEFAULT_WRITER_BATCH_SIZE = 256
    DEFAULT_CONFIG_NAME = "clean"
    BUILDER_CONFIGS = [
        LibrispeechASRConfig(name="clean", description="'Clean' speech."),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "file": datasets.Value("string"),
                    "audio": datasets.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,
        )

    def _split_generators(self, dl_manager):
        archive_path = dl_manager.download(_DL_URLS[self.config.name])
        # (Optional) In non-streaming mode, we can extract the archive locally to have actual local audio files:
        print(type(archive_path))
        local_extracted_archive = dl_manager.extract(archive_path)
        print(type(local_extracted_archive))
        if self.config.name == "clean":
            dev_splits = [
                datasets.SplitGenerator(
                    name=datasets.Split.VALIDATION,
                    gen_kwargs={
                        "local_extracted_archive": local_extracted_archive.get("dev"),
                        "files": dl_manager.iter_archive(archive_path["dev"]),
                    },
                )
            ]
            test_splits = [
                datasets.SplitGenerator(
                    name=datasets.Split.TEST,
                    gen_kwargs={
                        "local_extracted_archive": local_extracted_archive.get("test"),
                        "files": dl_manager.iter_archive(archive_path["test"]),
                    },
                )
            ]

        return dev_splits + test_splits

    def _generate_examples(self, files, local_extracted_archive):
        """Generate examples from a LibriSpeech archive_path."""
        key = 0
        audio_data = {}
        transcripts = []
        for path, f in files:
            if path.endswith(".flac"):
                id_ = path.split("/")[-1][: -len(".flac")]
                audio_data[id_] = f.read()
                #print(audio_data[id_])
            elif path.endswith(".trans.txt"):
                for line in f:
                    if line:
                        line = line.decode("utf-8").strip()
                        id_, transcript = line.split(" ", 1)
                        audio_file = f"{id_}.flac"
                        speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
                        audio_file = (
                            os.path.join(local_extracted_archive, audio_file)
                            if local_extracted_archive
                            else audio_file
                        )
                        transcripts.append(
                            {
                                "id": id_,
                                "speaker_id": speaker_id,
                                "chapter_id": chapter_id,
                                "file": audio_file,
                                "text": transcript,
                            }
                        )
            if audio_data and len(audio_data) == len(transcripts):
                for transcript in transcripts:
                    audio = {"path": transcript["file"], "bytes": audio_data[transcript["id"]]}
                   
                    yield key, {"audio": audio, **transcript}
                    key += 1
                audio_data = {}
                transcripts = []