A Gradio-based web UI for voice cloning and voice design, powered by Qwen3-TTS and VibeVoice. Supports both Whisper or VibeVoice-asr for automatic Transcription.

Clone voices from your own audio samples. Just provide a short reference audio clip with its transcript, and generate new speech in that voice. Choose Your Engine:

• Qwen Small/Fast or VibeVoice Small/Fast -

• Voice prompt caching - First generation processes the sample, subsequent ones are instant

• Seed control - Reproducible results with saved seeds

• Metadata tracking - Each output saves generation info (sample, seed, text)

Create multi-speaker dialogues using either Qwen's premium voices or your own custom voice samples using VibeVoice:

Choose Your Engine:

• Qwen - Fast generation with 9 preset voices, optimized for their native languages
• VibeVoice - High-quality custom voices, up to 90 minutes continuous, perfect for podcasts/audiobooks

Unified Script Format: Write scripts using [N]: format - works seamlessly with both engines:

[1]: Hey, how's it going?
[2]: I'm doing great, thanks for asking!
[3]: Mind if I join this conversation?

Qwen Mode:

• Mix any of the 9 premium speakers
• Adjustable pause duration between lines
• Fast generation with cached prompts

Speaker Mapping:

• [1] = Vivian, [2] = Serena, [3] = Uncle_Fu, [4] = Dylan, [5] = Eric
• [6] = Ryan, [7] = Aiden, [8] = Ono_Anna, [9] = Sohee

VibeVoice Mode:

• Up to 90 minutes of continuous speech
• Up to 4 distinct speakers using your own voice samples
• Cross-lingual support
• May spontaneously add background music/sounds for realism
• Numbers beyond 4 wrap around (5→1, 6→2, 7→3, 8→4, etc.)

Perfect for:

• Podcasts
• Audiobooks
• Long-form conversations
• Multi-speaker narratives

Models:

• Small - Faster generation (Qwen: 0.6B, VibeVoice: 1.5B)
• Large - Best quality (Qwen: 1.7B, VibeVoice: Large model)

Generate with premium pre-built voices with optional style instructions using Qwen3-TTS Custom Model:

• Style instructions supported (emotion, tone, speed)
• Each speaker works best in native language but supports all

Create voices from natural language descriptions - no audio needed, using Qwen3-TTS Voice Design Model:

• Describe age, gender, emotion, accent, speaking style
• Generate unique voices matching your description

Fine-tune your own custom voice models with your training data:

• Dataset Management - Organize training samples in the datasets/ folder
• Audio Preparation - Auto-converts to 24kHz 16-bit mono format
• Training Pipeline - Complete 3-step workflow (validation → extract codes → train)
• Epoch Selection - Compare different training checkpoints
• Live Progress - Real-time training logs and loss monitoring
• Voice Presets Integration - Use trained models alongside premium speakers

Requirements:

• CUDA GPU required
• Multiple audio samples with transcripts
• Training time: ~10-30 minutes depending on dataset size

Workflow:

1. Prepare audio files (WAV/MP3) and organize in datasets/YourSpeakerName/ folder
2. Use Batch Transcribe to automatically transcribe all files at once
3. Review and edit individual transcripts as needed
4. Configure training parameters (model size, epochs, learning rate)
5. Monitor training progress in real-time
6. Use trained model in Voice Presets tab

Full audio preparation workspace:

• Trim - Use waveform selection to cut audio
• Normalize - Balance audio levels
• Convert to Mono - Ensure single-channel audio
• Transcribe - Whisper or VibeVoice ASR automatic transcription
• Batch Transcribe - Process entire folders of audio files at once
• Save as Sample - One-click sample creation

View, play back, and manage your previously generated audio files.

• Python 3.10+ (recommended for all platforms)
• CUDA-compatible GPU (recommended: 8GB+ VRAM)
• SOX (Sound eXchange) - Required for audio processing
• FFMPEG - Multimedia framework required for audio format conversion
• Flash Attention 2 (optional but recommended)

Note for Linux users: The Linux installation skips openai-whisper (compatibility issues). VibeVoice ASR is used for transcription instead.

1. Clone the repository:

git clone https://github.com/FranckyB/Voice-Clone-Studio.git
cd Voice-Clone-Studio

2. Run the setup script:

setup-windows.bat

This will automatically:

• Install SOX (audio processing)
• Create virtual environment
• Install PyTorch with CUDA support
• Install all dependencies
• Display your Python version
• Show instructions for optional Flash Attention 2 installation

1. Clone the repository:

git clone https://github.com/FranckyB/Voice-Clone-Studio.git
cd Voice-Clone-Studio

2. Make the setup script executable and run it:

chmod +x setup-linux.sh
./setup-linux.sh

This will automatically:

• Detect your Python version
• Create virtual environment
• Install PyTorch with CUDA support
• Install all dependencies (using requirements file)
• Handle ONNX Runtime installation issues
• Warn about Whisper compatibility if needed

