forked from mrq/tortoise-tts
485 lines
18 KiB
Python
485 lines
18 KiB
Python
# Copyright (c) 2022 NVIDIA CORPORATION.
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# Licensed under the MIT license.
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# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
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# LICENSE is in incl_licenses directory.
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import json
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import os
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import torch, torch.utils.data
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import tortoise.models.activations as activations
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from torch.nn import Conv1d, ConvTranspose1d, Conv2d
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
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from tortoise.models.alias_free_torch import *
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from librosa.filters import mel as librosa_mel_fn
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LRELU_SLOPE = 0.1
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class AMPBlock1(torch.nn.Module):
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def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
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super(AMPBlock1, self).__init__()
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self.h = h
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self.convs1 = nn.ModuleList([
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
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padding=get_padding(kernel_size, dilation[2])))
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])
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self.convs1.apply(init_weights)
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self.convs2 = nn.ModuleList([
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
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padding=get_padding(kernel_size, 1)))
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])
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self.convs2.apply(init_weights)
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self.num_layers = len(self.convs1) + len(self.convs2) # total number of conv layers
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if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
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self.activations = nn.ModuleList([
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Activation1d(
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activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
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for _ in range(self.num_layers)
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])
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elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
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self.activations = nn.ModuleList([
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Activation1d(
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activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
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for _ in range(self.num_layers)
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])
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else:
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raise NotImplementedError(
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"activation incorrectly specified. check the config file and look for 'activation'.")
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def forward(self, x):
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acts1, acts2 = self.activations[::2], self.activations[1::2]
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for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
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xt = a1(x)
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xt = c1(xt)
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xt = a2(xt)
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xt = c2(xt)
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x = xt + x
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return x
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def remove_weight_norm(self):
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for l in self.convs1:
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remove_weight_norm(l)
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for l in self.convs2:
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remove_weight_norm(l)
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class AMPBlock2(torch.nn.Module):
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def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
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super(AMPBlock2, self).__init__()
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self.h = h
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self.convs = nn.ModuleList([
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
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padding=get_padding(kernel_size, dilation[0]))),
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weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
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padding=get_padding(kernel_size, dilation[1])))
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])
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self.convs.apply(init_weights)
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self.num_layers = len(self.convs) # total number of conv layers
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if activation == 'snake': # periodic nonlinearity with snake function and anti-aliasing
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self.activations = nn.ModuleList([
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Activation1d(
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activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
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for _ in range(self.num_layers)
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])
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elif activation == 'snakebeta': # periodic nonlinearity with snakebeta function and anti-aliasing
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self.activations = nn.ModuleList([
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Activation1d(
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activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
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for _ in range(self.num_layers)
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])
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else:
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raise NotImplementedError(
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"activation incorrectly specified. check the config file and look for 'activation'.")
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def forward(self, x):
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for c, a in zip(self.convs, self.activations):
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xt = a(x)
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xt = c(xt)
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x = xt + x
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return x
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def remove_weight_norm(self):
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for l in self.convs:
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remove_weight_norm(l)
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class AttrDict(dict):
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def __init__(self, *args, **kwargs):
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super(AttrDict, self).__init__(*args, **kwargs)
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self.__dict__ = self
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class BigVGAN(nn.Module):
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# this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
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def __init__(self, config=None, data=None):
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super(BigVGAN, self).__init__()
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"""
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with open(os.path.join(os.path.dirname(__file__), 'config.json'), 'r') as f:
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data = f.read()
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"""
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if config and data is None:
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with open(config, 'r') as f:
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data = f.read()
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jsonConfig = json.loads(data)
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elif data is not None:
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if isinstance(data, str):
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jsonConfig = json.loads(data)
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else:
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jsonConfig = data
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else:
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raise Exception("no config specified")
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global h
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h = AttrDict(jsonConfig)
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self.mel_channel = h.num_mels
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self.noise_dim = h.n_fft
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self.hop_length = h.hop_size
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self.num_kernels = len(h.resblock_kernel_sizes)
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self.num_upsamples = len(h.upsample_rates)
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# pre conv
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self.conv_pre = weight_norm(Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3))
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# define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
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resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2
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# transposed conv-based upsamplers. does not apply anti-aliasing
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self.ups = nn.ModuleList()
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for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
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self.ups.append(nn.ModuleList([
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weight_norm(ConvTranspose1d(h.upsample_initial_channel // (2 ** i),
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h.upsample_initial_channel // (2 ** (i + 1)),
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k, u, padding=(k - u) // 2))
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]))
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# residual blocks using anti-aliased multi-periodicity composition modules (AMP)
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self.resblocks = nn.ModuleList()
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for i in range(len(self.ups)):
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ch = h.upsample_initial_channel // (2 ** (i + 1))
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for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
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self.resblocks.append(resblock(h, ch, k, d, activation=h.activation))
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# post conv
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if h.activation == "snake": # periodic nonlinearity with snake function and anti-aliasing
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activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale)
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self.activation_post = Activation1d(activation=activation_post)
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elif h.activation == "snakebeta": # periodic nonlinearity with snakebeta function and anti-aliasing
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activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)
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self.activation_post = Activation1d(activation=activation_post)
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else:
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raise NotImplementedError(
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"activation incorrectly specified. check the config file and look for 'activation'.")
