NCSSD

🎉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 (📧: [email protected]) 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{10.1145/3664647.3681697,
  author = {Liu, Rui and Hu, Yifan and Ren, Yi and Yin, Xiang and Li, Haizhou},
  title = {Generative Expressive Conversational Speech Synthesis},
  year = {2024},
  isbn = {9798400706868},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3664647.3681697},
  doi = {10.1145/3664647.3681697},
  abstract = {Conversational Speech Synthesis (CSS) aims to express a target utterance with the proper speaking style in a user-agent conversation setting. Existing CSS methods employ effective multi-modal context modeling techniques to achieve empathy understanding and expression. However, they often need to design complex network architectures and meticulously optimize the modules within them. In addition, due to the limitations of small-scale datasets containing scripted recording styles, they often fail to simulate real natural conversational styles. To address the above issues, we propose a novel generative expressive CSS system, termed GPT-Talker.We transform the multimodal information of the multi-turn dialogue history into discrete token sequences and seamlessly integrate them to form a comprehensive user-agent dialogue context. Leveraging the power of GPT, we predict the token sequence, that includes both semantic and style knowledge, of response for the agent. After that, the expressive conversational speech is synthesized by the conversation-enriched VITS to deliver feedback to the user.Furthermore, we propose a large-scale Natural CSS Dataset called NCSSD, that includes both naturally recorded conversational speech in improvised styles and dialogues extracted from TV shows. It encompasses both Chinese and English languages, with a total duration of 236 hours. We conducted comprehensive experiments on the reliability of the NCSSD and the effectiveness of our GPT-Talker. Both subjective and objective evaluations demonstrate that our model outperforms other state-of-the-art CSS systems significantly in terms of naturalness and expressiveness. The Code, Dataset, and Pre-trained Model are available at: https://github.com/AI-S2-Lab/GPT-Talker.},
  booktitle = {Proceedings of the 32nd ACM International Conference on Multimedia},
  pages = {4187–4196},
  numpages = {10},
  keywords = {conversational speech synthesis (css), expressiveness, gpt, user-agent conversation},
  location = {Melbourne VIC, Australia},
  series = {MM '24}
}

⚠ The collected TV shows clips are all from public resources on the Internet. If there is any infringement, please contact us to delete them. (📧: [email protected])

Datasets:

walkerhyf
/

NCSSD

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