Datasets:
Tasks:
Automatic Speech Recognition
Multilinguality:
multilingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
Commit
•
df81181
1
Parent(s):
227c249
Update waves url
Browse files- ASCEND.py +10 -7
- dataset_infos.json +1 -1
ASCEND.py
CHANGED
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@@ -46,7 +46,7 @@ _URLS = {
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"train": _URL + "train_metadata.csv",
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"test": _URL + "test_metadata.csv",
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"validation": _URL + "validation_metadata.csv",
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"waves":
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}
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@@ -112,32 +112,35 @@ class ASCEND(datasets.GeneratorBasedBuilder):
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"metadata_path": downloaded_files["train"]
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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"metadata_path": downloaded_files["test"]
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"metadata_path": downloaded_files["validation"]
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},
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),
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]
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def _generate_examples(self, metadata_path):
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print(metadata_path)
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metadata_df = pd.read_csv(metadata_path)
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for index, row in metadata_df.iterrows():
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example = {
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"id": str(index).zfill(5),
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"path": os.path.join(
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"audio":
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"transcription": row["transcription"],
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"duration": row["duration"],
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"language": row["language"],
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"train": _URL + "train_metadata.csv",
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"test": _URL + "test_metadata.csv",
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"validation": _URL + "validation_metadata.csv",
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"waves": "https://huggingface.co/datasets/CAiRE/ASCEND/resolve/main/waves.tar.bz2",
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}
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"metadata_path": downloaded_files["train"],
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"wave_path": downloaded_files["waves"],
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={
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+
"metadata_path": downloaded_files["test"],
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"wave_path": downloaded_files["waves"],
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},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={
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"metadata_path": downloaded_files["validation"],
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"wave_path": downloaded_files["waves"],
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},
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),
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]
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def _generate_examples(self, metadata_path, wave_path):
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print(metadata_path)
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metadata_df = pd.read_csv(metadata_path)
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for index, row in metadata_df.iterrows():
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example = {
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"id": str(index).zfill(5),
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"path": os.path.join(wave_path, row["file_name"]),
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"audio": os.path.join(wave_path, row["file_name"]),
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"transcription": row["transcription"],
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"duration": row["duration"],
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"language": row["language"],
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dataset_infos.json
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@@ -1 +1 @@
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{"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":
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{"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": 4316724, "num_examples": 9869, "dataset_name": "ascend"}, "test": {"name": "test", "num_bytes": 559170, "num_examples": 1315, "dataset_name": "ascend"}, "validation": {"name": "validation", "num_bytes": 489562, "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"}, "https://huggingface.co/datasets/CAiRE/ASCEND/resolve/main/waves.tar.bz2": {"num_bytes": 929707032, "checksum": "b35cc295f1310535a8e250d534aee0adeb90bccbc027a442cdbef81146894529"}}, "download_size": 931034423, "post_processing_size": null, "dataset_size": 5365456, "size_in_bytes": 936399879}, "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": 4316724, "num_examples": 9869, "dataset_name": "ascend"}, "test": {"name": "test", "num_bytes": 559170, "num_examples": 1315, "dataset_name": "ascend"}, "validation": {"name": "validation", "num_bytes": 489562, "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"}, "https://huggingface.co/datasets/CAiRE/ASCEND/resolve/main/waves.tar.bz2": {"num_bytes": 929707032, "checksum": "b35cc295f1310535a8e250d534aee0adeb90bccbc027a442cdbef81146894529"}}, "download_size": 931034423, "post_processing_size": null, "dataset_size": 5365456, "size_in_bytes": 936399879}, "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": 4316724, "num_examples": 9869, "dataset_name": "ascend"}, "test": {"name": "test", "num_bytes": 559170, "num_examples": 1315, "dataset_name": "ascend"}, "validation": {"name": "validation", "num_bytes": 489562, "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"}, "https://huggingface.co/datasets/CAiRE/ASCEND/resolve/main/waves.tar.bz2": {"num_bytes": 929707032, "checksum": "b35cc295f1310535a8e250d534aee0adeb90bccbc027a442cdbef81146894529"}}, "download_size": 931034423, "post_processing_size": null, "dataset_size": 5365456, "size_in_bytes": 936399879}}
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