Step-Audio-TTS-3B
| Property | Value |
|---|---|
| Model Size | 3B parameters |
| Model Type | Text-to-Speech (TTS) |
| Architecture | Dual-codebook LLM |
| Hugging Face | stepfun-ai/Step-Audio-TTS-3B |
What is Step-Audio-TTS-3B?
Step-Audio-TTS-3B represents a breakthrough in text-to-speech technology as the industry's first TTS model trained using the LLM-Chat paradigm on a large-scale synthetic dataset. It achieves state-of-the-art performance in Character Error Rate (CER) on the SEED TTS Eval benchmark, outperforming existing models like GLM-4-Voice and MinMo with a CER of 1.53% for Chinese and 2.71% for English.
Implementation Details
The model utilizes a sophisticated dual-codebook architecture comprising two main components: a dual-codebook trained LLM for text-to-speech synthesis and specialized vocoders for both standard speech and humming generation. This unique architecture enables high-quality speech synthesis while maintaining excellent content consistency.
- Dual-codebook backbone with specialized vocoder implementation
- Advanced synthetic dataset training methodology
- Optimized performance for both Chinese and English languages
- Industry-leading CER and WER metrics
Core Capabilities
- Multi-language support with superior performance in Chinese and English
- Diverse emotional expression control
- Voice style customization
- Unique RAP and Humming generation capabilities
- Superior content consistency compared to existing models
Frequently Asked Questions
Q: What makes this model unique?
Step-Audio-TTS-3B is the first TTS model trained using the LLM-Chat paradigm and the first capable of generating RAP and Humming. It achieves industry-leading performance metrics while offering unprecedented versatility in voice generation capabilities.
Q: What are the recommended use cases?
The model is ideal for applications requiring high-quality speech synthesis, including multilingual content creation, emotional voice generation, RAP production, and humming synthesis. It's particularly effective for scenarios demanding high accuracy and natural-sounding speech output.
