NCSSD

Name: NCSSD
Creator: WALKER
License: https://choosealicense.com/licenses/cc-by-4.0/

🎉Introduction

This is the official repository for the NCSSD dataset and collecting pipeline to handle TV shows. 《Generative Expressive Conversational Speech Synthesis》 (Accepted by MM'2024)

Rui Liu *, Yifan Hu, Yi Ren, Xiang Yin, Haizhou Li.

📜NCSSD Overview

Includes Recording subsets: R-ZH, R-EN and Collection subsets: C-ZH, C-EN.

📣NCSSD Download

⭐ Huggingface download address: 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

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"

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"

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.

(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"

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. (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"

3. Dialogue Segment Extraction

We use the Volcengine 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 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

@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)

Datasets:

walkerhyf
/

NCSSD

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