configuration for hop_size 256

[yhgon]

when I configure factors for [4,4,4,2,2] to match with hop_size=256 but I could not find segment_length
Would you have idea for exactly matching segment_length to match dilation, padding for the configuration?

I got error below with segment_length=7200

Traceback (most recent call last):
  File "train.py", line 185, in <module>
    run(config, args)
  File "train.py", line 92, in run
    loss = model.compute_loss(mels, batch)
  File "/content/WaveGrad/model/diffusion_process.py", line 176, in compute_loss
    eps_recon = self.nn(mels, y_noisy, continuous_sqrt_alpha_cumprod)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/content/WaveGrad/model/nn.py", line 119, in forward
    ublock_outputs = ublock(x=ublock_outputs, scale=scale, shift=shift)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/content/WaveGrad/model/upsampling.py", line 82, in forward
    outputs = self.first_block_main_branch['modulation'](outputs, scale, shift)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/content/WaveGrad/model/upsampling.py", line 30, in forward
    outputs = self.featurewise_affine(x, scale, shift)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/content/WaveGrad/model/linear_modulation.py", line 98, in forward
    outputs = scale * x + shift
RuntimeError: The size of tensor a (450) must match the size of tensor b (448) at non-singleton dimension 2

full configuration

{
    "model_config": {
        "factors": [4,4,4,2,2],
        "upsampling_preconv_out_channels": 768,
        "upsampling_out_channels": [512, 512, 256, 128, 128],
        "upsampling_dilations": [
            [1, 2, 1, 2],
            [1, 2, 1, 2],
            [1, 2, 4, 8],
            [1, 2, 4, 8],
            [1, 2, 4, 8]
        ],
        "downsampling_preconv_out_channels": 32,
        "downsampling_out_channels": [128, 128, 256, 512],
        "downsampling_dilations": [
            [1, 2, 4], [1, 2, 4], [1, 2, 4], [1, 2, 4]
        ]
    },
    "data_config": {
        "sample_rate": 22050,
        "n_fft": 1024,
        "win_length": 1024,
        "hop_length": 256,
        "f_min": 0,
        "f_max": 8000,
        "n_mels": 80
    },

[ivanvovk]

Segment length maybe should match the condition segment_length % hop_length == 0. And 7200 doesn't match it. Try 7168, for example.

[yhgon]

thanks for your confirm. train start well with hop_size=256 and segment_size=7200 .