File size: 9,937 Bytes
3472b5c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
from pathlib import Path
from typing import Dict, List, Tuple
import datasets
import json
import os
from seacrowd.utils import schemas
from seacrowd.utils.configs import SEACrowdConfig
from seacrowd.utils.constants import Tasks
from zipfile import ZipFile
_CITATION = """\
@inproceedings{sakti-tcast-2008,
title = "Development of {I}ndonesian Large Vocabulary Continuous Speech Recognition System within {A-STAR} Project",
author = "Sakti, Sakriani and Kelana, Eka and Riza, Hammam and Sakai, Shinsuke and Markov, Konstantin and Nakamura, Satoshi",
booktitle = "Proc. IJCNLP Workshop on Technologies and Corpora for Asia-Pacific Speech Translation (TCAST)",
year = "2008",
pages = "19--24"
address = "Hyderabad, India"
}
@inproceedings{sakti-icslp-2004,
title = "Indonesian Speech Recognition for Hearing and Speaking Impaired People",
author = "Sakti, Sakriani and Hutagaol, Paulus and Arman, Arry Akhmad and Nakamura, Satoshi",
booktitle = "Proc. International Conference on Spoken Language Processing (INTERSPEECH - ICSLP)",
year = "2004",
pages = "1037--1040"
address = "Jeju Island, Korea"
}
@article{sakti-s2st-csl-2013,
title = "{A-STAR}: Toward Tranlating Asian Spoken Languages",
author = "Sakti, Sakriani and Paul, Michael and Finch, Andrew and Sakai, Shinsuke and Thang, Tat Vu, and Kimura, Noriyuki
and Hori, Chiori and Sumita, Eiichiro and Nakamura, Satoshi and Park, Jun and Wutiwiwatchai, Chai and Xu, Bo and Riza, Hammam
and Arora, Karunesh and Luong, Chi Mai and Li, Haizhou",
journal = "Special issue on Speech-to-Speech Translation, Computer Speech and Language Journal",
volume = "27",
number ="2",
pages = "509--527",
year = "2013",
publisher = "Elsevier"
}
"""
_LOCAL = False
_LANGUAGES = ["ind"] # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
_DATASETNAME = "indspeech_teldialog_lvcsr"
_DESCRIPTION = """
INDspeech_TELDIALOG_LVCSR is one of the first Indonesian speech datasets for large vocabulary continuous speech recognition (LVCSR) based on telephon application. R&D Division of PT Telekomunikasi Indonesia developed the data in 2005-2006, in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan, as the continuation of the Asia-Pacific Telecommunity (APT) project [Sakti et al., 2004]. It has also been successfully used for developing Indonesian LVCSR in the Asian speech translation advanced research (A-STAR) project [Sakti et al., 2013].
"""
_HOMEPAGE = "https://github.com/s-sakti/data_indsp_teldialog_lvcsr"
_LICENSE = "CC-BY-NC-SA 4.0"
URL_TEMPLATE = {
"lst": "https://raw.githubusercontent.com/s-sakti/data_indsp_teldialog_lvcsr/main/lst/", # transcript.lst
"speech": "https://github.com/s-sakti/data_indsp_teldialog_lvcsr/raw/main/speech/", # Ind3/Ind304.zip~Ind400.zip
"text": "https://github.com/s-sakti/data_indsp_teldialog_lvcsr/raw/main/text/", # all_transcript.zip
}
_URLS = {
"lst_spk_Ind": [URL_TEMPLATE["lst"] + "spk_Ind" + str(n) + ".lst" for n in range(0, 4)],
"lst_spk_all": URL_TEMPLATE["lst"] + "spk_all.lst",
"lst_spk_test": URL_TEMPLATE["lst"] + "spk_test.lst",
"lst_spk_train": URL_TEMPLATE["lst"] + "spk_train.lst",
"lst_transcript": URL_TEMPLATE["lst"] + "transcript.lst",
"speech_Ind": [URL_TEMPLATE["speech"] + "Ind" + str(n) + "/Ind" + str(p).zfill(3) + ".zip" for n in range(0, 4) for p in range(n * 100 + 1, n * 100 + 101)],
"transcript_all": URL_TEMPLATE["text"] + "all_transcript.zip",
"transcript_spk": URL_TEMPLATE["text"] + "spk_transcript.zip",
}
_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
_SOURCE_VERSION = "1.0.0"
_SEACROWD_VERSION = "2024.06.20"
class IndSpeechTelDialLVCSR(datasets.GeneratorBasedBuilder):
"""INDspeech_TELDIALOG_LVCSR is one of the first Indonesian speech datasets for large vocabulary continuous speech recognition (LVCSR) based on telephon application. R&D Division of PT Telekomunikasi Indonesia developed the data in 2005-2006, in collaboration with Advanced Telecommunication Research Institute International (ATR) Japan, as the continuation of the Asia-Pacific Telecommunity (APT) project [Sakti et al., 2004]. It has also been successfully used for developing Indonesian LVCSR in the Asian speech translation advanced research (A-STAR) project [Sakti et al., 2013]."""
SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
BUILDER_CONFIGS = [
SEACrowdConfig(
name=f"indspeech_teldialog_lvcsr_source",
version=_SOURCE_VERSION,
description="indspeech_teldialog_lvcsr source schema",
schema="source",
subset_id=f"indspeech_teldialog_lvcsr"
),
SEACrowdConfig(
name=f"indspeech_teldialog_lvcsr_seacrowd_sptext",
version=_SOURCE_VERSION,
description="indspeech_teldialog_lvcsr Nusantara schema",
schema="seacrowd_sptext",
subset_id=f"indspeech_teldialog_lvcsr"
),]
DEFAULT_CONFIG_NAME = "indspeech_teldialog_lvcsr_source"
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,
task_templates=[datasets.AutomaticSpeechRecognition(audio_column="audio", transcription_column="text")],
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
audio_files_dir = []
for aud_url in _URLS["speech_Ind"]:
onespeaker_folder = dl_manager.download_and_extract(aud_url)
audio_files_dir.append(Path(os.path.join(onespeaker_folder, aud_url.split("/")[-1][:-4])))
text_path = Path(dl_manager.download_and_extract(_URLS["lst_transcript"]))
speak_list = Path(dl_manager.download_and_extract(_URLS["lst_spk_all"]))
train_list = Path(dl_manager.download_and_extract(_URLS["lst_spk_train"]))
test_list = Path(dl_manager.download_and_extract(_URLS["lst_spk_test"]))
speaker_num2id = {}
with open(speak_list) as f:
for l in f.readlines():
l = l.strip()
speaker_num2id.update({l.split("_")[0]: l})
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"audio_files_dir": audio_files_dir,
"text_path": text_path,
"split": "train",
"file_list": train_list,
"speaker_num2id": speaker_num2id
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"audio_files_dir": audio_files_dir,
"text_path": text_path,
"split": "test",
"file_list": test_list,
"speaker_num2id": speaker_num2id
},
)
]
def _generate_examples(self, audio_files_dir: List, text_path: Path, split: str, file_list: Path, speaker_num2id: Dict) -> Tuple[int, Dict]:
speaker_nums = []
with open(file_list) as f:
for l in f.readlines():
speaker_nums.append(l.strip())
sentid = {}
with open(text_path) as f:
for i, l in enumerate(f.readlines()):
sentid.update({"appl_"+"%04d" % i: l.strip()})
for wav_one_speaker_folder in audio_files_dir: #XXXX/Ind0/Ind001
if wav_one_speaker_folder.name in speaker_nums:
speaker_num = wav_one_speaker_folder.name #Ind001
speaker_id = speaker_num2id[speaker_num] #Ind001_F_B
for wave_file in os.listdir(wav_one_speaker_folder):
audio_id = wave_file[:-4]
sentence_id = "appl_"+wave_file[:-4].split('_')[-1]
text = sentid[sentence_id]
wav_path = os.path.join(wav_one_speaker_folder, wave_file)
if self.config.schema == "source":
ex = {
"id": audio_id,
"speaker_id": speaker_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": speaker_id,
"path": wav_path,
"audio": wav_path,
"text": text,
"metadata": {
"speaker_age": None,
"speaker_gender": speaker_id.split("_")[1],
}
}
yield audio_id, ex
|