base on # LLVC: **L**ow-Latency **L**ow-Resource **V**oice **C**onversion 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/ ## Setup 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`. ## Inference `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. ## Training 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` ## Dataset 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` 2. ``` 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 ``` ## Evaluate results 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. ## Credits 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 ## Citation 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} } ``` ", Assign "at most 3 tags" to the expected json: {"id":"4679","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"