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# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Licenses, Tasks
# no bibtex citation
_CITATION = ""
_DATASETNAME = "asr_stidusc"
_DESCRIPTION = """\
This open-source dataset consists of 4.56 hours of transcribed Thai scripted
speech focusing on daily use sentences, where 5,431 utterances contributed by
ten speakers were contained.
"""
_HOMEPAGE = "https://magichub.com/datasets/thai-scripted-speech-corpus-daily-use-sentence/"
_LANGUAGES = ["tha"]
_LICENSE = Licenses.CC_BY_NC_ND_4_0.value
_LOCAL = False
_URLS = {
_DATASETNAME: "https://magichub.com/df/df.php?file_name=Thai_Scripted_Speech_Corpus_Daily_Use_Sentence.zip",
}
_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class ASRSTIDuSCDataset(datasets.GeneratorBasedBuilder):
"""ASR-STIDuSC consists transcribed Thai scripted speech focusing on daily use sentences"""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
SEACROWD_SCHEMA_NAME = "sptext"
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"{_DATASETNAME}_source",
version=SOURCE_VERSION,
description=f"{_DATASETNAME} source schema",
schema="source",
subset_id=_DATASETNAME,
),
SEACrowdConfig(
name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",
version=SEACROWD_VERSION,
description=f"{_DATASETNAME} SEACrowd schema",
schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
subset_id=_DATASETNAME,
),
]
DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
def _info(self) -> datasets.DatasetInfo:
if self.config.schema == "source":
features = datasets.Features(
{
"id": datasets.Value("string"),
"channel": datasets.Value("string"),
"uttrans_id": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"transcription": datasets.Value("string"),
"path": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
"speaker_gender": datasets.Value("string"),
"speaker_age": datasets.Value("int64"),
"speaker_region": datasets.Value("string"),
"speaker_device": datasets.Value("string"),
}
)
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
features = schemas.speech_text_features
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
"""Returns SplitGenerators."""
data_paths = {
_DATASETNAME: Path(dl_manager.download_and_extract(_URLS[_DATASETNAME])),
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_paths[_DATASETNAME],
"split": "train",
},
)
]
def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
"""Yields examples as (key, example) tuples."""
# read UTTRANSINFO file
# columns: channel, uttrans_id, speaker_id, prompt (empty field), transcription
uttransinfo_filepath = os.path.join(filepath, "UTTRANSINFO.txt")
with open(uttransinfo_filepath, "r", encoding="utf-8") as uttransinfo_file:
uttransinfo_data = uttransinfo_file.readlines()
uttransinfo_data = uttransinfo_data[1:] # remove header
uttransinfo_data = [s.strip("\n").split("\t") for s in uttransinfo_data]
# read SPKINFO file
# columns: channel, speaker_id, gender, age, region, device
spkinfo_filepath = os.path.join(filepath, "SPKINFO.txt")
with open(spkinfo_filepath, "r", encoding="utf-8") as spkinfo_file:
spkinfo_data = spkinfo_file.readlines()
spkinfo_data = spkinfo_data[1:] # remove header
spkinfo_data = [s.strip("\n").split("\t") for s in spkinfo_data]
for i, s in enumerate(spkinfo_data):
if s[2] == "M":
s[2] = "male"
elif s[2] == "F":
s[2] = "female"
else:
s[2] = None
# dictionary of metadata of each speaker
spkinfo_dict = {s[1]: {"speaker_gender": s[2], "speaker_age": int(s[3]), "speaker_region": s[4], "speaker_device": s[5]} for s in spkinfo_data}
for i, sample in enumerate(uttransinfo_data):
wav_path = os.path.join(filepath, "WAV", sample[2], sample[1])
if self.config.schema == "source":
example = {
"id": str(i),
"channel": sample[0],
"uttrans_id": sample[1],
"speaker_id": sample[2],
"transcription": sample[4],
"path": wav_path,
"audio": wav_path,
"speaker_gender": spkinfo_dict[sample[2]]["speaker_gender"],
"speaker_age": spkinfo_dict[sample[2]]["speaker_age"],
"speaker_region": spkinfo_dict[sample[2]]["speaker_region"],
"speaker_device": spkinfo_dict[sample[2]]["speaker_device"],
}
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
example = {
"id": str(i),
"speaker_id": sample[2],
"path": wav_path,
"audio": wav_path,
"text": sample[4],
"metadata": {"speaker_age": spkinfo_dict[sample[2]]["speaker_age"], "speaker_gender": spkinfo_dict[sample[2]]["speaker_gender"]},
}
yield i, example
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