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Building on HF
27.3
TFLOPS
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73
Dan
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Daankular
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๐ถ 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. โ End-to-end clip-embedding cosine 0.9999999 vs the original โ ONNX backbone parity < 1e-6 โ 62.35M params (paper: ~62.4M) โ 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) ``` ๐ค Weights: https://huggingface.co/matteospanio/mule ๐ป Code: https://github.com/matteospanio/mule-torch ๐ฆ PyPI: https://pypi.org/project/mule-torch/ The fun bug: parity was perfect through every conv but the block output was anti-correlated (cos = โ1). 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. โ ๏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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Running
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DramaBox TTS
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Expressive TTS with voice cloning
Running
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47
Sulphur
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Generate a video from an image and motion prompt
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