VibeVoice — MLX

VibeVoice Large converted and quantized for native MLX inference on Apple Silicon. Hybrid LLM + diffusion architecture for long-form speech, multi-speaker dialogue, and voice cloning.

Variants

Path	Precision
`mlx-int8/`	int8 quantized weights

How to Get Started

Single speaker:

python scripts/generate/vibevoice.py \
  --text "Hello from VibeVoice." \
  --output outputs/vibevoice.wav

Multi-speaker dialogue — speaker labels are 0-based:

python scripts/generate/vibevoice.py \
  --text "Speaker 0: Have you tried VibeVoice?
Speaker 1: Not yet. Does it need PyTorch?
Speaker 0: No. Pure MLX, runs locally on Apple Silicon.
Speaker 1: That is impressive." \
  --output outputs/dialogue.wav

Voice cloning — one reference WAV per speaker:

python scripts/generate/vibevoice.py \
  --text "Speaker 0: This is cloned from the reference." \
  --reference-audio-speaker0 ref_speaker0.wav \
  --output outputs/clone.wav

Up to 4 speakers supported: --reference-audio-speaker0 through --reference-audio-speaker3.

Default generation settings (matching upstream):

Greedy decoding (deterministic)
Seed: 42
Diffusion steps: 20

Add --no-greedy to enable temperature + top-p sampling.

Model Details

VibeVoice uses a 9B-parameter hybrid architecture combining a Qwen2 language model backbone with a continuous diffusion acoustic decoder. Converted to MLX with explicit weight remapping — no PyTorch at inference time.

See mlx-speech for the full runtime and conversion code.

License

Apache 2.0.

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MLX

Hardware compatibility

Quantized