Update asr_sundanese_2_hub.py

asr_sundanese_2_hub.py

@@ -1,164 +1,170 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import csv
-import os
-from pathlib import Path
-from typing import List
-
-import datasets
-
-from seacrowd.utils import schemas
-from seacrowd.utils.configs import SEACrowdConfig
-from seacrowd.utils.constants import Tasks
-
-_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},
-  }
-"""
-
-_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.
-"""
-
-_HOMEPAGE = "http://openslr.org/36/"
-_LANGUAGES = ["sun"]
-_LOCAL = False
-
-_LICENSE = "Attribution-ShareAlike 4.0 International"
-
-_URLs = {
-    "su_id_asr_train": "https://drive.google.com/uc?export=download&id=10YBMnKSfZQKCuYGXAsTeTfUM5t3rGLs-",
-    "su_id_asr_val": "https://drive.google.com/uc?export=download&id=1GvKjmV9ETd3wAFS5mcwA6hL0l4nwT-Xk",
-    "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]
-
-_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)
-
-    BUILDER_CONFIGS = [
-        SEACrowdConfig(
-            name="su_id_asr_source",
-            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",
-            schema="seacrowd_sptext",
-            subset_id="su_id_asr",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "su_id_asr_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,
-        )
-
-    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_val"])},
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={"filepath": dl_manager.download_and_extract(_URLs["su_id_asr_test"])},
-            )
-        ]
-
-    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")
-                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))
-
-                    if os.path.exists(wav_path):
-                        if self.config.schema == "source":
-                            ex = {
-                                "id": audio_id,
-                                "speaker_id": sp_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": sp_id,
-                                "path": wav_path,
-                                "audio": wav_path,
-                                "text": text,
-                                "metadata": {
-                                    "speaker_age": None,
-                                    "speaker_gender": None,
-                                },
-                            }
-                            yield audio_id, ex
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import csv
+import os
+from pathlib import Path
+from typing import List
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks
+
+_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},
+  }
+"""
+
+_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.
+"""
+
+_HOMEPAGE = "http://openslr.org/36/"
+_LANGUAGES = ["sun"]
+_LOCAL = False
+
+_LICENSE = "Attribution-ShareAlike 4.0 International"
+
+_URLs = {
+    "su_id_asr_train": "https://drive.google.com/uc?export=download&id=10YBMnKSfZQKCuYGXAsTeTfUM5t3rGLs-",
+    "su_id_asr_val": "https://drive.google.com/uc?export=download&id=1GvKjmV9ETd3wAFS5mcwA6hL0l4nwT-Xk",
+    "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]
+
+_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)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name="su_id_asr_source",
+            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",
+            schema="seacrowd_sptext",
+            subset_id="su_id_asr",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "su_id_asr_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,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        # As the data is already split manually, we only need to download and pass the paths
+        train_path = dl_manager.download_and_extract(_URLs["su_id_asr_train"])
+        val_path = dl_manager.download_and_extract(_URLs["su_id_asr_val"])
+        test_path = dl_manager.download_and_extract(_URLs["su_id_asr_test"])
+    
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": train_path},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": val_path},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": test_path},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path):
+        """Yields examples."""
+        tsv_file = os.path.join(filepath, "utt_spk_text.tsv")
+        
+        with open(tsv_file, "r", encoding="utf-8") as f:
+            tsv_reader = csv.reader(f, delimiter="\t")
+            for line in tsv_reader:
+                audio_id, sp_id, text = line[0], line[1], line[2]
+                wav_path = os.path.join(filepath, "data", f"{audio_id}.flac")
+    
+                if os.path.exists(wav_path):
+                    if self.config.schema == "source":
+                        ex = {
+                            "id": audio_id,
+                            "speaker_id": sp_id,
+                            "path": wav_path,
+                            "audio": wav_path,
+                            "text": text,
+                        }
+                    elif self.config.schema == "seacrowd_sptext":
+                        ex = {
+                            "id": audio_id,
+                            "speaker_id": sp_id,
+                            "path": wav_path,
+                            "audio": wav_path,
+                            "text": text,
+                            "metadata": {
+                                "speaker_age": None,
+                                "speaker_gender": None,
+                            },
+                        }
+                    yield audio_id, ex
             f.close()