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self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
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# weight initialization
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for i in range(len(self.ups)):
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self.ups[i].apply(init_weights)
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self.conv_post.apply(init_weights)
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def forward(self,x, c):
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# pre conv
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x = self.conv_pre(x)
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for i in range(self.num_upsamples):
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# upsampling
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for i_up in range(len(self.ups[i])):
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x = self.ups[i][i_up](x)
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# AMP blocks
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xs = None
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for j in range(self.num_kernels):
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if xs is None:
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xs = self.resblocks[i * self.num_kernels + j](x)
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else:
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xs += self.resblocks[i * self.num_kernels + j](x)
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x = xs / self.num_kernels
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# post conv
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x = self.activation_post(x)
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x = self.conv_post(x)
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x = torch.tanh(x)
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return x
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def remove_weight_norm(self):
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print('Removing weight norm...')
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for l in self.ups:
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for l_i in l:
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remove_weight_norm(l_i)
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for l in self.resblocks:
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l.remove_weight_norm()
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remove_weight_norm(self.conv_pre)
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remove_weight_norm(self.conv_post)
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def inference(self, c, z=None):
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# pad input mel with zeros to cut artifact
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# see https://github.com/seungwonpark/melgan/issues/8
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zero = torch.full((c.shape[0], h.num_mels, 10), -11.5129).to(c.device)
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mel = torch.cat((c, zero), dim=2)
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if z is None:
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z = torch.randn(c.shape[0], self.noise_dim, mel.size(2)).to(mel.device)
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audio = self.forward(mel, z)
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audio = audio[:, :, :-(self.hop_length * 10)]
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audio = audio.clamp(min=-1, max=1)
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return audio
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def eval(self, inference=False):
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super(BigVGAN, self).eval()
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# don't remove weight norm while validation in training loop
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if inference:
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self.remove_weight_norm()
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class DiscriminatorP(nn.Module):
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def __init__(self, h, period, kernel_size=5, stride=3, use_spectral_norm=False):
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super(DiscriminatorP, self).__init__()
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self.period = period
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self.d_mult = h.discriminator_channel_mult
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norm_f = weight_norm if use_spectral_norm == False else spectral_norm
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self.convs = nn.ModuleList([
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norm_f(Conv2d(1, int(32 * self.d_mult), (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(int(32 * self.d_mult), int(128 * self.d_mult), (kernel_size, 1), (stride, 1),
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padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(int(128 * self.d_mult), int(512 * self.d_mult), (kernel_size, 1), (stride, 1),
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padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(int(512 * self.d_mult), int(1024 * self.d_mult), (kernel_size, 1), (stride, 1),
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padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(int(1024 * self.d_mult), int(1024 * self.d_mult), (kernel_size, 1), 1, padding=(2, 0))),
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])
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self.conv_post = norm_f(Conv2d(int(1024 * self.d_mult), 1, (3, 1), 1, padding=(1, 0)))
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def forward(self, x):
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fmap = []
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# 1d to 2d
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b, c, t = x.shape
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if t % self.period != 0: # pad first
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n_pad = self.period - (t % self.period)
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x = F.pad(x, (0, n_pad), "reflect")
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t = t + n_pad
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x = x.view(b, c, t // self.period, self.period)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, LRELU_SLOPE)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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x = torch.flatten(x, 1, -1)
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return x, fmap
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class MultiPeriodDiscriminator(nn.Module):
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def __init__(self, h):
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super(MultiPeriodDiscriminator, self).__init__()
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self.mpd_reshapes = h.mpd_reshapes
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print("mpd_reshapes: {}".format(self.mpd_reshapes))
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discriminators = [DiscriminatorP(h, rs, use_spectral_norm=h.use_spectral_norm) for rs in self.mpd_reshapes]
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self.discriminators = nn.ModuleList(discriminators)
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def forward(self, y, y_hat):
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y_d_rs = []
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y_d_gs = []
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fmap_rs = []
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fmap_gs = []
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for i, d in enumerate(self.discriminators):
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y_d_r, fmap_r = d(y)
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y_d_g, fmap_g = d(y_hat)
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y_d_rs.append(y_d_r)
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fmap_rs.append(fmap_r)
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y_d_gs.append(y_d_g)
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fmap_gs.append(fmap_g)
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs
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class DiscriminatorR(nn.Module):
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def __init__(self, cfg, resolution):
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super().__init__()
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self.resolution = resolution
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assert len(self.resolution) == 3, \
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"MRD layer requires list with len=3, got {}".format(self.resolution)
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self.lrelu_slope = LRELU_SLOPE
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norm_f = weight_norm if cfg.use_spectral_norm == False else spectral_norm
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if hasattr(cfg, "mrd_use_spectral_norm"):
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print("INFO: overriding MRD use_spectral_norm as {}".format(cfg.mrd_use_spectral_norm))
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norm_f = weight_norm if cfg.mrd_use_spectral_norm == False else spectral_norm
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self.d_mult = cfg.discriminator_channel_mult
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if hasattr(cfg, "mrd_channel_mult"):
