Open-Unmix-Pytorch LabelNoise
收藏NIAID Data Ecosystem2026-03-14 收录
下载链接：
https://zenodo.org/record/7657180
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资源简介：
SDXDB21 LabelNoise 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_labelnoise_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_labelnoise_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_labelnoise_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_labelnoise_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
创建时间：
2023-02-20
