--- 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 ```bash 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). ```bash 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

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