Last active
November 26, 2020 11:25
-
-
Save albertz/01264cfbd2dfd73a19c1e2ac40bdb16b to your computer and use it in GitHub Desktop.
Parallel WaveGAN, converted PyTorch to RETURNN net dict, via https://github.com/albertz/pytorch-to-returnn
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
{ | |
'melgan': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'data'}, | |
'layer1': { | |
'class': 'conv', | |
'from': 'layer0', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 384, | |
'filter_size': (7,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'layer2': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer1'}, | |
'layer3': { | |
'class': 'transposed_conv', | |
'from': 'layer2', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (10,), | |
'strides': (5,), | |
'padding': 'valid', | |
'output_padding': (1,), | |
'remove_padding': (3,) | |
}, | |
'layer4': { | |
'class': 'subnetwork', | |
'from': 'layer3', | |
'subnetwork': { | |
'stack': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (1, 1), 'from': 'layer0'}, | |
'layer2': { | |
'class': 'conv', | |
'from': 'layer1', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (3,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
'layer4': { | |
'class': 'conv', | |
'from': 'layer3', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'output': {'class': 'copy', 'from': 'layer4'} | |
} | |
}, | |
'skip_layer': { | |
'class': 'conv', | |
'from': 'data', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
'output': {'class': 'copy', 'from': 'add'} | |
} | |
}, | |
'layer5': { | |
'class': 'subnetwork', | |
'from': 'layer4', | |
'subnetwork': { | |
'stack': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'layer0'}, | |
'layer2': { | |
'class': 'conv', | |
'from': 'layer1', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (3,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (3,) | |
}, | |
'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
'layer4': { | |
'class': 'conv', | |
'from': 'layer3', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'output': {'class': 'copy', 'from': 'layer4'} | |
} | |
}, | |
'skip_layer': { | |
'class': 'conv', | |
'from': 'data', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
'output': {'class': 'copy', 'from': 'add'} | |
} | |
}, | |
'layer6': { | |
'class': 'subnetwork', | |
'from': 'layer5', | |
'subnetwork': { | |
'stack': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (9, 9), 'from': 'layer0'}, | |
'layer2': { | |
'class': 'conv', | |
'from': 'layer1', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (3,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (9,) | |
}, | |
'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
'layer4': { | |
'class': 'conv', | |
'from': 'layer3', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'output': {'class': 'copy', 'from': 'layer4'} | |
} | |
}, | |
'skip_layer': { | |
'class': 'conv', | |
'from': 'data', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
'output': {'class': 'copy', 'from': 'add'} | |
} | |
}, | |
'layer7': { | |
'class': 'subnetwork', | |
'from': 'layer6', | |
'subnetwork': { | |
'stack': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (27, 27), 'from': 'layer0'}, | |
'layer2': { | |
'class': 'conv', | |
'from': 'layer1', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (3,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (27,) | |
}, | |
'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
'layer4': { | |
'class': 'conv', | |
'from': 'layer3', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'output': {'class': 'copy', 'from': 'layer4'} | |
} | |
}, | |
'skip_layer': { | |
'class': 'conv', | |
'from': 'data', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 192, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
'output': {'class': 'copy', 'from': 'add'} | |
} | |
}, | |
'layer8': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer7'}, | |
'layer9': { | |
'class': 'transposed_conv', | |
