• Voice Inference — Single & batch conversion, TTS, pitch shifting, formant shifting, audio cleaning, Whisper transcription
• Real-Time Conversion — Live mic voice conversion with VAD and low-latency processing
• 30+ F0 Methods — rmvpe, crepe, fcpe, harvest, hybrid, and many more
• F0 Autotune — Automatic pitch correction with configurable strength
• Audio Cleaning — Built-in denoising for cleaner output

• Audio Separation — Vocal/instrumental isolation (MDX-Net, Roformer, BS-Roformer), karaoke, reverb removal, denoising
• Auto Pretrained Download — Automatically downloads pretrained models from HuggingFace

• End-to-End Training — Dataset creation → preprocessing → feature extraction → training → model export
• 4 Vocoders — HiFi-GAN NSF (Default), BigVGAN, MRF-HiFi-GAN, RefineGAN
• 5 Optimizers — AdamW, RAdam, AnyPrecisionAdamW, AdaBelief, AdaBeliefV2
• Training Quality Improvements — Multi-scale mel spectrogram loss (8 scales), scaled v3 discriminator loss, proper feature loss gradient flow, cuDNN benchmark
• Advanced Options — Gradient accumulation, torch.compile(), 8-bit Adam, cosine annealing LR, overtraining detection
• Architecture Support — RVC and SVC (from Vietnamese-RVC)
• Embedder Mix — Layer-wise embedding mixing with configurable ratios (from Vietnamese-RVC)

• CLI — Full command-line interface via rvc-cli
• ZLUDA Support — Full AMD GPU support via ZLUDA
• XPU Support — Intel GPU support via XPU backend
• Push to Hub — Upload trained models directly to HuggingFace Hub
• 44 Languages — Full UI translation support

Advanced RVC Inference supports the same vocoders as Vietnamese-RVC:

When training without pitch guidance (pitch_guidance=False), a plain HiFi-GAN generator (no NSF) is used automatically regardless of the selected vocoder.

Advanced RVC Inference provides 5 carefully selected optimizers for model training, covering the most effective choices for RVC/audio model training:

See the Optimizer Reference Guide for detailed descriptions, hyperparameters, and recommendations.

git clone https://github.com/ArkanDash/Advanced-RVC-Inference.git
cd Advanced-RVC-Inference
pip install -r requirements.txt

Or install from PyPI:

pip install git+https://github.com/ArkanDash/Advanced-RVC-Inference.git

pip install git+https://github.com/ArkanDash/Advanced-RVC-Inference.git
pip install onnxruntime-gpu

# Follow the ZLUDA installation guide for your AMD GPU
# Then install Advanced RVC normally — ZLUDA is auto-detected
pip install git+https://github.com/ArkanDash/Advanced-RVC-Inference.git

# Launch the web UI
rvc-gui

# Or via Python module
python -m arvc.app.gui

# With a public share link
python -m arvc.app.gui --share

The interface will be available at http://localhost:7860.

# Voice conversion
rvc-cli infer -m model.pth -i input.wav -o output.wav

# Audio separation
rvc-cli uvr -i song.mp3

# Show all commands
rvc-cli --help

Notebook	Description
	Full Web UI
	CLI only — lightweight headless mode

arvc/
├── app/                    # Gradio web UI (tabs, pages, layouts)
│   ├── tabs/               #   inference, training, downloads, realtime, extra
├── engine/                 # Core logic (no UI dependency)
│   ├── inference/          #   Voice conversion pipeline, TTS
│   ├── training/           #   preprocess, extract, train, export
│   │   ├── preprocess/     #   Audio slicing & normalization
│   │   ├── extract/        #   Embedding & F0 extraction
│   │   └── runner/         #   Training loop, losses, data loading
│   ├── uvr/                #   Audio separation (UVR5)
│   ├── realtime/           #   Live mic conversion
│   └── models/             #   Model loading, generators, optimizers, embedders
│       ├── generators/     #   HiFi-GAN NSF, BigVGAN, MRF-HiFi-GAN, RefineGAN
│       ├── optimizers/     #   AdamW, RAdam, AnyPrecisionAdamW, AdaBelief, AdaBeliefV2
│       ├── embedders/      #   Hubert, ContentVec embedders
│       ├── predictors/     #   F0 predictors (RMVPE, Crepe, FCPE, etc.)
│       └── backends/       #   CUDA, DirectML, OpenCL, XPU, ZLUDA
├── services/               # Business logic layer (bridges UI <-> engine)
├── ui/                     # UI helpers (feedback, dropdown updates, formatting)
├── utils/                  # Shared utilities (variables, download helpers)
├── configs/                # Configuration files (training configs, model templates)
│   ├── v1/                 #   V1 model configs (32k, 40k, 48k)
│   ├── v2/                 #   V2 model configs (24k, 32k, 40k, 48k)
│   ├── ringformer_v2/      #   RingFormer V2 configs
│   └── pcph_gan/           #   PCPH-GAN configs
├── datasets/               # Training datasets (organized per model)
├── assets/                 # Runtime assets
│   ├── models/             #   Pretrained models, embedders, predictors, UVR5
│   │   ├── pretrained_v1/  #     V1 pretrained G/D weights
│   │   ├── pretrained_v2/  #     V2 pretrained G/D weights
│   │   ├── pretrained_custom/ #  Custom pretrained weights
│   │   ├── embedders/      #     Hubert/ContentVec models
│   │   ├── predictors/     #     F0 predictor models
│   │   └── uvr5/           #     UVR5 separation models
│   ├── logs/               #   Training logs, checkpoints, extracted features, indexes
│   ├── audios/             #   Audio files (input, output, TTS, UVR results)
│   ├── f0/                 #   F0 cache files
│   ├── binary/             #   Binary resources
│   ├── languages/          #   44 translation JSON files
│   └── presets/            #   Inference presets
└── _version.py             # Version management

Key rule: engine/ should never import from app/ or services/. Keep the core independent.

The use of the converted voice for the following purposes is strictly prohibited:

• Criticizing or attacking individuals
• Advocating for or opposing specific political positions, religions, or ideologies
• Publicly displaying strongly stimulating expressions without proper zoning
• Selling of voice models and generated voice clips
• Impersonation of the original owner of the voice with malicious intentions
• Fraudulent purposes that lead to identity theft or fraudulent phone calls

• VRVC Training Integration — Cloned Vietnamese-RVC training pipeline including architecture selector (RVC/SVC), embedder mix, include mutes, nprobe, alpha, and F0 autotune with configurable strength
• Training Quality Fixes — Multi-scale mel spectrogram loss (8 scales with dynamic windows from PolTrain), proper feature loss gradient flow (removed .detach() from Applio), scaled v3 discriminator loss for BigVGAN/RefineGAN, cuDNN benchmark enabled by default
• Optimizer Cleanup — Reduced from 43 optimizers to 5 proven choices (AdamW, RAdam, AnyPrecisionAdamW, AdaBelief, AdaBeliefV2)
• Directory Structure — Cleaned up assets: datasets moved to arvc/datasets/, weights merged into arvc/assets/logs/
• EasyGUI Removed — Deleted easy_gui.py and all references; Web UI is the only interface
• Bug Fixes — Fixed robotic chirping (#69), get_gpu_info() unpack error, faiss AVX512/AVX2 import crash, missing --predictor_onnx argument, synced all training params across UI → service → subprocess
• Colab Updates — Removed EasyGUI toggle and CLI Usage section from main notebook

This project builds upon the work of many open-source projects and contributors. We gratefully acknowledge the following:

This project is licensed under the MIT License — see the LICENSE file for details.