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+/demucs-4.0.0.tar.gz
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+%global _empty_manifest_terminate_build 0
+Name:		python-demucs
+Version:	4.0.0
+Release:	1
+Summary:	Music source separation in the waveform domain.
+License:	MIT License
+URL:		https://github.com/facebookresearch/demucs
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/7f/1e/c2c2cde6cd5826ea25dc132ec49cf5717474ce03e3479aad7f581adb29b1/demucs-4.0.0.tar.gz
+BuildArch:	noarch
+
+
+%description
+# Demucs Music Source Separation
+
+[![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB)](https://opensource.fb.com/support-ukraine)
+![tests badge](https://github.com/facebookresearch/demucs/workflows/tests/badge.svg)
+![linter badge](https://github.com/facebookresearch/demucs/workflows/linter/badge.svg)
+
+
+This is the 4th release of Demucs (v4), featuring Hybrid Transformer based source separation.
+**For the classic Hybrid Demucs (v3):** [Go this commit][demucs_v3].
+If you are experiencing issues and want the old Demucs back, please fill an issue, and then you can get back to the v3 with
+`git checkout v3`. You can also go [Demucs v2][demucs_v2].
+
+
+Demucs is a state-of-the-art music source separation model, currently capable of separating
+drums, bass, and vocals from the rest of the accompaniment.
+Demucs is based on a U-Net convolutional architecture inspired by [Wave-U-Net][waveunet].
+The v4 version features [Hybrid Transformer Demucs][htdemucs], a hybrid spectrogram/waveform separation model using Transformers.
+It is based on [Hybrid Demucs][hybrid_paper] (also provided in this repo) with the innermost layers are
+replaced by a cross-domain Transformer Encoder. This Transformer uses self-attention within each domain,
+and cross-attention across domains.
+The model achieves a SDR of 9.00 dB on the MUSDB HQ test set. Moreover, when using sparse attention
+kernels to extend its receptive field and per source fine-tuning, we achieve state-of-the-art 9.20 dB of SDR.
+
+Samples are available [on our sample page](https://ai.honu.io/papers/htdemucs/index.html).
+Checkout [our paper][htdemucs] for more information.
+It has been trained on the [MUSDB HQ][musdb] dataset + an extra training dataset of 800 songs.
+This model separates drums, bass and vocals and other stems for any song.
+
+
+As Hybrid Transformer Demucs is brand new, it is not activated by default, you can activate it in the usual
+commands described hereafter with `-n htdemucs_ft`.
+The single, non fine-tuned model is provided as `-n htdemucs`, and the retrained baseline
+as `-n hdemucs_mmi`. The Sparse Hybrid Transformer model decribed in our paper is not provided as its
+requires custom CUDA code that is not ready for release yet.
+We are also releasing an experimental 6 sources model, that adds a `guitar` and `piano` source.
+Quick testing seems to show okay quality for `guitar`, but a lot of bleeding and artifacts for the `piano` source.
+
+
+<p align="center">
+<img src="./demucs.png" alt="Schema representing the structure of Hybrid Transformer Demucs,
+    with a dual U-Net structure, one branch for the temporal domain,
+    and one branch for the spectral domain. There is a cross-domain Transformer between the Encoders and Decoders."
+width="800px"></p>
+
+
+
+## Important news if you are already using Demucs
+
+See the [release notes](./docs/release.md) for more details.
+
+- 07/12/2022: Demucs v4 now on PyPI. **htdemucs** model now used by default. Also releasing
+    a 6 sources models (adding `guitar` and `piano`, although the latter doesn't work so well at the moment).
+- 16/11/2022: Added the new **Hybrid Transformer Demucs v4** models.
+	Adding support for the [torchaudio implementation of HDemucs](https://pytorch.org/audio/stable/tutorials/hybrid_demucs_tutorial.html).
+- 30/08/2022: added reproducibility and ablation grids, along with an updated version of the paper.
+- 17/08/2022: Releasing v3.0.5: Set split segment length to reduce memory. Compatible with pyTorch 1.12.
+- 24/02/2022: Releasing v3.0.4: split into two stems (i.e. karaoke mode).
+    Export as float32 or int24.
+- 17/12/2021: Releasing v3.0.3: bug fixes  (thanks @keunwoochoi), memory drastically
+    reduced on GPU (thanks @famzah) and new multi-core evaluation on CPU (`-j` flag).
+- 12/11/2021: Releasing **Demucs v3** with hybrid domain separation. Strong improvements
+	on all sources. This is the model that won Sony MDX challenge.
+- 11/05/2021: Adding support for MusDB-HQ and arbitrary wav set, for the MDX challenge. For more information
+on joining the challenge with Demucs see [the Demucs MDX instructions](docs/mdx.md)
+
+
+## Comparison with other models
+
+We provide hereafter a summary of the different metrics presented in the paper.
+You can also compare Hybrid Demucs (v3), [KUIELAB-MDX-Net][kuielab], [Spleeter][spleeter], Open-Unmix, Demucs (v1), and Conv-Tasnet on one of my favorite
+songs on my [soundcloud playlist][soundcloud].
+
+### Comparison of accuracy
+
+`Overall SDR` is the mean of the SDR for each of the 4 sources, `MOS Quality` is a rating from 1 to 5
+of the naturalness and absence of artifacts given by human listeners (5 = no artifacts), `MOS Contamination`
+is a rating from 1 to 5 with 5 being zero contamination by other sources. We refer the reader to our [paper][hybrid_paper],
+for more details.
+
+| Model                        | Domain      | Extra data? | Overall SDR | MOS Quality | MOS Contamination |
+|------------------------------|-------------|-------------|-------------|-------------|-------------------|
+| [Wave-U-Net][waveunet]       | waveform    | no          | 3.2         | -           | -                 |
+| [Open-Unmix][openunmix]      | spectrogram | no          | 5.3         | -           | -                 |
+| [D3Net][d3net]               | spectrogram | no          | 6.0         | -           | -                 |
+| [Conv-Tasnet][demucs_v2]     | waveform    | no          | 5.7         | -           |                   |
+| [Demucs (v2)][demucs_v2]     | waveform    | no          | 6.3         | 2.37        | 2.36              |
+| [ResUNetDecouple+][decouple] | spectrogram | no          | 6.7         | -           | -                 |
+| [KUIELAB-MDX-Net][kuielab]   | hybrid      | no          | 7.5         | **2.86**    | 2.55              |
+| [Band-Spit RNN][bandsplit]   | spectrogram | no          | **8.2**     | -           | -                 |
+| **Hybrid Demucs (v3)**       | hybrid      | no          | 7.7         | **2.83**    | **3.04**          |
+| [MMDenseLSTM][mmdenselstm]   | spectrogram | 804 songs   | 6.0         | -           | -                 |
+| [D3Net][d3net]               | spectrogram | 1.5k songs  | 6.7         | -           | -                 |
+| [Spleeter][spleeter]         | spectrogram | 25k songs   | 5.9         | -           | -                 |
+| [Band-Spit RNN][bandsplit]   | spectrogram | 1.7k (mixes only)     | **9.0**     | -           | -                 |
+| **HT Demucs f.t. (v4)**      | hybrid      | 800 songs   | **9.0**     | -           | -                 |
+
+
+
+## Requirements
+
+You will need at least Python 3.7. See `requirements_minimal.txt` for requirements for separation only,
+and `environment-[cpu|cuda].yml` (or `requirements.txt`) if you want to train a new model.
