Datasets:

CAiRE
/

ASCEND

	# 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