Ultimate RVC Maker 🎵

A high-quality voice conversion tool focused on ease of use and performance.

Description

This project is all in one, easy-to-use voice conversion tool. With the goal of creating high-quality and high-performance voice conversion products, the project allows users to change voices smoothly and naturally.

Project Features

Feature	Description
Music Separation	Utilizes MDX-Net for separating audio tracks.
Voice Conversion	Supports file conversion, batch conversion, conversion with Whisper, and text-to-speech conversion.
Background Music Editing	Enables editing and manipulation of background music tracks.
Apply Effects to Audio	Allows application of various effects to enhance or modify audio output.
Generate Training Data	Creates training data from linked paths for model training.
Model Training	Supports v1 and v2 models with high-quality encoders for training.
Model Fusion	Facilitates combining multiple models for enhanced performance.
Read Model Information	Provides functionality to access and display model metadata.
Export Models to ONNX	Enables exporting trained models to ONNX format for compatibility.
Download from Pre-existing Model Repositories	Allows downloading models from established repositories.
Search for Models on the Web	Supports searching for models online for easy access.
Pitch Extraction	Extracts pitch information from audio inputs.
Support for Audio Conversion Inference Using ONNX Models	Enables inference for audio conversion using ONNX-compatible models.
ONNX RVC Models with Indexing	Supports ONNX RVC models with indexing for efficient inference.
Multiple Model Options
	F0: pm, dio, mangio-crepe-tiny, mangio-crepe-small, mangio-crepe-medium, mangio-crepe-large, mangio-crepe-full, crepe-tiny, crepe-small, crepe-medium, crepe-large, crepe-full, fcpe, fcpe-legacy, rmvpe, rmvpe-legacy, harvest, yin, pyin, swipe
	F0_ONNX: Some models converted to ONNX for accelerated pitch extraction.
	F0_HYBRID: Combines multiple options, e.g., hybrid[rmvpe+harvest], or all options together.
	EMBEDDERS: contentvec_base, hubert_base, japanese_hubert_base, korean_hubert_base, chinese_hubert_base, portuguese_hubert_base
	EMBEDDERS_ONNX: Pre-converted ONNX versions of embedding models for accelerated extraction.
	EMBEDDERS_TRANSFORMERS: Pre-converted Hugging Face versions of embedding models as an alternative to Fairseq.
	SPIN_EMBEDDERS: A new embedding extraction model offering potentially higher quality than older methods.

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Installation and Usage

• Step 1: Install Python from the official website or Python (REQUIRES PYTHON 3.10.x OR PYTHON 3.11.x)
• Step 2: Install FFmpeg from FFMPEG, extract it, and add it to PATH
• Step 3: Download and extract the source code
• Step 4: Navigate to the source code directory and open Command Prompt or Terminal
• Step 5: Run the command to install the required libraries

Install:

you can install URVC with runing `run_install.bat`

or simpily run pip install -r requirements.txt

Step 6:

Run the `run_app` file to open the user interface (Note: Do not close the Command Prompt or Terminal for the interface)

Alternatively, use Command Prompt or Terminal in the source code directory To allow the interface to access files outside the project, add --allow_all_disk to the command:

env\Scripts\python.exe main\app\app.py --open

To use TensorBoard for training monitoring:

Run the file: tensorboard or the command

env\Scripts\python.exe main\app\tensorboard.py

Command-Line Usage

python main\app\parser.py --help

NOTES

• This project only supports NVIDIA GPUs
• Currently, new encoders like MRF HIFIGAN do not yet have complete pre-trained datasets
• MRF HIFIGAN and REFINEGAN encoders do not support training without pitch training
• Models in the URVC repository are collected from AI Hub, HuggingFace, and other repositories. They may carry different licenses (For example, Audioldm2 has model weights with a "Non-Commercial" clause).
• This source code contains third party software components licensed under "non-commercial" terms. Any commercial use, including solicitation of donations or financialization of derivatives, may violate the license and will be subject to appropriate legal liability.

Terms of Use

• You must ensure that the audio content you upload and convert through this project does not violate the intellectual property rights of third parties.

• The project must not be used for any illegal activities, including but not limited to fraud, harassment, or causing harm to others.

• You are solely responsible for any damages arising from improper use of the product.

• I will not be responsible for any direct or indirect damages arising from the use of this project.

This Project is Built Based on the Following Projects

Project	Author/Organization	License
Vietnamese-RVC	Phạm Huỳnh Anh	MIT License
Applio	IAHispano	MIT License
Python-audio-separator	Nomad Karaoke	MIT License
Retrieval-based-Voice-Conversion-WebUI	RVC Project	MIT License
RVC-ONNX-INFER-BY-Anh	Phạm Huỳnh Anh	MIT License
Torch-Onnx-Crepe-By-Anh	Phạm Huỳnh Anh	MIT License
Hubert-No-Fairseq	Phạm Huỳnh Anh	MIT License
Local-attention	Phil Wang	MIT License
TorchFcpe	CN_ChiTu	MIT License
FcpeONNX	Yury	MIT License
ContentVec	Kaizhi Qian	MIT License
Mediafiredl	Santiago Ariel Mansilla	MIT License
Noisereduce	Tim Sainburg	MIT License
World.py-By-Anh	Phạm Huỳnh Anh	MIT License
Mega.py	Marco Trevisan	No License
Gdown	Kentaro Wada	MIT License
Whisper	OpenAI	MIT License
PyannoteAudio	pyannote	MIT License
AudioEditingCode	Hila Manor	MIT License
StftPitchShift	Jürgen Hock	MIT License
Codename-RVC-Fork-3	Codename;0	MIT License

Model Repository for Model Search Tool

• VOICE-MODELS.COM

Pitch Extraction Methods in RVC

This document provides detailed information on the pitch extraction methods used, including their advantages, limitations, strengths, and reliability based on personal experience.

Method	Type	Advantages	Limitations	Strength	Reliability
pm	Praat	Fast	Less accurate	Low	Low
dio	PYWORLD	Suitable for rap	Less accurate at high frequencies	Medium	Medium
harvest	PYWORLD	More accurate than DIO	Slower processing	High	Very high
crepe	Deep Learning	High accuracy	Requires GPU	Very high	Very high
mangio-crepe	Crepe finetune	Optimized for RVC	Sometimes less accurate than original crepe	Medium to high	Medium to high
fcpe	Deep Learning	Accurate, real-time	Requires powerful GPU	Good	Medium
fcpe-legacy	Old	Accurate, real-time	Older	Good	Medium
rmvpe	Deep Learning	Effective for singing voices	Resource-intensive	Very high	Excellent
rmvpe-legacy	Old	Supports older systems	Older	High	Good
yin	Librosa	Simple, efficient	Prone to octave errors	Medium	Low
pyin	Librosa	More stable than YIN	More complex computation	Good	Good
swipe	WORLD	High accuracy	Sensitive to noise	High	Good

Bug Reporting

• If you encounter an error while using this source code, I sincerely apologize for the poor experience. You can report the bug using the methods below.

• you can report bugs to us via ISSUE.