+
+### For Windows users
+
+Everytime you see `python3`, replace it with `python.exe`. You should always run commands from the
+Anaconda console.
+
+### For musicians
+
+If you just want to use Demucs to separate tracks, you can install it with
+
+```bash
+python3 -m pip install -U demucs
+```
+
+For bleeding edge versions, you can install directly from this repo using
+```bash
+python3 -m pip install -U git+https://github.com/facebookresearch/demucs#egg=demucs
+```
+
+Advanced OS support are provided on the following page, **you must read the page for your OS before posting an issues**:
+- **If you are using Windows:** [Windows support](docs/windows.md).
+- **If you are using MAC OS X:** [Mac OS X support](docs/mac.md).
+- **If you are using Linux:** [Linux support](docs/linux.md).
+
+### For machine learning scientists
+
+If you have anaconda installed, you can run from the root of this repository:
+
+```bash
+conda env update -f environment-cpu.yml  # if you don't have GPUs
+conda env update -f environment-cuda.yml # if you have GPUs
+conda activate demucs
+pip install -e .
+```
+
+This will create a `demucs` environment with all the dependencies installed.
+
+You will also need to install [soundstretch/soundtouch](https://www.surina.net/soundtouch/soundstretch.html): on Mac OSX you can do `brew install sound-touch`,
+and on Ubuntu `sudo apt-get install soundstretch`. This is used for the
+pitch/tempo augmentation.
+
+
+### Running in Docker
+
+Thanks to @xserrat, there is now a Docker image definition ready for using Demucs. This can ensure all libraries are correctly installed without interfering with the host OS. See his repo [Docker Facebook Demucs](https://github.com/xserrat/docker-facebook-demucs) for more information.
+
+
+### Running from Colab
+
+I made a Colab to easily separate track with Demucs. Note that
+transfer speeds with Colab are a bit slow for large media files,
+but it will allow you to use Demucs without installing anything.
+
+[Demucs on Google Colab](https://colab.research.google.com/drive/1dC9nVxk3V_VPjUADsnFu8EiT-xnU1tGH?usp=sharing)
+
+### Web Demo
+
+Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/demucs)
+
+### Graphical Interface
+
+@CarlGao4 has released a GUI for Demucs: [CarlGao4/Demucs-Gui](https://github.com/CarlGao4/Demucs-Gui). Downloads for Windows and macOS is available [here](https://github.com/CarlGao4/Demucs-Gui/releases). Use [FossHub mirror](https://fosshub.com/Demucs-GUI.html) to speed up your download.
+
+@Anjok07 is providing a self contained GUI in [UVR (Ultimate Vocal Remover)](https://github.com/facebookresearch/demucs/issues/334) that supports Demucs.
+
+### Other providers
+
+Audiostrip is providing free online separation with Demucs on their website [https://audiostrip.co.uk/](https://audiostrip.co.uk/).
+[MVSep](https://mvsep.com/) also provides free online separation, select `Demucs3 model B` for the best quality.
+
+Spleeter.io provides free online separation with Demucs on their website [https://www.spleeter.io/demucs](https://www.spleeter.io/demucs).
+
+
+## Separating tracks
+
+In order to try Demucs, you can just run from any folder (as long as you properly installed it)
+
+```bash
+demucs PATH_TO_AUDIO_FILE_1 [PATH_TO_AUDIO_FILE_2 ...]   # for Demucs
+# If you used `pip install --user` you might need to replace demucs with python3 -m demucs
+python3 -m demucs --mp3 --mp3-bitrate BITRATE PATH_TO_AUDIO_FILE_1  # output files saved as MP3
+# If your filename contain spaces don't forget to quote it !!!
+demucs "my music/my favorite track.mp3"
+# You can select different models with `-n` mdx_q is the quantized model, smaller but maybe a bit less accurate.
+demucs -n mdx_q myfile.mp3
+# If you only want to separate vocals out of an audio, use `--two-stems=vocal` (You can also set to drums or bass)
+demucs --two-stems=vocals myfile.mp3
+```
+
+
+If you have a GPU, but you run out of memory, please use `--segment SEGMENT` to reduce length of each split. `SEGMENT` should be changed to a integer. Personally recommend not less than 10 (the bigger the number is, the more memory is required, but quality may increase). Create an environment variable `PYTORCH_NO_CUDA_MEMORY_CACHING=1` is also helpful. If this still cannot help, please add `-d cpu` to the command line. See the section hereafter for more details on the memory requirements for GPU acceleration.
+
+Separated tracks are stored in the `separated/MODEL_NAME/TRACK_NAME` folder. There you will find four stereo wav files sampled at 44.1 kHz: `drums.wav`, `bass.wav`,
+`other.wav`, `vocals.wav` (or `.mp3` if you used the `--mp3` option).
+
+All audio formats supported by `torchaudio` can be processed (i.e. wav, mp3, flac, ogg/vorbis on Linux/Mac OS X etc.). On Windows, `torchaudio` has limited support, so we rely on `ffmpeg`, which should support pretty much anything.
+Audio is resampled on the fly if necessary.
+The output will be a wave file encoded as int16.
+You can save as float32 wav files with `--float32`, or 24 bits integer wav with `--int24`.
+You can pass `--mp3` to save as mp3 instead, and set the bitrate with `--mp3-bitrate` (default is 320kbps).
+
+It can happen that the output would need clipping, in particular due to some separation artifacts.
+Demucs will automatically rescale each output stem so as to avoid clipping. This can however break
+the relative volume between stems. If instead you prefer hard clipping, pass `--clip-mode clamp`.
+You can also try to reduce the volume of the input mixture before feeding it to Demucs.
+
+
+Other pre-trained models can be selected with the `-n` flag.
