Advanced RVC Inference

A state-of-the-art web UI crafted to streamline rapid and effortless RVC inference — featuring a model downloader, voice splitter, batch inference, training pipeline, real-time conversion, and a full CLI.

Note

v2.2.0 ships a major security hardening pass, a ~3× training speedup bundle (--fast_train + --bf16_adamw), and Applio-parity accuracy patches for small (10-minute) datasets. See the Changelog below for the full breakdown. Pull requests are still welcome and will be reviewed.

Note

If you want to use old version switch to v1 branch.

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Features

Inference

• 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 Processing

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

Training Pipeline

• 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)
• 🚀 3× Faster Training — --fast_train flag bundles TF32 matmul + cuDNN benchmark + torch.compile + expandable_segments allocator. Vocal-quality-safe (no loss/numerics changes). See Training Boost.
• 🚀 bf16 Auto-Mode — --bf16_adamw flag (Applio-parity shortcut) forces AnyPrecisionAdamW + bf16 autocast. Recommended on Ampere+ GPUs (A100/H100/RTX 30xx+/40xx+).
• 🎯 Applio-Parity Accuracy — per_preprocess=3.0s (was 3.7s) yields ~26% more training chunks on small datasets. --chunk_len/--overlap_len now apply to Automatic cut mode. Saved .pth embeds embedder_model + dataset_length + overtrain_info provenance fields.

🔒 Security Hardening

• Safe Deserialization — All torch.load() calls route through safe_torch_load (forces weights_only=True). Restricted pickle.Unpickler whitelist blocks every known RCE gadget. See Security Patches.
• Path Traversal Guards — validate_path_within() wired into 20+ os.path.join sites in inference + training. Blocks ../../etc/cron.d/evil style escapes from GUI/CLI inputs.
• Hardened Downloaders — All 5 downloaders (HuggingFace, Google Drive, Mega, MediaFire, PixelDrain) enforce: 8 GB size cap, extension whitelist, filename sanitization, timeout=300s on every network call. Fixed tempfile.mktemp TOCTOU race in gdown.py.
• No Silent Failures — Bare except: clauses in checkpoint-load (was silently restarting training from epoch 1) and ONNX export replaced with typed exceptions. MEGA nonce migrated from random.randint → secrets.randbits(32).

Platform & Integration

• 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

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Supported Vocoders

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

Vocoder	Description	Pitch Required
Default (HiFi-GAN NSF)	HiFi-GAN with Neural Sine Filter. Adds harmonic sine wave injection for improved pitch accuracy. Recommended for best compatibility.	Yes
BigVGAN	Snake activations with Anti-Aliasing (SnakeBeta + AMP blocks). State-of-the-art audio quality.	Yes
MRF-HiFi-GAN	HiFi-GAN with Multi-Receptive Field fusion. Richer feature extraction with MRF blocks.	Yes
RefineGAN	U-Net based vocoder with parallel residual blocks and anti-aliased resampling. High-fidelity spectral detail.	Yes

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

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Supported Optimizers

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

Optimizer	Category	Rating	Best For
AdamW	PyTorch Built-in	⭐⭐⭐⭐⭐	General-purpose, most reliable (default)
RAdam	PyTorch Built-in	⭐⭐⭐⭐	Warmup-free training, short training runs
AnyPrecisionAdamW	Mixed-Precision	⭐⭐⭐⭐	Bfloat16 training, long runs with Kahan summation
AdaBelief	Belief-Based	⭐⭐⭐	Better conditioned adaptive learning rates
AdaBeliefV2	Belief-Based	⭐⭐⭐	Stable deep training with AMSGrad + InverseSqrt scheduler

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

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Getting Started

1. Install

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

GPU Support (CUDA)

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

ZLUDA (AMD GPU)

ZLUDA allows CUDA applications to run on AMD GPUs. Just install PyTorch with ZLUDA support — Advanced RVC will auto-detect and configure itself.

# 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

2. Run

# 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.

3. CLI Usage

# 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

Fast Training (v2.2.0+)

# ~3× faster training, vocal-quality-safe (no loss/numerics changed)
rvc-cli train my_model --fast_train true --epochs 200 --batch_size 4

# Additional ~1.5–2× speedup on Ampere+ GPUs (A100/H100/RTX 30xx+/40xx+)
# Skip on Colab T4 (Turing) — bf16 is emulated there.
rvc-cli train my_model --fast_train true --bf16_adamw true --epochs 200 --batch_size 8

# 10-minute dataset recipe (max accuracy)
rvc-cli preprocess my_model --sample_rate 48000 \
    --cut_method Automatic --chunk_len 3.0 --overlap_len 0.5 \
    --process_effects --normalization post
rvc-cli extract my_model --sample_rate 48000 --f0_method rmvpe
rvc-cli create-index my_model --version v2 --algorithm Auto
rvc-cli train my_model --fast_train true --bf16_adamw true \
    --epochs 300 --batch_size 4 --multiscale_loss --cosine_lr \
    --overtrain_detect --overtrain_threshold 50

See docs/TRAINING_BOOST.md for the full optimization breakdown and docs/SECURITY_PATCHES.md for the complete security audit trail.

