SoftVC VITS Singing Voice Conversion

Notice

This repo adds an inference GUI for so-vits-svc 4.0, inference_gui2.py.
- Inference GUI 2 features experimental TalkNet integration, in-program recording, as well as other features like timestretching with rubberband and crepe pitch detection. Instructions can be found below under Inference GUI 2 header.
4.0 is now the default branch for this repo.

Inference GUI 2 - Installation

On Windows, try the script under releases. Otherwise: pip install -r requirements.txt in a Python 3.8/3.9 (conda) environment. Additional features may be available based on other dependencies:

OPTIONAL - You PROBABLY DO NOT NEED THIS: For timestretching support, you need to install BOTH the rubberband standalone program, ensuring the rubberband executable is on your PATH, and the python module pip install pyrubberband. Note that installing pyrubberband installs PySoundFile which needs to be uninstalled, and SoundFile will need to be reinstalled.
OPTIONAL - For TalkNet support, you need to pip install requests and also install this ControllableTalkNet fork.
OPTIONAL - For crepe pitch detection support, pip install tensorflow crepe
OPTIONAL - For auto playback, pip install pygame
OPTIONAL - For auto recording/playback using a hotkey without window focus, pip install keyboard. Requires sudo on Linux. Please note that keyboard uses OS keyboard hooks. This allows it to read your keystrokes without window focus. This also could result in unexpected keyboard behavior in the event of a crash.
OPTIONAL - For VST3 support, pip install pedalboard. Please note that pedalboard's support for VST3 is fairly temperamental and crashes may result. Additionally, plugins are not guaranteed to function correctly.

Basic Usage

Models should be placed in separate folders within a folder called models, in the same directory as inference_gui2.py by default. Specifically, the file structure should be:

so-vits-svc-eff\
    models\
        TwilightSparkle
            G_*****.pth
            D_*****.pth
            kmeans_*****.pt {may or may not be present for some models}
            config.json

If the proper libraries are installed, the GUI can be run simply by running inference_gui2.py. If everything goes well you should see something like this (some features may not be available depending on what extra libraries you have installed):

All basic workflow occurs under the leftmost UI panel.

Select a speaker based on the listed names under Speaker:.
Drag and drop reference audio files to be converted onto Files to Convert. Alternatively, click on Files to Convert or Recent Directories to open a file dialog.
Set desired transpose (for m2f vocal conversion this is usually 12 i.e. an octave, or leave it 0 if the reference audio is female) under Transpose.
Click Convert. The resulting file should appear under results.

The right UI panel allows for recording audio directly into the GUI for quick fixes and tests. Simply select the proper audio device and click Record to begin recording. Recordings will automatically be saved to a recordings folder. The resulting recording can be transferred to the so-vits-svc panel by pressing Push last output to so-vits-svc.

Common issues

When converting: TypeError: Invalid file: WindowsPath('...') Ensure that PySoundFile is not installed (pip show pysoundfile). This may occur after installing rubberband. PySoundFile is a deprecated version of SoundFile. After uninstalling pysoundfile, run pip install soundfile==0.10.3.post1 --force-reinstall
When trying to run with TalkNet: Couldn't parse TalkNet response. Ensure that you are running alt_server.py in the TalkNet fork, and not controllable_talknet.py (or related).