+The list of pre-trained models is:
+- `htdemucs`: first version of Hybrid Transformer Demucs. Trained on MusDB + 800 songs. Default model.
+- `htdemucs_ft`: fine-tuned version of `htdemucs`, separation will take 4 times more time
+    but might be a bit better. Same training set as `htdemucs`.
+- `htdemucs_6s`: 6 sources version of `htdemucs`, with `piano` and `guitar` being added as sources.
+    Note that the `piano` source is not working great at the moment.
+- `hdemucs_mmi`: Hybrid Demucs v3, retrained on MusDB + 800 songs.
+- `mdx`: trained only on MusDB HQ, winning model on track A at the [MDX][mdx] challenge.
+- `mdx_extra`: trained with extra training data (including MusDB test set), ranked 2nd on the track B
+    of the [MDX][mdx] challenge.
+- `mdx_q`, `mdx_extra_q`: quantized version of the previous models. Smaller download and storage
+    but quality can be slightly worse.
+- `SIG`: where `SIG` is a single model from the [model zoo](docs/training.md#model-zoo).
+
+The `--two-stems=vocals` option allows to separate vocals from the rest (e.g. karaoke mode).
+`vocals` can be changed into any source in the selected model.
+This will mix the files after separating the mix fully, so this won't be faster or use less memory.
+
+The `--shifts=SHIFTS` performs multiple predictions with random shifts (a.k.a the *shift trick*) of the input and average them. This makes prediction `SHIFTS` times
+slower. Don't use it unless you have a GPU.
+
+The `--overlap` option controls the amount of overlap between prediction windows. Default is 0.25 (i.e. 25%) which is probably fine.
+It can probably be reduced to 0.1 to improve a bit speed.
+
+
+The `-j` flag allow to specify a number of parallel jobs (e.g. `demucs -j 2 myfile.mp3`).
+This will multiply by the same amount the RAM used so be careful!
+
+### Memory requirements for GPU acceleration
+
+If you want to use GPU acceleration, you will need at least 3GB of RAM on your GPU for `demucs`. However, about 7GB of RAM will be required if you use the default arguments. Add `--segment SEGMENT` to change size of each split. If you only have 3GB memory, set SEGMENT to 8 (though quality may be worse if this argument is too small). Creating an environment variable `PYTORCH_NO_CUDA_MEMORY_CACHING=1` can help users with even smaller RAM such as 2GB (I separated a track that is 4 minutes but only 1.5GB is used), but this would make the separation slower.
+
+If you do not have enough memory on your GPU, simply add `-d cpu` to the command line to use the CPU. With Demucs, processing time should be roughly equal to 1.5 times the duration of the track.
+
+
+## Training Demucs
+
+If you want to train (Hybrid) Demucs, please follow the [training doc](docs/training.md).
+
+## MDX Challenge reproduction
+
+In order to reproduce the results from the Track A and Track B submissions, checkout the [MDX Hybrid Demucs submission repo][mdx_submission].
+
+
+
+## How to cite
+
+```
+@inproceedings{defossez2021hybrid,
+  title={Hybrid Spectrogram and Waveform Source Separation},
+  author={D{\'e}fossez, Alexandre},
+  booktitle={Proceedings of the ISMIR 2021 Workshop on Music Source Separation},
+  year={2021}
+}
+```
+
+## License
+
+Demucs is released under the MIT license as found in the [LICENSE](LICENSE) file.
+
+[hybrid_paper]: https://arxiv.org/abs/2111.03600
+[waveunet]: https://github.com/f90/Wave-U-Net
+[musdb]: https://sigsep.github.io/datasets/musdb.html
+[openunmix]: https://github.com/sigsep/open-unmix-pytorch
+[mmdenselstm]: https://arxiv.org/abs/1805.02410
+[demucs_v2]: https://github.com/facebookresearch/demucs/tree/v2
+[demucs_v3]: https://github.com/facebookresearch/demucs/tree/v3
+[spleeter]: https://github.com/deezer/spleeter
+[soundcloud]: https://soundcloud.com/honualx/sets/source-separation-in-the-waveform-domain
+[d3net]: https://arxiv.org/abs/2010.01733
+[mdx]: https://www.aicrowd.com/challenges/music-demixing-challenge-ismir-2021
+[kuielab]: https://github.com/kuielab/mdx-net-submission
+[decouple]: https://arxiv.org/abs/2109.05418
+[mdx_submission]: https://github.com/adefossez/mdx21_demucs
+[bandsplit]: https://arxiv.org/abs/2209.15174
+[htdemucs]: https://arxiv.org/abs/2211.08553
+
+%package -n python3-demucs
+Summary:	Music source separation in the waveform domain.
+Provides:	python-demucs
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-demucs
+# Demucs Music Source Separation
+
+[![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB)](https://opensource.fb.com/support-ukraine)
+![tests badge](https://github.com/facebookresearch/demucs/workflows/tests/badge.svg)
+![linter badge](https://github.com/facebookresearch/demucs/workflows/linter/badge.svg)
+
+
+This is the 4th release of Demucs (v4), featuring Hybrid Transformer based source separation.
+**For the classic Hybrid Demucs (v3):** [Go this commit][demucs_v3].
+If you are experiencing issues and want the old Demucs back, please fill an issue, and then you can get back to the v3 with
+`git checkout v3`. You can also go [Demucs v2][demucs_v2].
+
+
+Demucs is a state-of-the-art music source separation model, currently capable of separating
+drums, bass, and vocals from the rest of the accompaniment.
+Demucs is based on a U-Net convolutional architecture inspired by [Wave-U-Net][waveunet].
+The v4 version features [Hybrid Transformer Demucs][htdemucs], a hybrid spectrogram/waveform separation model using Transformers.
+It is based on [Hybrid Demucs][hybrid_paper] (also provided in this repo) with the innermost layers are
+replaced by a cross-domain Transformer Encoder. This Transformer uses self-attention within each domain,
+and cross-attention across domains.
+The model achieves a SDR of 9.00 dB on the MUSDB HQ test set. Moreover, when using sparse attention
+kernels to extend its receptive field and per source fine-tuning, we achieve state-of-the-art 9.20 dB of SDR.
+
+Samples are available [on our sample page](https://ai.honu.io/papers/htdemucs/index.html).
+Checkout [our paper][htdemucs] for more information.
+It has been trained on the [MUSDB HQ][musdb] dataset + an extra training dataset of 800 songs.
+This model separates drums, bass and vocals and other stems for any song.
+
+
+As Hybrid Transformer Demucs is brand new, it is not activated by default, you can activate it in the usual
+commands described hereafter with `-n htdemucs_ft`.
