Advanced RVC Inference CLI

A comprehensive command-line interface for the Advanced RVC Inference framework. This CLI provides access to all the powerful features of RVC including voice conversion, model training, audio separation, and more - all from the terminal.

Installation
Quick Start
Command Reference
Configuration
Examples
Troubleshooting

Installation

From Source

# Clone the repository
git clone https://github.com/ArkanDash/Advanced-RVC-Inference.git
cd Advanced-RVC-Inference

# Install the package
pip install -e .

# Or install with all dependencies
pip install -e .[all]

Using the CLI Directly

# Run directly with Python
python rvc_cli.py --help

# Or use the wrapper script (Linux/macOS)
./rvc-cli --help

# Make the wrapper executable (Linux/macOS)
chmod +x rvc-cli

Quick Start

Basic Voice Conversion

# Convert voice with default settings
rvc-cli infer -m mymodel.pth -i input.wav -o output.wav

# Convert with pitch shift (12 semitones = octave up)
rvc-cli infer -m mymodel.pth -i input.wav -p 12

# Convert with custom F0 method
rvc-cli infer -m mymodel.pth -i input.wav --f0_method harvest

Audio Separation

# Separate vocals from music
rvc-cli uvr -i music.wav

# Separate with specific model
rvc-cli uvr -i music.wav --model MDXNET_Main --output ./separated

Launch Web Interface

# Start web interface on default port
rvc-cli serve

# Start on custom port with public share
rvc-cli serve --port 8080 --share

Command Reference

infer - Voice Conversion

Convert voice in an audio file using an RVC model.

rvc-cli infer -m <model> -i <input> [options]

Required Arguments:

-m, --model: Path to RVC model file (.pth or .onnx)
-i, --input: Path to input audio file

Optional Arguments:

-o, --output: Output file path (auto-generated if not specified)
-p, --pitch: Pitch shift in semitones (default: 0)
-f, --format: Output format - wav, mp3, flac, ogg (default: wav)
--index: Path to .index file for better quality
--f0_method: F0 extraction method (default: rmvpe)
--filter_radius: Filter radius for F0 smoothing (default: 3)
--index_rate: Index strength 0.0-1.0 (default: 0.5)
--rms_mix_rate: RMS mix rate (default: 1.0)
--protect: Protect consonants 0.0-1.0 (default: 0.33)
--hop_length: Hop length for processing (default: 64)
--embedder_model: Embedding model (default: hubert_base)
--resample_sr: Resample rate (0 = original, default: 0)
--split_audio: Split audio before processing
--checkpointing: Enable memory checkpointing
--f0_autotune: Enable F0 autotune
--f0_autotune_strength: Autotune strength (default: 1.0)
--formant_shifting: Enable formant shifting
--formant_qfrency: Formant frequency coefficient (default: 0.8)
--formant_timbre: Formant timbre coefficient (default: 0.8)
--clean_audio: Apply audio cleaning
--clean_strength: Cleaning strength (default: 0.7)

Example:

rvc-cli infer -m artist_model.pth -i speech.wav -o converted.wav -p 2 \
    --index_rate 0.75 --f0_method rmvpe --clean_audio

uvr - Audio Separation

Separate vocals from instrumentals using UVR5.

rvc-cli uvr -i <input> [options]

Required Arguments:

-i, --input: Path to input audio file

Optional Arguments:

-o, --output: Output directory (default: ./audios/uvr)
-f, --format: Output format (default: wav)
--model: Separation model (default: MDXNET_Main)
--karaoke_model: Karaoke model (default: MDX-Version-1)
--reverb_model: Reverb model (default: MDX-Reverb)
--denoise_model: Denoise model (default: Normal)
--sample_rate: Output sample rate (default: 44100)
--shifts: Number of predictions (default: 2)
--batch_size: Batch size (default: 1)
--overlap: Overlap between segments (default: 0.25)
--aggression: Extraction intensity (default: 5)
--hop_length: Hop length (default: 1024)
--window_size: Window size (default: 512)
--enable_tta: Enable test-time augmentation
--enable_denoise: Enable denoising
--separate_backing: Separate backing vocals
--separate_reverb: Separate reverb

Available Models:

MDXNET_Main, MDXNET_9482
HP-Vocal-1, HP-Vocal-2
Inst_HQ_1 through Inst_HQ_5
Kim_Vocal_1, Kim_Vocal_2

Example:

rvc-cli uvr -i song.wav --model HP-Vocal-2 --aggression 10 \
    --enable_denoise --output ./vocals

create-dataset - Create Training Data

Create training dataset from YouTube videos or local audio files.

rvc-cli create-dataset -u <url> [options]
# or
rvc-cli create-dataset -i <directory> [options]

Required Arguments (one of):

-u, --url: YouTube URL (separate multiple with commas)
-i, --input: Input directory with audio files

Optional Arguments:

