Testing the network on music datasets

I’ve started to play around with the MagnaTagATune dataset. There’s a small change that needs to be made to the code when training on this dataset: Because it uses mp3 instead of wav, the pattern in wavenet/audio_reader.py needs to be adjusted. It would be nice to write a MagnaReader class that inherits from the AudioReader (or contains one), and that’s able to filter the content by genre using the provided metadata.

[robinsloan]

Just another music test to share… I think this is pretty nice/interesting!

https://soundcloud.com/robinsloan/sets/tensorflow-wavenet-temperature-demo

Model trained on this album to a loss of ~2.8 with these params:

{
    "filter_width": 2,
    "sample_rate": 8000,
    "dilations": [1, 2, 4, 8, 16, 32, 64, 128, 256, 512,
                  1, 2, 4, 8, 16, 32, 64, 128, 256, 512,
                  1, 2, 4, 8, 16, 32, 64, 128, 256, 512,
                  1, 2, 4, 8, 16, 32, 64],
    "residual_channels": 32,
    "dilation_channels": 32,
    "quantization_channels": 256,
    "skip_channels": 1024,
    "use_biases": false
}

[dunnevan]

I’ve been training on the MagnaTagATune dataset with clips that are tagged as solo piano.

https://soundcloud.com/evan-dunn-676478257/sets/magnatagatune-solo-piano

I am training with batches of 4 with a loss fluctuating between ~1.7 to ~2.4. The first second of audio is seeded.

{
    "filter_width": 2,
    "sample_rate": 8192,
    "dilations": [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024,
                  1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024,
                  1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024,
                  1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024],
    "residual_channels": 32,
    "dilation_channels": 32,
    "quantization_channels": 256,
    "skip_channels": 1024,
    "use_biases": true,
    "scalar_input": false,
    "initial_filter_width": 32
}