- Better MacOS installation script. For inference, all that needs to be done is running the `run.sh` from the extracted zip folder, where it will install Python 3.8, Homebrew, and other dependencies for you automatically. M1 Macs are natively supported for GPU acceleration, and training should work if you choose to download the pretrained models.
- Auto-detect Index path for models selected + Auto-detect paths, no more default values like this: `E:\codes\py39\vits_vc_gpu_train\logs\mi-test-1key\total_fea.npy`, We're getting Root Dir and subfolders using
```python
os.path.abspath(os.getcwd())
```
- Audio files dropdown by autodetecting files in `/audios/` folder
- More stable version of Gradio(3.34.0) with support of Themes
- Removed `One Click Training` button in `Training` Tab, since it was glitchy and has confused a lot of users.
- Changed default training settings to be more optimal for newer users.
- Auto-open TensorBoard localhost URL when `tensor-launch.py` is executed
A fork of an easy-to-use SVC framework based on VITS with top1 retrieval 💯. In general, this fork provides a CLI interface in addition. And also gives you more f0 methods to use, as well as a personlized 'hybrid' f0 estimation method using nanmedian. <br><br>
<b>Im developing alone. Help support me in the rapid development of open-source A.I Deep NN audio tools and frameworks. By donating, you making a commitment to the improvement of audio A.I as a whole. You should donate if you merely want to "support" me, not because you are then expecting a finished product. There are no guarantees. Thank you</b>
> 💓 Please support the original [RVC repository](https://www.bilibili.com/video/BV1pm4y1z7Gm/). Without it, obviously this fork wouldn't have been possible. The Mangio-RVC-Fork aims to essentially enhance the features that the original RVC repo has in my own way. Please note that this fork is NOT STABLE and was forked with the intention of experimentation. Do not use this Fork thinking it is a "better" version of the original repo. Think of it more like another "version" of the original repo. Please note that this doesn't have a google colab. If you want to use google colab, go to the original repository. This fork is intended to be used with paperspace and local machines for now.
Special thanks to discord user @kalomaze#2983 for creating a temporary colab notebook for this fork for the time being. Eventually, an official, more stable notebook will be included with this fork. Please use paperspace instead if you can as it is much more stable.
[](https://colab.research.google.com/drive/1iWOLYE9znqT6XE5Rw2iETE19ZlqpziLx?usp=sharing)
+ f0 Crepe Pitch Extraction for training. 🌟 (EXPERIMENTAL) Works on paperspace machines but not local mac/windows machines. Potential memory leak. Watch out.
+ f0 hybrid (median) estimation method by calculating nanmedian for a specified array of f0 methods to get the best of all worlds for all specified f0 methods. Only for CLI right now. Soon to be implemented into GUI 🌟
+ f0 hybrid (median) estimation method on f0 feature extraction (training). (VERY EXPERIMENTAL PROBABLY EXTREMELY BUGGY). Feature extraction with the hybrid method will take MUCH longer.
# About this fork's f0 Hybrid feature on inference
Right now, the hybrid f0 method is only available on CLI, not GUI yet. But basically during inference, we can specify an array of f0 methods, E.G ["pm", "harvest", "crepe"], get f0 calculations of all of them and 'combine' them with nanmedian to get a hybrid f0 signal to get the 'best of all worlds' of the f0 methods provided.
Here's how we would infer with the hybrid f0 method in cli:
# the crepe calculation will be at the 'end' of the computational stack.
# the parselmouth calculation will be at the 'start' of the computational stack.
# the 'hybrid' method will calculate the nanmedian of both pm, harvest and crepe
```
Many f0 methods may be used. But are to be split with a delimiter of the '+' character. Keep in mind that inference will take much longer as we are calculating f0 X more times.
The original repos crepe f0 computation method is slightly different to mine. Its arguable that in some areas, my crepe implementation sounds more stable in some parts. However, the orginal repo's crepe implementation gets rid of noise and artifacts much better. In this fork, my own crepe implementation (mangio-crepe) uses a customizable crepe_hop_length feature on both the GUI and the CLI which the original crepe doesnt have. Please let it be known, that each implementation sounds slightly different, and there isn't a clear "better" or "worse". It all depends on the context!
If one must be chosen, I highly recommend using the original crepe implementation (not this fork's) as the developers of RVC have more control on fixing issues than I have.
Crepe training is still incredibly instable and there's been report of a memory leak. This will be fixed in the future, however it works quite well on paperspace machines. Please note that crepe training adds a little bit of difference against a harvest trained model. Crepe sounds clearer on some parts, but sounds more robotic on some parts too. Both I would say are equally good to train with, but I still think crepe on INFERENCE is not only quicker, but more pitch stable (especially with vocal layers). Right now, its quite stable to train with a harvest model and infer it with crepe. If you are training with crepe however (f0 feature extraction), please make sure your datasets are as dry as possible to reduce artifacts and unwanted harmonics as I assume the crepe pitch estimation latches on to reverb more.
Only for CLI (not implemented in GUI yet). Basically the same as usage described in this readme's f0 hybrid on inference section. Instead of stating "harvest" into your arguments in the f0 feature extraction page, you would use "hybrid[harvest+dio+pm+crepe]" for example. This f0 nanmedian hybrid method will take very long during feature extraction. Please, if you're willing to use hybrid f0, be patient.
## If you get CUDA issues with crepe training, or pm and harvest etc.
This is due to the number of processes (n_p) being too high. Make sure to cut the number of threads down. Please lower the value of the "Number of CPU Threads to use" slider on the feature extraction GUI.
Please note that version 2 pre-trained models only support 40k model sample rates. If you want to use 32k or 48k however, please use version 1 pre-trained models.
**Notice**: `faiss 1.7.2` will raise Segmentation Fault: 11 under `MacOS`, please use `pip install faiss-cpu==1.7.0` if you use pip to install it manually. `Swig` can be installed via `brew` under `MacOS`
If you want to test the v2 version model (the v2 version model changes the feature from the 256-dimensional input of 9-layer hubert+final_proj to the 768-dimensional input of 12-layer hubert, and adds 3 cycle discriminators), an additional download is required
#If you are using Windows, you may need this file, skip if ffmpeg and ffprobe are installed; ubuntu/debian users can install these two libraries through apt install ffmpeg
Welcome to the CLI version of RVC. Please read the documentation on https://github.com/Mangio621/Mangio-RVC-Fork (README.MD) to understand how to use this app.
Typing 'go infer' for example will take you to the infer page where you can then enter in your arguments that you wish to use for that specific page. For example typing 'go infer' will take you here:
3.3 Copy theme variable(in this case, it's `theme='freddyaboulton/dracula_revamped'`)
4. Replace `theme='HaleyCH/HaleyCH_Theme'` in `infer-web.py` with any value of a theme from [Gradio Theme Gallery](https://huggingface.co/spaces/gradio/theme-gallery)