1. Clone the repository:

git clone https://github.com/FranckyB/Voice-Clone-Studio.git
cd Voice-Clone-Studio

2. Create a virtual environment:

python -m venv venv
# Windows
venv\Scripts\activate
# Linux/MacOs
source venv/bin/activate

3. (NVIDIA GPU) Install PyTorch with CUDA support:

# Linux/Windows
pip install torch==2.9.1 torchaudio --index-url https://download.pytorch.org/whl/cu130

4. Install dependencies:

# All platforms (Windows, Linux, macOS)
pip install -r requirements.txt

Note: The requirements file uses platform markers to automatically install the correct packages:

• Windows: Includes openai-whisper for transcription
• Linux/macOS: Excludes openai-whisper (uses VibeVoice ASR instead)

5. Install Sox

# Windows
winget install -e --id ChrisBagwell.SoX

# Linux
# Debian/Ubuntu
sudo apt install sox libsox-dev
# Fedora/RHEL
sudo dnf install sox sox-devel

# MacOs
brew install sox

6. Install ffmpeg

# Windows
winget install -e --id Gyan.FFmpeg

# Linux
# Debian/Ubuntu
sudo apt install ffmpeg
# Fedora/RHEL
sudo dnf install ffmpeg

# MacOs
brew install ffmpeg

7. (Optional) Install FlashAttention 2 for faster generation: Note: The application automatically detects and uses the best available attention mechanism. Configure in Settings tab: auto (recommended) → flash_attention_2 → sdpa → eager

For troubleshooting solutions, see docs/troubleshooting.md.

1. Install NVIDIA Drivers (Windows Side)

   • Install the latest standard NVIDIA driver (Game Ready or Studio) for Windows from the NVIDIA Drivers page.
   • Crucial: Do not try to install NVIDIA drivers inside your WSL Linux terminal. It will conflict with the host driver.

2. Update WSL 2

   • Open PowerShell as Administrator and ensure your WSL kernel is up to date:

     wsl --update

   • (If you don't have WSL installed yet, run wsl --install and restart your computer).

3. Configure Docker Desktop

   • Install the latest version of Docker Desktop for Windows.
   • Open Docker Desktop Settings (gear icon).
   • Under General, ensure "Use the WSL 2 based engine" is checked.
   • Under Resources > WSL Integration, ensure the switch is enabled for your default Linux distro (e.g., Ubuntu).

4. Run with Docker Compose

   • Run the following command in the repository root:

     docker-compose up --build

   • The application will be accessible at http://127.0.0.1:7860.

To verify the installation and features (like the DeepFilterNet denoiser), runs the integration tests inside the container:

# Run the Denoiser Integration Test
docker-compose exec voice-clone-studio python tests/integration_test_denoiser.py

python voice_clone_studio.py

Or use the batch file (Windows):

launch.bat

The UI will open at http://127.0.0.1:7860

1. Go to the Prep Samples tab
2. Upload or record audio (3-10 seconds of clear speech)
3. Trim and normalize as needed
4. Transcribe or manually enter the text
5. Save as a sample with a name

1. Go to the Voice Clone tab
2. Select your sample from the dropdown
3. Enter the text you want to speak
4. Click Generate

1. Go to the Voice Design tab
2. Enter the text to speak
3. Describe the voice (e.g., "Young female, warm and friendly, slight British accent")
4. Click Generate

Qwen3-TTS-Voice-Clone-Studio/
├── voice_clone_ui.py      # Main Gradio application
├── requirements.txt       # Python dependencies
├── __Launch_UI.bat        # Windows launcher
├── samples/               # Voice samples (.wav + .txt pairs)
│   └── example.wav
│   └── example.txt
├── output/                # Generated audio outputs
├── vendor                 # Included Technology
│   └── vibevoice_asr      # newest version of vibevoice with asr support
│   └── vibevoice_tts      # prior version of vibevoice with tts support

Each tab lets you choose between model sizes:

• Small = Faster, less VRAM (Qwen: 0.6B ~4GB, VibeVoice: 1.5B)
• Large = Better quality, more expressive (Qwen: 1.7B ~8GB, VibeVoice: Large model)
• 4 Bit Quantized version of the Large model is also included for VibeVoice.

Models are automatically downloaded on first use via HuggingFace.

• Reference Audio: Use clear, noise-free recordings (3-10 seconds)
• Transcripts: Should exactly match what's spoken in the audio
• Caching: Voice prompts are cached - first generation is slow, subsequent ones are fast
• Seeds: Use the same seed to reproduce identical outputs

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

This project is based on and uses code from:

• Qwen3-TTS - Apache 2.0 License (Alibaba)
• VibeVoice - MIT License
• Gradio - Apache 2.0 License
• OpenAI Whisper - MIT License

• Qwen3-TTS by Alibaba
• VibeVoice by Microsoft
• Gradio for the web UI framework
• OpenAI Whisper for transcription

For detailed version history and release notes, see docs/updates.md.

Latest Version: 0.6.0 - Enhanced Model Support & Settings (January 27, 2026)