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| license: mit | |
| title: RVC MAKER | |
| sdk: gradio | |
| emoji: 👀 | |
| colorFrom: blue | |
| app_file: main/app/app.py | |
| colorTo: blue | |
| pinned: true | |
| <div align="center"> | |
| <img alt="LOGO" src="assets/ico.png" width="300" height="300" /><div align="center"> | |
| <img src="https://count.nett.moe/get/foo/img?theme=rule34" alt="yeh"></div> | |
| </div> | |
| A high-quality voice conversion tool focused on ease of use and performance. | |
| [](https://github.com/TheNeodev/RVC-MAKER) | |
| [](https://colab.research.google.com/github/TheNeodev/RVC-MAKER/blob/main/webui.ipynb) | |
| [](https://github.com/TheNeodev/RVC-MAKER/blob/main/LICENSE) | |
| </div> | |
| <div align="center"> | |
| </div> | |
| # 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 | |
| - Music separation (MDX-Net/Demucs) | |
| - Voice conversion (File conversion/Batch conversion/Conversion with Whisper/Text-to-speech conversion) | |
| - Background music editing | |
| - Apply effects to audio | |
| - Generate training data (From linked paths) | |
| - Model training (v1/v2, high-quality encoders) | |
| - Model fusion | |
| - Read model information | |
| - Export models to ONNX | |
| - Download from pre-existing model repositories | |
| - Search for models on the web | |
| - Pitch extraction | |
| - Support for audio conversion inference using ONNX models | |
| - ONNX RVC models also support indexing for 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 are converted to ONNX to support accelerated extraction | |
| **F0_HYBRID**: Multiple options can be combined, such as `hybrid[rmvpe+harvest]`, or you can try combining all options together | |
| **EMBEDDERS**: `contentvec_base, hubert_base, japanese_hubert_base, korean_hubert_base, chinese_hubert_base, portuguese_hubert_base` | |
| **EMBEDDERS_ONNX**: All the above embedding models have ONNX versions pre-converted for accelerated embedding extraction | |
| **EMBEDDERS_TRANSFORMERS**: All the above embedding models have versions pre-converted to Hugging Face for use as an alternative to Fairseq | |
| **SPIN_EMBEDDERS**: A new embedding extraction model that may provide higher quality than older extractions | |
| # Usage Instructions | |
| **Will be provided if I’m truly free...** | |
| # Installation and Usage | |
| - **Step 1**: Install Python from the official website or [Python](https://www.python.org/ftp/python/3.10.7/python-3.10.7-amd64.exe) (**REQUIRES PYTHON 3.10.x OR PYTHON 3.11.x**) | |
| - **Step 2**: Install FFmpeg from [FFMPEG](https://github.com/BtbN/FFmpeg-Builds/releases), 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 | |
| python -m venv envenv\Scripts\activate | |
| If you have an NVIDIA GPU, run this step depending on your CUDA version (you may need to change cu117 to cu128, etc.): | |
| If using Torch 2.3.1 | |
| python -m pip install torch==2.3.1 torchaudio==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu117 | |
| If using Torch 2.6.0 | |
| python -m pip install torch==2.6.0 torchaudio==2.6.0 torchvision==0.21.0 --index-url https://download.pytorch.org/whl/cu117 | |
| Then run: | |
| python -m 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 | |
| </pre> | |
| # 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** | |
| # 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](https://github.com/PhamHuynhAnh16/Vietnamese-RVC) | Phạm Huỳnh Anh | Apache License 2.0 | | |
| | [Applio](https://github.com/IAHispano/Applio/tree/main) | IAHispano | MIT License | | |
| | [Python-audio-separator](https://github.com/nomadkaraoke/python-audio-separator/tree/main) | Nomad Karaoke | MIT License | | |
| | [Retrieval-based-Voice-Conversion-WebUI](https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI/tree/main) | RVC Project | MIT License | | |
| | [RVC-ONNX-INFER-BY-Anh](https://github.com/PhamHuynhAnh16/RVC_Onnx_Infer) | Phạm Huỳnh Anh | MIT License | | |
| | [Torch-Onnx-Crepe-By-Anh](https://github.com/PhamHuynhAnh16/TORCH-ONNX-CREPE) | Phạm Huỳnh Anh | MIT License | | |
| | [Hubert-No-Fairseq](https://github.com/PhamHuynhAnh16/hubert-no-fairseq) | Phạm Huỳnh Anh | MIT License | | |
| | [Local-attention](https://github.com/lucidrains/local-attention) | Phil Wang | MIT License | | |
| | [TorchFcpe](https://github.com/CNChTu/FCPE/tree/main) | CN_ChiTu | MIT License | | |
| | [FcpeONNX](https://github.com/deiteris/voice-changer/blob/master-custom/server/utils/fcpe_onnx.py) | Yury | MIT License | | |
| | [ContentVec](https://github.com/auspicious3000/contentvec) | Kaizhi Qian | MIT License | | |
| | [Mediafiredl](https://github.com/Gann4Life/mediafiredl) | Santiago Ariel Mansilla | MIT License | | |
| | [Noisereduce](https://github.com/timsainb/noisereduce) | Tim Sainburg | MIT License | | |
| | [World.py-By-Anh](https://github.com/PhamHuynhAnh16/world.py) | Phạm Huỳnh Anh | MIT License | | |
| | [Mega.py](https://github.com/odwyersoftware/mega.py) | O'Dwyer Software | Apache 2.0 License | | |
| | [Gdown](https://github.com/wkentaro/gdown) | Kentaro Wada | MIT License | | |
| | [Whisper](https://github.com/openai/whisper) | OpenAI | MIT License | | |
| | [PyannoteAudio](https://github.com/pyannote/pyannote-audio) | pyannote | MIT License | | |
| | [AudioEditingCode](https://github.com/HilaManor/AudioEditingCode) | Hila Manor | MIT License | | |
| | [StftPitchShift](https://github.com/jurihock/stftPitchShift) | Jürgen Hock | MIT License | | |
| | [Codename-RVC-Fork-3](https://github.com/codename0og/codename-rvc-fork-3) | Codename;0 | MIT License | | |
| # Model Repository for Model Search Tool | |
| - **[VOICE-MODELS.COM](https://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](https://github.com/unchCrew/RVC-MAKER/issues).** |