CSM-1B
| Property | Value |
|---|---|
| Author | Sesame |
| Model Type | Speech Generation |
| Architecture | Llama backbone with audio decoder |
| Model URL | https://huggingface.co/sesame/csm-1b |
What is CSM-1B?
CSM-1B (Conversational Speech Model) is an innovative speech generation model developed by Sesame that transforms text and audio inputs into RVQ audio codes. The model leverages a Llama backbone architecture combined with a specialized audio decoder that produces Mimi audio codes, enabling high-quality speech synthesis with contextual awareness.
Implementation Details
The model architecture combines the powerful Llama language model backbone with a dedicated audio decoder optimized for speech generation. It supports multiple speakers and can process contextual information to maintain conversation coherence. The implementation requires Python 3.10 and access to both sesame/csm-1b and meta-llama/Llama-3.2-1B models.
- Supports variable audio length generation up to specified milliseconds
- Includes context-aware processing for improved conversational flow
- Implements speaker identification system
- Uses RVQ audio codes for high-quality output
Core Capabilities
- Text-to-speech generation with multiple speaker support
- Contextual speech generation using previous conversation history
- High-quality audio synthesis using Mimi audio codes
- Flexible integration through Python API
Frequently Asked Questions
Q: What makes this model unique?
CSM-1B stands out for its ability to generate contextually aware speech using a hybrid architecture that combines Llama's language understanding capabilities with specialized audio generation. It supports multiple speakers and can maintain conversation coherence through context processing.
Q: What are the recommended use cases?
The model is primarily designed for research and educational purposes in speech generation. It's particularly useful for applications requiring contextual speech synthesis, though it's important to note that it's a base generation model without fine-tuning for specific voices. The model works best with English language content.
