Rust Implementation of Mistral's Voxtral Mini ASR and TTS Models for Native and Browser Deployment
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Summary
This article presents a Rust implementation of Mistral's Voxtral Mini 4B Realtime ASR (Automatic Speech Recognition) and Voxtral 4B TTS (Text-to-Speech) models using the Burn ML framework. The project enables streaming speech recognition and text-to-speech functionality that runs both natively and in web browsers. It includes performance benchmarks showing metrics for different configurations including Q4 GGUF native, BF16 native, and Q4 GGUF WASM (WebAssembly) versions, with details on processing times, real-time factors, token rates, and memory usage for both ASR and TTS operations.
Key quotes
· 5 pulledStreaming speech recognition and text-to-speech running natively and in the browser.
A pure Rust implementation of Mistral's Voxtral Mini 4B Realtime (ASR) and Voxtral 4B TTS models using the Burn ML framework.
ASR (Speech Recognition) 16s test audio, 3-run average:
Q4 GGUF native: 1021 ms Encode, 5578 ms Decode, 6629 ms Total, 0.416 RTF, 19.4 Tok/s, 703 MB Memory
TTS (Text-to-Speech) 'The quick brown fox jumps over the lazy dog' (9 words)
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