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@@ -0,0 +1 @@ +/demucs-4.0.0.tar.gz diff --git a/python-demucs.spec b/python-demucs.spec new file mode 100644 index 0000000..29bae8b --- /dev/null +++ b/python-demucs.spec @@ -0,0 +1,927 @@ +%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 + +[](https://opensource.fb.com/support-ukraine) + + + + +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: [](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 + +[](https://opensource.fb.com/support-ukraine) + + + + +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: [](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 + +[](https://opensource.fb.com/support-ukraine) + + + + +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: [](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 @@ -0,0 +1 @@ +bcf89c0205f3c9a81b28e14244cfb50f demucs-4.0.0.tar.gz |