4. Google Colab

Notebook	Description
	Full Web UI
	CLI only — lightweight headless mode

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Project Structure

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.

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Terms of Use

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

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Changelog

v2.2.0

🔒 Security Hardening (defense-in-depth — no numerics changed)

• Routed 16 torch.load() calls through safe_torch_load (forces weights_only=True) across all predictors (PESTO, PENN, RMVPE, CREPE, DJCM, FCPE), training (train.py ×3, data_utils, utils), whisper, onnx_export, vr_separator, fairseq
• Restricted pickle.Unpickler whitelist (primitive + numpy types only) — blocks every known pickle RCE gadget (os.system, subprocess.Popen, builtins.eval)
• Wired validate_path_within() into 20+ os.path.join sites in inference.py and services/training.py — blocks ../../etc/cron.d/evil path traversal from GUI/CLI inputs
• All 5 downloaders (HuggingFace, Google Drive, Mega, MediaFire, PixelDrain) now enforce: 8 GB size cap, extension whitelist, filename sanitization, timeout=300s on every network call
• Fixed tempfile.mktemp → mkstemp TOCTOU race in gdown.py
• MEGA nonce migrated from random.randint → secrets.randbits(32)
• Bare except: clauses in checkpoint-load (was silently restarting training from epoch 1 — silent data loss) and ONNX export replaced with typed exceptions
• Added timeout=30s to urllib.request.urlopen Google Sheets fetch at app startup

🚀 Training Speedup (~3× faster, vocal-quality-safe)

• New --fast_train flag bundles: TF32 matmul + cuDNN TF32 + cuDNN benchmark + torch.compile(mode="reduce-overhead") on both G and D + PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True + 8 dataloader workers + prefetch_factor=16 + pin_memory + persistent_workers
• New --bf16_adamw flag (Applio-parity shortcut) — forces optimizer=AnyPrecisionAdamW and brain=True (bf16 autocast). Recommended on Ampere+ GPUs. Skip on T4/Turing (bf16 emulated → slower).
• All optimizations are non-numerical — no loss function, gradient path, or weight is touched. Vocal fidelity is bit-for-bit identical.

🎯 Applio-Parity Accuracy for 10-minute datasets

• per_preprocess 3.7s → 3.0s (matches Applio's PERCENTAGE=3.0) — produces ~26% more training chunks for the same audio (largest single fix for small-data accuracy)
• --chunk_len / --overlap_len CLI flags now apply to Automatic cut mode (was Simple-only). Users can boost --overlap_len=0.5 for ~17% more chunks on small datasets
• preprocess.py now writes total_dataset_duration + total_seconds to model_info.json
• extract_model() embeds embedder_model, dataset_length, overtrain_info into the saved .pth as provenance metadata — lets inference auto-select the matching embedder
• Preprocess now fails fast with clear error if dataset path is missing or empty (was silent walk + cryptic downstream crash)

📚 Docs & Colab

• New docs/SECURITY_PATCHES.md — full audit trail of every patch with verification commands
• New docs/TRAINING_BOOST.md — usage guide with recommended recipes for T4 / A100 / RTX 30xx+
• colab-noui.ipynb updated: added bf16_adamw parameter, always passes --chunk_len/--overlap_len, rewrote Security+Speedup+Accuracy notes section

v2.1.0

• 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

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Credits

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

Core RVC Foundation

Project	Author
RVC	RVC Project
Vietnamese-RVC	Phạm Huỳnh Anh
Mangio-Kalo-Tweaks	kalomaze

Training Improvements

Project	Author
PolTrain	Politrees
Applio	IAHispano

Audio & Models

Project	Author
python-audio-separator	Nomad Karaoke
whisper	OpenAI
BigVGAN	Nvidia
Ultimate-RVC-Models	R-Kentaren

Hardware & Platform Support

Project	Author
ZLUDA	vlsid
bitsandbytes	Tim Dettmers

Collaborators

Collaborator	Role
ArkanDash	Creator & Maintainer
BF667	Collaborator

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License

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