Voxtral Mini 4B Realtime 2602 GGUF

Voxtral-Mini-4B-Realtime-2602: transcribe.cpp GGUF

GGUF conversions of mistralai/Voxtral-Mini-4B-Realtime-2602 for use with transcribe.cpp.

Ported from upstream commit 2769294, pinned 2026-06-06. Validated against the Transformers reference at transcribe.cpp commit 483c122 on 2026-06-06.

Streaming audio-LLM speech-to-text. A ~970M causal audio encoder (left-pad causal conv stem + 32-layer sliding-window RoPE transformer) feeds a 4-frame-group projector whose audio embeddings are added onto a ~3.4B Ministral decoder (26 layers, GQA 32/8, NEOX RoPE) with delay-token latency conditioning, emitting one text token per 80 ms audio slot (12.5 Hz). Takes a 16 kHz mono WAV and supports both incremental streaming (configurable latency/quality via --stream-chunk-ms and --stream-voxtral-delay) and offline transcription with a byte-equal final transcript. Architecturally distinct from the offline Voxtral 2507 family — own arch, streaming frontend, causal encoder, additive audio fusion.

Downloads

Quantization	Download	Size	WER (LibriSpeech test-clean)
BF16	Voxtral-Mini-4B-Realtime-2602-BF16.gguf	8.87 GB	2.08%
F16	Voxtral-Mini-4B-Realtime-2602-F16.gguf	8.88 GB	2.09%
Q8_0	Voxtral-Mini-4B-Realtime-2602-Q8_0.gguf	4.73 GB	2.07%
Q6_K	Voxtral-Mini-4B-Realtime-2602-Q6_K.gguf	3.66 GB	2.08%
Q5_K_M	Voxtral-Mini-4B-Realtime-2602-Q5_K_M.gguf	3.28 GB	2.08%
Q4_K_M	Voxtral-Mini-4B-Realtime-2602-Q4_K_M.gguf	2.83 GB	2.08%

WER measured on the full LibriSpeech test-clean split (2620 utterances) with the Whisper English text normalizer, offline path, batch size 8 on an NVIDIA L40S. Same-machine HuggingFace transformers reference (VoxtralRealtimeForConditionalGeneration, BF16, greedy): 2.08%; the BF16 GGUF matches at 2.08%. Every shipped quant stays within bootstrap noise (2.07-2.09%), so the quantization ladder is WER-neutral down to Q4_K_M. The model is multilingual (13 languages, auto-detect); the published WER is English only.

Usage

Build transcribe.cpp from source:

git clone [email protected]:handy-computer/transcribe.cpp.git
cd transcribe.cpp
cmake -B build && cmake --build build

Run on a 16 kHz mono WAV:

build/bin/transcribe-cli \
  -m Voxtral-Mini-4B-Realtime-2602-Q8_0.gguf \
  input.wav

If your audio isn't already 16 kHz mono WAV, convert it first:

ffmpeg -i input.mp3 -ar 16000 -ac 1 output.wav

See the transcribe.cpp model page for performance numbers, numerical validation, and reproduction steps.

License

Inherited from the base model: Apache-2.0. See the upstream model card for full terms.

---

Original Model Card

The section below is reproduced from mistralai/Voxtral-Mini-4B-Realtime-2602 at commit 2769294 for offline reference. The upstream card is the authoritative source.

Voxtral Mini 4B Realtime 2602

Voxtral Mini 4B Realtime 2602 is a multilingual, realtime speech-transcription model and among the first open-source solutions to achieve accuracy comparable to offline systems with a delay of <500ms. It supports 13 languages and outperforms existing open-source baselines across a range of tasks, making it ideal for applications like voice assistants and live subtitling.

Built with a natively streaming architecture and a custom causal audio encoder - it allows configurable transcription delays (240ms to 2.4s), enabling users to balance latency and accuracy based on their needs. At a 480ms delay, it matches the performance of leading offline open-source transcription models, as well as realtime APIs.

As a 4B-parameter model, is optimized for on-device deployment, requiring minimal hardware resources. It runs in realtime with on devices minimal hardware with throughput exceeding 12.5 tokens/second.

This model is released in BF16 under the Apache-2 license, ensuring flexibility for both research and commercial use.