Other options

Formant Shift - This option is largely useless now, as the ContentVec speech representation (which replaces HuBERT in 4.0) is intended to be agnostic to speaker formants; however, it may yield amusing nonsense results as a result of artifacting when taken to extreme values.
Timestretch - Timestretches the input using rubberband.
Clustering
- Separate k-means cluster models can be trained on speaker data which can be used to improve similarity to the target speaker at the cost of intelligibility. These models can be distributed alongside the speaker models or separately as .pt files.
- To enable clustering (if it is available), click Use clustering.
- To select a custom cluster model, use Select custom cluster model...
- Clustering can be used as a ratio from 0.0 to 1.0 by adjusting Clustering ratio.
- For larger-dataset speakers, a cluster ratio from 0.5 to 1.0; for smaller-dataset speakers, lower ratios may be desired. Generally, the more data there is for a speaker, the higher the cluster ratio can be set without frequent artifacting.
Noise scale - adjusts the variability of variational inference. Usually low values (0.2) work.
Automatic f0 prediction - For 4.0, a pitch predictor is trained alongside the speaker model which can be used to impose predicted pitch on speech features.
Use old f0 detection for inference - Uses praat's to_pitch_ac instead of to_pitch_cc. MAY be preferred for speaking inputs with rapidly varying pitch.
Use crepe for f0 estimation - (requires crepe) - Captures generally smoother pitch with fewer artifacts at the cost of GPU time and resources; however, may not be suitable for inputs with rapidly varying pitch.
Voicing threshold - Alters the tendency for the input (as well as background noise) to be considered voiced vs. unvoiced. 0.0 means that everything is considered to be voiced.

Cool features

You can drag and drop files from the audio preview by dragging from the play button, if a sound is present.
Speaker embedding mixing is supported for speakers within the same model file. This feature is disabled by default as it seems to be quite temperamental, but it can be enabled using the commandline switch custom_merge.

Running with TalkNet

For TalkNet support, you need to pip install requests and also install this ControllableTalkNet fork. Instead of running talknet_offline.py, run alt_server.py (if you use a batch script or conda environment to run TalkNet, you should use it to run alt_server.py). This will start a server that can interface with Inference GUI 2. The TalkNet server should be started before Inference GUI 2.

Next, starting Inference GUI 2 should show a UI like this:

The rightmost panel shows controls for TalkNet which are similar to those used in the web interface. Some items special to this interface:

There is currently no "Custom model" option. To add additional models you should modify the model jsons in Controllable TalkNet and download the appropriate model files.
Recordings can be also be transferred from the recording panel to the TalkNet panel.
Files can be provided under Provide input audio through clicking for a file dialog or drag-and-drop.
TalkNet can now be used to generate without reference audio using Disable reference audio
In order to push output from TalkNet through so-vits-svc, you can use Manual push TalkNet output to so-vits-svc section; alternatively, automatically check Auto push TalkNet output to so-vits-svc.
By default, Auto push TalkNet output to so-vits-svc ignores so-vits-svc parameters on the left side panel, since the intended use of this panel is to improve the quality of so-vits-svc generations; however, this can be changed with Apply left-side parameters to so-vits-svc gens

Model Overview

A singing voice coversion (SVC) model, using the SoftVC encoder to extract features from the input audio, sent into VITS along with the F0 to replace the original input to acheive a voice conversion effect. Additionally, changing the vocoder to NSF HiFiGAN to fix the issue with unwanted staccato.

Notice

Note: 3.0 models are not compatible with 4.0.
This is a fork of the 4.0 branch of so-vits-svc. It implements the same inference GUI as found in the eff branch of this repository, with a few extra features relating to 4.0 models (such as automatic pitch prediction and clustering). For instructions on using the GUI see the eff branch

4.0 Features

Feature input replaced with Content Vec
Sampling rate changed to 44100hz
Due to change of parameters such as hop size and simplification of model structures，VRAM usage for inference has been greatly reduced compared to version 3.0.
Code refactor
Dataset production and training process are consistent with 3.0; however, models and preprocessed data are not compatible.
Added automatic pitch f0 prediction for voice conversion (will be out of tune if used with singing voices)
Reduced timbre leakage through k-means clustering scheme

Demo：

Required downloads

Download ContentVec model:checkpoint_best_legacy_500.pt
- Place under hubert.
Download pretrained models G_0.pth and D_0.pth
- Place under logs/44k.
- Pretrained models are required, because from experiments, training from scratch can be rather unpredictable to say the least, and training with a pretrained model can greatly improve training speeds.
- The pretrained model includes云灏, 即霜, 辉宇·星AI, 派蒙, and 绫地宁宁, covering the common ranges of both male and female voices, and so it can be seen as a rather universal pretrained model.

