
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]

python

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

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

Voxtral Mini 4B Realtime — MLX fp16

Key Details

Usage

Recommended Settings

Benchmarks (from upstream)

FLEURS (13 languages, WER%)

Long-form English (WER%)

Architecture

More Info

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