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su_id_asr.py

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+import csv
+import os
+from typing import Dict, List
+
+import datasets
+
+from nusacrowd.utils import schemas
+from nusacrowd.utils.configs import NusantaraConfig
+from nusacrowd.utils.constants import (DEFAULT_NUSANTARA_VIEW_NAME,
+                                       DEFAULT_SOURCE_VIEW_NAME, Tasks)
+
+_DATASETNAME = "su_id_asr"
+_SOURCE_VIEW_NAME = DEFAULT_SOURCE_VIEW_NAME
+_UNIFIED_VIEW_NAME = DEFAULT_NUSANTARA_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": "https://www.openslr.org/resources/36/asr_sundanese_{}.zip",
+}
+
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
+
+_SOURCE_VERSION = "1.0.0"
+_NUSANTARA_VERSION = "1.0.0"
+
+
+class SuIdASR(datasets.GeneratorBasedBuilder):
+    """su_id contains ~220K utterances for Sundanese ASR training data."""
+
+    BUILDER_CONFIGS = [
+        NusantaraConfig(
+            name="su_id_asr_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description="SU_ID_ASR source schema",
+            schema="source",
+            subset_id="su_id_asr",
+        ),
+        NusantaraConfig(
+            name="su_id_asr_nusantara_sptext",
+            version=datasets.Version(_NUSANTARA_VERSION),
+            description="SU_ID_ASR Nusantara schema",
+            schema="nusantara_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 == "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 = {}
+        for id in range(10):
+            base_path[id] = dl_manager.download_and_extract(_URLs["su_id_asr"].format(str(id)))
+        for id in ["a", "b", "c", "d", "e", "f"]:
+            base_path[id] = dl_manager.download_and_extract(_URLs["su_id_asr"].format(str(id)))
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": base_path},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Dict):
+
+        if self.config.schema == "source" or self.config.schema == "nusantara_sptext":
+
+            for key, each_filepath in filepath.items():
+
+                tsv_file = os.path.join(each_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(each_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 == "nusantara_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}")