'from': 'layer8', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (10,), | |
'strides': (5,), | |
'padding': 'valid', | |
'output_padding': (1,), | |
'remove_padding': (3,) | |
}, | |
'layer10': { | |
'class': 'subnetwork', | |
'from': 'layer9', | |
'subnetwork': { | |
'stack': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (1, 1), 'from': 'layer0'}, | |
'layer2': { | |
'class': 'conv', | |
'from': 'layer1', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (3,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
'layer4': { | |
'class': 'conv', | |
'from': 'layer3', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'output': {'class': 'copy', 'from': 'layer4'} | |
} | |
}, | |
'skip_layer': { | |
'class': 'conv', | |
'from': 'data', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
'output': {'class': 'copy', 'from': 'add'} | |
} | |
}, | |
'layer11': { | |
'class': 'subnetwork', | |
'from': 'layer10', | |
'subnetwork': { | |
'stack': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'layer0'}, | |
'layer2': { | |
'class': 'conv', | |
'from': 'layer1', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (3,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (3,) | |
}, | |
'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
'layer4': { | |
'class': 'conv', | |
'from': 'layer3', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'output': {'class': 'copy', 'from': 'layer4'} | |
} | |
}, | |
'skip_layer': { | |
'class': 'conv', | |
'from': 'data', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
'output': {'class': 'copy', 'from': 'add'} | |
} | |
}, | |
'layer12': { | |
'class': 'subnetwork', | |
'from': 'layer11', | |
'subnetwork': { | |
'stack': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (9, 9), 'from': 'layer0'}, | |
'layer2': { | |
'class': 'conv', | |
'from': 'layer1', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (3,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (9,) | |
}, | |
'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
'layer4': { | |
'class': 'conv', | |
'from': 'layer3', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'output': {'class': 'copy', 'from': 'layer4'} | |
} | |
}, | |
'skip_layer': { | |
'class': 'conv', | |
'from': 'data', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
'output': {'class': 'copy', 'from': 'add'} | |
} | |
}, | |
'layer13': { | |
'class': 'subnetwork', | |
'from': 'layer12', | |
'subnetwork': { | |
'stack': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (27, 27), 'from': 'layer0'}, | |
'layer2': { | |
'class': 'conv', | |
'from': 'layer1', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (3,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (27,) | |
}, | |
'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
'layer4': { | |
'class': 'conv', | |
'from': 'layer3', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'output': {'class': 'copy', 'from': 'layer4'} | |
} | |
}, | |
'skip_layer': { | |
'class': 'conv', | |
'from': 'data', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 96, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
'output': {'class': 'copy', 'from': 'add'} | |
} | |
}, | |
'layer14': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer13'}, | |
'layer15': { | |
'class': 'transposed_conv', | |
'from': 'layer14', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (4,), | |
'strides': (2,), | |
'padding': 'valid', | |
'output_padding': (0,), | |
'remove_padding': (1,) | |
}, | |
'layer16': { | |
'class': 'subnetwork', | |
'from': 'layer15', | |
'subnetwork': { | |
'stack': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (1, 1), 'from': 'layer0'}, | |
'layer2': { | |
'class': 'conv', | |
'from': 'layer1', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (3,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
'layer4': { | |
'class': 'conv', | |
'from': 'layer3', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'output': {'class': 'copy', 'from': 'layer4'} | |
} | |
}, | |
'skip_layer': { | |
