Update asr_sundanese_2_hub.py

asr_sundanese_2_hub.py

@@ -15,43 +15,40 @@
 
 import csv
 import os
-from pathlib import Path
-from typing import List
+from typing import Dict, List
 
 import datasets
 
 from seacrowd.utils import schemas
 from seacrowd.utils.configs import SEACrowdConfig
-from seacrowd.utils.constants import Tasks
+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{kjartansson-etal-sltu2018,
-    title = {{Crowd-Sourced Speech Corpora for Javanese, Sundanese,  Sinhala, Nepali, and Bangladeshi Bengali}},
-    author = {Oddur Kjartansson and Supheakmungkol Sarin and Knot Pipatsrisawat and Martin Jansche and Linne Ha},
-    booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},
-    year  = {2018},
-    address = {Gurugram, India},
-    month = aug,
-    pages = {52--55},
-    URL   = {http://dx.doi.org/10.21437/SLTU.2018-11},
-  }
+@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}
+}
 """
 
-_DATASETNAME = "su_id_asr"
-
 _DESCRIPTION = """\
-This data set contains transcribed audio data for Sundanese, specifically asr_sundanese_2 dataset from OpenSLR.
-The data set consists of wave files, and a TSV file.
-The file utt_spk_text.tsv contains a FileID, UserID and the transcription of audio in the file.
-The data set has been manually quality checked, but there might still be errors.
-This dataset was collected by Google in collaboration with Reykjavik University and Universitas Gadjah Mada in Indonesia.
+Sundanese ASR training data set containing ~220K utterances.
+This dataset was collected by Google in Indonesia.
 """
 
-_HOMEPAGE = "http://openslr.org/36/"
-_LANGUAGES = ["sun"]
-_LOCAL = False
+_HOMEPAGE = "https://indonlp.github.io/nusa-catalogue/card.html?su_id_asr"
 
-_LICENSE = "Attribution-ShareAlike 4.0 International"
+_LICENSE = "Attribution-ShareAlike 4.0 International."
 
 _URLs = {
     "su_id_asr_train": "https://drive.google.com/uc?export=download&id=10YBMnKSfZQKCuYGXAsTeTfUM5t3rGLs-",
@@ -59,31 +56,26 @@ _URLs = {
     "su_id_asr_test": "https://drive.google.com/uc?export=download&id=1P6mtQJoZ2QV7AC9zbR2nDbW6s6YrJ_XU",
 }
 
-_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]  # example: [Tasks.TRANSLATION, Tasks.NAMED_ENTITY_RECOGNITION, Tasks.RELATION_EXTRACTION]
+_SUPPORTED_TASKS = [Tasks.SPEECH_RECOGNITION]
 
 _SOURCE_VERSION = "1.0.0"
-
 _SEACROWD_VERSION = "2024.06.20"
 
-
 class SuIdASR(datasets.GeneratorBasedBuilder):
-    """Sundanese ASR training data set."""
-
-    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
-    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    """su_id contains ~220K utterances for Sundanese ASR training data."""
 
     BUILDER_CONFIGS = [
         SEACrowdConfig(
             name="su_id_asr_source",
-            version=SOURCE_VERSION,
-            description="su_id_asr source schema",
+            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=SEACROWD_VERSION,
-            description="su_id_asr Nusantara schema",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description="SU_ID_ASR Nusantara schema",
             schema="seacrowd_sptext",
             subset_id="su_id_asr",
         ),
@@ -91,7 +83,7 @@ class SuIdASR(datasets.GeneratorBasedBuilder):
 
     DEFAULT_CONFIG_NAME = "su_id_asr_source"
 
-    def _info(self) -> datasets.DatasetInfo:
+    def _info(self):
         if self.config.schema == "source":
             features = datasets.Features(
                 {
@@ -111,6 +103,7 @@ class SuIdASR(datasets.GeneratorBasedBuilder):
             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]:
@@ -121,7 +114,7 @@ class SuIdASR(datasets.GeneratorBasedBuilder):
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": dl_manager.download_and_extract(_URLs["su_id_asr_val"])},
+                gen_kwargs={"filepath": dl_manager.download_and_extract(_URLs["su_id_asr_dev"])},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
@@ -129,36 +122,43 @@ class SuIdASR(datasets.GeneratorBasedBuilder):
             )
         ]
 
-    def _generate_examples(self, filepath: Path):
-        for key, fp in filepath.items():
-            tsv_file = os.path.join(fp, "utt_spk_text.tsv")
-            with open(tsv_file, "r") as f:
-                tsv_file = csv.reader(f, delimiter="\t")
+    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, sp_id, text = line[0], line[1], line[2]
-                    wav_path = os.path.join(fp, "data", "{}.flac".format(audio_id))
+                    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": sp_id,
+                                "speaker_id": speaker_id,
                                 "path": wav_path,
                                 "audio": wav_path,
-                                "text": text,
+                                "text": transcription_text,
                             }
                             yield audio_id, ex
                         elif self.config.schema == "seacrowd_sptext":
                             ex = {
                                 "id": audio_id,
-                                "speaker_id": sp_id,
+                                "speaker_id": speaker_id,
                                 "path": wav_path,
                                 "audio": wav_path,
-                                "text": text,
+                                "text": transcription_text,
                                 "metadata": {
                                     "speaker_age": None,
                                     "speaker_gender": None,
                                 },
                             }
                             yield audio_id, ex
-            f.close()
+                        
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")