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programer01 edited this page Jan 20, 2026 · 1 revision

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.

Table of Contents

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

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