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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_indocsc"
_DESCRIPTION = """\
This open-source dataset consists of 4.54 hours of transcribed Indonesian
conversational speech on certain topics, where seven conversations between two
pairs of speakers were contained. Please create an account and be logged in on
https://magichub.com to download the data.
"""
_HOMEPAGE = "https://magichub.com/datasets/indonesian-conversational-speech-corpus/"
_LANGUAGES = ["ind"]
_LICENSE = Licenses.CC_BY_NC_ND_4_0.value
_LOCAL = False
_URLS = {
_DATASETNAME: "https://magichub.com/df/df.php?file_name=Indonesian_Conversational_Speech_Corpus.zip",
}
_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class ASRIndocscDataset(datasets.GeneratorBasedBuilder):
"""ASR-Indocsc consists transcribed Indonesian conversational speech on certain topics"""
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"),
"topic": datasets.Value("string"),
"text": 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 AUDIOINFO file
# columns: channel, uttrans_id, speaker_id, topic
audioinfo_filepath = os.path.join(filepath, "AUDIOINFO.txt")
with open(audioinfo_filepath, "r", encoding="utf-8") as audioinfo_file:
audioinfo_data = audioinfo_file.readlines()
audioinfo_data = audioinfo_data[1:] # remove header
audioinfo_data = [s.strip("\n").split("\t") for s in audioinfo_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}
num_sample = len(audioinfo_data)
for i in range(num_sample):
# wav file
wav_path = os.path.join(filepath, "WAV", audioinfo_data[i][1])
# transcription file
transcription_path = os.path.join(filepath, "TXT", audioinfo_data[i][1].replace("wav", "txt"))
with open(transcription_path, "r", encoding="utf-8") as transcription_file:
transcription = transcription_file.readlines()
# remove redundant speaker info from transcription file
transcription = [s.strip("\n").split("\t") for s in transcription]
transcription = [s[-1] for s in transcription]
text = " \n ".join(transcription)
if self.config.schema == "source":
example = {
"id": audioinfo_data[i][1].strip(".wav"),
"channel": audioinfo_data[i][0],
"uttrans_id": audioinfo_data[i][1],
"speaker_id": audioinfo_data[i][2],
"topic": audioinfo_data[i][3],
"text": text,
"path": wav_path,
"audio": wav_path,
"speaker_gender": spkinfo_dict[audioinfo_data[i][2]]["speaker_gender"],
"speaker_age": spkinfo_dict[audioinfo_data[i][2]]["speaker_age"],
"speaker_region": spkinfo_dict[audioinfo_data[i][2]]["speaker_region"],
"speaker_device": spkinfo_dict[audioinfo_data[i][2]]["speaker_device"],
}
elif self.config.schema == f"seacrowd_{self.SEACROWD_SCHEMA_NAME}":
example = {
"id": audioinfo_data[i][1].strip(".wav"),
"speaker_id": audioinfo_data[i][2],
"path": wav_path,
"audio": wav_path,
"text": text,
"metadata": {"speaker_age": spkinfo_dict[audioinfo_data[i][2]]["speaker_age"], "speaker_gender": spkinfo_dict[audioinfo_data[i][2]]["speaker_gender"]},
}
yield i, example
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