'class': 'conv', | |
'from': 'data', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
'output': {'class': 'copy', 'from': 'add'} | |
} | |
}, | |
'layer17': { | |
'class': 'subnetwork', | |
'from': 'layer16', | |
'subnetwork': { | |
'stack': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'layer0'}, | |
'layer2': { | |
'class': 'conv', | |
'from': 'layer1', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (3,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (3,) | |
}, | |
'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
'layer4': { | |
'class': 'conv', | |
'from': 'layer3', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'output': {'class': 'copy', 'from': 'layer4'} | |
} | |
}, | |
'skip_layer': { | |
'class': 'conv', | |
'from': 'data', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
'output': {'class': 'copy', 'from': 'add'} | |
} | |
}, | |
'layer18': { | |
'class': 'subnetwork', | |
'from': 'layer17', | |
'subnetwork': { | |
'stack': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (9, 9), 'from': 'layer0'}, | |
'layer2': { | |
'class': 'conv', | |
'from': 'layer1', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (3,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (9,) | |
}, | |
'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
'layer4': { | |
'class': 'conv', | |
'from': 'layer3', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'output': {'class': 'copy', 'from': 'layer4'} | |
} | |
}, | |
'skip_layer': { | |
'class': 'conv', | |
'from': 'data', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
'output': {'class': 'copy', 'from': 'add'} | |
} | |
}, | |
'layer19': { | |
'class': 'subnetwork', | |
'from': 'layer18', | |
'subnetwork': { | |
'stack': { | |
'class': 'subnetwork', | |
'from': 'data', | |
'subnetwork': { | |
'layer0': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'data'}, | |
'layer1': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (27, 27), 'from': 'layer0'}, | |
'layer2': { | |
'class': 'conv', | |
'from': 'layer1', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (3,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (27,) | |
}, | |
'layer3': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer2'}, | |
'layer4': { | |
'class': 'conv', | |
'from': 'layer3', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'output': {'class': 'copy', 'from': 'layer4'} | |
} | |
}, | |
'skip_layer': { | |
'class': 'conv', | |
'from': 'data', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 48, | |
'filter_size': (1,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'add': {'class': 'combine', 'kind': 'add', 'from': ['stack', 'skip_layer']}, | |
'output': {'class': 'copy', 'from': 'add'} | |
} | |
}, | |
'layer20': {'class': 'eval', 'eval': 'tf.nn.leaky_relu(source(0), alpha=0.2)', 'from': 'layer19'}, | |
'layer21': {'class': 'pad', 'mode': 'reflect', 'axes': 'spatial', 'padding': (3, 3), 'from': 'layer20'}, | |
'layer22': { | |
'class': 'conv', | |
'from': 'layer21', | |
'activation': None, | |
'with_bias': True, | |
'n_out': 4, | |
'filter_size': (7,), | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'layer23': {'class': 'activation', 'activation': 'tanh', 'from': 'layer22'}, | |
'output': {'class': 'copy', 'from': 'layer23'} | |
} | |
}, | |
'PQMF_Cast': {'class': 'cast', 'from': 'PQMF_Cast_unnamed_const', 'dtype': 'float32'}, | |
'PQMF_Cast_unnamed_const': {'class': 'constant', 'value': numpy.array(4, dtype=numpy.int32)}, | |
'PQMF_mul': {'class': 'combine', 'kind': 'mul', 'from': ['PQMF_updown_filter', 'PQMF_Cast']}, | |
'PQMF_updown_filter': { | |
'class': 'constant', | |
'value': numpy.array([ | |
[[1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]], | |
[[0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]], | |
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]], | |
[[0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [1.0, 0.0, 0.0, 0.0]] | |
], dtype=numpy.float32) | |
}, | |
'PQMF_FunctionalConvTransposed1d': { | |