+The single, non fine-tuned model is provided as `-n htdemucs`, and the retrained baseline
+as `-n hdemucs_mmi`. The Sparse Hybrid Transformer model decribed in our paper is not provided as its
+requires custom CUDA code that is not ready for release yet.
+We are also releasing an experimental 6 sources model, that adds a `guitar` and `piano` source.
+Quick testing seems to show okay quality for `guitar`, but a lot of bleeding and artifacts for the `piano` source.
+
+
+<p align="center">
+<img src="./demucs.png" alt="Schema representing the structure of Hybrid Transformer Demucs,
+    with a dual U-Net structure, one branch for the temporal domain,
+    and one branch for the spectral domain. There is a cross-domain Transformer between the Encoders and Decoders."
+width="800px"></p>
+
+
+
+## Important news if you are already using Demucs
+
+See the [release notes](./docs/release.md) for more details.
+
+- 07/12/2022: Demucs v4 now on PyPI. **htdemucs** model now used by default. Also releasing
+    a 6 sources models (adding `guitar` and `piano`, although the latter doesn't work so well at the moment).
+- 16/11/2022: Added the new **Hybrid Transformer Demucs v4** models.
+	Adding support for the [torchaudio implementation of HDemucs](https://pytorch.org/audio/stable/tutorials/hybrid_demucs_tutorial.html).
+- 30/08/2022: added reproducibility and ablation grids, along with an updated version of the paper.
+- 17/08/2022: Releasing v3.0.5: Set split segment length to reduce memory. Compatible with pyTorch 1.12.
+- 24/02/2022: Releasing v3.0.4: split into two stems (i.e. karaoke mode).
+    Export as float32 or int24.
+- 17/12/2021: Releasing v3.0.3: bug fixes  (thanks @keunwoochoi), memory drastically
+    reduced on GPU (thanks @famzah) and new multi-core evaluation on CPU (`-j` flag).
+- 12/11/2021: Releasing **Demucs v3** with hybrid domain separation. Strong improvements
+	on all sources. This is the model that won Sony MDX challenge.
+- 11/05/2021: Adding support for MusDB-HQ and arbitrary wav set, for the MDX challenge. For more information
+on joining the challenge with Demucs see [the Demucs MDX instructions](docs/mdx.md)
+
+
+## Comparison with other models
+
+We provide hereafter a summary of the different metrics presented in the paper.
+You can also compare Hybrid Demucs (v3), [KUIELAB-MDX-Net][kuielab], [Spleeter][spleeter], Open-Unmix, Demucs (v1), and Conv-Tasnet on one of my favorite
+songs on my [soundcloud playlist][soundcloud].
+
+### Comparison of accuracy
+
+`Overall SDR` is the mean of the SDR for each of the 4 sources, `MOS Quality` is a rating from 1 to 5
+of the naturalness and absence of artifacts given by human listeners (5 = no artifacts), `MOS Contamination`
+is a rating from 1 to 5 with 5 being zero contamination by other sources. We refer the reader to our [paper][hybrid_paper],
+for more details.
+
+| Model                        | Domain      | Extra data? | Overall SDR | MOS Quality | MOS Contamination |
+|------------------------------|-------------|-------------|-------------|-------------|-------------------|
+| [Wave-U-Net][waveunet]       | waveform    | no          | 3.2         | -           | -                 |
+| [Open-Unmix][openunmix]      | spectrogram | no          | 5.3         | -           | -                 |
+| [D3Net][d3net]               | spectrogram | no          | 6.0         | -           | -                 |
+| [Conv-Tasnet][demucs_v2]     | waveform    | no          | 5.7         | -           |                   |
+| [Demucs (v2)][demucs_v2]     | waveform    | no          | 6.3         | 2.37        | 2.36              |
+| [ResUNetDecouple+][decouple] | spectrogram | no          | 6.7         | -           | -                 |
+| [KUIELAB-MDX-Net][kuielab]   | hybrid      | no          | 7.5         | **2.86**    | 2.55              |
+| [Band-Spit RNN][bandsplit]   | spectrogram | no          | **8.2**     | -           | -                 |
+| **Hybrid Demucs (v3)**       | hybrid      | no          | 7.7         | **2.83**    | **3.04**          |
+| [MMDenseLSTM][mmdenselstm]   | spectrogram | 804 songs   | 6.0         | -           | -                 |
+| [D3Net][d3net]               | spectrogram | 1.5k songs  | 6.7         | -           | -                 |
+| [Spleeter][spleeter]         | spectrogram | 25k songs   | 5.9         | -           | -                 |
+| [Band-Spit RNN][bandsplit]   | spectrogram | 1.7k (mixes only)     | **9.0**     | -           | -                 |
+| **HT Demucs f.t. (v4)**      | hybrid      | 800 songs   | **9.0**     | -           | -                 |
+
+
+
+## Requirements
+
+You will need at least Python 3.7. See `requirements_minimal.txt` for requirements for separation only,
+and `environment-[cpu|cuda].yml` (or `requirements.txt`) if you want to train a new model.
+
+### For Windows users
+
+Everytime you see `python3`, replace it with `python.exe`. You should always run commands from the
+Anaconda console.
+
+### For musicians
+
+If you just want to use Demucs to separate tracks, you can install it with
+
+```bash
+python3 -m pip install -U demucs
+```
+
+For bleeding edge versions, you can install directly from this repo using
+```bash
+python3 -m pip install -U git+https://github.com/facebookresearch/demucs#egg=demucs
+```
+
+Advanced OS support are provided on the following page, **you must read the page for your OS before posting an issues**:
+- **If you are using Windows:** [Windows support](docs/windows.md).
+- **If you are using MAC OS X:** [Mac OS X support](docs/mac.md).
+- **If you are using Linux:** [Linux support](docs/linux.md).
+
+### For machine learning scientists
+
+If you have anaconda installed, you can run from the root of this repository:
+
+```bash
+conda env update -f environment-cpu.yml  # if you don't have GPUs
+conda env update -f environment-cuda.yml # if you have GPUs
+conda activate demucs
+pip install -e .
+```
+
+This will create a `demucs` environment with all the dependencies installed.
+
+You will also need to install [soundstretch/soundtouch](https://www.surina.net/soundtouch/soundstretch.html): on Mac OSX you can do `brew install sound-touch`,
+and on Ubuntu `sudo apt-get install soundstretch`. This is used for the
+pitch/tempo augmentation.
+
+
+### Running in Docker
+
+Thanks to @xserrat, there is now a Docker image definition ready for using Demucs. This can ensure all libraries are correctly installed without interfering with the host OS. See his repo [Docker Facebook Demucs](https://github.com/xserrat/docker-facebook-demucs) for more information.
