speech-recognition mistral-ai audio-processing developer-tools

Pure C, CPU-only inference with Mistral Voxtral Realtime 4B speech to text model

GitHub - antirez/voxtral.c: Pure C inference of Mistral Voxtral Realtime 4B speech to text model

github.com

February 10, 2026

9 min read

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62/100

Summary

The GitHub repository provides a pure C implementation of the inference pipeline for Mistral AI's Voxtral Realtime 4B speech-to-text model, requiring only the C standard library. It features fast MPS inference, a chunked audio processing encoder to manage memory usage, and supports audio input from stdin or live microphone capture.

Key Takeaways

The project provides a pure C implementation of the Mistral Voxtral Realtime 4B speech-to-text model with no external dependencies beyond the C standard library.
It supports live microphone input on macOS and allows audio to be piped from stdin for transcription.
The implementation features a streaming C API that enables incremental audio feeding and real-time token string output.
The model uses a chunked encoder for audio processing, which manages memory usage effectively regardless of input length.

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Pure C, CPU-only inference with Mistral Voxtral Realtime 4B speech to text model ↗

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Analysis ready

Community Sentiment

Mixed

Positives

The Mistral Voxtral Transcription API offers impressive performance and affordability, making it an attractive option for real-time transcription tasks.
Installation on Linux is straightforward, which lowers the barrier for users wanting to experiment with the model.

Concerns

Real-time transcription is currently not functioning as expected, indicating potential limitations in the model's usability compared to alternatives like Whisper.cpp.
The model's performance is deemed too slow for practical applications, especially when integrating voice input support.