# Vec-Tok Speech: speech vectorization and tokenization for neural speech generation

**Authors:** Xinfa Zhu, Yuanjun Lv, Yi Lei, Tao Li, Wendi He, Hongbin Zhou, Heng Lu, Lei Xie  
**arXiv:** [2310.07246](https://arxiv.org/abs/2310.07246)  
**Date:** 2023-10-11  
**Summary page:** https://www.summarizepaper.com/en/paper/2310.07246v2/

## Key points

- Language models (LMs) have advanced in natural language processing and computer vision, producing high-quality texts and images.
- Speech generative models have faced challenges in achieving comparable speech quality and task generalization.
- Vec-Tok Speech is introduced by a team of researchers to support multiple speech generation tasks with expressive and high-fidelity speech.
- The framework integrates speech vectors for acoustic details and semantic tokens for linguistic content to enhance language modeling capabilities.
- Vec-Tok Speech utilizes an LM at its core for efficient speech generation, incorporating Byte-Pair Encoding (BPE) to reduce token length and bit rate.
- The versatility of Vec-Tok Speech extends to applications like voice conversion, speaking style transfer, translation, denoising, and speaker de-identification/anonymization.
- Experimental results show that Vec-Tok Speech outperforms other models when trained on a substantial dataset of 50k hours of speech data.
- The code for Vec-Tok Speech will be available on GitHub at https://github.com/BakerBunker/VecTok.

## Summary

In recent years, language models (LMs) have seen significant advancements in natural language processing and computer vision, producing high-quality texts and images across various tasks. However, speech generative models have faced challenges in achieving comparable speech quality and task generalization. To address this gap, a team of researchers including Xinfa Zhu, Yuanjun Lv, Yi Lei, Tao Li, Wendi He, Hongbin Zhou, Heng Lu, and Lei Xie have introduced Vec-Tok Speech. This innovative framework is designed to support multiple speech generation tasks by generating expressive and high-fidelity speech. Vec-Tok Speech introduces a novel approach to speech codec through the integration of speech vectors and semantic tokens. <br>
Speech vectors capture acoustic details essential for reconstructing high-fidelity speech,<br>
while semantic tokens focus on the linguistic content of the speech to enhance language modeling capabilities.<br>
Leveraging this unique codec design,<br>
Vec-Tok Speech utilizes an LM at its core for efficient speech generation.<br>
Furthermore,<br>
the researchers incorporate Byte-Pair Encoding (BPE) into Vec-Tok Speech to reduce token length and bit rate.<br>
This optimization not only minimizes exposure bias but also enables longer context coverage,<br> 
thereby enhancing LM performance.<br>

The versatility of Vec-Tok Speech extends to various applications such as intra- and cross-lingual zero-shot voice conversion (VC), zero-shot speaking style transfer text-to-speech (TTS), speech-to-speech translation (S2ST), speech denoising, and speaker de-identification and anonymization. Experimental results demonstrate that Vec-Tok Speech outperforms other state-of-the-art models when trained on a substantial dataset comprising 50k hours of speech data. The research team plans to make the code for Vec-Tok Speech available on GitHub at https://github.com/BakerBunker/VecTok. Overall, Vec-Tok Speech represents a significant advancement in neural speech generation technology with its focus on high-fidelity output and task flexibility across a range of applications.