---
language:
- en
- zh
license: cc-by-4.0
extra_gated_prompt: >-
  Subject to other terms Subject to other terms:
  1. The corpus is not used for commercial purposes and is only provided free of charge to “universities and research institutes” for scientific research.

  2、When publishing papers and applying for results, if you use this dataset, please indicate the reference:
    @inproceedings{
      liu2024generative.
      title={Generative Expressive Conversational Speech Synthesis},
      author={Rui Liu and Yifan Hu and Yi Ren and Xiang Yin and Haizhou Li},
      booktitle={ACM Multimedia 2024}, year={2024}, url={}
      url={https://openreview.net/forum?id=eK9ShhDqwu}
    }

  3. The final interpretation of this corpus belongs to S2LAB Lab, Inner Mongolia University, China.
  
extra_gated_fields:
  First Name: text
  Last Name: text
  Date of birth: date_picker
  Country: country
  Affiliation: text
  Job title:
    type: select
    options:
    - Student
    - Research Graduate
    - AI researcher
    - AI developer/engineer
    - Reporter
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  geo: ip_location
  By clicking submit below, I accept the terms of the license and acknowledge that the information I provide will be collected, stored and processed by S2LAB: checkbox
extra_gated_description: >-
  The information you provide will be collected, stored and processed by S2LAB.
extra_gated_button_content: Submit
extra_gated_eu_disallowed: true
task_categories:
- text-to-speech
tags:
- CSS
- Dialog
- Conversational Speech Synthesis
pretty_name: NCSSD
size_categories:
- 100M<n<1B
---

# NCSSD
## 🎉Introduction

This is the official repository for the NCSSD dataset and collecting pipeline to handle TV shows. [《Generative Expressive Conversational Speech Synthesis》](https://arxiv.org/pdf/2407.21491)
 (Accepted by MM'2024)

[Rui Liu *](https://ttslr.github.io/), Yifan Hu, [Yi Ren](https://rayeren.github.io/), Xiang Yin, [Haizhou Li](https://colips.org/~eleliha/).

## 📜NCSSD Overview 
Includes Recording subsets: R-ZH, R-EN and Collection subsets: C-ZH, C-EN.
<div align=center><img width="500" height="340" src="image-1.png"/></div>

## 📣NCSSD Download
  ⭐ Huggingface download address: [NCSSD](https://huggingface.co/datasets/walkerhyf/NCSSD).

  ⭐ Users in China can contact the email (📧: ``hyfwalker@163.com``) to obtain the Baidu Cloud address, but you need to provide necessary information such as name, organization, profession, etc.


## 💻Collection Subset Pipeline
<div align=center><img width="800" height="220" src="image.png"/></div>


### 1. Video Selection
#### 1.1 Prepare TV shows and name it: **TV name-episode number**.

#### 1.2. Extract the audios from MKV videos (video_file: input video file name, output_file: output audio file name).
```
python ./step-0.py --input_video_path "xxx.mkv" --output_audio_path "xxx.wav"
```

<!-- Dialogue Scene Extraction -->
### 2. Dialogue Scene Extraction
#### 2.1 Use VAD to segment speech audio, split into two segments if the silent interval is greater than 4 seconds, and retain segments with more than 30% valid speech duration and longer than 15 seconds.
```
python ./step-1.py --audio_root_path "xxx"
```

<!-- Demucs -->
#### 2.2 Use Demucs for vocal and background separation.
##### (1) To install Demucs, you can refer to the official documentation or installation instructions provided at the following link: [https://github.com/facebookresearch/demucs](https://github.com/facebookresearch/demucs). 

##### (2) Use the Demucs mentioned above to separate vocals and background sounds, and keep the vocals part with SNR<=4.
```
python ./step-2.py --audio_root_path "xxx"
```

<!-- sepformer -->
#### 2.3 Use SepFormer for voice enhancement.
##### (1) To install SepFormer, you can refer to the official documentation or installation instructions provided at the following link: [https://huggingface.co/speechbrain/sepformer-dns4-16k-enhancement](https://huggingface.co/speechbrain/sepformer-dns4-16k-enhancement). (*vocals_16k_path* is the folder generated in a previous step, located in the **one-step** directory.)
```
python ./step-3.py --vocals_16k_path "yyy"
```

<!-- Speaker -->
### 3. Dialogue Segment Extraction
We use the [Volcengine](https://console.volcengine.com/speech/app) for speaker recognition, extracting different conversation scenes. Please configure ASR information such as ``appid``,``token``, and OSS information such as ``access_key_id``,``access_key_secret``,``bucket_name`` (for generating URLs to be used for ASR)
```
python ./step-4.py --audio_root_path "xxx"
```



### 4. Dialogue Script Recognition
#### Use Aliyun's ASR service for re-recognition and correction.

We use the [Aliyun's ASR](https://ai.aliyun.com/nls/filetrans?spm=5176.28508143.nav-v2-dropdown-menu-0.d_main_9_1_1_1.5421154aIHmaWo&scm=20140722.X_data-b7a761a1c730419a6c79._.V_1) for dialogue script recognition. Please configure ASR information such as ``accessKeyId``,``accessKeySecret``, and OSS information such as ``access_key_id``,``access_key_secret``,``bucket_name`` (for generating URLs to be used for ASR). 

⚠ ``appkey``: Pay attention to the Chinese and English settings.

```
python ./step-5.py --audio_root_path "xxx"
```


### 5. Organizing the Data
Organize the data from the above steps into a standard format, with *result_path* as the output result path.
```
python step-6.py --audio_root_path "xxx" --result_path "yyy"
```


🎉🎉🎉 ***Congratulations! The dataset was created successfully!***

## Citations

```bibtex
@inproceedings{
liu2024generative,
    title={Generative Expressive Conversational Speech Synthesis},
    author={Rui Liu and Yifan Hu and Yi Ren and Xiang Yin and Haizhou Li},
    booktitle={ACM Multimedia 2024},
    year={2024},
    url={https://openreview.net/forum?id=eK9ShhDqwu}
}
```


⚠ The collected TV shows clips are all from public resources on the Internet. If there is any infringement, please contact us to delete them. (📧: ``hyfwalker@163.com``)