Create su_id_asr_split.py

su_id_asr_split.py

@@ -0,0 +1,150 @@
+import csv
+import os
+from typing import Dict, List
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import (DEFAULT_SEACROWD_VIEW_NAME,
+                                       DEFAULT_SOURCE_VIEW_NAME, Tasks)
+
+_DATASETNAME = "su_id_asr"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_SEACROWD_VIEW_NAME
+
+_LANGUAGES = ["sun"]
+_LOCAL = False
+_CITATION = """\
+@inproceedings{sodimana18_sltu,
+  author={Keshan Sodimana and Pasindu {De Silva} and Supheakmungkol Sarin and Oddur Kjartansson and Martin Jansche and Knot Pipatsrisawat and Linne Ha},
+  title={{A Step-by-Step Process for Building TTS Voices Using Open Source Data and Frameworks for Bangla, Javanese, Khmer, Nepali, Sinhala, and Sundanese}},
+  year=2018,
+  booktitle={Proc. 6th Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU 2018)},
+  pages={66--70},
+  doi={10.21437/SLTU.2018-14}
+}
+"""
+
+_DESCRIPTION = """\
+Sundanese ASR training data set containing ~220K utterances.
+This dataset was collected by Google in Indonesia.
+"""
+
+_HOMEPAGE = "https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr"
+
+_LICENSE = "Attribution-ShareAlike 4.0 International."
+
+_URLs = {
+    "su_id_asr_test": "https://univindonesia-my.sharepoint.com/:u:/g/personal/sabrina_aviana_office_ui_ac_id/Eds4-Yht811OrI56EdLSEpkBg4SuudSazxSRmSo3d_SqLg?e=WMuSdC&download=1",
+    "su_id_asr_val": "https://univindonesia-my.sharepoint.com/:u:/g/personal/sabrina_aviana_office_ui_ac_id/EamAbFjx5J1AjUO1apFjFDwBuoMfPeB1oB_vsL6YxxGjkw?e=vFdjzW&download=1",
+    "su_id_asr_train": "https://univindonesia-my.sharepoint.com/:u:/g/personal/sabrina_aviana_office_ui_ac_id/ESwkEg0Fx_FKhoZbUZx6qPABZFXO8LpEEcUZGisLhrzTQQ?e=tzyFwr&download=1",
+}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class SuIdASR(datasets.GeneratorBasedBuilder):
+    """su_id contains ~220K utterances for Sundanese ASR training data."""
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="su_id_asr_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="SU_ID_ASR source schema",
+            schema="source",
+            subset_id="su_id_asr",
+        ),
+        SEACrowdConfig(
+            name="su_id_asr_seacrowd_sptext",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description="SU_ID_ASR Nusantara schema",
+            schema="seacrowd_sptext",
+            subset_id="su_id_asr",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "su_id_asr_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 == "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]:
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": dl_manager.download_and_extract(_URLs["su_id_asr_train"])},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": dl_manager.download_and_extract(_URLs["su_id_asr_dev"])},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": dl_manager.download_and_extract(_URLs["su_id_asr_test"])},
+            )
+        ]
+
+    def _generate_examples(self, filepath: str):
+
+        if self.config.schema == "source" or self.config.schema == "seacrowd_sptext":
+
+            tsv_file = os.path.join(filepath, "asr_sundanese", "utt_spk_text.tsv")
+
+            with open(tsv_file, "r") as file:
+                tsv_file = csv.reader(file, delimiter="\t")
+
+                for line in tsv_file:
+                    audio_id, speaker_id, transcription_text = line[0], line[1], line[2]
+
+                    wav_path = os.path.join(filepath, "asr_sundanese", "data", "{}".format(audio_id[:2]), "{}.flac".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": transcription_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": transcription_text,
+                                "metadata": {
+                                    "speaker_age": None,
+                                    "speaker_gender": None,
+                                },
+                            }
+                            yield audio_id, ex
+                        
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")