Published February 20, 2023
| Version v1
Dataset
Open
Open-Unmix Pytorch Bleeding
Description
SDXDB21 Bleeding Baseline
We split the training data into train and valid. For valid, the following songs were used:
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
The following commands were used to create the models:
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
Files
bass.json
Files
(569.8 MB)
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