+
+
+### Running from Colab
+
+I made a Colab to easily separate track with Demucs. Note that
+transfer speeds with Colab are a bit slow for large media files,
+but it will allow you to use Demucs without installing anything.
+
+[Demucs on Google Colab](https://colab.research.google.com/drive/1dC9nVxk3V_VPjUADsnFu8EiT-xnU1tGH?usp=sharing)
+
+### Web Demo
+
+Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/demucs)
+
+### Graphical Interface
+
+@CarlGao4 has released a GUI for Demucs: [CarlGao4/Demucs-Gui](https://github.com/CarlGao4/Demucs-Gui). Downloads for Windows and macOS is available [here](https://github.com/CarlGao4/Demucs-Gui/releases). Use [FossHub mirror](https://fosshub.com/Demucs-GUI.html) to speed up your download.
+
+@Anjok07 is providing a self contained GUI in [UVR (Ultimate Vocal Remover)](https://github.com/facebookresearch/demucs/issues/334) that supports Demucs.
+
+### Other providers
+
+Audiostrip is providing free online separation with Demucs on their website [https://audiostrip.co.uk/](https://audiostrip.co.uk/).
+[MVSep](https://mvsep.com/) also provides free online separation, select `Demucs3 model B` for the best quality.
+
+Spleeter.io provides free online separation with Demucs on their website [https://www.spleeter.io/demucs](https://www.spleeter.io/demucs).
+
+
+## Separating tracks
+
+In order to try Demucs, you can just run from any folder (as long as you properly installed it)
+
+```bash
+demucs PATH_TO_AUDIO_FILE_1 [PATH_TO_AUDIO_FILE_2 ...]   # for Demucs
+# If you used `pip install --user` you might need to replace demucs with python3 -m demucs
+python3 -m demucs --mp3 --mp3-bitrate BITRATE PATH_TO_AUDIO_FILE_1  # output files saved as MP3
+# If your filename contain spaces don't forget to quote it !!!
+demucs "my music/my favorite track.mp3"
+# You can select different models with `-n` mdx_q is the quantized model, smaller but maybe a bit less accurate.
+demucs -n mdx_q myfile.mp3
+# If you only want to separate vocals out of an audio, use `--two-stems=vocal` (You can also set to drums or bass)
+demucs --two-stems=vocals myfile.mp3
+```
+
+
+If you have a GPU, but you run out of memory, please use `--segment SEGMENT` to reduce length of each split. `SEGMENT` should be changed to a integer. Personally recommend not less than 10 (the bigger the number is, the more memory is required, but quality may increase). Create an environment variable `PYTORCH_NO_CUDA_MEMORY_CACHING=1` is also helpful. If this still cannot help, please add `-d cpu` to the command line. See the section hereafter for more details on the memory requirements for GPU acceleration.
+
+Separated tracks are stored in the `separated/MODEL_NAME/TRACK_NAME` folder. There you will find four stereo wav files sampled at 44.1 kHz: `drums.wav`, `bass.wav`,
+`other.wav`, `vocals.wav` (or `.mp3` if you used the `--mp3` option).
+
+All audio formats supported by `torchaudio` can be processed (i.e. wav, mp3, flac, ogg/vorbis on Linux/Mac OS X etc.). On Windows, `torchaudio` has limited support, so we rely on `ffmpeg`, which should support pretty much anything.
+Audio is resampled on the fly if necessary.
+The output will be a wave file encoded as int16.
+You can save as float32 wav files with `--float32`, or 24 bits integer wav with `--int24`.
+You can pass `--mp3` to save as mp3 instead, and set the bitrate with `--mp3-bitrate` (default is 320kbps).
+
+It can happen that the output would need clipping, in particular due to some separation artifacts.
+Demucs will automatically rescale each output stem so as to avoid clipping. This can however break
+the relative volume between stems. If instead you prefer hard clipping, pass `--clip-mode clamp`.
+You can also try to reduce the volume of the input mixture before feeding it to Demucs.
+
+
+Other pre-trained models can be selected with the `-n` flag.
+The list of pre-trained models is:
+- `htdemucs`: first version of Hybrid Transformer Demucs. Trained on MusDB + 800 songs. Default model.
+- `htdemucs_ft`: fine-tuned version of `htdemucs`, separation will take 4 times more time
+    but might be a bit better. Same training set as `htdemucs`.
+- `htdemucs_6s`: 6 sources version of `htdemucs`, with `piano` and `guitar` being added as sources.
+    Note that the `piano` source is not working great at the moment.
+- `hdemucs_mmi`: Hybrid Demucs v3, retrained on MusDB + 800 songs.
+- `mdx`: trained only on MusDB HQ, winning model on track A at the [MDX][mdx] challenge.
+- `mdx_extra`: trained with extra training data (including MusDB test set), ranked 2nd on the track B
+    of the [MDX][mdx] challenge.
+- `mdx_q`, `mdx_extra_q`: quantized version of the previous models. Smaller download and storage
+    but quality can be slightly worse.
+- `SIG`: where `SIG` is a single model from the [model zoo](docs/training.md#model-zoo).
+
+The `--two-stems=vocals` option allows to separate vocals from the rest (e.g. karaoke mode).
+`vocals` can be changed into any source in the selected model.
+This will mix the files after separating the mix fully, so this won't be faster or use less memory.
+
+The `--shifts=SHIFTS` performs multiple predictions with random shifts (a.k.a the *shift trick*) of the input and average them. This makes prediction `SHIFTS` times
+slower. Don't use it unless you have a GPU.
+
+The `--overlap` option controls the amount of overlap between prediction windows. Default is 0.25 (i.e. 25%) which is probably fine.
+It can probably be reduced to 0.1 to improve a bit speed.
+
+
+The `-j` flag allow to specify a number of parallel jobs (e.g. `demucs -j 2 myfile.mp3`).
+This will multiply by the same amount the RAM used so be careful!
+
+### Memory requirements for GPU acceleration
+
+If you want to use GPU acceleration, you will need at least 3GB of RAM on your GPU for `demucs`. However, about 7GB of RAM will be required if you use the default arguments. Add `--segment SEGMENT` to change size of each split. If you only have 3GB memory, set SEGMENT to 8 (though quality may be worse if this argument is too small). Creating an environment variable `PYTORCH_NO_CUDA_MEMORY_CACHING=1` can help users with even smaller RAM such as 2GB (I separated a track that is 4 minutes but only 1.5GB is used), but this would make the separation slower.
