Logicino/LASAFT-Net-v2_reference

A PyTorch implementation: "LASAFT-Net-v2: Listen, Attend and Separate by Attentively aggregating Frequency Transformation"

LASAFT-Net-v2

我修改了安装依赖，在kino_requirements.txt里

Listen, Attend and Separate by Attentively aggregating Frequency Transformation

Woosung Choi, Yeong-Seok Jeong, Jinsung Kim, Jaehwa Chung, Soonyoung Jung, and Joshua D. Reiss

Demonstration (under construction)

Experimental Results

• Musdb 18

model	vocals	drums	bass	other	AVG
Meta-TasNet	6.40	5.91	5.58	4.19	5.52
AMSS-Net	6.78	5.92	5.10	4.51	5.58
LaSAFT-Net-v1	7.33	5.68	5.63	4.87	5.88
LASAFT-Net-v2	7.57	6.13	5.28	4.87	5.96

• MDX Challenge (Leaderboard A)

model	model type	vocals	drums	bass	other	AVG
KUILAB-MDX-Net	dedicated (1 source/ 1 model)	8.901	7.173	7.232	5.636	7.236
LaSAFT-Net-v1 (light)	conditioned (4 sources/ 1 model)	7.275	5.935	5.823	4.557	5.897
LASAFT-Net-v2 (light)	conditioned (4 sources/ 1 model)	7.324	5.976	5.884	4.642	5.957

How to reproduce

1. Environment

• Ubuntu 20.04
• wandb for logging

You must create .env file by copying .env.sample to set environmental variables.

wandb_api_key=[Your Key] # "xxxxxxxxxxxxxxxxxxxxxxxx"
data_dir=[Your Path] # "/home/ielab/repos/musdbHQ"

• about wandb_api_key
  • we currently only support wandb for logging.
  • for wandb_api_key, visit wandb, go to setting, and then copy your api key
• about data_dir
  • the absolute path where datasets are stored

2. Installation (cuda)

conda env create -f environment.yaml -n lasaftv2
conda activate lasaftv2
pip install -r requirements.txt