Open-Unmix Pytorch Bleeding

SDXDB21 Bleeding Baseline <pre>We split the training data into train and valid. For valid, the following songs were used:</pre> <pre>bc1f2967-f834-43bd-aadc-95afc897cfe7 cc3e4991-6cce-40fe-a917-81a4fbb92ea6 ed90a89a-bf22-444d-af3d-d9ac3896ebd2 f4b735de-14b1-4091-a9ba-c8b30c0740a7 bc964128-da16-4e4c-af95-4d1211e78c70 cc7f7675-d3c8-4a49-a2d7-a8959b694004 f40ffd10-4e8b-41e6-bd8a-971929ca9138</pre> <pre>The following commands were used to create the models:</pre> <pre><code class="language-bash">OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=3 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file vocals.wav \ --interferer-files bass.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=4 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file bass.wav \ --interferer-files vocals.wav drums.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=5 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file drums.wav \ --interferer-files bass.wav vocals.wav other.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap OMP_NUM_THREADS=1 CUDA_VISIBLE_DEVICES=7 python train.py \ --root /sdxdb23_bleeding_v1.0 \ --dataset trackfolder_fix \ --target-file other.wav \ --interferer-files bass.wav drums.wav vocals.wav \ --random-track-mix \ --lr-decay-patience 160 \ --source-augmentations gain channelswap</code></pre>