+
+If you do not have enough memory on your GPU, simply add `-d cpu` to the command line to use the CPU. With Demucs, processing time should be roughly equal to 1.5 times the duration of the track.
+
+
+## Training Demucs
+
+If you want to train (Hybrid) Demucs, please follow the [training doc](docs/training.md).
+
+## MDX Challenge reproduction
+
+In order to reproduce the results from the Track A and Track B submissions, checkout the [MDX Hybrid Demucs submission repo][mdx_submission].
+
+
+
+## How to cite
+
+```
+@inproceedings{defossez2021hybrid,
+  title={Hybrid Spectrogram and Waveform Source Separation},
+  author={D{\'e}fossez, Alexandre},
+  booktitle={Proceedings of the ISMIR 2021 Workshop on Music Source Separation},
+  year={2021}
+}
+```
+
+## License
+
+Demucs is released under the MIT license as found in the [LICENSE](LICENSE) file.
+
+[hybrid_paper]: https://arxiv.org/abs/2111.03600
+[waveunet]: https://github.com/f90/Wave-U-Net
+[musdb]: https://sigsep.github.io/datasets/musdb.html
+[openunmix]: https://github.com/sigsep/open-unmix-pytorch
+[mmdenselstm]: https://arxiv.org/abs/1805.02410
+[demucs_v2]: https://github.com/facebookresearch/demucs/tree/v2
+[demucs_v3]: https://github.com/facebookresearch/demucs/tree/v3
+[spleeter]: https://github.com/deezer/spleeter
+[soundcloud]: https://soundcloud.com/honualx/sets/source-separation-in-the-waveform-domain
+[d3net]: https://arxiv.org/abs/2010.01733
+[mdx]: https://www.aicrowd.com/challenges/music-demixing-challenge-ismir-2021
+[kuielab]: https://github.com/kuielab/mdx-net-submission
+[decouple]: https://arxiv.org/abs/2109.05418
+[mdx_submission]: https://github.com/adefossez/mdx21_demucs
+[bandsplit]: https://arxiv.org/abs/2209.15174
+[htdemucs]: https://arxiv.org/abs/2211.08553
+
+%package help
+Summary:	Development documents and examples for demucs
+Provides:	python3-demucs-doc
+%description help
+# Demucs Music Source Separation
+
+[![Support Ukraine](https://img.shields.io/badge/Support-Ukraine-FFD500?style=flat&labelColor=005BBB)](https://opensource.fb.com/support-ukraine)
+![tests badge](https://github.com/facebookresearch/demucs/workflows/tests/badge.svg)
+![linter badge](https://github.com/facebookresearch/demucs/workflows/linter/badge.svg)
+
+
+This is the 4th release of Demucs (v4), featuring Hybrid Transformer based source separation.
+**For the classic Hybrid Demucs (v3):** [Go this commit][demucs_v3].
+If you are experiencing issues and want the old Demucs back, please fill an issue, and then you can get back to the v3 with
+`git checkout v3`. You can also go [Demucs v2][demucs_v2].
+
+
+Demucs is a state-of-the-art music source separation model, currently capable of separating
+drums, bass, and vocals from the rest of the accompaniment.
+Demucs is based on a U-Net convolutional architecture inspired by [Wave-U-Net][waveunet].
+The v4 version features [Hybrid Transformer Demucs][htdemucs], a hybrid spectrogram/waveform separation model using Transformers.
+It is based on [Hybrid Demucs][hybrid_paper] (also provided in this repo) with the innermost layers are
+replaced by a cross-domain Transformer Encoder. This Transformer uses self-attention within each domain,
+and cross-attention across domains.
+The model achieves a SDR of 9.00 dB on the MUSDB HQ test set. Moreover, when using sparse attention
+kernels to extend its receptive field and per source fine-tuning, we achieve state-of-the-art 9.20 dB of SDR.
+
+Samples are available [on our sample page](https://ai.honu.io/papers/htdemucs/index.html).
+Checkout [our paper][htdemucs] for more information.
+It has been trained on the [MUSDB HQ][musdb] dataset + an extra training dataset of 800 songs.
+This model separates drums, bass and vocals and other stems for any song.
+
+
+As Hybrid Transformer Demucs is brand new, it is not activated by default, you can activate it in the usual
+commands described hereafter with `-n htdemucs_ft`.
+The single, non fine-tuned model is provided as `-n htdemucs`, and the retrained baseline
+as `-n hdemucs_mmi`. The Sparse Hybrid Transformer model decribed in our paper is not provided as its
+requires custom CUDA code that is not ready for release yet.
+We are also releasing an experimental 6 sources model, that adds a `guitar` and `piano` source.
+Quick testing seems to show okay quality for `guitar`, but a lot of bleeding and artifacts for the `piano` source.
+
+
+<p align="center">
+<img src="./demucs.png" alt="Schema representing the structure of Hybrid Transformer Demucs,
+    with a dual U-Net structure, one branch for the temporal domain,
+    and one branch for the spectral domain. There is a cross-domain Transformer between the Encoders and Decoders."
+width="800px"></p>
+
+
+
+## Important news if you are already using Demucs
+
+See the [release notes](./docs/release.md) for more details.
+
+- 07/12/2022: Demucs v4 now on PyPI. **htdemucs** model now used by default. Also releasing
+    a 6 sources models (adding `guitar` and `piano`, although the latter doesn't work so well at the moment).
+- 16/11/2022: Added the new **Hybrid Transformer Demucs v4** models.
+	Adding support for the [torchaudio implementation of HDemucs](https://pytorch.org/audio/stable/tutorials/hybrid_demucs_tutorial.html).
+- 30/08/2022: added reproducibility and ablation grids, along with an updated version of the paper.
+- 17/08/2022: Releasing v3.0.5: Set split segment length to reduce memory. Compatible with pyTorch 1.12.
+- 24/02/2022: Releasing v3.0.4: split into two stems (i.e. karaoke mode).
+    Export as float32 or int24.
+- 17/12/2021: Releasing v3.0.3: bug fixes  (thanks @keunwoochoi), memory drastically
+    reduced on GPU (thanks @famzah) and new multi-core evaluation on CPU (`-j` flag).
+- 12/11/2021: Releasing **Demucs v3** with hybrid domain separation. Strong improvements
+	on all sources. This is the model that won Sony MDX challenge.
+- 11/05/2021: Adding support for MusDB-HQ and arbitrary wav set, for the MDX challenge. For more information
+on joining the challenge with Demucs see [the Demucs MDX instructions](docs/mdx.md)
+
+
+## Comparison with other models
+
+We provide hereafter a summary of the different metrics presented in the paper.
