This repository contains the code necessary to train Koe AI's LLVC models and to reproduce the LLVC paper.

LLVC paper: https://koe.ai/papers/llvc.pdf

LLVC samples: https://koeai.github.io/llvc-demo/

Windows executable: https://koe.ai/recast/download/

Koe AI homepage: https://koe.ai/

1. Create a Python environment with e.g. conda: conda create -n llvc python=3.11
2. Activate the new environment: conda activate llvc
3. Install torch and torchaudio from https://pytorch.org/get-started/locally/
4. Install requirements with pip install -r requirements.txt
5. Download models with python download_models.py
6. eval.py has requirements that conflict with requirements.txt, so before running this file, create a seperate new Python virtual environment with python 3.9 and run pip install -r eval_requirements.txt

You should now be able to run python infer.py and convert all of the files in test_wavs with the pretrained llvc checkpoint, with the resulting files saved to converted_out.

python infer.py -p my_checkpoint.pth -c my_config.pth -f input_file -o my_out_dir will convert a single audio file or folder of audio files using the given LLVC checkpoint and save the output to the folder my_out_dir. The -s argument simulate a streaming environment for conversion. The -n argument allows the user to specify the size of input audio chunks in streaming mode, trading increased latency for better RTF.

compare_infer.py allows you to reproduce our streaming no-f0 RVC and QuickVC conversions on input audio of your choice. By default, window_ms and extra_convert_size are set to the values used for no-f0 RVC conversion. See the linked paper for the QuickVC conversion parameters.

1. Create a folder experiments/my_run containing a config.json (see experiments/llvc/config.json for an example)
2. Edit the config.json to reflect the location of your dataset and desired architectural modifications
3. python train.py -d experiments/my_run
4. The run will be logged to Tensorboard in the directory experiments/my_run/logs

Datasets are comprised of a folder containing three subfolders: dev, train and val. Each of these folders contains audio files of the form PREFIX_original.wav, which are audio clips recorded by a variety of input speakers, and PREFIX_converted.wav, which are the original audio clips converted to a single target speaker. val contains clips from the same speakers as test. dev contains clips from different speakers than test.

To recreate the dataset that we use in our paper:

1. Download dev-clean.tar.gz and train-clean-360.tar.gz from https://www.openslr.org/12 and unzip to llvc/LibriSpeech

python -m minimal_rvc._infer_folder \
                                    --train_set_path "LibriSpeech/train-clean-360" \
                                    --dev_set_path "LibriSpeech/dev-clean" \
                                    --out_path "f_8312_ls360" \
                                    --flatten \
                                    --model_path "llvc_models/models/rvc/f_8312_32k-325.pth" \
                                    --model_name "f_8312" \
                                    --target_sr 16000 \
                                    --f0_method "rmvpe" \
                                    --val_percent 0.02 \
                                    --random_seed 42 \
                                    --f0_up_key 12

1. Download test-clean.tar.gz from https://www.openslr.org/12
2. Use infer.py to convert the test-clean folder using the checkpoint that you want to evaluate
3. Activate the eval environment and run eval.py on your converted audio and directory of ground-truth audio files.

Many of the modules written in minimal_rvc/ are based on the following repositories:

• https://github.com/ddPn08/rvc-webui
• https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI
• https://github.com/teftef6220/Voice_Separation_and_Selection

If you find out work relevant to your research, please cite:

@misc{sadov2023lowlatency,
      title={Low-latency Real-time Voice Conversion on CPU}, 
      author={Konstantine Sadov and Matthew Hutter and Asara Near},
      year={2023},
      eprint={2311.00873},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}