wget -P logs/44k/ https://huggingface.co/therealvul/so-vits-svc-4.0-init/resolve/main/G_0.pth
wget -P logs/44k/ https://huggingface.co/therealvul/so-vits-svc-4.0-init/resolve/main/D_0.pth

Colab notebook scripts

Colab training notebook (EN)

Colab inference notebook (EN)

Note that the following notebooks are not maintained by me.

Dataset preparation

All that is required is that the data be put under the dataset_raw folder in the structure format provided below.

dataset_raw
├───speaker0
│   ├───xxx1-xxx1.wav
│   ├───...
│   └───Lxx-0xx8.wav
└───speaker1
    ├───xx2-0xxx2.wav
    ├───...
    └───xxx7-xxx007.wav

Data pre-processing.

Resample to 44100hz

python resample.py

Automatically sort out training set, validation set, test set, and automatically generate configuration files.
```
python preprocess_flist_config.py
```
Generate hubert and F0 features/
```
python preprocess_hubert_f0.py
```
After running the step above, the dataset folder will contain all the pre-processed data, you can delete the dataset_raw folder after that.

Training.

python train.py -c configs/config.json -m 44k

Note: The old model will be automatically cleared during training, and only the latest 5 models will be kept. If you want to prevent overfitting, you need to manually back up the model record points, or modify the configuration file keep_ckpts 0 to never clear.

To train a cluster model, train a so-vits-svc 4.0 model first (as above), then execute python cluster/train_cluster.py.

Inference

For instructions on using the GUI see the eff branch Otherwise use inference_main.py Command line support has been added for inference

# Example
python inference_main.py -m "logs/44k/G_30400.pth" -c "configs/config.json" -n "君の知らない物語-src.wav" -t 0 -s "nen"

Required fields

-m, --model_path: model path
-c, --config_path: configuration file path
-n, --clean_names: list of wav file names placed in raw folder
-t, --trans: pitch transpose (semitones)
-s, --spk_list: target speaker names

Optional fields

-a, --auto_predict_f0:Automatic pitch prediction; do not enable when converting singing or it will be out of tune.
-cm, --cluster_model_path:Path of cluster model
-cr, --cluster_infer_ratio:Ratio of clustering to use

Optional fields

Automatic f0 prediction

The 4.0 model training process will train an f0 predictor. For voice conversion you can enable automatic pitch prediction. Do not enable this function when converting singing voices unless you want it to be out of tune.

Cluster timbre leakage

Clustering is used to make the model trained more like the target timbre at the cost of articulation/intelligibility. The model can linearly control the proportion of non-clustering scheme (more intelligible, 0) vs. clustering scheme (more speaker-like, 1).

Onnx export

Use onnx_export.py

Create a new folder:checkpoints and open it
Create a new folder in the checkpoints folder and name it after your project such as aziplayer
Rename your model to model.pth，rename the config file to config.json，and place it in the project folder (aziplayer )
In onnx_export.py change path = "NyaruTaffy" to your project name e.g. path = "aziplayer"
Run onnx_export.py
After execution is completed，A model.onnx file will be generated in your project folder, which is the exported model
Onnx UI
- MoeSS

effusiveperiscope / so-vits-svc

readme

SoftVC VITS Singing Voice Conversion

Notice

Inference GUI 2 - Installation

Basic Usage

Common issues

Other options

Cool features

Running with TalkNet

Model Overview

Notice

4.0 Features

Required downloads

Colab notebook scripts

Dataset preparation

Data pre-processing.

Training.

Inference

Optional fields

Automatic f0 prediction

Cluster timbre leakage

Onnx export

Onnx UI