ComfyUI-VoxCPM

A custom node for ComfyUI that integrates VoxCPM, a novel tokenizer-free TTS system for context-aware speech generation and true-to-life voice cloning.

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About The Project

VoxCPM is a novel tokenizer-free Text-to-Speech system that redefines realism in speech synthesis by modeling speech in a continuous space. Built on the MiniCPM-4 backbone, it excels at generating highly expressive speech and performing accurate zero-shot voice cloning.

This custom node handles everything from model downloading and memory management to audio processing, allowing you to generate high-quality speech directly from a text script and optional reference audio files.

✨ Key Features:

• Context-Aware Expressive Speech: The model understands text context to generate appropriate prosody and vocal expression.
• True-to-Life Voice Cloning: Clone a voice's timbre, accent, and emotional tone from a short audio sample.
• Zero-Shot TTS: Generate high-quality speech without any reference audio.
• Automatic Model Management: The required VoxCPM model is downloaded automatically and managed efficiently by ComfyUI to save VRAM.
• Fine-Grained Control: Adjust parameters like CFG scale and inference steps to tune the performance and style of the generated speech.
• High-Efficiency Synthesis: VoxCPM is designed for speed, enabling fast generation even on consumer-grade hardware.

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🚀 Getting Started

The easiest way to install is via ComfyUI Manager. Search for ComfyUI-VoxCPM and click "Install".

Alternatively, to install manually:

1. Clone the Repository: Navigate to your ComfyUI/custom_nodes/ directory and clone this repository:

   git clone https://github.com/wildminder/ComfyUI-VoxCPM.git

2. Install Dependencies: Open a terminal or command prompt, navigate into the cloned ComfyUI-VoxCPM directory, and install the required Python packages:

   cd ComfyUI-VoxCPM
   pip install -r requirements.txt

3. Start/Restart ComfyUI: Launch ComfyUI. The "VoxCPM TTS" node will appear under the audio/tts category. The first time you use the node, it will automatically download the selected model to your ComfyUI/models/tts/VoxCPM/ folder.

Models

This node automatically downloads the required model files.

Model	Parameters	Hugging Face Link
VoxCPM-0.5B	0.5B	openbmb/VoxCPM-0.5B

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🛠️ Usage

1. Add Nodes: Add the VoxCPM TTS node to your graph. For voice cloning, add a Load Audio node to load your reference voice file.
2. Connect Voice (for Cloning): Connect the AUDIO output from the Load Audio node to the prompt_audio input on the VoxCPM TTS node.
3. Write Text:
   • For voice cloning, provide the transcript of your reference audio in the prompt_text field.
   • Enter the text you want to generate in the main text field.
4. Generate: Queue the prompt. The node will process the text and generate a single audio file.

Note

Denoising: The original VoxCPM library includes a built-in denoiser (ZipEnhancer). This feature has been intentionally removed from the node. The ComfyUI philosophy encourages modular, single-purpose nodes. For denoising, please use a dedicated audio processing node before passing the prompt_audio to this one. This keeps the workflow clean and flexible.

Node Inputs

• model_name: Select the VoxCPM model to use. Official models are downloaded automatically.
• text: The target text to synthesize into speech.
• prompt_audio (Optional): A reference audio clip for voice cloning.
• prompt_text (Optional): The exact transcript of the prompt_audio. This is required for voice cloning.
• cfg_value: Classifier-Free Guidance scale. Higher values increase adherence to the voice prompt but may reduce naturalness.
• inference_timesteps: Number of diffusion steps for audio generation. More steps can improve quality but take longer.
• normalize_text: Enable to automatically process numbers, abbreviations, and punctuation. Disable for precise control with phoneme inputs like {ni3}{hao3}.
• seed: A seed for reproducibility. Set to -1 for a random seed on each run.
• force_offload: Forces the model to be completely offloaded from VRAM after generation.

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🎤 Achieving High-Quality Voice Clones

To achieve the best voice cloning results, providing an accurate prompt_text is critical. This text acts as a transcript that aligns the sound of the prompt_audio with the words being spoken, teaching the model the speaker's unique vocal characteristics.

Warning

prompt_text is the exact transcript of the prompt_audio. It's not a general description of the voice, nor is it for providing emotional cues. Its job is to create a precise, moment-by-moment alignment between the words being spoken and the sounds being made.

1. Provide a Verbatim Transcript

The prompt_text must be a word-for-word transcript of the prompt_audio. Do not summarize or describe the audio.