+You can also compare Hybrid Demucs (v3), [KUIELAB-MDX-Net][kuielab], [Spleeter][spleeter], Open-Unmix, Demucs (v1), and Conv-Tasnet on one of my favorite
+songs on my [soundcloud playlist][soundcloud].
+
+### Comparison of accuracy
+
+`Overall SDR` is the mean of the SDR for each of the 4 sources, `MOS Quality` is a rating from 1 to 5
+of the naturalness and absence of artifacts given by human listeners (5 = no artifacts), `MOS Contamination`
+is a rating from 1 to 5 with 5 being zero contamination by other sources. We refer the reader to our [paper][hybrid_paper],
+for more details.
+
+| Model                        | Domain      | Extra data? | Overall SDR | MOS Quality | MOS Contamination |
+|------------------------------|-------------|-------------|-------------|-------------|-------------------|
+| [Wave-U-Net][waveunet]       | waveform    | no          | 3.2         | -           | -                 |
+| [Open-Unmix][openunmix]      | spectrogram | no          | 5.3         | -           | -                 |
+| [D3Net][d3net]               | spectrogram | no          | 6.0         | -           | -                 |
+| [Conv-Tasnet][demucs_v2]     | waveform    | no          | 5.7         | -           |                   |
+| [Demucs (v2)][demucs_v2]     | waveform    | no          | 6.3         | 2.37        | 2.36              |
+| [ResUNetDecouple+][decouple] | spectrogram | no          | 6.7         | -           | -                 |
+| [KUIELAB-MDX-Net][kuielab]   | hybrid      | no          | 7.5         | **2.86**    | 2.55              |
+| [Band-Spit RNN][bandsplit]   | spectrogram | no          | **8.2**     | -           | -                 |
+| **Hybrid Demucs (v3)**       | hybrid      | no          | 7.7         | **2.83**    | **3.04**          |
+| [MMDenseLSTM][mmdenselstm]   | spectrogram | 804 songs   | 6.0         | -           | -                 |
+| [D3Net][d3net]               | spectrogram | 1.5k songs  | 6.7         | -           | -                 |
+| [Spleeter][spleeter]         | spectrogram | 25k songs   | 5.9         | -           | -                 |
+| [Band-Spit RNN][bandsplit]   | spectrogram | 1.7k (mixes only)     | **9.0**     | -           | -                 |
+| **HT Demucs f.t. (v4)**      | hybrid      | 800 songs   | **9.0**     | -           | -                 |
+
+
+
+## Requirements
+
+You will need at least Python 3.7. See `requirements_minimal.txt` for requirements for separation only,
+and `environment-[cpu|cuda].yml` (or `requirements.txt`) if you want to train a new model.
+
+### For Windows users
+
+Everytime you see `python3`, replace it with `python.exe`. You should always run commands from the
+Anaconda console.
+
+### For musicians
+
+If you just want to use Demucs to separate tracks, you can install it with
+
+```bash
+python3 -m pip install -U demucs
+```
+
+For bleeding edge versions, you can install directly from this repo using
+```bash
+python3 -m pip install -U git+https://github.com/facebookresearch/demucs#egg=demucs
+```
+
+Advanced OS support are provided on the following page, **you must read the page for your OS before posting an issues**:
+- **If you are using Windows:** [Windows support](docs/windows.md).
+- **If you are using MAC OS X:** [Mac OS X support](docs/mac.md).
+- **If you are using Linux:** [Linux support](docs/linux.md).
+
+### For machine learning scientists
+
+If you have anaconda installed, you can run from the root of this repository:
+
+```bash
+conda env update -f environment-cpu.yml  # if you don't have GPUs
+conda env update -f environment-cuda.yml # if you have GPUs
+conda activate demucs
+pip install -e .
+```
+
+This will create a `demucs` environment with all the dependencies installed.
+
+You will also need to install [soundstretch/soundtouch](https://www.surina.net/soundtouch/soundstretch.html): on Mac OSX you can do `brew install sound-touch`,
+and on Ubuntu `sudo apt-get install soundstretch`. This is used for the
+pitch/tempo augmentation.
+
+
+### Running in Docker
+
+Thanks to @xserrat, there is now a Docker image definition ready for using Demucs. This can ensure all libraries are correctly installed without interfering with the host OS. See his repo [Docker Facebook Demucs](https://github.com/xserrat/docker-facebook-demucs) for more information.
+
+
+### Running from Colab
+
+I made a Colab to easily separate track with Demucs. Note that
+transfer speeds with Colab are a bit slow for large media files,
+but it will allow you to use Demucs without installing anything.
+
+[Demucs on Google Colab](https://colab.research.google.com/drive/1dC9nVxk3V_VPjUADsnFu8EiT-xnU1tGH?usp=sharing)
+
+### Web Demo
+
+Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/akhaliq/demucs)
+
+### Graphical Interface
+
+@CarlGao4 has released a GUI for Demucs: [CarlGao4/Demucs-Gui](https://github.com/CarlGao4/Demucs-Gui). Downloads for Windows and macOS is available [here](https://github.com/CarlGao4/Demucs-Gui/releases). Use [FossHub mirror](https://fosshub.com/Demucs-GUI.html) to speed up your download.
+
+@Anjok07 is providing a self contained GUI in [UVR (Ultimate Vocal Remover)](https://github.com/facebookresearch/demucs/issues/334) that supports Demucs.
+
+### Other providers
+
+Audiostrip is providing free online separation with Demucs on their website [https://audiostrip.co.uk/](https://audiostrip.co.uk/).
+[MVSep](https://mvsep.com/) also provides free online separation, select `Demucs3 model B` for the best quality.
+
+Spleeter.io provides free online separation with Demucs on their website [https://www.spleeter.io/demucs](https://www.spleeter.io/demucs).
+
+
+## Separating tracks
+
+In order to try Demucs, you can just run from any folder (as long as you properly installed it)
+
+```bash
+demucs PATH_TO_AUDIO_FILE_1 [PATH_TO_AUDIO_FILE_2 ...]   # for Demucs
+# If you used `pip install --user` you might need to replace demucs with python3 -m demucs
+python3 -m demucs --mp3 --mp3-bitrate BITRATE PATH_TO_AUDIO_FILE_1  # output files saved as MP3
+# If your filename contain spaces don't forget to quote it !!!
+demucs "my music/my favorite track.mp3"
+# You can select different models with `-n` mdx_q is the quantized model, smaller but maybe a bit less accurate.
