VODER - Voice Blender

VODER is a Local, Free, Offline, professional-grade voice processing and transformation tool that enables seamless conversion between speech, text, and music. Built for creators, developers, and audio professionals, VODER delivers high-quality synthesis, voice cloning, transcription, music generation, sound effects, and speech enhancement capabilities through an intuitive interface.

🚀 Ready in Colab: Open VODER in Google Colab

🤖 For AI agents and automated tools: See Bots.md

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Quick Start

Run from Source

# Clone the repository
git clone https://github.com/HAKORADev/VODER.git
cd VODER

# Install dependencies
pip install -r requirements.txt

# IMPORTANT: After installing requirements, upgrade protobuf to avoid compatibility issues
pip install --upgrade protobuf==5.29.6

# Launch GUI
python src/voder.py

# Or use CLI mode
python src/voder.py cli

Run in Google Colab

Open the link, connect to a runtime, and press Run All (or run cells one by one until the last one). Once execution completes, VODER is ready to use directly in your browser — no installation required.

Installation Requirements

# Install FFmpeg (required for audio processing)
# Windows: winget install FFmpeg
# macOS: brew install ffmpeg
# Linux: sudo apt install ffmpeg

# Additional system dependencies (required for new features)
# Linux: sudo apt install sox
# macOS: brew install sox
# yt-dlp: pip install yt-dlp

New Dependencies (v04/08/2026 update): VODER now requires yt-dlp (for YouTube/Bilibili/TikTok URL support), easyocr and onnxruntime (for image text extraction), lightning (for pyannote model loading), sox (for audio manipulation), einx, x-transformers, safetensors, soxr (for UniSE speech enhancement), tqdm/packaging, rotary_embedding_torch, beartype, and ml_collections (for BS-RoFormer vocal/music separation), and huggingface-hub==0.34.0 (pinned for model download compatibility). These are included in requirements.txt — simply run pip install -r requirements.txt after pulling the latest version.

New Model Directories: VODER now downloads additional models for BS-RoFormer (vocal/music separation) and VibeVoice ASR (advanced transcription). Ensure sufficient disk space is available — model files are cached in the standard Hugging Face cache directory.

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Core Capabilities

🎤 10 Processing Modes

VODER offers ten distinct voice processing modes, each designed for specific audio transformation needs:

Mode	Description	Input	Output
STT+TTS	Speech-to-Text then Text-to-Speech	Audio	Audio
TTS	Text-to-Speech with Voice Design & Cloning	Text + Optional Reference	Audio
STS	Speech-to-Speech (Voice Conversion)	Audio/Video + Reference	Audio/Video
TTM	Text-to-Music Generation & Manipulation	Text + Audio	Audio
STT	Speech-to-Text (Transcription & Translation)	Audio / Video / Image / URL	Text
SE	Speech Enhancement (Denoise/Dereverb)	Audio / Video	Audio / Video
SFX	Sound Effects Generation	Text	Audio
SVS	Song Voice Separate (Vocal/Music Isolation)	Audio / Video / URL	Audio
SLC	Speaker Language Conversion	Audio / URL	Audio
SS	Speakers Separator	Audio / Video	Audio per speaker

Note: tts+vc and ttm+vc are no longer available as standalone modes. Voice cloning in TTS is handled via the target parameter, and voice conversion in TTM is handled via the vc flag. Use tts and ttm respectively.

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System Requirements

Minimum Requirements

Component	Specification
CPU	4-6 cores
RAM	12GB+ system memory
GPU (CUDA)	Optional (CPU-only operation supported)
VRAM	4GB minimum (6GB recommended, 16GB for best performance)
Storage	SSD recommended

Note: VODER runs entirely on CPU. No GPU is required for any mode. However, having a GPU with sufficient VRAM can significantly improve processing speed for certain modes.

SVS Mode Requirements

SVS mode requires the BS-RoFormer Resurrection model, which is downloaded automatically on first use. The model adds approximately 1.5GB to disk storage in the Hugging Face model cache.

SS / VibeVoice ASR Requirements

The SS mode (Speakers Separator) and STT overdose mode use Microsoft VibeVoice ASR, which has significant memory requirements:

• VRAM: 24GB+ GPU VRAM recommended, or
• RAM: 48GB+ system memory for CPU/offload operation
• If VibeVoice ASR cannot load due to insufficient resources, SS falls back to Whisper + pyannote speaker diarization

ACE-Step Overdose / Complete Requirements

TTM mode with overdose or complete quality tiers uses larger ACE-Step models:

• VRAM: 32GB+ GPU VRAM recommended, or
• RAM: 48GB+ system memory for CPU/offload operation

Speaker Diarization Requirements

Speaker diarization (STT with diarization or multi-speaker analysis) adds additional memory requirements:

• RAM: Expect approximately 2–3GB more system memory when using speaker diarization, as the pyannote model loads alongside the transcription pipeline
• HF_TOKEN: The pyannote speaker-diarization-community-1 model requires a Hugging Face access token with accepted terms of use. Set the HF_TOKEN environment variable before running:

  export HF_TOKEN="hf_your_token_here"

  You can obtain a token from huggingface.co/settings/tokens after accepting the model's license on its Hugging Face page.

Recommended Requirements

VODER is designed to maximize output quality rather than speed. Meeting the minimum requirements ensures reliable operation — the focus is on achieving professional-grade audio results, not processing benchmarks. More RAM allows for longer audio generation and more complex workflows. For the best experience with all features (including speaker diarization, speech enhancement, VibeVoice ASR, and BS-RoFormer separation), 32GB+ RAM is recommended.

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Documentation

• READ.md — Detailed mode descriptions, CLI examples, notes, and technical deep-dives
• Guide.md — Comprehensive usage guide, technical implementation, and creative techniques
• COMMAND_CATALOG.md — Complete oneline command reference — every mode, flag, keyword, and syntax with examples and a Quick Jump table
• CHANGELOG.md — Development history and version changes
• Bots.md — Guidelines for AI agents and automated systems
• voder-skill.md — Direct Agent skill
• Languages.md — Supported languages across all components, auto‑detection capabilities, and language configuration

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Version Information

Note: VODER does not maintain PyPI packages or pre-built binaries. Running from source ensures access to the most recent features and improvements.

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Contributing

VODER is open-source (AGPL-3.0 License) and welcomes contributions:

• New voice processing modes
• Additional model integrations
• UI/UX improvements
• Performance optimizations
• Documentation and translations
• Bug reports and feature requests

Please submit pull requests or issues via GitHub.

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License

AGPL v3.0 License — See LICENSE for full details.

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Acknowledgments

Built with appreciation for the open-source AI voice synthesis community and the amazing models that power VODER.