VOICECLONE-QC

VOICECLONE-QC is a Windows-first local voice conversion workflow built around RVC (Retrieval-based Voice Conversion). It prepares voice datasets locally, uses Google Drive as a bridge to an external Google Colab training notebook, and runs local RMVPE voice conversion from a browser-based LAN interface.

The project prioritizes reproducible training, audio quality, and keeping private assets on the local machine.

What It Does

• Cleans voice datasets with Demucs and creates training-ready audio segments.
• Provides a Split Only mode for source material that is already clean.
• Uploads a Colab-ready dataset/ ZIP to Google Drive.
• Watches RVC_Output on Google Drive and imports matching .pth and .index model pairs into the local model bank.
• Runs local RVC conversion with RMVPE pitch extraction.
• Produces 24-bit / 48 kHz WAV outputs.
• Includes a local audio toolbox for isolation, denoising, and restoration.
• Supports resumable Colab training with checkpoint backups stored on Google Drive.
• Provides visual progress feedback and completion alerts for long-running local tasks.

Local Audio Toolbox

The Non-Colab Features page contains independent local processors. These tools do not require a Google Colab training session and do not modify the RVC model bank.

Dataset Preparation

• Demucs dataset cleaning: isolates vocals before automatic slicing, normalization, and dataset export.
• Split Only: skips Demucs while retaining the automatic slicing and export workflow for sources that are already clean.

Standalone Processing

• Demucs vocal isolation: produces a voice-focused file without slicing. Its quality control trades processing time for additional separation passes.
• DeepFilterNet: applies adjustable-strength speech denoising for a faster, alternative cleanup pass.
• UVR: offers an alternative vocal-isolation pipeline and selectable source separation models, with a separate quality/speed control.
• Resemble Enhance: performs high-fidelity generative audio restoration. Solver selection and reconstruction quality allow slower, more careful restoration when required.
• VoiceFixer: restores degraded vocal recordings through selectable restoration modes.

Each processor writes a separately named WAV output, keeps the original input unchanged, and shows an active processing indicator while the task runs.

Local Workflow Controls

• A single-task queue prevents multiple heavy audio operations from competing for the same machine resources.
• The RVC conversion controls expose pitch transposition, index influence, and consonant/breath protection, while RMVPE remains the selected F0 method.
• The local model bank can be refreshed from the interface after Drive imports.
• The Colab notebook URL can be configured from the interface with either a Google Drive notebook file link or a direct Colab link.
• Transient folders can be cleared without deleting trained models, credentials, runtimes, or application settings.

Architecture

Local audio files
  -> VOICECLONE-QC preprocessing
  -> Google Drive dataset ZIP
  -> Google Colab RVC training
  -> Google Drive RVC_Output
  -> Local models_bank
  -> Local RVC conversion

Requirements

• Windows 10 or 11
• Python 3.11
• FFmpeg available on PATH
• Google Drive access and an OAuth client configured for the application
• An NVIDIA GPU is optional for local conversion; the application also supports CPU execution
• A Google Colab runtime for RVC model training

Setup

1. Clone the repository into the intended local project directory.
2. Create the Python environment using the setup script in scripts/.
3. Copy config/settings.example.json to config/settings.json and configure local paths and Google Drive OAuth settings.
4. Keep OAuth credentials, tokens, trained models, audio files, and runtime downloads outside Git. The provided .gitignore is designed for this.
5. Start the application with scripts/run_app.bat.
6. Open the local web interface at http://localhost:7860/.

Colab Workflow

The included VOICECLONE_QC_RVC_Bridge.ipynb is a reproducible training bridge. Run the Google Drive mount cell immediately after its configuration cell to authorize Drive before the longer dependency and model-download steps begin.

For a new model, run the notebook from top to bottom. For a stopped training session, set RUN_MODE to resume; the notebook restores the experiment and checkpoint backup from Google Drive before continuing toward the configured total epoch count.

Security and Privacy

Do not commit the contents of config/settings.json, OAuth client files, OAuth tokens, service-account credentials, trained models, audio datasets, or generated outputs. These are intentionally ignored by Git.

The Gradio interface can be exposed on a LAN. Only grant access to trusted users and networks. Use voice data and trained models only with the necessary permission from the voice owner.

Project Notes

This is a practical local workflow rather than a hosted service. Training is performed externally in Google Colab; VOICECLONE-QC does not automate or control the Colab user interface.

The code and user interface are currently in French.

Conçu par Sébastien Bédard