Convert dataset to Parquet

ASCEND.py

@@ -1,139 +0,0 @@
-# coding=utf-8
-# Copyright 2021 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.
-""" Common Voice Dataset"""
-
-from datasets import AutomaticSpeechRecognition
-
-
-import datasets
-import os
-import pandas as pd
-
-
-_CITATION = """\
-@inproceedings{lovenia2021ascend,
-  title     = {ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation},
-  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},
-  booktitle = {Proceedings of the International Conference on Language Resources and Evaluation, {LREC} 2022, 20-25 June 2022, Lu Palais du Pharo, France},
-  publisher = {European Language Resources Association},
-  year      = {2022},
-  pages = {}
-}
-"""
-
-_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.
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/CAiRE/ASCEND"
-
-_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",
-    "waves": "https://huggingface.co/datasets/CAiRE/ASCEND/resolve/main/waves.tar.bz2",
-}
-
-
-class ASCENDConfig(datasets.BuilderConfig):
-    """BuilderConfig for ASCEND."""
-
-    def __init__(self, name="main", **kwargs):
-        """
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(ASCENDConfig, self).__init__(name, **kwargs)
-
-
-class ASCEND(datasets.GeneratorBasedBuilder):
-    """ASCEND: A Spontaneous Chinese-English Dataset for code-switching. Snapshot date: 5 January 2022."""
-
-    BUILDER_CONFIGS = [
-        ASCENDConfig(
-            name="main",
-            version=datasets.Version("1.0.0", ""),
-            description=_DESCRIPTION,
-        )
-    ]
-
-    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"),
-                "duration": datasets.Value("float32"),
-                "language": datasets.Value("string"),
-                "original_speaker_id": datasets.Value("int64"),
-                "session_id": datasets.Value("int64"),
-                "topic": datasets.Value("string"),
-            }
-        )
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            task_templates=[AutomaticSpeechRecognition(audio_column="audio", transcription_column="transcription")],
-        )
-
-    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": downloaded_files["train"],
-                    "wave_path": downloaded_files["waves"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "metadata_path": downloaded_files["test"],
-                    "wave_path": downloaded_files["waves"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "metadata_path": downloaded_files["validation"],
-                    "wave_path": downloaded_files["waves"],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, metadata_path, wave_path):
-        print(metadata_path)
-        metadata_df = pd.read_csv(metadata_path)
-
-        for index, row in metadata_df.iterrows():
-            example = {
-                "id": str(index).zfill(5),
-                "path": os.path.join(wave_path, row["file_name"]),
-                "audio": os.path.join(wave_path, 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

README.md

@@ -22,6 +22,51 @@ pretty_name: 'ASCEND: A Spontaneous Chinese-English Dataset for Code-switching i
 tags:
 - speech-recognition
 - code-switching
+dataset_info:
+  config_name: main
+  features:
+  - name: id
+    dtype: string
+  - name: path
+    dtype: string
+  - name: audio
+    dtype:
+      audio:
+        sampling_rate: 16000
+  - name: transcription
+    dtype: string
+  - name: duration
+    dtype: float32
+  - name: language
+    dtype: string
+  - name: original_speaker_id
+    dtype: int64
+  - name: session_id
+    dtype: int64
+  - name: topic
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1014573740.14
+    num_examples: 9869
+  - name: test
+    num_bytes: 106171230.135
+    num_examples: 1315
+  - name: validation
+    num_bytes: 106772517.43
+    num_examples: 1130
+  download_size: 1223536062
+  dataset_size: 1227517487.7050002
+configs:
+- config_name: main
+  data_files:
+  - split: train
+    path: main/train-*
+  - split: test
+    path: main/test-*
+  - split: validation
+    path: main/validation-*
+  default: true
 ---
 
 # Dataset Card for ASCEND

dataset_infos.json

@@ -1 +0,0 @@
-{"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}}

waves.tar.bz2 → main/test-00000-of-00001.parquet

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main/train-00002-of-00003.parquet

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main/validation-00000-of-00001.parquet

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speakers.csv

@@ -1,24 +0,0 @@
-speaker_id,age,gender,split,years_in_english_study,english_score
-1,23,female,train,17,6
-2,22,male,train,13,6
-3,25,male,test,15,6
-4,24,male,train,10,7.5
-5,24,female,train,12,7
-6,30,female,train,12,7
-7,23,female,val,17,7
-8,23,female,train,15,6
-9,27,male,train,12,6
-10,23,male,train,12,6
-11,23,female,train,16,8
-12,23,male,val,10,5.5
-13,23,female,train,16,7
-14,22,female,train,12,6.5
-15,19,female,train,8,7
-16,26,male,train,15,6.5
-17,24,female,test,11,6.5
-18,23,male,train,5,6.5
-19,26,male,train,16,7
-20,26,female,val,18,7.5
-21,23,female,train,15,6.5
-24,27,male,train,17,6
-26,23,female,train,10,7