openslr-nepali-asr-cleaned.py

import os
import csv
import datasets

_CITATION = """\
@inproceedings{kjartansson-etal-sltu2018,
title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese,  Sinhala, Nepali, and Bangladeshi Bengali}},
author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha},
booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},
year  = {2018},
address = {Gurugram, India},
month = aug,
pages = {52--55},
URL   = {http://dx.doi.org/10.21437/SLTU.2018-11}
}
"""

_DESCRIPTION = """\
This data set contains transcribed audio data for Nepali. The data set consists of flac files, and a TSV file. The file utt_spk_text.tsv contains a FileID, anonymized UserID and the transcription of audio in the file.
The data set has been manually quality checked, but there might still be errors.
The audio files are sampled at rate of 16KHz, and leading and trailing silences are trimmed using torchaudio's voice activity detection.
"""

_HOMEPAGE = "https://www.openslr.org/54/"
_LICENSE = "license:cc-by-sa-4.0"

_URLS = {
    'cleaned': {
        "index_file": "https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned/resolve/main/data/utt_spk_text_clean.tsv",
        "zipfiles": [
            f"https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned/resolve/main/data/asr_nepali_{k}.zip"
            for k in [*range(10), *'abcdef']
        ],
    },
    'original': {
        "index_file": "https://huggingface.co/datasets/spktsagar/openslr-nepali-asr-cleaned/resolve/main/data/utt_spk_text_orig.tsv",
        "zipfiles": [
            f"https://www.openslr.org/resources/54/asr_nepali_{k}.zip"
            for k in [*range(10), *'abcdef']
        ],
    },
}


class OpenslrNepaliAsrCleaned(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="original", version=VERSION, description="All original utterances, speaker id and transcription from Openslr Large Nepali ASR Dataset"),
        datasets.BuilderConfig(name="cleaned", version=VERSION, description="All cleaned utterances, speaker id and transcription from Openslr Large Nepali ASR Dataset"),
    ]

    DEFAULT_CONFIG_NAME = "original"

    def _info(self):
        features = datasets.Features(
            {
                "utterance_id": datasets.Value("string"),
                "speaker_id": datasets.Value("string"),
                "utterance": datasets.Audio(sampling_rate=16000),
                "transcription": datasets.Value("string"),
                "num_frames": datasets.Value("int32"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
            task_templates=[datasets.tasks.AutomaticSpeechRecognition(
                audio_column="utterance", transcription_column="transcription"
            )]
        )

    def _split_generators(self, dl_manager):
        index_file = dl_manager.download(_URLS[self.config.name]['index_file'])
        zip_paths = [item for sublist in [
            dl_manager.download(
                _URLS[self.config.name]['zipfiles'][i:i+4]
            ) for i in range(0, len(_URLS[self.config.name]['zipfiles']), 4)
        ] for item in sublist]
        audio_paths = dict(zip([url[-5] for url in _URLS[self.config.name]["zipfiles"]],
                           dl_manager.extract(zip_paths)))
        for path in zip_paths:
            if os.path.exists(path):
                os.remove(path)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "index_file": index_file,
                    "audio_paths": audio_paths,
                },
            ),
        ]

    def _generate_examples(self, index_file, audio_paths):
        with open(index_file, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter='\t')
            for key, row in enumerate(reader):
                if self.config.name == 'cleaned':
                    path = os.path.join(
                        audio_paths[row['utterance_id'][0]], 'cleaned',
                        'asr_nepali', 'data', row['utterance_id'][:2],
                        f"{row['utterance_id']}.flac"
                    )
                else:
                    path = os.path.join(
                        audio_paths[row['utterance_id'][0]],
                        'asr_nepali', 'data', row['utterance_id'][:2],
                        f"{row['utterance_id']}.flac"
                    )
                yield key, {
                    "utterance_id": row['utterance_id'],
                    "speaker_id": row['speaker_id'],
                    "utterance": path,
                    "transcription": row['transcription'],
                    "num_frames": int(row['num_frames']),
                }