+demucs -n mdx_q myfile.mp3
+# If you only want to separate vocals out of an audio, use `--two-stems=vocal` (You can also set to drums or bass)
+demucs --two-stems=vocals myfile.mp3
+```
+
+
+If you have a GPU, but you run out of memory, please use `--segment SEGMENT` to reduce length of each split. `SEGMENT` should be changed to a integer. Personally recommend not less than 10 (the bigger the number is, the more memory is required, but quality may increase). Create an environment variable `PYTORCH_NO_CUDA_MEMORY_CACHING=1` is also helpful. If this still cannot help, please add `-d cpu` to the command line. See the section hereafter for more details on the memory requirements for GPU acceleration.
+
+Separated tracks are stored in the `separated/MODEL_NAME/TRACK_NAME` folder. There you will find four stereo wav files sampled at 44.1 kHz: `drums.wav`, `bass.wav`,
+`other.wav`, `vocals.wav` (or `.mp3` if you used the `--mp3` option).
+
+All audio formats supported by `torchaudio` can be processed (i.e. wav, mp3, flac, ogg/vorbis on Linux/Mac OS X etc.). On Windows, `torchaudio` has limited support, so we rely on `ffmpeg`, which should support pretty much anything.
+Audio is resampled on the fly if necessary.
+The output will be a wave file encoded as int16.
+You can save as float32 wav files with `--float32`, or 24 bits integer wav with `--int24`.
+You can pass `--mp3` to save as mp3 instead, and set the bitrate with `--mp3-bitrate` (default is 320kbps).
+
+It can happen that the output would need clipping, in particular due to some separation artifacts.
+Demucs will automatically rescale each output stem so as to avoid clipping. This can however break
+the relative volume between stems. If instead you prefer hard clipping, pass `--clip-mode clamp`.
+You can also try to reduce the volume of the input mixture before feeding it to Demucs.
+
+
+Other pre-trained models can be selected with the `-n` flag.
+The list of pre-trained models is:
+- `htdemucs`: first version of Hybrid Transformer Demucs. Trained on MusDB + 800 songs. Default model.
+- `htdemucs_ft`: fine-tuned version of `htdemucs`, separation will take 4 times more time
+    but might be a bit better. Same training set as `htdemucs`.
+- `htdemucs_6s`: 6 sources version of `htdemucs`, with `piano` and `guitar` being added as sources.
+    Note that the `piano` source is not working great at the moment.
+- `hdemucs_mmi`: Hybrid Demucs v3, retrained on MusDB + 800 songs.
+- `mdx`: trained only on MusDB HQ, winning model on track A at the [MDX][mdx] challenge.
+- `mdx_extra`: trained with extra training data (including MusDB test set), ranked 2nd on the track B
+    of the [MDX][mdx] challenge.
+- `mdx_q`, `mdx_extra_q`: quantized version of the previous models. Smaller download and storage
+    but quality can be slightly worse.
+- `SIG`: where `SIG` is a single model from the [model zoo](docs/training.md#model-zoo).
+
+The `--two-stems=vocals` option allows to separate vocals from the rest (e.g. karaoke mode).
+`vocals` can be changed into any source in the selected model.
+This will mix the files after separating the mix fully, so this won't be faster or use less memory.
+
+The `--shifts=SHIFTS` performs multiple predictions with random shifts (a.k.a the *shift trick*) of the input and average them. This makes prediction `SHIFTS` times
+slower. Don't use it unless you have a GPU.
+
+The `--overlap` option controls the amount of overlap between prediction windows. Default is 0.25 (i.e. 25%) which is probably fine.
+It can probably be reduced to 0.1 to improve a bit speed.
+
+
+The `-j` flag allow to specify a number of parallel jobs (e.g. `demucs -j 2 myfile.mp3`).
+This will multiply by the same amount the RAM used so be careful!
+
+### Memory requirements for GPU acceleration
+
+If you want to use GPU acceleration, you will need at least 3GB of RAM on your GPU for `demucs`. However, about 7GB of RAM will be required if you use the default arguments. Add `--segment SEGMENT` to change size of each split. If you only have 3GB memory, set SEGMENT to 8 (though quality may be worse if this argument is too small). Creating an environment variable `PYTORCH_NO_CUDA_MEMORY_CACHING=1` can help users with even smaller RAM such as 2GB (I separated a track that is 4 minutes but only 1.5GB is used), but this would make the separation slower.
+
+If you do not have enough memory on your GPU, simply add `-d cpu` to the command line to use the CPU. With Demucs, processing time should be roughly equal to 1.5 times the duration of the track.
+
+
+## Training Demucs
+
+If you want to train (Hybrid) Demucs, please follow the [training doc](docs/training.md).
+
+## MDX Challenge reproduction
+
+In order to reproduce the results from the Track A and Track B submissions, checkout the [MDX Hybrid Demucs submission repo][mdx_submission].
+
+
+
+## How to cite
+
+```
+@inproceedings{defossez2021hybrid,
+  title={Hybrid Spectrogram and Waveform Source Separation},
+  author={D{\'e}fossez, Alexandre},
+  booktitle={Proceedings of the ISMIR 2021 Workshop on Music Source Separation},
+  year={2021}
+}
+```
+
+## License
+
+Demucs is released under the MIT license as found in the [LICENSE](LICENSE) file.
+
+[hybrid_paper]: https://arxiv.org/abs/2111.03600
+[waveunet]: https://github.com/f90/Wave-U-Net
+[musdb]: https://sigsep.github.io/datasets/musdb.html
+[openunmix]: https://github.com/sigsep/open-unmix-pytorch
+[mmdenselstm]: https://arxiv.org/abs/1805.02410
+[demucs_v2]: https://github.com/facebookresearch/demucs/tree/v2
+[demucs_v3]: https://github.com/facebookresearch/demucs/tree/v3
+[spleeter]: https://github.com/deezer/spleeter
+[soundcloud]: https://soundcloud.com/honualx/sets/source-separation-in-the-waveform-domain
+[d3net]: https://arxiv.org/abs/2010.01733
+[mdx]: https://www.aicrowd.com/challenges/music-demixing-challenge-ismir-2021
+[kuielab]: https://github.com/kuielab/mdx-net-submission
+[decouple]: https://arxiv.org/abs/2109.05418
+[mdx_submission]: https://github.com/adefossez/mdx21_demucs
+[bandsplit]: https://arxiv.org/abs/2209.15174
+[htdemucs]: https://arxiv.org/abs/2211.08553
+
+%prep
+%autosetup -n demucs-4.0.0
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-demucs -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 4.0.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..4972949
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+bcf89c0205f3c9a81b28e14244cfb50f  demucs-4.0.0.tar.gz