timit_asr.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
"""TIMIT automatic speech recognition dataset."""


import os
from pathlib import Path

import datasets
from datasets.tasks import AutomaticSpeechRecognition


_CITATION = """\
@inproceedings{
  title={TIMIT Acoustic-Phonetic Continuous Speech Corpus},
  author={Garofolo, John S., et al},
  ldc_catalog_no={LDC93S1},
  DOI={https://doi.org/10.35111/17gk-bn40},
  journal={Linguistic Data Consortium, Philadelphia},
  year={1983}
}
"""

_DESCRIPTION = """\
The TIMIT corpus of reading speech has been developed to provide speech data for acoustic-phonetic research studies
and for the evaluation of automatic speech recognition systems.

TIMIT contains high quality recordings of 630 individuals/speakers with 8 different American English dialects,
with each individual reading upto 10 phonetically rich sentences.

More info on TIMIT dataset can be understood from the "README" which can be found here:
https://catalog.ldc.upenn.edu/docs/LDC93S1/readme.txt
"""

_HOMEPAGE = "https://catalog.ldc.upenn.edu/LDC93S1"


class TimitASRConfig(datasets.BuilderConfig):
    """BuilderConfig for TimitASR."""

    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(TimitASRConfig, self).__init__(version=datasets.Version("2.0.1", ""), **kwargs)


class TimitASR(datasets.GeneratorBasedBuilder):
    """TimitASR dataset."""

    BUILDER_CONFIGS = [TimitASRConfig(name="clean", description="'Clean' speech.")]

    @property
    def manual_download_instructions(self):
        return (
            "To use TIMIT you have to download it manually. "
            "Please create an account and download the dataset from https://catalog.ldc.upenn.edu/LDC93S1 \n"
            "Then extract all files in one folder and load the dataset with: "
            "`datasets.load_dataset('timit_asr', data_dir='path/to/folder/folder_name')`"
        )

    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"),
                    "phonetic_detail": datasets.Sequence(
                        {
                            "start": datasets.Value("int64"),
                            "stop": datasets.Value("int64"),
                            "utterance": datasets.Value("string"),
                        }
                    ),
                    "word_detail": datasets.Sequence(
                        {
                            "start": datasets.Value("int64"),
                            "stop": datasets.Value("int64"),
                            "utterance": datasets.Value("string"),
                        }
                    ),
                    "dialect_region": datasets.Value("string"),
                    "sentence_type": datasets.Value("string"),
                    "speaker_id": datasets.Value("string"),
                    "id": datasets.Value("string"),
                }
            ),
            supervised_keys=("file", "text"),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
        )

    def _split_generators(self, dl_manager):

        data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir))

        if not os.path.exists(data_dir):
            raise FileNotFoundError(
                f"{data_dir} does not exist. Make sure you insert a manual dir via `datasets.load_dataset('timit_asr', data_dir=...)` that includes files unzipped from the TIMIT zip. Manual download instructions: {self.manual_download_instructions}"
            )

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"split": "train", "data_dir": data_dir}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"split": "test", "data_dir": data_dir}),
        ]

    def _generate_examples(self, split, data_dir):
        """Generate examples from TIMIT archive_path based on the test/train csv information."""
        # Iterating the contents of the data to extract the relevant information
        for key, wav_path in enumerate(sorted(Path(data_dir).glob(f"**/{split.upper()}/**/*.WAV"))):

            # extract transcript
            with open(wav_path.with_suffix(".TXT"), encoding="utf-8") as op:
                transcript = " ".join(op.readlines()[0].split()[2:])  # first two items are sample number

            # extract phonemes
            with open(wav_path.with_suffix(".PHN"), encoding="utf-8") as op:
                phonemes = [
                    {
                        "start": i.split(" ")[0],
                        "stop": i.split(" ")[1],
                        "utterance": " ".join(i.split(" ")[2:]).strip(),
                    }
                    for i in op.readlines()
                ]

            # extract words
            with open(wav_path.with_suffix(".WRD"), encoding="utf-8") as op:
                words = [
                    {
                        "start": i.split(" ")[0],
                        "stop": i.split(" ")[1],
                        "utterance": " ".join(i.split(" ")[2:]).strip(),
                    }
                    for i in op.readlines()
                ]

            dialect_region = wav_path.parents[1].name
            sentence_type = wav_path.name[0:2]
            speaker_id = wav_path.parents[0].name[1:]
            id_ = wav_path.stem

            example = {
                "file": str(wav_path),
                "audio": str(wav_path),
                "text": transcript,
                "phonetic_detail": phonemes,
                "word_detail": words,
                "dialect_region": dialect_region,
                "sentence_type": sentence_type,
                "speaker_id": speaker_id,
                "id": id_,
            }

            yield key, example