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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://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] # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
_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 JvIdASR(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() |