• ✅ Correct: The quick brown fox jumps over the lazy dog.
• ❌ Incorrect: A person saying a sentence about a fox.

2. Punctuation is Important

Use accurate punctuation to capture the speaker's intonation. The model learns how the speaker ends sentences, asks questions, or shows excitement.

• For a statement: This is a great example.
• For a question: Is this a great example?
• For excitement: This is a great example!

3. Match Audio and Text Length

The audio clip should be long enough to capture the speaker's natural pacing and rhythm.

• 👍 Good: A 5-15 second clip of continuous, clear speech.
• 👌 Okay: A 3-5 second clip.
• ⚠️ Warning: Very short clips (< 3 seconds) may result in a less stable or robotic-sounding clone.

👩‍🍳 A Voice Chef's Guide

🥚 Step 1: Prepare Your Base Ingredients (Content)

First, choose how you’d like to input your text:

1. Regular Text (Classic Mode)
   • ✅ Keep normalize_text ON. Type naturally (e.g., "Hello, world! 123"). The system will automatically process numbers and punctuation.
2. Phoneme Input (Native Mode)
   • ❌ Turn normalize_text OFF. Enter phoneme text like {HH AH0 L OW1} (EN) or {ni3}{hao3} (ZH) for precise pronunciation control.

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🍳 Step 2: Choose Your Flavor Profile (Voice Style)

This is the secret sauce that gives your audio its unique sound.

1. With a Prompt (Voice Cloning)
   • A prompt_audio file provides the desired acoustic characteristics. The speaker's timbre, speaking style, and even ambiance can be replicated.
   • For best results, use a clean, high-quality audio recording as the prompt.
2. Without a Prompt (Zero-Shot Generation)
   • If no prompt is provided, VoxCPM becomes a creative chef! It will infer a fitting speaking style based on the text itself, thanks to its foundation model, MiniCPM-4.

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🧂 Step 3: The Final Seasoning (Fine-Tuning)

For master chefs who want to tweak the flavor, here are two key spices:

• cfg_value (How Closely to Follow the Recipe)
  • Default (2.0): A great starting point.
  • Lower it: If the cloned voice sounds strained or weird, lowering this value tells the model to be more relaxed and improvisational.
  • Raise it slightly: To maximize clarity and adherence to the prompt voice or text.
• inference_timesteps (Simmering Time: Quality vs. Speed)
  • Lower (e.g., 5-10): For a quick snack. Perfect for fast drafts and experiments.
  • Higher (e.g., 15-25): For a gourmet meal. This lets the model "simmer" longer, refining the audio for superior detail and naturalness.

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📊 Performance Benchmarks

Click to view model performance highlights

VoxCPM achieves competitive results on public zero-shot TTS benchmarks:

Seed-TTS-eval Benchmark

Model	Parameters	Open-Source	test-EN		test-ZH		test-Hard
			WER/%⬇	SIM/%⬆	CER/%⬇	SIM/%⬆	CER/%⬇	SIM/%⬆
VoxCPM	0.5B	✅	1.85	72.9	0.93	77.2	8.87	73.0
MegaTTS3	0.5B	❌	2.79	77.1	1.52	79.0	-	-
DiTAR	0.6B	❌	1.69	73.5	1.02	75.3	-	-

CV3-eval Benchmark

Model	zh	en	hard-zh			hard-en
	CER/%⬇	WER/%⬇	CER/%⬇	SIM/%⬆	DNSMOS⬆	WER/%⬇	SIM/%⬆	DNSMOS⬆
VoxCPM	3.40	4.04	12.9	66.1	3.59	7.89	64.3	3.74
CosyVoice2	4.08	6.32	12.58	72.6	3.81	11.96	66.7	3.95
IndexTTS2	3.58	4.45	12.8	74.6	3.65	-	-	-

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⚠️ Risks and Limitations

• Potential for Misuse: The voice cloning capability is powerful and could be misused for creating convincing deepfakes. Users of this node must not use it to create content that infringes upon the rights of individuals. It is strictly forbidden to use this for any illegal or unethical purposes.
• Technical Limitations: The model may occasionally exhibit instability with very long or complex inputs.
• Bilingual Model: VoxCPM is trained primarily on Chinese and English data. Performance on other languages is not guaranteed.
• This node is released for research and development purposes. Please use it responsibly.

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License

The VoxCPM model and its components are subject to the Apache-2.0 License provided by OpenBMB.

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Acknowledgments

• OpenBMB & ModelBest for creating and open-sourcing the incredible VoxCPM project.
• The ComfyUI team for their powerful and extensible platform.

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