su_id_asr_split.py

import csv
import os
from typing import Dict, List

import datasets

from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import (
    DEFAULT_SEACROWD_VIEW_NAME,
    DEFAULT_SOURCE_VIEW_NAME,
    Tasks,
)

_DATASETNAME = "su_id_asr"
_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME

_LANGUAGES = ["sun"]
_LOCAL = False
_CITATION = """\
@inproceedings{sodimana18_sltu,
  author={Keshan Sodimana and Pasindu {De Silva} and Supheakmungkol Sarin and Oddur Kjartansson and Martin Jansche and Knot Pipatsrisawat and Linne Ha},
  title={{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Frameworks for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}},
  year=2018,
  booktitle={Proc. 6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)},
  pages={66--70},
  doi={10.21437/SLTU.2018-14}
}
"""

_DESCRIPTION = """\
Sundanese ASR training data set containing ~220K utterances.
This dataset was collected by Google in Indonesia.
"""

_HOMEPAGE = "https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr"

_LICENSE = "Attribution-ShareAlike 4.0 International."

_URLs = {
    "su_id_asr_train": "https://univindonesia-my.sharepoint.com/personal/patrick_samuel_office_ui_ac_id/_layouts/15/download.aspx?share=ESbYerhrepxPsggILmK8hZwB9ywXeZzLX7fF885Yo9F7JA",
    "su_id_asr_dev": "https://univindonesia-my.sharepoint.com/personal/patrick_samuel_office_ui_ac_id/_layouts/15/download.aspx?share=EdmZ2KYglRBJrKacGRklGD4BEcZXqY6txIrEhj2csx3I3g",
    "su_id_asr_test": "https://univindonesia-my.sharepoint.com/personal/patrick_samuel_office_ui_ac_id/_layouts/15/download.aspx?share=ET_Yu0vwbk9Mu-2vg68mSnkBJ-CnY1DOBjm8GVjGLKFZxQ",
}

_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]

_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"


class SuIdASR(datasets.GeneratorBasedBuilder):
    """su_id contains ~220K utterances for Sundanese ASR training data."""

    BUILDER_CONFIGS = [
        SEACrowdConfig(
            name="su_id_asr_source",
            version=datasets.Version(_SOURCE_VERSION),
            description="SU_ID_ASR source schema",
            schema="source",
            subset_id="su_id_asr",
        ),
        SEACrowdConfig(
            name="su_id_asr_seacrowd_sptext",
            version=datasets.Version(_SEACROWD_VERSION),
            description="SU_ID_ASR Nusantara schema",
            schema="seacrowd_sptext",
            subset_id="su_id_asr",
        ),
    ]

    DEFAULT_CONFIG_NAME = "su_id_asr_source"

    def _info(self):
        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "speaker_id": datasets.Value("string"),
                    "path": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=16_000),
                    "text": datasets.Value("string"),
                }
            )
        elif self.config.schema == "seacrowd_sptext":
            features = schemas.speech_text_features

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
            task_templates=[datasets.AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
        )

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        base_path_train = dl_manager.download_and_extract(_URLs["su_id_asr_train"])
        base_path_validation = dl_manager.download_and_extract(_URLs["su_id_asr_dev"])
        base_path_test = dl_manager.download_and_extract(_URLs["su_id_asr_test"])

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": base_path_train, "split": "train"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": base_path_validation, "split": "validation"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": base_path_test, "split": "test"},
            ),
        ]

    def _generate_examples(self, filepath: str):
        # Construct the path for the TSV file
        tsv_file = os.path.join(filepath, "utt_spk_text.tsv")

        # Check if the TSV file exists
        if not os.path.exists(tsv_file):
            raise FileNotFoundError(f"TSV file not found at: {tsv_file}")

        with open(tsv_file, "r") as file:
            tsv_reader = csv.reader(file, delimiter="\t")

            for line in tsv_reader:
                audio_id, speaker_id, transcription_text = line[0], line[1], line[2]
                wav_path = os.path.join(filepath, "{}.flac".format(audio_id))

                if os.path.exists(wav_path):
                    ex = {
                        "id": audio_id,
                        "speaker_id": speaker_id,
                        "path": wav_path,
                        "audio": wav_path,
                        "text": transcription_text,
                    }
                    if self.config.schema == "seacrowd_sptext":
                        ex["metadata"] = {
                            "speaker_age": None,
                            "speaker_gender": None,
                        }
                    yield audio_id, ex