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titml_idn.py

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+from pathlib import Path
+from typing import List
+
+import datasets
+import json
+import os
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import Tasks, DEFAULT_SOURCE_VIEW_NAME, DEFAULT_NUSANTARA_VIEW_NAME
+
+_DATASETNAME = "titml_idn"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_VIEW_NAME
+
+_LANGUAGES = ["ind"]  # We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LOCAL = False
+_CITATION = """\
+@inproceedings{lestari2006titmlidn,
+  title={A large vocabulary continuous speech recognition system for Indonesian language},
+  author={Lestari, Dessi Puji and Iwano, Koji and Furui, Sadaoki},
+  booktitle={15th Indonesian Scientific Conference in Japan Proceedings},
+  pages={17--22},
+  year={2006}
+}
+"""
+
+_DESCRIPTION = """\
+TITML-IDN (Tokyo Institute of Technology Multilingual - Indonesian) is collected to build a pioneering Indonesian Large Vocabulary Continuous Speech Recognition (LVCSR) System. In order to build an LVCSR system, high accurate acoustic models and large-scale language models are essential. Since Indonesian speech corpus was not available yet, we tried to collect speech data from 20 Indonesian native speakers (11 males and 9 females) to construct a speech corpus for training the acoustic model based on Hidden Markov Models (HMMs). A text corpus which was collected by ILPS, Informatics Institute, University of Amsterdam, was used to build a 40K-vocabulary dictionary and a n-gram language model.
+"""
+
+_HOMEPAGE = "http://research.nii.ac.jp/src/en/TITML-IDN.html"
+
+_LICENSE = "For research purposes only. If you use this corpus, you have to cite (Lestari et al, 2006)."
+
+_URLs = {"titml-idn": "https://huggingface.co/datasets/holylovenia/TITML-IDN/resolve/main/IndoLVCSR.zip"}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class TitmlIdn(datasets.GeneratorBasedBuilder):
+    """TITML-IDN is a speech recognition dataset containing Indonesian speech collected with transcriptions from newpaper and magazine articles."""
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="titml_idn_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="TITML-IDN source schema",
+            schema="source",
+            subset_id="titml_idn",
+        ),
+        NusantaraConfig(
+            name="titml_idn_nusantara_sptext",
+            version=datasets.Version(_NUSANTARA_VERSION),
+            description="TITML-IDN Nusantara schema",
+            schema="nusantara_sptext",
+            subset_id="titml_idn",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "titml_idn_source"
+
+    def _info(self):
+        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 == "nusantara_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]:
+        base_path = dl_manager.download_and_extract(_URLs["titml-idn"])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": base_path},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, n_speakers=20):
+
+        if self.config.schema == "source" or self.config.schema == "nusantara_sptext":
+
+            for speaker_id in range(1, n_speakers + 1):
+                speaker_id = str(speaker_id).zfill(2)
+                dir_path = os.path.join(filepath, speaker_id)
+                transcription_path = os.path.join(dir_path, "script~")
+
+                with open(transcription_path, "r+") as f:
+                    for line in f:
+                        audio_id = line[2:8]
+                        text = line[9:].strip()
+                        wav_path = os.path.join(dir_path, "{}.wav".format(audio_id))
+
+                        if os.path.exists(wav_path):
+                            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 == "nusantara_sptext":
+                                ex = {
+                                    "id": audio_id,
+                                    "speaker_id": speaker_id,
+                                    "path": wav_path,
+                                    "audio": wav_path,
+                                    "text": text,
+                                    "metadata": {
+                                        "speaker_age": None,
+                                        "speaker_gender": None,
+                                    }
+                                }
+                                yield audio_id, ex
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")