MLX Speech Models
Collection
Speech AI models for Apple Silicon via MLX. ASR, TTS, VAD, diarization, speaker embedding. • 92 items • Updated • 5
How to use aufklarer/Voxtral-Mini-3B-2507-MLX-5bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Voxtral-Mini-3B-2507-MLX-5bit aufklarer/Voxtral-Mini-3B-2507-MLX-5bit
MLX weights for Voxtral Mini 3B 2507, ready for local speech-to-text with speech-swift.
The FP16, INT5, and INT8 family passed the isolated WER, RTF, throughput, RSS, and physical-footprint compatibility gate on 2026-07-22 using English FLEURS.
| Parameters | Precision | Sample rate | Languages |
|---|---|---|---|
| 3B | 5-bit affine | 16 kHz | 8 |
| File | Description |
|---|---|
model*.safetensors |
MLX model weights |
config.json and params.json |
Model and runtime configuration |
tekken.json |
Tekken tokenizer data |
speech transcribe recording.wav --engine voxtral --model int5
import VoxtralASR
let model = try await VoxtralModel.load(
"aufklarer/Voxtral-Mini-3B-2507-MLX-5bit"
)
let text = model.transcribe(
audio: samples,
sampleRate: sourceSampleRate,
language: "en"
)
English FLEURS read speech, 80 utterances (759.56 seconds), Apple M5 Pro:
| Variant | Bundle | WER | Mean RTF | Peak RSS |
|---|---|---|---|---|
| FP16 | 8.71 GiB | 4.633% | 0.1305 | 9,091 MiB |
| INT5 | 3.77 GiB | 4.744% | 0.0739 | 4,035 MiB |
| INT8 | 5.18 GiB | 4.578% | 0.0906 | 5,487 MiB |
These read-speech measurements are not Artificial Analysis Conversational or ElevenLabs Scribe results.
The base model is mistralai/Voxtral-Mini-3B-2507 and is released under Apache 2.0.
5-bit
Base model
mistralai/Voxtral-Mini-3B-2507