YAMNet-transfer-learning

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YAMNet-transfer-learning

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Transfer learning and fine-tuning with YAMNet

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Code for transfer learning and fine-tuning with YAMNet

This repo requires the code and weights file from the official yamnet directory to be downloaded: https://github.com/tensorflow/models/tree/master/research/audioset/yamnet.

- `generate YAMNet input.ipynb` pre-computes spectrograms from audio files and stores them as `.npy`. These are then loaded during training using a custom Tensorflow data generator defined in `datagen_yamnet.py`. Pre-computing the spectrograms greatly increases the speed of training / experimentation.

- `fine-tune YAMNet.ipynb` sets up spectrogram data generators, loads AudioSet weights into YAMNet and adds a custom network top, and runs transfer learning and fine-tuning.

See also: [tfk-audio](https://github.com/jackgle/tfk-audio) for additional resources for Tensorflow/Keras audio recognition and transfer learning.

##### References
* https://github.com/tensorflow/models/tree/master/research/audioset/yamnet
* Gemmeke, J. et. al.,
  [AudioSet: An ontology and human-labelled dataset for audio events](https://research.google.com/pubs/pub45857.html),
  ICASSP 2017
* Hershey, S. et. al.,
  [CNN Architectures for Large-Scale Audio Classification](https://research.google.com/pubs/pub45611.html),
  ICASSP 2017

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