'class': 'transposed_conv', | |
'from': 'melgan', | |
'n_out': 4, | |
'activation': None, | |
'with_bias': False, | |
'bias': None, | |
'filter_size': (4,), | |
'filter': 'PQMF_mul', | |
'filter_perm': {'static:0': 'F', 'static:1': 'static:1', 'F': 'static:0'}, | |
'padding': 'valid', | |
'output_padding': (0,), | |
'remove_padding': (0,), | |
'strides': (4,) | |
}, | |
'pad_fn': { | |
'class': 'pad', | |
'mode': 'constant', | |
'axes': 'spatial', | |
'padding': (31, 31), | |
'from': 'PQMF_FunctionalConvTransposed1d', | |
'value': 0.0 | |
}, | |
'PQMF_FunctionalConv1d': { | |
'class': 'conv', | |
'from': 'pad_fn', | |
'n_out': 1, | |
'activation': None, | |
'with_bias': False, | |
'bias': None, | |
'filter_size': (63,), | |
'filter': 'PQMF_synthesis_filter', | |
'filter_perm': {'static:0': 'F', 'static:1': 'static:1', 'F': 'static:0'}, | |
'padding': 'valid', | |
'strides': (1,), | |
'dilation_rate': (1,) | |
}, | |
'PQMF_synthesis_filter': { | |
'class': 'constant', | |
'value': numpy.array([ | |
[ | |
[ | |
1.653344588703476e-05, | |
3.907531936420128e-05, | |
3.7993191654095426e-05, | |
-1.1118547263322398e-05, | |
-6.70973167871125e-05, | |
-6.19814497996956e-19, | |
0.000319937797030434, | |
0.0008386399713344872, | |
0.0012031772639602423, | |
0.0009228164562955499, | |
-0.00017188044148497283, | |
-0.0014911156613379717, | |
-0.0017811377765610814, | |
2.2266204893018784e-18, | |
0.00346964574418962, | |
0.006319895852357149, | |
0.005423387512564659, | |
-0.0006891031516715884, | |
-0.009650670923292637, | |
-0.015545716509222984, | |
-0.012525110505521297, | |
7.202603219092196e-18, | |
0.01440473459661007, | |
0.01749838888645172, | |
-0.0012722874525934458, | |
-0.04052716866135597, | |
-0.08317920565605164, | |
-0.10245303809642792, | |
-0.07593876123428345, | |
1.490708503996114e-17, | |
0.10464989393949509, | |
0.20081833004951477, | |
0.25264719128608704, | |
0.24345116317272186, | |
0.1833323836326599, | |
0.10245303809642792, | |
0.03445395454764366, | |
2.481573477317513e-18, | |
-0.0005269987159408629, | |
0.01749838888645172, | |
0.03477610647678375, | |
0.03920914605259895, | |
0.03023829124867916, | |
0.015545716509222984, | |
0.003997439052909613, | |
1.265861891747689e-19, | |
0.0022464406210929155, | |
0.006319895852357149, | |
0.008376466110348701, | |
0.007272694259881973, | |
0.0043000467121601105, | |
0.0014911156613379717, | |
7.119520887499675e-05, | |
-1.1021566446688507e-18, | |
0.0004983723629266024, | |
0.0008386399713344872, | |
0.0007723981398157775, | |
0.0004598950035870075, | |
0.0001619872491573915, | |
1.1118547263322398e-05, | |
-1.5737294233986177e-05, | |
-8.616042507323048e-20, | |
6.848377779533621e-06 | |
], | |
[ | |
-6.848377779533621e-06, | |
-3.907531936420128e-05, | |
-1.5737294233986177e-05, | |
-1.1118547263322398e-05, | |
-0.0001619872491573915, | |
1.351986075970332e-18, | |
0.0007723981398157775, | |
0.0008386399713344872, | |
-0.0004983723629266024, | |
-0.0009228164562955499, | |
7.119520887499675e-05, | |
-0.0014911156613379717, | |
-0.0043000467121601105, | |
1.8708113957901537e-17, | |
0.008376466110348701, | |
0.006319895852357149, | |
-0.0022464406210929155, | |
0.0006891031516715884, | |
0.003997439052909613, | |
-0.015545716509222984, | |
-0.03023829124867916, | |
8.645551472572356e-17, | |
0.03477610647678375, | |
0.01749838888645172, | |
0.0005269987159408629, | |
0.04052716866135597, | |
0.03445395454764366, | |
-0.10245303809642792, | |
-0.1833323836326599, | |
1.490708503996114e-17, | |
0.25264719128608704, | |
0.20081833004951477, | |
-0.10464989393949509, | |
-0.24345116317272186, | |
-0.07593876123428345, | |
0.10245303809642792, | |
0.08317920565605164, | |
1.2407867179792413e-17, | |
-0.0012722874525934458, | |
0.01749838888645172, | |
-0.01440473459661007, | |
-0.03920914605259895, | |
-0.012525110505521297, | |
0.015545716509222984, | |
0.009650670923292637, | |
-7.599437017507494e-19, | |
0.005423387512564659, | |
0.006319895852357149, | |
