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@@ -21,6 +21,48 @@ configs:
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  - split: train
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  path: data/train-*
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  ---
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- # Dataset Card for "Zhengzhou_Dialect_Conversational_Speech_Corpus"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 
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  - split: train
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  path: data/train-*
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  ---
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+ # Corpus
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+
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+ This dataset is built from Magicdata [ASR-CZDIACSC: A CHINESE ZHENGZHOU DIALECT CONVERSATIONAL SPEECH CORPUS](https://magichub.com/datasets/zhengzhou-dialect-conversational-speech-corpus/)
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+
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+ This corpus is licensed under a [Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License](http://creativecommons.org/licenses/by-nc-nd/4.0/). Please refer to the license for further information.
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+ Modifications: The audio is split in sentences based on the time span on the transcription file. Sentences that span less than 1 second is discarded. Topics of conversation is removed.
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+
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+ # Usage
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+
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+ To load this dataset, use
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+ ```python
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+ from datasets import load_dataset
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+ dialect_corpus = load_dataset("TingChen-ppmc/Zhengzhou_Dialect_Conversational_Speech_Corpus")
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+ ```
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+
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+ This dataset only has train split. To split out a test split, use
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+
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+ ```python
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+ from datasets import load_dataset
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+ train_split = load_dataset("TingChen-ppmc/Zhengzhou_Dialect_Conversational_Speech_Corpus", split="train")
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+ # where test=0.5 denotes 0.5 of the dataset will be split to test split
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+ corpus = train_split.train_test_split(test=0.5)
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+ ```
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+ A sample data would be
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+
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+ ```python
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+ # note this data is from the Nanchang Dialect corpus, the data format is shared
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+ {'audio':
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+ {'path': 'A0001_S001_0_G0001_0.WAV',
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+ 'array': array([-0.00030518, -0.00039673,
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+ -0.00036621, ..., -0.00064087,
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+ -0.00015259, -0.00042725]),
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+ 'sampling_rate': 16000},
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+ 'gender': '女',
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+ 'speaker_id': 'G0001',
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+ 'transcription': '北京爱数智慧语音采集'
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+ }
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+ ```
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+
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+
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  [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)