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mp1704
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upvoted an article about 8 hours ago
From Zero to GPU: A Guide to Building and Scaling Production-Ready CUDA Kernels
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nvidia/nemotron-3.5-asr-streaming-0.6b
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🎶 Released mule-torch — 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. ✅ End-to-end clip-embedding cosine 0.9999999 vs the original ✅ ONNX backbone parity < 1e-6 ✅ 62.35M params (paper: ~62.4M) ✅ Batched, GPU-native, ONNX-exportable — 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) ``` 🤗 Weights: https://huggingface.co/matteospanio/mule 💻 Code: https://github.com/matteospanio/mule-torch 📦 PyPI: https://pypi.org/project/mule-torch/ The fun bug: parity was perfect through every conv but the block output was anti-correlated (cos = −1). Cause: the learnable skip-init gains couldn't be mapped by layer name (Keras scrambles the order) — they had to be recovered from the graph. ⚠️ Unofficial, community port — 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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