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--- |
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title: Music Splitter |
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emoji: 🎶 |
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colorFrom: indigo |
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colorTo: yellow |
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sdk: docker |
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pinned: true |
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--- |
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |
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# Music Source Splitter 🎶 |
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<a href="https://huggingface.co/spaces/fabiogra/st-music-splitter"><img src="https://img.shields.io/badge/🤗%20Hugging%20Face-Spaces-blue" alt="Hugging Face Spaces"></a> |
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This is a streamlit demo of the [Music Source Separation](https://huggingface.co/spaces/fabiogra/st-music-splitter). |
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The model can separate the vocals, drums, bass, and other from a music track. |
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## Usage |
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You can use the demo [here](https://huggingface.co/spaces/fabiogra/st-music-splitter), or run it locally with: |
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```bash |
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streamlit run app.py |
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``` |
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> **Note**: In order to run the demo locally, you need to install the dependencies with `pip install -r requirements.txt`. |
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## How it works |
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The app uses a pretrained model called Hybrid Spectrogram and Waveform Source Separation from <a href="https://github.com/facebookresearch/demucs">facebook/htdemucs</a>. |
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## Acknowledgements |
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- HtDemucs model from <a href="https://github.com/facebookresearch/demucs">facebook/htdemucs</a> |
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- Streamlit Audio Recorder from <a href="https://github.com/stefanrmmr/streamlit_audio_recorder">stefanrmmr/streamlit_audio_recorder</a> |
