add test_model_breaks

README.md

@@ -18,9 +18,9 @@ license: gpl-3.0
 
 ![mel spectrogram](mel.png)
 
-Audio can be represented as images by transforming to a [mel spectrogram](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum), such as the one shown above. The class `Mel` in `mel.py` can convert a slice of audio into a mel spectrogram of `x_res` x `y_res` and vice versa. The higher the resolution, the less audio information will be lost. You can see how this works in the `test-mel.ipynb` notebook.
+Audio can be represented as images by transforming to a [mel spectrogram](https://en.wikipedia.org/wiki/Mel-frequency_cepstrum), such as the one shown above. The class `Mel` in `mel.py` can convert a slice of audio into a mel spectrogram of `x_res` x `y_res` and vice versa. The higher the resolution, the less audio information will be lost. You can see how this works in the `test_mel.ipynb` notebook.
 
-A DDPM model is trained on a set of mel spectrograms that have been generated from a directory of audio files. It is then used to synthesize similar mel spectrograms, which are then converted back into audio. See the `test-model.ipynb` notebook for an example.
+A DDPM model is trained on a set of mel spectrograms that have been generated from a directory of audio files. It is then used to synthesize similar mel spectrograms, which are then converted back into audio. See the `test_model.ipynb` and `test_model_breaks.ipynb` notebooks for examples.
 
 You can play around with the model I trained on about 500 songs from my Spotify "liked" playlist on [Google Colab](https://colab.research.google.com/github/teticio/audio-diffusion/blob/master/notebooks/test-model.ipynb) or [Hugging Face spaces](https://huggingface.co/spaces/teticio/audio-diffusion). Check out some samples I generated [here](https://soundcloud.com/teticio2/sets/audio-diffusion).