mlx-community/Voxtral-Mini-4B-Realtime-2602-fp16

Voxtral Mini 4B Realtime — MLX fp16

This is a float16 MLX conversion of mistralai/Voxtral-Mini-4B-Realtime-2602, Mistral AI's streaming speech-to-text model.

Runs via mlx-audio.

Key Details

Parameters	4B (~3.4B LM + ~0.6B Audio Encoder)
Precision	float16 (full precision)
Base model	mistralai/Voxtral-Mini-4B-Realtime-2602
Languages	13 (Arabic, German, English, Spanish, French, Hindi, Italian, Dutch, Portuguese, Chinese, Japanese, Korean, Russian)
License	Apache 2.0

See also: int4 variant (smaller, faster)

Usage

pip install mlx-audio[stt]

from mlx_audio.stt.utils import load

model = load("mlx-community/Voxtral-Mini-4B-Realtime-2602-fp16")

# Transcribe audio
result = model.generate("audio.wav")
print(result.text)

# Streaming transcription
for chunk in model.generate("audio.wav", stream=True):
    print(chunk, end="", flush=True)

# Adjust transcription delay (lower = faster but less accurate)
result = model.generate("audio.wav", transcription_delay_ms=480)

Recommended Settings

Setting	Value	Notes
Temperature	0.0	Always use greedy decoding
Transcription delay	480ms	Sweet spot of accuracy vs. latency
Delay range	80ms – 2400ms	Multiples of 80ms

Benchmarks (from upstream)

FLEURS (13 languages, WER%)

Delay	AVG	EN	FR	DE	ES	ZH	JA	KO
160ms	12.60	6.46	9.75	9.50	5.34	17.67	19.17	19.81
480ms	8.72	4.90	6.42	6.19	3.31	10.45	9.59	15.74
960ms	7.70	4.34	5.68	4.87	2.98	8.99	6.80	14.90
2400ms	6.73	4.05	5.23	4.15	2.71	8.48	5.50	14.30

Long-form English (WER%)

Delay	Meanwhile	Earnings-21	Earnings-22	TEDLIUM
480ms	5.05	10.23	12.30	3.17

Architecture

• Causal audio encoder (~0.6B) with sliding window attention — enables true streaming
• Language model decoder (~3.4B) based on Ministral-3B with adaptive RMS norm conditioned on transcription delay
• 4x downsampling from encoder to decoder (frame rate = 12.5 Hz)
• Both components use sliding window attention for unbounded audio length

More Info

• Original model card
• mlx-audio GitHub
• Mistral announcement