| # 🎙️ VoxCPM2 - Full Capability Tools |
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| **Complete toolkit for [openbmb/VoxCPM2](https://huggingface.co/openbmb/VoxCPM2)** — a 2B parameter tokenizer-free diffusion TTS model with voice cloning, voice design, and multilingual synthesis. |
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|
| [](https://huggingface.co/openbmb/VoxCPM2) |
| [](https://arxiv.org/abs/2509.24650) |
| [](https://voxcpm.readthedocs.io/) |
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| --- |
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| ## 📦 What's Included |
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| | File | Purpose | |
| |------|---------| |
| | `voxcpm2_local_laptop.py` | **Local inference script** — optimized for Ryzen 7 + 16GB RAM CPU | |
| | `VoxCPM2_Colab_Notebook.ipynb` | **Google Colab notebook** — free T4 GPU, all capabilities + Gradio UI | |
| | `README.md` | This file — full documentation | |
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| --- |
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| ## 🚀 Quick Start (Google Colab — FREE) |
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| 1. **Open the notebook** in Colab: |
| - Download: [`VoxCPM2_Colab_Notebook.ipynb`](https://huggingface.co/SWAG456/voxcpm2-tools/blob/main/VoxCPM2_Colab_Notebook.ipynb) |
| - Or open directly: [Open in Colab](https://colab.research.google.com/) |
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| 2. **Run all cells top to bottom** — installs `voxcpm`, downloads ~4.6GB model, then: |
| - 🔊 Basic TTS |
| - 🎨 Voice Design |
| - 🌐 Multilingual (30+ languages) |
| - 👤 Zero-Shot Voice Cloning (upload your voice!) |
| - 🎵 Hi-Fi Ultimate Cloning |
| - 📡 Streaming Generation |
| - 🖥️ **Interactive Gradio UI** with public URL |
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| 3. **GPU memory optimized** for free T4 tier (~8GB VRAM used out of 16GB) |
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| --- |
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| ## 💻 Local Laptop (Ryzen 7 + 16GB RAM) |
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| ### Install |
| ```bash |
| pip install voxcpm soundfile torch |
| ``` |
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|
| ### Run All Demos |
| ```bash |
| python voxcpm2_local_laptop.py --demo |
| ``` |
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|
| ### Run Specific Modes |
| ```bash |
| # Basic TTS |
| python voxcpm2_local_laptop.py --text "Hello world" |
| |
| # Voice Design (natural language voice control) |
| python voxcpm2_local_laptop.py --mode design \ |
| --description "warm female voice" \ |
| --text "Hello there" |
| |
| # Voice Cloning (needs reference WAV file) |
| python voxcpm2_local_laptop.py --mode clone \ |
| --text "This is my cloned voice" \ |
| --reference my_voice.wav |
| |
| # Multilingual demo |
| python voxcpm2_local_laptop.py --mode multilingual |
| |
| # Speed mode (lower timesteps = faster) |
| python voxcpm2_local_laptop.py --text "Hello" --timesteps 5 |
| ``` |
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| --- |
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| ## 🎛️ All Capabilities Explained |
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| ### 1. 🔊 Basic TTS |
| Just text → audio. Fastest mode. 48kHz studio-quality output. |
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| ### 2. 🎨 Voice Design |
| Control voice characteristics with natural language descriptions: |
| ```python |
| "(A young woman, gentle and soothing voice) Hello!" |
| "(A deep male narrator, professional tone) Welcome." |
| "(A robot, monotone synthetic voice) System online." |
| ``` |
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| ### 3. 👤 Zero-Shot Voice Cloning |
| Clone ANY voice from a 3-10 second audio sample. Upload a WAV and the model mimics the speaker perfectly. |
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| ### 4. 🎵 Hi-Fi Ultimate Cloning |
| Best quality cloning combining: |
| - **Prompt audio** + transcript (for prosody/style) |
| - **Reference audio** (for timbre) |
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| ### 5. 🌐 Multilingual (30+ Languages) |
| No language tags needed. Just write in the target language: |
| - English, Chinese, Spanish, French, German, Japanese, Korean |
| - Arabic, Hindi, Portuguese, Russian, Italian, Dutch, Polish |
| - Turkish, Vietnamese, Thai, Indonesian, and more |
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| ### 6. 📡 Streaming Generation |
| Generate long texts chunk-by-chunk. Memory-efficient for audiobooks. |
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| --- |
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| ## ⚙️ Speed vs Quality |
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| | Timesteps | Quality | Speed | Best For | |
| |-----------|---------|-------|----------| |
| | 4-5 | Draft | ⚡ Fast | Testing | |
| | 8-10 | Good | 🚀 Normal | Default, balanced | |
| | 15-20 | High | 🐢 Slow | Voice cloning | |
| | 25-30 | Best | 🐌 Very Slow | Audiobooks | |
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| **Parameter: `inference_timesteps`** — lower = faster, higher = better quality. |
| |
| --- |
| |
| ## 🖥️ Hardware Requirements |
| |
| | Setup | VRAM/RAM | Feasibility | Notes | |
| |-------|----------|-------------|-------| |
| | **Colab T4 (Free)** | 16GB GPU | ✅ Perfect | `load_denoiser=False` saves ~500MB | |
| | **Ryzen 7 + 16GB RAM** | 16GB CPU | ✅ Works | CPU mode, slower but functional | |
| | **RTX 3060/4060** | 12GB GPU | ✅ Good | Same settings as Colab | |
| | **Apple Silicon M1-M3** | Unified | ✅ Works | `device="mps"` | |
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| --- |
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| ## 🔧 Memory Optimizations Applied |
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| Both scripts use these settings to fit in 16GB: |
| - `load_denoiser=False` — Skip ZipEnhancer (~500MB saved) |
| - `optimize=False` on CPU — Skip torch.compile overhead |
| - `optimize=True` on GPU — Enable torch.compile for speed |
| - `device="auto"` / `"cpu"` / `"cuda"` — Proper device selection |
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| --- |
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| ## 📚 Model Info |
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| - **Parameters:** 2B |
| - **Architecture:** MiniCPM-4 → LocEnc → TSLM → RALM → LocDiT → AudioVAE V2 |
| - **Output:** 48kHz WAV |
| - **License:** Apache-2.0 (commercial use OK) |
| - **Paper:** [arXiv:2509.24650](https://arxiv.org/abs/2509.24650) |
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| --- |
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| ## 📝 License |
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| These scripts are provided as-is for personal/educational use. The VoxCPM2 model is Apache-2.0 licensed. |
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| --- |
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| ## 🔗 Links |
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| - [Model on HuggingFace](https://huggingface.co/openbmb/VoxCPM2) |
| - [Official Documentation](https://voxcpm.readthedocs.io/) |
| - [Paper on arXiv](https://arxiv.org/abs/2509.24650) |
| - [OpenBMB GitHub](https://github.com/OpenBMB/VoxCPM) |
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