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TensorFlow implementation for audio neural style.
| Date | Stars |
|---|---|
| 2026-07-24 | 449 |
| 2026-07-25 | 449 |
| 2026-07-28 | 449 |
| 2026-07-30 | 449 |
| 2026-08-06 | 449 |
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# Audio Style Transfer This is a TensorFlow reimplementation of [Vadim's Lasagne code](https://github.com/vadim-v-lebedev/audio_style_tranfer) for style transfer algorithm for audio, which uses convolutions with random weights to represent audio features. To listen to examples go to the [blog post](http://dmitryulyanov.github.io/audio-texture-synthesis-and-style-transfer/). Also check out [Torch implementation](https://github.com/DmitryUlyanov/neural-style-audio-torch). So far it is CPU only, but if you are proficient in TensorFlow it should be easy to switch. Actually it runs fast on CPU. ### Dependencies - python (tested with 2.7) - TensorFlow ([installation instructions](https://www.tensorflow.org/get_started/os_setup)) - librosa ``` pip install librosa ``` - numpy and matplotlib The easiest way to install python is to use [Anaconda](https://www.continuum.io/downloads). ### How to run - Open `neural-style-audio-tf.ipynb` in Jupyter. - In case you want to use your own audio files as inputs, first cut them to 10s length with: ``` ffmpeg -i yourfile.mp3 -ss 00:00:00 -t 10 yourfile_10s.mp3 ``` - Set `CONTENT_FILENAME` and `STYLE_FILENAME` in the third cell of Jupyter notebook to your input files. - Run all cells. The most frequent problem is domination of either content or style in the output. To fight this problem, adjust `ALPHA` parameter. Larger `ALPHA` means more content in the output, and `ALPHA=0` means no content, which reduces stylization to texture generation. Example output `outputs/imperial_usa.wav`, the result of mixing content of imperial march from star wars with style of U.S. National Anthem, was obtained with default value `ALPHA=1e-2`. ### References - Original paper on style transfer: [A Neural Algorithm of Artistic Style](https://arxiv.org/abs/1508.06576) - [Neural style TensorFlow implementation](https://github.com/anishathalye/neural-style) - Publications on texture generation with random convolutions: - [Extreme Style Machines](https://nucl.ai/blog/extreme-style-machines/) - [Texture Synthesis Using Shallow Convolutional Networks with Random Filters](https://arxiv.org/abs/1606.00021) - [A Powerful Generative Model Using Random Weights for the Deep Image Representation](https://arxiv.org/pdf/1606.04801)
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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