ComfyUI node for highly expressive speech and realistic zero-shot voice cloning
509
stars
86
commits
Python
primary language
Aug 6, 2026
updated
ComfyUI custom node integrating VoxCPM — a tokenizer-free TTS system for expressive speech generation and voice cloning.
VoxCPM models speech in a continuous space using a MiniCPM-4 backbone, producing highly expressive speech and accurate zero-shot voice cloning. This node handles model downloading, memory management, and audio processing end-to-end.
Via ComfyUI Manager: Search ComfyUI-VoxCPM → Install.
Manual install:
cd ComfyUI/custom_nodes/
git clone https://github.com/wildminder/ComfyUI-VoxCPM.git
cd ComfyUI-VoxCPM
pip install -r requirements.txt
Restart ComfyUI. Nodes appear under audio/tts. Models auto-download to ComfyUI/models/tts/VoxCPM/ on first use.
| Model | Params | Sample Rate | Languages | Link |
|---|---|---|---|---|
| VoxCPM2 | 2B | 48kHz | 30+ | openbmb/VoxCPM2 |
| VoxCPM1.5 | 800M | 44.1kHz | 2 | openbmb/VoxCPM1.5 |
| VoxCPM-0.5B | 640M | 16kHz | 2 | openbmb/VoxCPM-0.5B |
VoxCPM TTSUnified TTS node supporting VoxCPM1.5 and VoxCPM2: zero-shot TTS, voice design, voice cloning, ultimate cloning, LoRA support.
VoxCPM Voice CloningConfigures audio-based cloning (prompt/reference audio, VAD trimming). Connect to TTS node's voice_config input.
VoxCPM Advanced ParamsConfigures diffusion parameters (temperature, sway sampling, CFG, timesteps, retry). Connect to TTS node's advanced_params input.
VoxCPM Train Config — LoRA training parametersVoxCPM Dataset Maker — create training datasets from audioVoxCPM LoRA Trainer — train custom LoRA modelsNote:
voice_designis a direct parameter on the TTS node, not part of the Voice Cloning config.
Config precedence: Direct parameters > config node values > defaults.
Add VoxCPM TTS → select model → enter text → generate.
Connect Load Audio → prompt_audio, provide exact transcript in prompt_text → generate.
Select VoxCPM2 model → enter description in voice_design (e.g., "warm female voice") → generate. Voice design is applied in plain TTS and reference cloning modes. Ignored when prompt audio is used (continuation cloning).
Connect reference audio to reference_audio (no transcript needed) → generate. Voice design instructions can be combined with reference audio for controllable cloning (e.g., style control).
Connect reference_audio (identity) + prompt_audio with transcript (prosody) → generate.
[!NOTE] Denoising: The built-in ZipEnhancer denoiser is disabled by default to keep dependencies light.
| Parameter | Default | Range | Description |
|---|---|---|---|
temperature | 1.0 | 0.1-2.0 | Lower = stable, higher = expressive |
sway_sampling_coef | 1.0 | 0.0-2.0 | Sway sampling trajectory |
use_cfg_zero_star | True | — | CFG-Zero* optimization |
cfg_value | 2.0 | 0.1-10.0 | Guidance scale |
inference_timesteps | 10 | 1-100 | More steps = higher quality, slower |
| Parameter | Default | Options | Description |
|---|---|---|---|
device | auto | cuda, cpu, mps, xpu, npu | Inference device |
dtype | auto | auto, bf16, fp16, fp32 | Model precision |
AudioVAE always runs in FP32 for numerical stability.
| Parameter | Default | Range | Description |
|---|---|---|---|
trim_silence | False | — | VAD silence trimming |
max_silence_ms | 200.0 | 0-1000 | Max silence at boundaries (ms) |
top_db | 35.0 | 10-60 | Lower = more aggressive trimming |
Inference: Place .safetensors LoRA files in ComfyUI/models/loras/, refresh, select in lora_name dropdown.
Training: 👉 Full LoRA Training Guide
| Description | Result |
|---|---|
warm female voice | Soft, gentle female voice |
deep male voice | Low-pitched male voice |
cheerful young girl | Energetic, high-pitched |
professional announcer | Clear, authoritative |
whispering voice | Quiet, intimate |
Combine descriptions: "warm female voice with slight British accent"
prompt_text must match audio word-for-word[!Warning]
prompt_textis the exact transcript, not a description of the voice.
VoxCPM model and components: Apache-2.0 License by OpenBMB.
