Audio
pyannote-audio
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
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View guide →An audio quality metric and loss for training cleaner models.
torch-log-wmse is an open-source ai music project by crlandsc (crlandsc/torch-log-wmse on GitHub). It has 50 GitHub stars and 1 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 1 September 2026.
Add it to your training environment.
Use it as a loss or metric.
Configure digital-silence behavior.
Optimize your audio model with it.
Audio quality measurement in PyTorch.
Train directly against it.
Handles digital silence correctly.
Built for enhancement tasks.
Works for source-separation training.
Free for research and products.
Download the source and enter the folder.
git clone https://github.com/crlandsc/torch-log-wmse.git
cd torch-log-wmseInstall the Python package.
pip install -e .Import logWMSE in your loop.
python -c "import torch_log_wmse; print(torch_log_wmse.__file__)"logWMSE, an audio quality metric and loss function for PyTorch, at crlandsc/torch-log-wmse.
Training and evaluating audio models like denoisers and separators.
Training targets where the correct output is silence; the metric handles them properly.
Yes. Apache-2.0 licensed.
Audio ML researchers and engineers training enhancement models.
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
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Free and open — grab the source, star the repo, and start creating.