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.gitattributes

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+*.pt filter=lfs diff=lfs merge=lfs -text
+*.th filter=lfs diff=lfs merge=lfs -text

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LICENSE

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+MIT License
+
+Copyright (c) Meta Platforms, Inc. and affiliates.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+license: mit
+tags:
+  - audio
+  - music-source-separation
+  - sound-separation
+  - demucs
+  - htdemucs
+  - stem-separation
+  - inference
+pipeline_tag: audio-to-audio
+---
+
+## Music Source Separation
+
+This is the Demucs model, serialized from Facebook Research's pretrained models. 
+
+---
+
+## What is HTDemucs?
+
+[HTDemucs (Hybrid Transformer Demucs)](https://github.com/facebookresearch/demucs) is Meta AI's fourth-generation music source separation model, introduced in [*Hybrid Transformers for Music Source Separation* (Rouard et al., ICASSP 2023)](https://arxiv.org/abs/2211.08553).
+
+Where earlier Demucs generations processed audio purely in the time domain, HTDemucs runs **two parallel encoders simultaneously** — one operating on the raw waveform, the other on the STFT spectrogram — with a **Transformer Encoder with cross-attention** at the bottleneck connecting them. This lets the model correlate time-domain and frequency-domain features before decoding, yielding measurably better separation quality — especially on spectrally complex, temporally sparse instruments like piano and guitar.
+
+The `htdemucs_6s` variant adds dedicated guitar and piano stems on top of the standard drums/bass/other/vocals quad, making it the most capable publicly available separation model for music production use.
+
+---
+
+From Facebook research:
+
+    Demucs is based on U-Net convolutional architecture inspired by Wave-U-Net and SING, with GLUs, a BiLSTM between the encoder and decoder, specific initialization of weights and transposed convolutions in the decoder.
+
+See [facebookresearch's repository](https://github.com/facebookresearch/demucs) for more information on Demucs. 

configuration.json

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+{
+  "framework": "pytorch",
+  "task": "music-source-separation",
+  "allow_remote": true
+}

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htdemucs.yaml

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+models: ['955717e8']

htdemucs_6s.yaml

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+models: ['5c90dfd2']

htdemucs_ft.yaml

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+models: ['f7e0c4bc', 'd12395a8', '92cfc3b6', '04573f0d']
+weights: [
+  [1., 0., 0., 0.],
+  [0., 1., 0., 0.],
+  [0., 0., 1., 0.],
+  [0., 0., 0., 1.],
+]