══════════════════════════════════
86 commits
Python
89.5%
JavaScript
10.5%
ComfyUI node for highly expressive speech and realistic zero-shot voice cloning
509
stars
86
commits
Python
primary language
Aug 6, 2026
updated
ComfyUI custom node integrating VoxCPM — a tokenizer-free TTS system for expressive speech generation and voice cloning.
VoxCPM models speech in a continuous space using a MiniCPM-4 backbone, producing highly expressive speech and accurate zero-shot voice cloning. This node handles model downloading, memory management, and audio processing end-to-end.
Via ComfyUI Manager: Search ComfyUI-VoxCPM → Install.
Manual install:
cd ComfyUI/custom_nodes/
git clone https://github.com/wildminder/ComfyUI-VoxCPM.git
cd ComfyUI-VoxCPM
pip install -r requirements.txt
Restart ComfyUI. Nodes appear under audio/tts. Models auto-download to ComfyUI/models/tts/VoxCPM/ on first use.
| Model | Params | Sample Rate | Languages | Link |
|---|---|---|---|---|
| VoxCPM2 | 2B | 48kHz | 30+ | openbmb/VoxCPM2 |
| VoxCPM1.5 | 800M | 44.1kHz | 2 | openbmb/VoxCPM1.5 |
| VoxCPM-0.5B | 640M | 16kHz | 2 | openbmb/VoxCPM-0.5B |
VoxCPM TTSUnified TTS node supporting VoxCPM1.5 and VoxCPM2: zero-shot TTS, voice design, voice cloning, ultimate cloning, LoRA support.
VoxCPM Voice CloningConfigures audio-based cloning (prompt/reference audio, VAD trimming). Connect to TTS node's voice_config input.
VoxCPM Advanced ParamsConfigures diffusion parameters (temperature, sway sampling, CFG, timesteps, retry). Connect to TTS node's advanced_params input.
VoxCPM Train Config — LoRA training parametersVoxCPM Dataset Maker — create training datasets from audioVoxCPM LoRA Trainer — train custom LoRA modelsNote:
voice_designis a direct parameter on the TTS node, not part of the Voice Cloning config.
Config precedence: Direct parameters > config node values > defaults.
Add VoxCPM TTS → select model → enter text → generate.
Connect Load Audio → prompt_audio, provide exact transcript in prompt_text → generate.
Select VoxCPM2 model → enter description in voice_design (e.g., "warm female voice") → generate. Voice design is applied in plain TTS and reference cloning modes. Ignored when prompt audio is used (continuation cloning).
Connect reference audio to reference_audio (no transcript needed) → generate. Voice design instructions can be combined with reference audio for controllable cloning (e.g., style control).
Connect reference_audio (identity) + prompt_audio with transcript (prosody) → generate.
[!NOTE] Denoising: The built-in ZipEnhancer denoiser is disabled by default to keep dependencies light.
| Parameter | Default | Range | Description |
|---|---|---|---|
temperature | 1.0 | 0.1-2.0 | Lower = stable, higher = expressive |
sway_sampling_coef | 1.0 | 0.0-2.0 | Sway sampling trajectory |
use_cfg_zero_star | True | — | CFG-Zero* optimization |
cfg_value | 2.0 | 0.1-10.0 | Guidance scale |
inference_timesteps | 10 | 1-100 | More steps = higher quality, slower |
| Parameter | Default | Options | Description |
|---|---|---|---|
device | auto | cuda, cpu, mps, xpu, npu | Inference device |
dtype | auto | auto, bf16, fp16, fp32 | Model precision |
AudioVAE always runs in FP32 for numerical stability.
| Parameter | Default | Range | Description |
|---|---|---|---|
trim_silence | False | — | VAD silence trimming |
max_silence_ms | 200.0 | 0-1000 | Max silence at boundaries (ms) |
top_db | 35.0 | 10-60 | Lower = more aggressive trimming |
Inference: Place .safetensors LoRA files in ComfyUI/models/loras/, refresh, select in lora_name dropdown.
Training: 👉 Full LoRA Training Guide
| Description | Result |
|---|---|
warm female voice | Soft, gentle female voice |
deep male voice | Low-pitched male voice |
cheerful young girl | Energetic, high-pitched |
professional announcer | Clear, authoritative |
whispering voice | Quiet, intimate |
Combine descriptions: "warm female voice with slight British accent"
prompt_text must match audio word-for-word[!Warning]
prompt_textis the exact transcript, not a description of the voice.
VoxCPM model and components: Apache-2.0 License by OpenBMB.
══════════════════════════════════
86 commits
Python
89.5%
JavaScript
10.5%