Upload indspeech_teldialog_lvcsr.py with huggingface_hub

indspeech_teldialog_lvcsr.py

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+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