-o, --output: Output directory (default: ./dataset)
--sample_rate: Sample rate (default: 48000)
--clean_dataset: Apply data cleaning
--clean_strength: Cleaning strength (default: 0.7)
--separate: Separate vocals (default: True)
--separator_model: Separation model (default: MDXNET_Main)
--skip_start: Seconds to skip at start (default: 0)
--skip_end: Seconds to skip at end (default: 0)

Example:

rvc-cli create-dataset -u "https://youtube.com/watch?v=xxx" \
    --sample_rate 48000 --separate --output ./my_dataset

create-index - Create Model Index

Create .index file for voice retrieval.

rvc-cli create-index <model_name> [options]

Arguments:

model_name: Name of the model

Optional Arguments:

--version: RVC version - v1 or v2 (default: v2)
--algorithm: Index algorithm - Auto, Faiss, or KMeans (default: Auto)

Example:

rvc-cli create-index mymodel --version v2 --algorithm Faiss

extract - Feature Extraction

Extract embeddings and F0 from training data.

rvc-cli extract <model_name> --sample_rate <rate> [options]

Required Arguments:

model_name: Name of the model
--sample_rate: Sample rate of input audio

Optional Arguments:

--version: RVC version - v1 or v2 (default: v2)
--f0_method: F0 extraction method (default: rmvpe)
--f0_onnx: Use ONNX F0 predictor
--pitch_guidance: Use pitch guidance (default: True)
--hop_length: Hop length (default: 128)
--cpu_cores: CPU cores (default: 2)
--gpu: GPU index (default: - for CPU)
--embedder_model: Embedder model (default: hubert_base)
--rms_extract: Extract RMS energy

Example:

rvc-cli extract mymodel --sample_rate 48000 --f0_method rmvpe \
    --gpu 0 --pitch_guidance

preprocess - Data Preprocessing

Slice and normalize training audio.

rvc-cli preprocess <model_name> --sample_rate <rate> [options]

Required Arguments:

model_name: Name of the model
--sample_rate: Sample rate

Optional Arguments:

--dataset_path: Dataset path (default: ./dataset)
--cpu_cores: CPU cores (default: 2)
--cut_method: Cutting method - Automatic, Simple, or Skip (default: Automatic)
--process_effects: Apply preprocessing effects
--clean_dataset: Clean dataset
--chunk_len: Chunk length for Simple method (default: 3.0)
--overlap_len: Overlap length (default: 0.3)
--normalization: Normalization mode - none, pre, or post (default: none)

Example:

rvc-cli preprocess mymodel --sample_rate 48000 --cut_method Automatic \
    --process_effects --normalization pre

train - Model Training

Train a new RVC voice model.

rvc-cli train <model_name> [options]

Required Arguments:

model_name: Name of the model

Optional Arguments:

--version: RVC version - v1 or v2 (default: v2)
--author: Model author name
--epochs: Total training epochs (default: 300)
--batch_size: Batch size (default: 8)
--save_every: Save checkpoint every N epochs (default: 50)
--save_latest: Save only latest checkpoint (default: True)
--save_weights: Save all model weights (default: True)
--gpu: GPU index (default: 0)
--cache_gpu: Cache data in GPU
--pitch_guidance: Use pitch guidance (default: True)
--pretrained_g: Path to pre-trained G weights
--pretrained_d: Path to pre-trained D weights
--vocoder: Vocoder - Default, MRF-HiFi-GAN, or RefineGAN (default: Default)
--energy: Use RMS energy
--overtrain_detect: Enable overtraining detection
--optimizer: Optimizer - AdamW, RAdam, or AnyPrecisionAdamW (default: AdamW)
--multiscale_loss: Use multi-scale mel loss
--use_reference: Use custom reference set
--reference_path: Path to reference set

Example:

rvc-cli train mymodel --version v2 --epochs 500 --batch_size 8 \
    --gpu 0 --save_every 100 --vocoder "MRF-HiFi-GAN"

create-ref - Create Reference Set

Create reference audio for better inference quality.

rvc-cli create-ref <audio_file> [options]

Required Arguments:

audio_file: Path to audio file

Optional Arguments:

-n, --name: Reference name (default: reference)
--version: RVC version - v1 or v2 (default: v2)
--pitch_guidance: Use pitch guidance (default: True)
--energy: Use RMS energy
--embedder_model: Embedder model (default: hubert_base)
--f0_method: F0 extraction method (default: rmvpe)
--pitch_shift: Pitch shift (default: 0)
--filter_radius: Filter radius (default: 3)
--f0_autotune: Enable F0 autotune
--alpha: Alpha blending (default: 0.5)

Example:

rvc-cli create-ref reference_audio.wav -n myref --f0_method rmvpe

download - Download Models/Audio

Download models from HuggingFace or audio from YouTube.

rvc-cli download -l <link> [options]

Required Arguments:

-l, --link: Download link (HuggingFace or YouTube URL)

Optional Arguments:

-t, --type: Download type - model, audio, or index (default: model)
-n, --name: Name to save as

Example:

rvc-cli download -l "https://huggingface.co/user/model/resolve/main/model.pth"

serve - Web Interface

Launch the Gradio web UI.

rvc-cli serve [options]

Optional Arguments:

--host: Host to bind (default: 0.0.0.0)
--port: Port to bind (default: 7860)
--share: Create public share URL

Example:

rvc-cli serve --port 7860 --share

info - System Information

Show system and environment information.

rvc-cli info

Displays:

Operating system and version
CPU information
Memory and disk space
GPU information (if available)
Python and package versions

version - Version Info

Show version and dependency information.

rvc-cli version

list-models - List Available Models

List installed models in the weights folder.

rvc-cli list-models

list-f0-methods - List F0 Methods

Show all available pitch extraction methods.

rvc-cli list-f0-methods

Configuration

Environment Variables

Variable	Description	Default
`ARVC_ASSETS_PATH`	Path to assets directory	`assets`
`ARVC_CONFIGS_PATH`	Path to configs directory	`configs`
`ARVC_WEIGHTS_PATH`	Path to weights directory	`assets/weights`
`ARVC_LOGS_PATH`	Path to logs directory	`assets/logs`

Model and Index Files

Place your model files (.pth or .onnx) in:

advanced_rvc_inference/assets/weights/

Place index files (.index) in:

advanced_rvc_inference/assets/logs/<model_name>/

Examples

Complete Training Workflow

# 1. Create dataset from YouTube
rvc-cli create-dataset -u "https://youtube.com/watch?v=xxx" \
    --output ./dataset --sample_rate 48000 --separate

# 2. Create index for the model (after training)
rvc-cli create-index mymodel --version v2

# 3. Extract features
rvc-cli extract mymodel --sample_rate 48000 --f0_method rmvpe --gpu 0

# 4. Preprocess data
rvc-cli preprocess mymodel --sample_rate 48000 --cut_method Automatic

# 5. Train model
rvc-cli train mymodel --version v2 --epochs 300 --batch_size 8 --gpu 0

Voice Conversion with Different F0 Methods

# Using RMVPE (recommended)
rvc-cli infer -m model.pth -i input.wav -o output_rmvpe.wav --f0_method rmvpe

# Using Harvest (faster)
rvc-cli infer -m model.pth -i input.wav -o output_harvest.wav --f0_method harvest

# Using Crepe (most accurate but slow)
rvc-cli infer -m model.pth -i input.wav -o output_crepe.wav --f0_method crepe-medium

Batch Processing

# Process all audio files in a directory
for file in ./inputs/*.wav; do
    rvc-cli infer -m model.pth -i "$file" -o "./outputs/$(basename $file)"
done

Multiple Voice Models

# Convert with different models
rvc-cli infer -m model_a.pth -i voice.wav -o voice_model_a.wav -p 0
rvc-cli infer -m model_b.pth -i voice.wav -o voice_model_b.wav -p 2
rvc-cli infer -m model_c.pth -i voice.wav -o voice_model_c.wav -p -2

Troubleshooting

Common Issues

"Model file not found"

Ensure the model path is correct
Check that the model file has .pth or .onnx extension
Verify file permissions

"CUDA out of memory"

Reduce batch size: --batch_size 4
Enable checkpointing: --checkpointing
Use CPU: --gpu -

"Audio format not supported"

Convert audio to WAV first: ffmpeg -i input.mp3 output.wav
Supported formats: wav, mp3, flac, ogg, opus, m4a, aac

"F0 method not available"

Install ONNX runtime for some methods
Some methods require specific embedders

Getting Help

# General help
rvc-cli --help

# Command-specific help
rvc-cli infer --help
rvc-cli uvr --help
rvc-cli train --help

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please read the contributing guidelines in CONTRIBUTING.md.

Support

GitHub Issues: https://github.com/ArkanDash/Advanced-RVC-Inference/issues
Discord: https://discord.gg/hvmsukmBHE

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Advanced RVC Inference CLI

Table of Contents

Installation

From Source

Using the CLI Directly

Quick Start

Basic Voice Conversion

Audio Separation

Launch Web Interface

Command Reference

infer - Voice Conversion

uvr - Audio Separation

create-dataset - Create Training Data

create-index - Create Model Index

extract - Feature Extraction

preprocess - Data Preprocessing

train - Model Training

create-ref - Create Reference Set

download - Download Models/Audio

serve - Web Interface

info - System Information

version - Version Info

list-models - List Available Models

list-f0-methods - List F0 Methods

Configuration

Environment Variables

Model and Index Files

Examples

Complete Training Workflow

Voice Conversion with Different F0 Methods

Batch Processing

Multiple Voice Models

Troubleshooting

Common Issues

"Model file not found"

"CUDA out of memory"

"Audio format not supported"

"F0 method not available"

Getting Help

License

Contributing

Support