Fix loading script

ascend.py → ASCEND.py

@@ -14,11 +14,11 @@
 # limitations under the License.
 """ Common Voice Dataset"""
 
+from datasets import AutomaticSpeechRecognition
 
-import os
 
 import datasets
-from datasets.tasks import AutomaticSpeechRecognition
+import os
 import pandas as pd
 
 
@@ -39,6 +39,15 @@ ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resourc
 
 _HOMEPAGE = "https://huggingface.co/datasets/CAiRE/ASCEND"
 
+DEFAULT_CONFIG_NAME = "train"
+
+_URL = "https://huggingface.co/datasets/CAiRE/ASCEND/raw/main/"
+_URLS = {
+    "train": _URL + "train_metadata.csv",
+    "test": _URL + "test_metadata.csv",
+    "validation": _URL + "validation_metadata.csv",
+}
+
 
 class ASCENDConfig(datasets.BuilderConfig):
     """BuilderConfig for ASCEND."""
@@ -75,6 +84,7 @@ class ASCEND(datasets.GeneratorBasedBuilder):
     def _info(self):
         features = datasets.Features(
             {
+                "id": datasets.Value("string"),
                 "path": datasets.Value("string"),
                 "audio": datasets.Audio(sampling_rate=16_000),
                 "transcription": datasets.Value("string"),
@@ -95,37 +105,43 @@ class ASCEND(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "metadata_path": "train_metadata.csv",
-                    "split": "train",
+                    "metadata_path": downloaded_files["train"]
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "metadata_path": "test_metadata.csv",
-                    "split": "test"
+                    "metadata_path": downloaded_files["test"]
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
                 gen_kwargs={
-                    "metadata_path": "validation_metadata.csv",
-                    "split": "validation",
+                    "metadata_path": downloaded_files["validation"]
                 },
             ),
         ]
 
     def _generate_examples(self, metadata_path):
+        print(metadata_path)
         metadata_df = pd.read_csv(metadata_path)
 
         for index, row in metadata_df.iterrows():
             example = {
-                "id": index,
-                "audio": row["audio"],
-                "transcription": row["transcription"]
+                "id": str(index).zfill(5),
+                "path": row["file_name"],
+                "audio": row["file_name"],
+                "transcription": row["transcription"],
+                "duration": row["duration"],
+                "language": row["language"],
+                "original_speaker_id": row["original_speaker_id"],
+                "session_id": row["session_id"],
+                "topic": row["topic"],
             }
             yield index, example

