README.md

---
dataset_info:
  features:
  - name: audio
    dtype: audio
  - name: gender
    dtype: string
  - name: speaker_id
    dtype: string
  - name: transcription
    dtype: string
  splits:
  - name: train
    num_bytes: 422057259.808
    num_examples: 3792
  download_size: 436738370
  dataset_size: 422057259.808
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# Corpus

This dataset is built from Magicdata [ASR-CZDIACSC: A CHINESE SHANGHAI DIALECT CONVERSATIONAL SPEECH CORPUS](https://magichub.com/datasets/shanghai-dialect-conversational-speech-corpus/)

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.

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.

# Usage

To load this dataset, use

```python
from datasets import load_dataset
dialect_corpus = load_dataset("TingChen-ppmc/Shanghai_Dialect_Conversational_Speech_Corpus")
```

This dataset only has train split. To split out a test split, use

```python
from datasets import load_dataset
train_split = load_dataset("TingChen-ppmc/Shanghai_Dialect_Conversational_Speech_Corpus", split="train")
# where test_size=0.5 denotes 0.5 of the dataset will be split to test split
corpus = train_split.train_test_split(test_size=0.5)
```

A sample data would be

```python
# note this data is from the Nanchang Dialect corpus, the data format is shared
{'audio': 
	{'path': 'A0001_S001_0_G0001_0.WAV',
  	 'array': array([-0.00030518, -0.00039673, 
                     -0.00036621, ..., 	-0.00064087,
         			 -0.00015259, -0.00042725]),
  	 'sampling_rate': 16000},
 'gender': '女',
 'speaker_id': 'G0001',
 'transcription': '北京爱数智慧语音采集'
}
```



[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)