metadata
license: cc-by-nc-4.0
tags:
- audio
- music-source-separation
- source-separation
pipeline_tag: audio-to-audio
Piano Source Separation Model
This repository contains a 17 MB piano separation model and inference script for running it.
The model takes an audio track as input and outputs the isolated piano.
Examples
Listen to some examples here https://tjpurdy.github.io/Piano-Separation-Model-small/
Input and output
- Supported input formats:
wav,flac,mp3 - Supported output formats:
wav,flac(--output_format wav / --output_format flac) - --input_dir can point to either a single file or a directory containing multiple files
Installation
pip install torch einops rotary-embedding-torch numpy soundfile safetensors
Usage
Download the inference.py file then run the code below after setting the --input_dir (model and config will be auto-downloaded).
python inference.py --input_dir 'Insert path to file or directory containing file(s) here'
Extra options
- --output_dir to choose where the outputs are saved, default is the same as --input_dir (output filenames will have _piano at the end)
- --checkpoint_path where the model is located, if not found the code will automatically download it
- --config_path where the config.json is located, if not found the code will automatically download it
Notes
- This model is trained for the typical common piano only, it will not work on variants such as the electric piano.
- Uses GPU (3GB VRAM required) automatically if available, CPU is used otherwise
- The model is trained with 44.1 kHz audio
- Processing speed of ~1 second per 1 minute of audio on a google colab T4.
Citation
Please cite this repository if you use this model in research or a project.
Credit
Wei-Tsung Lu, Ju-Chiang Wang, Qiuqiang Kong, Yun-Ning Hung - https://arxiv.org/abs/2309.02612 lucidrains - https://github.com/lucidrains/BS-RoFormer
