su_id_asr_split.py

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

import datasets

from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import 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://drive.google.com/file/d/1-9oCkIQSok_STemyNBLx2EDQXfmWabsU/view?usp=sharing",
    "su_id_asr_dev": "https://drive.google.com/file/d/1IkqEuGrIyKbCSDo9q6F6_r_vkeJ1pcrp/view?usp=sharing",
    "su_id_asr_test": "https://drive.google.com/file/d/1-7aLW9Tzs4lxm9ImWho91FjpgpVC6wAc/view?usp=sharing",
}

_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]

_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"

def download_from_gdrive(url, output_dir):
    """Download a file from Google Drive and save it to the specified directory."""
    file_id = url.split("/d/")[-1].split("/")[0]  # Extract FILE_ID from URL
    gdrive_url = f"https://drive.google.com/uc?id={file_id}"
    output_path = os.path.join(output_dir, f"{file_id}.zip")  # Save file
    gdown.download(gdrive_url, output_path, quiet=False)
    return output_path


class SuIdASR(datasets.GeneratorBasedBuilder):
    """Javanese ASR training data set containing ~185K utterances."""

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)

    BUILDER_CONFIGS = [
        SEACrowdConfig(
            name="su_id_asr_source",
            version=SOURCE_VERSION,
            description="su_id_asr source schema",
            schema="source",
            subset_id="su_id_asr",
        ),
        SEACrowdConfig(
            name="su_id_asr_seacrowd_sptext",
            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 _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        """Returns SplitGenerators."""
        
        def download_from_gdrive(url, name):
            # Create a temporary directory for downloads
            with tempfile.TemporaryDirectory() as temp_dir:
                file_id = url.split("/d/")[-1].split("/")[0]
                output_path = os.path.join(temp_dir, f"{name}.zip")
                
                # Download using gdown with fuzzy=True
                gdown.download(url, output_path, fuzzy=True)
                
                # Use dl_manager to extract and manage the downloaded file
                extracted_path = dl_manager.extract(output_path)
                return extracted_path

        # Download and extract all splits
        paths = {
            "train": download_from_gdrive(_URLs["su_id_asr_train"], 'asr_sundanese_train'),
            "dev": download_from_gdrive(_URLs["su_id_asr_dev"], 'asr_sundanese_dev'),
            "test": download_from_gdrive(_URLs["su_id_asr_test"], 'asr_sundanese_test')
        }

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": paths["train"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": paths["dev"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": paths["test"]},
            ),
        ]
    
    def _info(self) -> datasets.DatasetInfo:
        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,
        )

    def _generate_examples(self, filepath: str):
        tsv_file = os.path.join(filepath, "asr_sundanese", "utt_spk_text.tsv")
        with open(tsv_file, "r") as f:
            tsv_file = csv.reader(f, delimiter="\t")
            for line in tsv_file:
                audio_id, sp_id, text = line[0], line[1], line[2]
                wav_path = os.path.join(filepath, "asr_sundanese", "data", "{}".format(audio_id[:2]), "{}.flac".format(audio_id))

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