HifiGAN resblocks are applied sequentially.

https://github.com/TensorSpeech/TensorFlowTTS/blob/master/tensorflow_tts/models/hifigan.py#L162

  for j in range(config.stacks):
      layers += [
          TFHifiResBlock(
              kernel_size=config.stack_kernel_size[j],
              filters=config.filters // (2 ** (i + 1)),
              dilation_rate=config.stack_dilation_rate[j],
              use_bias=config.use_bias,
              nonlinear_activation=config.nonlinear_activation,
              nonlinear_activation_params=config.nonlinear_activation_params,
              is_weight_norm=config.is_weight_norm,
              initializer_seed=config.initializer_seed,
              name="hifigan_resblock_._{}._._{}".format(i, j),
          )
      ]

HifiGAN resblocks should be applied in parallel.

[dathudeptrai]

@hccho2 I know it, but I think it does not affect a result much. I will fix an implementation in the next release. I will also check if the model inference faster or not 😄 .

[hccho2]

@dathudeptrai The issue I’m talking about isn’t the efficient side of the computation.

origin implementation:

xs=None
for j in range(config.stacks):
    if xs is None:
        xs = resblocks[j](x)
    else:
        xs += resblocks[j](x)

this repo:

for j in range(config.stacks):
    x = resblocks[j](x)