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print("INFO: overriding mrd channel multiplier as {}".format(cfg.mrd_channel_mult))
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self.d_mult = cfg.mrd_channel_mult
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self.convs = nn.ModuleList([
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norm_f(nn.Conv2d(1, int(32 * self.d_mult), (3, 9), padding=(1, 4))),
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norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
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norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
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norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
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norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 3), padding=(1, 1))),
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])
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self.conv_post = norm_f(nn.Conv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1)))
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def forward(self, x):
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fmap = []
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x = self.spectrogram(x)
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x = x.unsqueeze(1)
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for l in self.convs:
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x = l(x)
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x = F.leaky_relu(x, self.lrelu_slope)
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fmap.append(x)
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x = self.conv_post(x)
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fmap.append(x)
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x = torch.flatten(x, 1, -1)
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return x, fmap
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def spectrogram(self, x):
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n_fft, hop_length, win_length = self.resolution
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x = F.pad(x, (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), mode='reflect')
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x = x.squeeze(1)
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x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=False, return_complex=True)
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x = torch.view_as_real(x) # [B, F, TT, 2]
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mag = torch.norm(x, p=2, dim=-1) # [B, F, TT]
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return mag
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class MultiResolutionDiscriminator(nn.Module):
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def __init__(self, cfg, debug=False):
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super().__init__()
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self.resolutions = cfg.resolutions
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assert len(self.resolutions) == 3, \
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"MRD requires list of list with len=3, each element having a list with len=3. got {}". \
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format(self.resolutions)
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self.discriminators = nn.ModuleList(
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[DiscriminatorR(cfg, resolution) for resolution in self.resolutions]
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)
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def forward(self, y, y_hat):
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y_d_rs = []
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y_d_gs = []
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fmap_rs = []
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fmap_gs = []
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for i, d in enumerate(self.discriminators):
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y_d_r, fmap_r = d(x=y)
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y_d_g, fmap_g = d(x=y_hat)
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y_d_rs.append(y_d_r)
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fmap_rs.append(fmap_r)
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y_d_gs.append(y_d_g)
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fmap_gs.append(fmap_g)
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs
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def get_mel(x):
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return mel_spectrogram(x, h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax)
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def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
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if torch.min(y) < -1.:
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print('min value is ', torch.min(y))
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if torch.max(y) > 1.:
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print('max value is ', torch.max(y))
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global mel_basis, hann_window
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if fmax not in mel_basis:
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mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
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mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
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hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
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y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
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y = y.squeeze(1)
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# complex tensor as default, then use view_as_real for future pytorch compatibility
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spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
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center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
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spec = torch.view_as_real(spec)
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spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
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spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
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spec = torch.nn.utils.spectral_normalize_torch(spec)
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return spec
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def feature_loss(fmap_r, fmap_g):
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loss = 0
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for dr, dg in zip(fmap_r, fmap_g):
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for rl, gl in zip(dr, dg):
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loss += torch.mean(torch.abs(rl - gl))
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return loss * 2
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def init_weights(m, mean=0.0, std=0.01):
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classname = m.__class__.__name__
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if classname.find("Conv") != -1:
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m.weight.data.normal_(mean, std)
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|
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def get_padding(kernel_size, dilation=1):
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return int((kernel_size * dilation - dilation) / 2)
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|
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def discriminator_loss(disc_real_outputs, disc_generated_outputs):
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|
loss = 0
|
|
r_losses = []
|
|
g_losses = []
|
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for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
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r_loss = torch.mean((1 - dr) ** 2)
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|
g_loss = torch.mean(dg ** 2)
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|
loss += (r_loss + g_loss)
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r_losses.append(r_loss.item())
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g_losses.append(g_loss.item())
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|
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return loss, r_losses, g_losses
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|
|
|
|
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def generator_loss(disc_outputs):
|
|
loss = 0
|
|
gen_losses = []
|
|
for dg in disc_outputs:
|
|
l = torch.mean((1 - dg) ** 2)
|
|
gen_losses.append(l)
|
|
loss += l
|
|
|
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return loss, gen_losses
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|
|
|
|
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if __name__ == '__main__':
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model = BigVGAN()
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|
|
|
c = torch.randn(3, 100, 10)
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|
z = torch.randn(3, 64, 10)
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|
print(c.shape)
|
|
|
|
y = model(c, z)
|
|
print(y.shape)
|
|
assert y.shape == torch.Size([3, 1, 2560])
|
|
|
|
pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
|
|
print(pytorch_total_params) |