-0.00346964574418962, | |
-0.007272694259881973, | |
-0.0017811377765610814, | |
0.0014911156613379717, | |
0.00017188044148497283, | |
6.78645702812047e-19, | |
0.0012031772639602423, | |
0.0008386399713344872, | |
-0.000319937797030434, | |
-0.0004598950035870075, | |
-6.70973167871125e-05, | |
1.1118547263322398e-05, | |
-3.7993191654095426e-05, | |
-1.244431968521913e-19, | |
1.653344588703476e-05 | |
], | |
[ | |
6.848377779533621e-06, | |
-3.907531936420128e-05, | |
1.5737294233986177e-05, | |
-1.1118547263322398e-05, | |
0.0001619872491573915, | |
-3.3801075166899776e-18, | |
-0.0007723981398157775, | |
0.0008386399713344872, | |
0.0004983723629266024, | |
-0.0009228164562955499, | |
-7.119520887499675e-05, | |
-0.0014911156613379717, | |
0.0043000467121601105, | |
-2.3161354936505294e-17, | |
-0.008376466110348701, | |
0.006319895852357149, | |
0.0022464406210929155, | |
0.0006891031516715884, | |
-0.003997439052909613, | |
-0.015545716509222984, | |
0.03023829124867916, | |
3.8438156623053137e-17, | |
-0.03477610647678375, | |
0.01749838888645172, | |
-0.0005269987159408629, | |
0.04052716866135597, | |
-0.03445395454764366, | |
-0.10245303809642792, | |
0.1833323836326599, | |
-4.4721255119883424e-17, | |
-0.25264719128608704, | |
0.20081833004951477, | |
0.10464989393949509, | |
-0.24345116317272186, | |
0.07593876123428345, | |
0.10245303809642792, | |
-0.08317920565605164, | |
-8.936173108986737e-17, | |
0.0012722874525934458, | |
0.01749838888645172, | |
0.01440473459661007, | |
-0.03920914605259895, | |
0.012525110505521297, | |
0.015545716509222984, | |
-0.009650670923292637, | |
-1.9414148179481886e-18, | |
-0.005423387512564659, | |
0.006319895852357149, | |
0.00346964574418962, | |
-0.007272694259881973, | |
0.0017811377765610814, | |
0.0014911156613379717, | |
-0.00017188044148497283, | |
-3.392086125935511e-18, | |
-0.0012031772639602423, | |
0.0008386399713344872, | |
0.000319937797030434, | |
-0.0004598950035870075, | |
6.70973167871125e-05, | |
1.1118547263322398e-05, | |
3.7993191654095426e-05, | |
1.9117200550086035e-20, | |
-1.653344588703476e-05 | |
], | |
[ | |
-1.653344588703476e-05, | |
3.907531936420128e-05, | |
-3.7993191654095426e-05, | |
-1.1118547263322398e-05, | |
6.70973167871125e-05, | |
-4.502824872857176e-19, | |
-0.000319937797030434, | |
0.0008386399713344872, | |
-0.0012031772639602423, | |
0.0009228164562955499, | |
0.00017188044148497283, | |
-0.0014911156613379717, | |
0.0017811377765610814, | |
2.4951655023478314e-17, | |
-0.00346964574418962, | |
0.006319895852357149, | |
-0.005423387512564659, | |
-0.0006891031516715884, | |
0.009650670923292637, | |
-0.015545716509222984, | |
0.012525110505521297, | |
-1.1046418715961388e-16, | |
-0.01440473459661007, | |
0.01749838888645172, | |
0.0012722874525934458, | |
-0.04052716866135597, | |
0.08317920565605164, | |
-0.10245303809642792, | |
0.07593876123428345, | |
-4.4721255119883424e-17, | |
-0.10464989393949509, | |
0.20081833004951477, | |
-0.25264719128608704, | |
0.24345116317272186, | |
-0.1833323836326599, | |
0.10245303809642792, | |
-0.03445395454764366, | |
4.469340891174244e-17, | |
0.0005269987159408629, | |
0.01749838888645172, | |
-0.03477610647678375, | |
0.03920914605259895, | |
-0.03023829124867916, | |
0.015545716509222984, | |
-0.003997439052909613, | |
-4.7271643199925296e-18, | |
-0.0022464406210929155, | |
0.006319895852357149, | |
-0.008376466110348701, | |
0.007272694259881973, | |
-0.0043000467121601105, | |
0.0014911156613379717, | |
-7.119520887499675e-05, | |
3.842993444776436e-18, | |
-0.0004983723629266024, | |
0.0008386399713344872, | |
-0.0007723981398157775, | |
0.0004598950035870075, | |
-0.0001619872491573915, | |
1.1118547263322398e-05, | |
1.5737294233986177e-05, | |
5.739997556022112e-20, | |
-6.848377779533621e-06 | |
] | |
] | |
], dtype=numpy.float32) | |
}, | |
'output': {'class': 'copy', 'from': 'PQMF_FunctionalConv1d'} | |
} |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment