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:

MyModel.pth saudio/Source.wav Output.wav logs/mi-test/added.index 0 -2 hybrid[pm+crepe] 128 3 0 1 0.95 0.33

Notice that the method is "hybrid[pm+crepe]" instead of a singular method like "harvest".

hybrid[pm+harvest+crepe]
# 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.

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.

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

brew install swig

Install requirements:

pip install -r requirements.txt

If you're experiencing httpx invalid port errors please insteall httpx==0.23.0

RVC requires other pre-models to infer. You need to download them from our Huggingface space.

Here's a list of Pre-models and other files that RVC needs:

hubert_base.pt

./pretrained 

./uvr5_weights


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

./pretrained_v2 

Simply call infer.py as follows:

python infer.py <model_path> <input_audio> <output_audio> <index_path> 1 0 harvest 160 3 0 1 0.78 0.33