For more details, see our:

• Blog post
• Demo
• Technical report
• vLLM's blog on streaming input

Key Features

Voxtral Mini 4B Realtime consists of two main architectural components:

• ≈3.4B Language Model
• ≈970M Audio Encoder
• The audio encoder was trained from scratch with causal attention enabling streaming capability
• Both the audio encoder as well as the LLM backbone use sliding window attention allowing for "infinite" streaming
• For more details, refer to the technical report

Voxtral-Realtime Architecture

The Voxtral Mini 4B Realtime model offers the following capabilities:

• High-Quality Transcription: Transcribe audio to text with confidence.
• Multilingual: Supports dozens of languages, making it perfect for multilingual transcription tasks.
• Real-Time: Fast streaming ASR model, enabling real-time transcription use cases.
• Configurable Transcription Delays: Customize the transcription delay to balance quality and latency, from 80ms to 2.4s.

Use Cases

Real-Time Transcription Purposes:

• Private meeting transcriptions
• Live subtitle creation
• Real-time assistants with speech understanding
• And more

Bringing real-time transcription capabilities to all.

Recommended Settings

We recommend deploying with the following best practices:

• Always set the temperature to 0.0
• A single text-token is worth 80ms. Hence, make sure to set your --max-model-len accordingly. To live-record a 1h meeting, you need to set --max-model-len >= 3600 / 0.8 = 45000. In theory, you should be able to record with no limit; in practice, pre-allocations of RoPE parameters among other things limits --max-model-len. For the best user experience, we recommend to simply instantiate vLLM with the default parameters which will automatically set a maximum model length of 131072 (~ca. 3h).
• We strongly recommend using websockets to set up audio streaming sessions. For more info on how to do so, check Usage.
• We recommend using a delay of 480ms as we found it to be the sweet spot of performance and low latency. If, however, you want to adapt the delay, you can change the "transcription_delay_ms": 480 parameter in the tekken.json file to any multiple of 80ms between 80 and 1200, as well as 2400 as a standalone value.

Benchmark Results

We compare Voxtral Mini 4B Realtime to similar models - both offline models and realtime. Voxtral Mini 4B Realtime is competitive to leading offline models and shows significant gains over existing open-source realtime solutions.

Fleurs

Model	Delay	AVG	Arabic	German	English	Spanish	French	Hindi	Italian	Dutch	Portuguese	Chinese	Japanese	Korean	Russian
Voxtral Mini Transcribe 2.0	Offline	5.90%	13.54%	3.54%	3.32%	2.63%	4.32%	10.33%	2.17%	4.78%	3.56%	7.30%	4.14%	12.29%	4.75%
Voxtral Mini 4B Realtime 2602	480 ms	8.72%	22.53%	6.19%	4.90%	3.31%	6.42%	12.88%	3.27%	7.07%	5.03%	10.45%	9.59%	15.74%	6.02%
	160 ms	12.60%	24.33%	9.50%	6.46%	5.34%	9.75%	15.28%	5.59%	11.39%	10.01%	17.67%	19.17%	19.81%	9.53%
	240 ms	10.80%	23.95%	8.15%	5.91%	4.59%	8.00%	14.26%	4.41%	9.23%	7.51%	13.84%	15.17%	17.56%	7.87%
	960 ms	7.70%	20.32%	4.87%	4.34%	2.98%	5.68%	11.82%	2.46%	6.76%	4.57%	8.99%	6.80%	14.90%	5.56%
	2400 ms	6.73%	14.71%	4.15%	4.05%	2.71%	5.23%	10.73%	2.37%	5.91%	3.93%	8.48%	5.50%	14.30%	5.41%

Long-form English

Model	Delay	Meanwhile (<10m)	E-21 (<10m)	E-22 (<10m)	TEDLIUM (<20m)
Voxtral Mini Transcribe 2.0	Offline	4.08%	9.81%	11.69%	2.86%
Voxtral Mini 4B Realtime 2602	480ms	5.05%	10.23%	12.30%	3.17%

Short-form English

Model	Delay	CHiME-4	GigaSpeech 2k Subset	AMI IHM	SwitchBoard	CHiME-4 SP	GISpeech 2k Subset
Voxtral Mini Transcribe 2.0	Offline	10.39%	6.81%	14.43%	11.54%	10.42%	1.74%
Voxtral Mini 4B Realtime 2602	480ms	10.50%	7.35%	15.05%	11.65%	12.41%	1.73%

Usage

The model can also be deployed with the following libraries:

• vllm (recommended): See here
• transformers: See...