| name | voxtral-tts |
| description | Generate speech from text using Mistral's Voxtral TTS model locally on Apple Silicon via mlx-audio. 20 preset voices across 9 languages (English, French, German, Spanish, Portuguese, Italian, Dutch, Arabic, Hindi). Does NOT support Chinese/Japanese/Korean. Use when: user asks to "generate speech", "text to speech", "TTS", "voxtral", "read aloud", "speak this", or needs high-quality multilingual voice synthesis locally. Preferred over cloud TTS for privacy and cost.
|
Voxtral TTS
Local text-to-speech on Apple Silicon using Mistral's Voxtral-4B via MLX.
Requirements
- Apple Silicon Mac (M1+)
- Python 3.10+
- ~2.5GB RAM (4-bit quantized) or ~8GB (bf16)
Setup
pip install "mlx-audio @ git+https://github.com/lucasnewman/mlx-audio.git@main"
Note: As of March 2026, Voxtral support requires mlx-audio from git (PR #607). Once released to PyPI, pip install -U mlx-audio will suffice.
Quick Usage
from mlx_audio.tts.utils import load
import numpy as np
import soundfile as sf
model = load("mlx-community/Voxtral-4B-TTS-2603-mlx-4bit")
for result in model.generate(text="Hello world!", voice="cheerful_female"):
audio = np.array(result.audio)
sf.write("output.wav", audio, result.sample_rate)
Always use result.sample_rate — never hardcode the sample rate.
Model Variants
| Model | Size | RAM | Speed |
|---|
mlx-community/Voxtral-4B-TTS-2603-mlx-4bit | ~2.5GB | ~3GB | RTF ~0.70 |
mlx-community/Voxtral-4B-TTS-2603-mlx-6bit | ~3.5GB | ~4GB | RTF ~0.75 |
mlx-community/Voxtral-4B-TTS-2603-mlx-bf16 | ~8GB | ~9GB | Best quality |
4-bit recommended for most use cases. RTF < 1.0 means faster than real-time.
Voices (20 total)
English (5)
casual_female, casual_male, cheerful_female, neutral_female, neutral_male
Other Languages (15)
| Language | Voices |
|---|
| Portuguese 🇧🇷 | pt_male, pt_female |
| Dutch 🇳🇱 | nl_male, nl_female |
| Italian 🇮🇹 | it_male, it_female |
| French 🇫🇷 | fr_male, fr_female |
| Spanish 🇪🇸 | es_male, es_female |
| German 🇩🇪 | de_male, de_female |
| Arabic 🇸🇦 | ar_male |
| Hindi 🇮🇳 | hi_male, hi_female |
Script Usage
Generate speech from the command line:
python scripts/voxtral_generate.py --text "Hello world" --voice cheerful_female --output output.wav
Options:
--text — Text to synthesize (required)--voice — Voice name (default: cheerful_female)--model — Model variant (default: mlx-community/Voxtral-4B-TTS-2603-mlx-4bit)--output — Output file path (default: output.wav)--format — Output format: wav, ogg, mp3 (default: wav; ogg/mp3 require ffmpeg)
First Run
The first run downloads the model from HuggingFace (~2.5GB for 4-bit). The script auto-detects this and prints a notice. Subsequent runs load from cache in ~2-5 seconds.
Performance (M5 MacBook Pro 32GB, 4-bit)
- Load time: ~2-5s (first load downloads model)
- Generation RTF: ~0.70 (10s audio ≈ 7s generation)
- Sample rate: 24000 Hz
- Languages not supported: Chinese, Japanese, Korean — use
spark-tts skill for Chinese
Integration Pattern
from mlx_audio.tts.utils import load
import numpy as np
import sounddevice as sd
model = load("mlx-community/Voxtral-4B-TTS-2603-mlx-4bit")
def speak(text: str, voice: str = "cheerful_female"):
for result in model.generate(text=text, voice=voice):
audio = np.array(result.audio)
sd.play(audio, result.sample_rate)
sd.wait()
Telegram Voice Message
To send as Telegram voice, convert to OGG Opus:
ffmpeg -y -i output.wav -c:a libopus -b:a 96k output.ogg
Then send with asVoice: true.
