appautomaton/step-audio-editx-8bit-mlx

Step-Audio-EditX — MLX 8-bit

This repository contains a self-contained pure-MLX int8 conversion of Step-Audio-EditX for local voice cloning and expressive audio editing on Apple Silicon. All pipeline components are stored as .safetensors — no PyTorch, ONNX, or NumPy files are required at inference time.

Model Details

• Developed by: AppAutomaton
• Upstream model: stepfun-ai/Step-Audio-EditX
• Task: zero-shot voice cloning, expressive audio editing
• Runtime: MLX on Apple Silicon
• Precision: int8 for Step1 LM, Flow model, and VQ02 tokenizer; bf16 for the rest
• Total size: ~4.1 GB (down from ~7.7 GB upstream)

Bundle Contents

This bundle is self-contained — all weights are packaged in one repository.

File	Component	Format	Size
model.safetensors	Step1 LM (3.5B params)	int8	3.5 GB
flow-model.safetensors	Flow model (DiT + conformer)	int8	181 MB
vq02.safetensors	VQ02 audio tokenizer	int8	162 MB
vq06.safetensors	VQ06 audio tokenizer	bf16	249 MB
hift.safetensors	HiFT vocoder	bf16	40 MB
campplus.safetensors	CampPlus speaker embedding	bf16	13 MB
flow-conditioner.safetensors	Flow conditioner	bf16	2.5 MB
config.json	Step1 LM config + quantization	JSON	—
flow-model-config.json	Flow model config	JSON	—
vq02-config.json, vq06-config.json	Tokenizer configs	JSON	—
step-audio-tokenizer-assets.safetensors	VQ02 codebook + CMVN	FP32	~2 MB
step-audio-tokenizer-config.json	Tokenizer runtime config	JSON	—
frontend-config.json	Prompt mel frontend config	JSON	—
hift-config.json, campplus-config.json, flow-conditioner-config.json	Component configs	JSON	—
tokenizer.json, tokenizer_config.json	Step1 tokenizer	JSON	—

How to Get Started

Download the bundle:

hf download appautomaton/step-audio-editx-8bit-mlx \
  --local-dir models/stepfun/step_audio_editx/mlx-int8

Voice cloning:

python scripts/generate/step_audio_editx.py \
  --prompt-audio reference.wav \
  --prompt-text "Transcript of reference audio." \
  -o cloned.wav \
  clone --target-text "New speech in the cloned voice."

Audio editing (change emotion):

python scripts/generate/step_audio_editx.py \
  --prompt-audio input.wav \
  --prompt-text "Transcript of input audio." \
  -o happy.wav \
  edit --edit-type emotion --edit-info happy

Supported Edit Types

Edit type	Description	--edit-info examples
emotion	Change the emotion of speech	happy, sad, angry, surprised
style	Change speaking style	whispering, broadcasting, formal
speed	Change speaking speed	fast, slow
denoise	Remove noise from audio	not used
vad	Remove silences from audio	not used
paralinguistic	Add non-verbal sounds	requires --target-text

Architecture

Five-stage pipeline, all running pure MLX with bf16 activations:

1. Step1 LM (3.5B params, int8) — autoregressive dual-codebook token generation
2. CampPlus (bf16) — speaker embedding extraction from reference audio
3. Flow conditioner (bf16) — conditions generation on speaker embedding
4. Flow model (int8) — flow-matching mel spectrogram generation
5. HiFT vocoder (bf16) — mel spectrogram to waveform

The VQ02 and VQ06 tokenizers encode reference audio into dual codebook tokens consumed by Step1.

Links

• Source code: mlx-speech
• Project page: appautomaton.com/mlx-speech
• Upstream model: stepfun-ai/Step-Audio-EditX
• Technical report: arXiv:2511.03601
• More examples: AppAutomaton

License

Apache 2.0 — following the upstream license published with stepfun-ai/Step-Audio-EditX.