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Norod78
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https://linktr.ee/Norod78

AI & ML interests

Fooling around with Generative machine learning models.

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reacted to matteospanio's post with ๐Ÿš€ 1 day ago
๐ŸŽถ Released mule-torch โ€” an unofficial PyTorch port of MULE (SF-NFNet-F0), SiriusXM/Pandora's music-audio embedding model (McCallum et al., ISMIR 2022). No retraining: I re-implemented the architecture in pure PyTorch and transferred the original TensorFlow weights, then checked it layer by layer against the genuine TF pipeline. โœ… End-to-end clip-embedding cosine 0.9999999 vs the original โœ… ONNX backbone parity < 1e-6 โœ… 62.35M params (paper: ~62.4M) โœ… Batched, GPU-native, ONNX-exportable โ€” none of which the original `Analysis` pipeline does ```python pip install mule-torch ``` ```python from mule_torch import MuleModel emb = MuleModel.from_pretrained()(waveform) # (B, T)@16kHz -> (B, 1728) ``` ๐Ÿค— Weights: https://huggingface.co/matteospanio/mule ๐Ÿ’ป Code: https://github.com/matteospanio/mule-torch ๐Ÿ“ฆ PyPI: https://pypi.org/project/mule-torch/ The fun bug: parity was perfect through every conv but the block output was anti-correlated (cos = โˆ’1). Cause: the learnable skip-init gains couldn't be mapped by layer name (Keras scrambles the order) โ€” they had to be recovered from the graph. โš ๏ธ Unofficial, community port โ€” not affiliated with or endorsed by the original authors. All credit to them; please cite the paper. Weights inherit CC-BY-NC-4.0.
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