Moshiko-Candle-Q8
| Property | Value |
|---|---|
| Parameter Count | 7.69B |
| License | CC-BY-4.0 |
| Language | English |
| Framework | Candle (Rust) |
| Paper | Research Paper |
What is moshiko-candle-q8?
Moshiko-candle-q8 is an 8-bit quantized version of Moshi, a groundbreaking speech-text foundation model designed for real-time dialogue. This implementation uses Candle, a Rust-based framework, to optimize performance while maintaining model quality. The model represents a significant advancement in spoken dialogue systems, capable of processing and generating speech with remarkably low latency of just 160-200ms.
Implementation Details
The model employs a sophisticated architecture that generates speech as tokens from a residual quantizer of a neural audio codec. It uniquely models both user and system speech in parallel streams, eliminating the need for explicit speaker turns. A key innovation is the "Inner Monologue" method, which predicts time-aligned text tokens before audio tokens, enhancing linguistic quality.
- 8-bit quantization for efficient deployment
- Rust-based implementation using Candle framework
- Neural audio codec with 12Hz processing rate
- 1.1kbps bitrate for speech processing
Core Capabilities
- Real-time full-duplex spoken dialogue
- Streaming speech recognition and text-to-speech
- Natural conversational dynamics
- Casual conversation handling
- Basic fact-based interactions and advice
- Recipe and trivia discussions
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to process speech in real-time with extremely low latency (160-200ms) while maintaining natural conversation flow makes it unique. Its parallel stream processing and Inner Monologue method represent innovative approaches to speech-text modeling.
Q: What are the recommended use cases?
The model is best suited for casual conversations, basic fact-based interactions, and simple advice-giving scenarios. It's particularly effective for natural dialogues that don't require complex task completion or tool usage. However, it's important to note that it's intended for research purposes only and not recommended for professional applications.
