jv_id_tts.py

import csv
import os
from pathlib import Path
from typing import List

import datasets

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

_DATASETNAME = "jv_id_tts"
_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME

_LANGUAGES = ["jav"]
_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 = """\
This data set contains high-quality transcribed audio data for Javanese.
The data set consists of wave files, and a TSV file.
The file line_index.tsv contains a filename and the transcription of audio in the file.
Each filename is prepended with a speaker identification number.
The data set has been manually quality checked, but there might still be errors.
This dataset was collected by Google in collaboration with Gadjah Mada University in Indonesia.
"""

_HOMEPAGE = "http://openslr.org/41/"

_LICENSE = Licenses.CC_BY_SA_4_0.value

_URLs = {
    _DATASETNAME: {
        "female": "https://www.openslr.org/resources/41/jv_id_female.zip",
        "male": "https://www.openslr.org/resources/41/jv_id_male.zip",
    }
}

_SUPPORTED_TASKS = [Tasks.TEXT_TO_SPEECH]

_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"


class JvIdTTS(datasets.GeneratorBasedBuilder):
    """jv_id_tts contains high-quality Multi-speaker TTS data for Javanese (jv-ID)."""

    BUILDER_CONFIGS = [
        SEACrowdConfig(
            name="jv_id_tts_source",
            version=datasets.Version(_SOURCE_VERSION),
            description="JV_ID_TTS source schema",
            schema="source",
            subset_id="jv_id_tts",
        ),
        SEACrowdConfig(
            name="jv_id_tts_seacrowd_sptext",
            version=datasets.Version(_SEACROWD_VERSION),
            description="JV_ID_TTS Nusantara schema",
            schema="seacrowd_sptext",
            subset_id="jv_id_tts",
        ),
    ]

    DEFAULT_CONFIG_NAME = "jv_id_tts_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]:
        male_path = Path(dl_manager.download_and_extract(_URLs[_DATASETNAME]["male"]))
        female_path = Path(dl_manager.download_and_extract(_URLs[_DATASETNAME]["female"]))

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "male_filepath": male_path,
                    "female_filepath": female_path,
                },
            ),
        ]

    def _generate_examples(self, male_filepath: Path, female_filepath: Path):

        if self.config.schema == "source" or self.config.schema == "seacrowd_sptext":
            tsv_file = os.path.join(male_filepath, "jv_id_male", "line_index.tsv")
            with open(tsv_file, "r") as file:
                tsv_data = csv.reader(file, delimiter="\t")

                for line in tsv_data:
                    # for male data, the tsv contains three columns
                    audio_id, _, transcription_text = line[0], line[1], line[2]
                    speaker_id = audio_id.split("_")[1]
                    wav_path = os.path.join(male_filepath, "jv_id_male", "wavs", "{}.wav".format(audio_id))

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

            tsv_file = os.path.join(female_filepath, "jv_id_female", "line_index.tsv")
            with open(tsv_file, "r") as file:
                tsv_data = csv.reader(file, delimiter="\t")

                for line in tsv_data:
                    audio_id, transcription_text = line[0], line[1]
                    speaker_id = audio_id.split("_")[1]
                    wav_path = os.path.join(female_filepath, "jv_id_female", "wavs", "{}.wav".format(audio_id))

                    if os.path.exists(wav_path):
                        if self.config.schema == "source":
                            ex = {
                                "id": audio_id,
                                "speaker_id": speaker_id,
                                "path": wav_path,
                                "audio": wav_path,
                                "text": transcription_text,
                            }
                            yield audio_id, ex
                        elif self.config.schema == "seacrowd_sptext":
                            ex = {
                                "id": audio_id,
                                "speaker_id": speaker_id,
                                "path": wav_path,
                                "audio": wav_path,
                                "text": transcription_text,
                                "metadata": {
                                    "speaker_age": None,
                                    "speaker_gender": "female",
                                },
                            }
                            yield audio_id, ex
        else:
            raise ValueError(f"Invalid config: {self.config.name}")