Comments (5)
from music_source_separation.
Yes, both vocals and accompaniment are supported.
from music_source_separation.
the pretrained model doesn't seem to work. I tried to download the checkpoints from your script, after that run the separate_vocals.sh
but the size mismatch
problem was raised:
size mismatch for stft.conv_real.weight: copying a param with shape torch.Size([1025, 1, 2048]) from checkpoint, the shape in current model is torch.Size([257, 1, 512]).
size mismatch for stft.conv_imag.weight: copying a param with shape torch.Size([1025, 1, 2048]) from checkpoint, the shape in current model is torch.Size([257, 1, 512]).
size mismatch for istft.ola_window: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for istft.conv_real.weight: copying a param with shape torch.Size([2048, 2048, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 1]).
size mismatch for istft.conv_imag.weight: copying a param with shape torch.Size([2048, 2048, 1]) from checkpoint, the shape in current model is torch.Size([512, 512, 1]).
size mismatch for bn0.weight: copying a param with shape torch.Size([1025]) from checkpoint, the shape in current model is torch.Size([257]).
size mismatch for bn0.bias: copying a param with shape torch.Size([1025]) from checkpoint, the shape in current model is torch.Size([257]).
size mismatch for bn0.running_mean: copying a param with shape torch.Size([1025]) from checkpoint, the shape in current model is torch.Size([257]).
size mismatch for bn0.running_var: copying a param with shape torch.Size([1025]) from checkpoint, the shape in current model is torch.Size([257]).
size mismatch for encoder_block1.conv_block1.bn1.weight: copying a param with shape torch.Size([2]) from checkpoint, the shape in current model is torch.Size([8]).
size mismatch for encoder_block1.conv_block1.bn1.bias: copying a param with shape torch.Size([2]) from checkpoint, the shape in current model is torch.Size([8]).
size mismatch for encoder_block1.conv_block1.bn1.running_mean: copying a param with shape torch.Size([2]) from checkpoint, the shape in current model is torch.Size([8]).
size mismatch for encoder_block1.conv_block1.bn1.running_var: copying a param with shape torch.Size([2]) from checkpoint, the shape in current model is torch.Size([8]).
size mismatch for encoder_block1.conv_block1.conv1.weight: copying a param with shape torch.Size([32, 2, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 8, 3, 3]).
size mismatch for encoder_block1.conv_block1.shortcut.weight: copying a param with shape torch.Size([32, 2, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 8, 1, 1]).
size mismatch for after_conv2.weight: copying a param with shape torch.Size([8, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 32, 1, 1]).
size mismatch for after_conv2.bias: copying a param with shape torch.Size([8]) from checkpoint, the shape in current model is torch.Size([32]).
from music_source_separation.
from music_source_separation.
Hey @qiuqiangkong, In the separate_scripts/download_checkpoints.sh
you downloaded the ismir2021
checkpoint, but in the separate_scripts/separate_vocals.sh
the default model was resunet_subbandtime
. That leads to mismatch error 😄.
I've tried the resunet_ismir2021
model to separate vocals, it reduced about 70% the accompaniment in the audio, that's awesome. I can improve more by finetuning your pretrained model ? Btw you can release resunet_subbandtime
model ?
Again, thanks for your amazing work.
from music_source_separation.
Related Issues (20)
- 模型权重链接是失效了吗? HOT 5
- 权重下载命令有问题
- poor peformance using short segment <1s HOT 1
- 现在完全无法转换
- 主分支 使用发现两个命令和参数问题 HOT 3
- scipy.io.matlab.miobase.MatReadError: Mat file appears to be empty HOT 1
- Readme: SDR incorrectly defined
- 我把数据扩展到了10多个类别 但很多类别分离不出来 是不是要设置什么参数呢
- 混音数据是否需要归一化保护
- How to convert audio pytorch models to tflite model HOT 1
- Model download problem HOT 1
- python3 -m bytesep download_checkpoints 无法正常运行
- /home/dzy/bytesep_data/filters/f_4_64.mat HOT 4
- Can we separate out vocal and accompaniment together? I don't see any option to that. HOT 1
- I can't create indexes
- stft and istft are placed outside forward
- No module named '_bz2' HOT 1
- invalid choice: 'download_checkpoints'
- scipy.io.matlab.miobase.MatReadError: Mat file appears to be empty
- How to generate visualizations during the training phase?
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from music_source_separation.