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README.md

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-# Music Mixing Style Transfer
-
-This repository includes source code and pre-trained models of the work *Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects* by [Junghyun Koo](https://linkedin.com/in/junghyun-koo-525a31251), [Marco A. Martínez-Ramírez](https://m-marco.com/about/), [Wei-Hsiang Liao](https://jp.linkedin.com/in/wei-hsiang-liao-66283154), [Stefan Uhlich](https://scholar.google.de/citations?user=hja8ejYAAAAJ&hl=de), [Kyogu Lee](https://linkedin.com/in/kyogu-lee-7a93b611), and [Yuki Mitsufuji](https://www.yukimitsufuji.com/).
-
-
-[![arXiv](https://img.shields.io/badge/arXiv-2211.02247-b31b1b.svg)](https://arxiv.org/abs/2211.02247)
-[![Web](https://img.shields.io/badge/Web-Demo_Page-green.svg)](https://jhtonyKoo.github.io/MixingStyleTransfer/)
-[![Supplementary](https://img.shields.io/badge/Supplementary-Materials-white.svg)](https://tinyurl.com/4math4pm)
-
-
-
-## Pre-trained Models
-| Model | Configuration | Training Dataset |
-|-------------|-------------|-------------|
-[FXencoder (Φ<sub>p.s.</sub>)](https://drive.google.com/file/d/1BFABsJRUVgJS5UE5iuM03dbfBjmI9LT5/view?usp=sharing) | Used *FX normalization* and *probability scheduling* techniques for training | Trained with [MUSDB18](https://sigsep.github.io/datasets/musdb.html) Dataset
-[MixFXcloner](https://drive.google.com/file/d/1Qu8rD7HpTNA1gJUVp2IuaeU_Nue8-VA3/view?usp=sharing) | Mixing style converter trained with Φ<sub>p.s.</sub> | Trained with [MUSDB18](https://sigsep.github.io/datasets/musdb.html) Dataset
-
-
-## Installation
-```
-pip install -r "requirements.txt"
-```
-
-# Inference
-
-## Mixing Style Transfer
-
-To run the inference code for <i>mixing style transfer</i>, 
-1. Download pre-trained models above and place them under the folder named 'weights' (default)
-2. Prepare input and reference tracks under the folder named 'samples/style_transfer' (default)
-Target files should be organized as follow:
-```
-    "path_to_data_directory"/"song_name_#1"/"input_file_name".wav
-    "path_to_data_directory"/"song_name_#1"/"reference_file_name".wav
-    ...
-    "path_to_data_directory"/"song_name_#n"/"input_file_name".wav
-    "path_to_data_directory"/"song_name_#n"/"reference_file_name".wav
-```
-3. Run 'inference/style_transfer.py'
-```
-python inference/style_transfer.py \
-    --ckpt_path_enc "path_to_checkpoint_of_FXencoder" \
-    --ckpt_path_conv "path_to_checkpoint_of_MixFXcloner" \
-    --target_dir "path_to_directory_containing_inference_samples"
-```
-4. Outputs will be stored under the same folder to inference data directory (default)
-
-*Note: The system accepts WAV files of stereo-channeled, 44.1kHZ, and 16-bit rate. We recommend to use audio samples that are not too loud: it's better for the system to transfer these samples by reducing the loudness of mixture-wise inputs (maintaining the overall balance of each instrument).*
-
-
-
-## Interpolation With 2 Different Reference Tracks
-
-Inference code for <interpolating> two reference tracks is almost the same as <i>mixing style transfer</i>.
-1. Download pre-trained models above and place them under the folder named 'weights' (default)
-2. Prepare input and 2 reference tracks under the folder named 'samples/style_transfer' (default)
-Target files should be organized as follow:
-```
-    "path_to_data_directory"/"song_name_#1"/"input_track_name".wav
-    "path_to_data_directory"/"song_name_#1"/"reference_file_name".wav
-    "path_to_data_directory"/"song_name_#1"/"reference_file_name_2interpolate".wav
-    ...
-    "path_to_data_directory"/"song_name_#n"/"input_track_name".wav
-    "path_to_data_directory"/"song_name_#n"/"reference_file_name".wav
-    "path_to_data_directory"/"song_name_#n"/"reference_file_name_2interpolate".wav
-```
-3. Run 'inference/style_transfer.py'
-```
-python inference/style_transfer.py \
-    --ckpt_path_enc "path_to_checkpoint_of_FXencoder" \
-    --ckpt_path_conv "path_to_checkpoint_of_MixFXcloner" \
-    --target_dir "path_to_directory_containing_inference_samples" \
-    --interpolation True \
-    --interpolate_segments "number of segments to perform interpolation"
-```
-4. Outputs will be stored under the same folder to inference data directory (default)
-
-*Note: This example of interpolating 2 different reference tracks is not mentioned in the paper, but this example implies a potential for controllable style transfer using latent space.*
-
-
-
-## Feature Extraction Using *FXencoder*
-
-This inference code will extracts audio effects-related embeddings using our proposed <i>FXencoder</i>. This code will process all the .wav files under the target directory.
-
-1. Download <i>FXencoder</i>'s pre-trained model above and place it under the folder named 'weights' (default)=
-2. Run 'inference/style_transfer.py'
-```
-python inference/feature_extraction.py \
-    --ckpt_path_enc "path_to_checkpoint_of_FXencoder" \
-    --target_dir "path_to_directory_containing_inference_samples"
-```
-3. Outputs will be stored under the same folder to inference data directory (default)
-
-
-
-
-# Implementation
-
-All the details of our system implementation are under the folder "mixing_style_transfer".
-
-<li><i>FXmanipulator</i></li>
-&emsp;&emsp;-> mixing_style_transfer/mixing_manipulator/
-<li>network architectures</li>
-&emsp;&emsp;-> mixing_style_transfer/networks/
-<li>configuration of each sub-networks</li>
-&emsp;&emsp;-> mixing_style_transfer/networks/configs.yaml
-<li>data loader</li>
-&emsp;&emsp;-> mixing_style_transfer/data_loader/
-
-
-# Citation
-
-Please consider citing the work upon usage.
-
-```
-@article{koo2022music,
-  title={Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects},
-  author={Koo, Junghyun and Martinez-Ramirez, Marco A and Liao, Wei-Hsiang and Uhlich, Stefan and Lee, Kyogu and Mitsufuji, Yuki},
-  journal={arXiv preprint arXiv:2211.02247},
-  year={2022}
-}
-```
-
-
-
+---
+license: mit
+title: Music Mixing Style Transfer Demo
+sdk: gradio
+emoji: 🎶
+pinned: true
+colorFrom: black
+colorTo: white
+---