dataset_infos.json

@@ -0,0 +1 @@
+{"train": {"description": "ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set.\n", "citation": "@inproceedings{lovenia2021ascend,\n  title     = {ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation},\n  author    = {Lovenia, Holy and Cahyawijaya, Samuel and Winata, Genta Indra and Xu, Peng and Yan, Xu and Liu, Zihan and Frieske, Rita and Yu, Tiezheng and Dai, Wenliang and Barezi, Elham J and others},\n  booktitle = {Proceedings of the International Conference on Language Resources and Evaluation, {LREC} 2022, 20-25 June 2022, Lu Palais du Pharo, France},\n  publisher = {European Language Resources Association},\n  year      = {2022},\n  pages = {}\n}\n", "homepage": "https://huggingface.co/datasets/CAiRE/ASCEND", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "path": {"dtype": "string", "id": null, "_type": "Value"}, "audio": {"sampling_rate": 16000, "mono": true, "decode": true, "id": null, "_type": "Audio"}, "transcription": {"dtype": "string", "id": null, "_type": "Value"}, "duration": {"dtype": "float32", "id": null, "_type": "Value"}, "language": {"dtype": "string", "id": null, "_type": "Value"}, "original_speaker_id": {"dtype": "int64", "id": null, "_type": "Value"}, "session_id": {"dtype": "int64", "id": null, "_type": "Value"}, "topic": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "automatic-speech-recognition", "audio_column": "audio", "transcription_column": "transcription"}], "builder_name": "ascend", "config_name": "train", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1869212, "num_examples": 9869, "dataset_name": "ascend"}, "test": {"name": "test", "num_bytes": 233050, "num_examples": 1315, "dataset_name": "ascend"}, "validation": {"name": "validation", "num_bytes": 209322, "num_examples": 1130, "dataset_name": "ascend"}}, "download_checksums": {"https://huggingface.co/datasets/CAiRE/ASCEND/raw/main/train_metadata.csv": {"num_bytes": 1081181, "checksum": "4cbdf90fe9bf53640bfc285e2539b468a6e412daeb17c36a1b5da478cd9f5b29"}, "https://huggingface.co/datasets/CAiRE/ASCEND/raw/main/test_metadata.csv": {"num_bytes": 127658, "checksum": "15689bc1c1a0bc29b250f63221576392b627da9cc1d80e51bb1a422118b9732c"}, "https://huggingface.co/datasets/CAiRE/ASCEND/raw/main/validation_metadata.csv": {"num_bytes": 118552, "checksum": "6e53e362991b23ffa49ed991c6062a51d8f286747f341e566c897c02bee72459"}}, "download_size": 1327391, "post_processing_size": null, "dataset_size": 2311584, "size_in_bytes": 3638975}, "validation": {"description": "ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set.\n", "citation": "@inproceedings{lovenia2021ascend,\n  title     = {ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation},\n  author    = {Lovenia, Holy and Cahyawijaya, Samuel and Winata, Genta Indra and Xu, Peng and Yan, Xu and Liu, Zihan and Frieske, Rita and Yu, Tiezheng and Dai, Wenliang and Barezi, Elham J and others},\n  booktitle = {Proceedings of the International Conference on Language Resources and Evaluation, {LREC} 2022, 20-25 June 2022, Lu Palais du Pharo, France},\n  publisher = {European Language Resources Association},\n  year      = {2022},\n  pages = {}\n}\n", "homepage": "https://huggingface.co/datasets/CAiRE/ASCEND", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "path": {"dtype": "string", "id": null, "_type": "Value"}, "audio": {"sampling_rate": 16000, "mono": true, "decode": true, "id": null, "_type": "Audio"}, "transcription": {"dtype": "string", "id": null, "_type": "Value"}, "duration": {"dtype": "float32", "id": null, "_type": "Value"}, "language": {"dtype": "string", "id": null, "_type": "Value"}, "original_speaker_id": {"dtype": "int64", "id": null, "_type": "Value"}, "session_id": {"dtype": "int64", "id": null, "_type": "Value"}, "topic": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "automatic-speech-recognition", "audio_column": "audio", "transcription_column": "transcription"}], "builder_name": "ascend", "config_name": "validation", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1869212, "num_examples": 9869, "dataset_name": "ascend"}, "test": {"name": "test", "num_bytes": 233050, "num_examples": 1315, "dataset_name": "ascend"}, "validation": {"name": "validation", "num_bytes": 209322, "num_examples": 1130, "dataset_name": "ascend"}}, "download_checksums": {"https://huggingface.co/datasets/CAiRE/ASCEND/raw/main/train_metadata.csv": {"num_bytes": 1081181, "checksum": "4cbdf90fe9bf53640bfc285e2539b468a6e412daeb17c36a1b5da478cd9f5b29"}, "https://huggingface.co/datasets/CAiRE/ASCEND/raw/main/test_metadata.csv": {"num_bytes": 127658, "checksum": "15689bc1c1a0bc29b250f63221576392b627da9cc1d80e51bb1a422118b9732c"}, "https://huggingface.co/datasets/CAiRE/ASCEND/raw/main/validation_metadata.csv": {"num_bytes": 118552, "checksum": "6e53e362991b23ffa49ed991c6062a51d8f286747f341e566c897c02bee72459"}}, "download_size": 1327391, "post_processing_size": null, "dataset_size": 2311584, "size_in_bytes": 3638975}, "test": {"description": "ASCEND (A Spontaneous Chinese-English Dataset) introduces a high-quality resource of spontaneous multi-turn conversational dialogue Chinese-English code-switching corpus collected in Hong Kong. ASCEND consists of 10.62 hours of spontaneous speech with a total of ~12.3K utterances. The corpus is split into 3 sets: training, validation, and test with a ratio of 8:1:1 while maintaining a balanced gender proportion on each set.\n", "citation": "@inproceedings{lovenia2021ascend,\n  title     = {ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation},\n  author    = {Lovenia, Holy and Cahyawijaya, Samuel and Winata, Genta Indra and Xu, Peng and Yan, Xu and Liu, Zihan and Frieske, Rita and Yu, Tiezheng and Dai, Wenliang and Barezi, Elham J and others},\n  booktitle = {Proceedings of the International Conference on Language Resources and Evaluation, {LREC} 2022, 20-25 June 2022, Lu Palais du Pharo, France},\n  publisher = {European Language Resources Association},\n  year      = {2022},\n  pages = {}\n}\n", "homepage": "https://huggingface.co/datasets/CAiRE/ASCEND", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "path": {"dtype": "string", "id": null, "_type": "Value"}, "audio": {"sampling_rate": 16000, "mono": true, "decode": true, "id": null, "_type": "Audio"}, "transcription": {"dtype": "string", "id": null, "_type": "Value"}, "duration": {"dtype": "float32", "id": null, "_type": "Value"}, "language": {"dtype": "string", "id": null, "_type": "Value"}, "original_speaker_id": {"dtype": "int64", "id": null, "_type": "Value"}, "session_id": {"dtype": "int64", "id": null, "_type": "Value"}, "topic": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": [{"task": "automatic-speech-recognition", "audio_column": "audio", "transcription_column": "transcription"}], "builder_name": "ascend", "config_name": "test", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1869212, "num_examples": 9869, "dataset_name": "ascend"}, "test": {"name": "test", "num_bytes": 233050, "num_examples": 1315, "dataset_name": "ascend"}, "validation": {"name": "validation", "num_bytes": 209322, "num_examples": 1130, "dataset_name": "ascend"}}, "download_checksums": {"https://huggingface.co/datasets/CAiRE/ASCEND/raw/main/train_metadata.csv": {"num_bytes": 1081181, "checksum": "4cbdf90fe9bf53640bfc285e2539b468a6e412daeb17c36a1b5da478cd9f5b29"}, "https://huggingface.co/datasets/CAiRE/ASCEND/raw/main/test_metadata.csv": {"num_bytes": 127658, "checksum": "15689bc1c1a0bc29b250f63221576392b627da9cc1d80e51bb1a422118b9732c"}, "https://huggingface.co/datasets/CAiRE/ASCEND/raw/main/validation_metadata.csv": {"num_bytes": 118552, "checksum": "6e53e362991b23ffa49ed991c6062a51d8f286747f341e566c897c02bee72459"}}, "download_size": 1327391, "post_processing_size": null, "dataset_size": 2311584, "size_in_bytes": 3638975}}