added BigVGAN in place of default vocoder (credit to https://github.com/deviandice/tortoise-tts-BigVGAN)
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a9de016230
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# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
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# LICENSE is in incl_licenses directory.
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from .filter import *
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from .resample import *
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from .act import *
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# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
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# LICENSE is in incl_licenses directory.
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import torch.nn as nn
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from .resample import UpSample1d, DownSample1d
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class Activation1d(nn.Module):
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def __init__(self,
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activation,
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up_ratio: int = 2,
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down_ratio: int = 2,
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up_kernel_size: int = 12,
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down_kernel_size: int = 12):
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super().__init__()
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self.up_ratio = up_ratio
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self.down_ratio = down_ratio
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self.act = activation
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self.upsample = UpSample1d(up_ratio, up_kernel_size)
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self.downsample = DownSample1d(down_ratio, down_kernel_size)
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# x: [B,C,T]
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def forward(self, x):
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x = self.upsample(x)
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x = self.act(x)
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x = self.downsample(x)
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return x
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# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
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# LICENSE is in incl_licenses directory.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import math
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if 'sinc' in dir(torch):
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sinc = torch.sinc
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else:
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# This code is adopted from adefossez's julius.core.sinc under the MIT License
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# https://adefossez.github.io/julius/julius/core.html
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# LICENSE is in incl_licenses directory.
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def sinc(x: torch.Tensor):
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"""
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Implementation of sinc, i.e. sin(pi * x) / (pi * x)
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__Warning__: Different to julius.sinc, the input is multiplied by `pi`!
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"""
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return torch.where(x == 0,
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torch.tensor(1., device=x.device, dtype=x.dtype),
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torch.sin(math.pi * x) / math.pi / x)
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# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
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# https://adefossez.github.io/julius/julius/lowpass.html
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# LICENSE is in incl_licenses directory.
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def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size]
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even = (kernel_size % 2 == 0)
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half_size = kernel_size // 2
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#For kaiser window
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delta_f = 4 * half_width
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A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
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if A > 50.:
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beta = 0.1102 * (A - 8.7)
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elif A >= 21.:
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beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
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else:
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beta = 0.
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window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
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# ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
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if even:
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time = (torch.arange(-half_size, half_size) + 0.5)
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else:
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time = torch.arange(kernel_size) - half_size
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if cutoff == 0:
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filter_ = torch.zeros_like(time)
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else:
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filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
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# Normalize filter to have sum = 1, otherwise we will have a small leakage
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# of the constant component in the input signal.
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filter_ /= filter_.sum()
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filter = filter_.view(1, 1, kernel_size)
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return filter
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class LowPassFilter1d(nn.Module):
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def __init__(self,
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cutoff=0.5,
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half_width=0.6,
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stride: int = 1,
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padding: bool = True,
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padding_mode: str = 'replicate',
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kernel_size: int = 12):
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# kernel_size should be even number for stylegan3 setup,
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# in this implementation, odd number is also possible.
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super().__init__()
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if cutoff < -0.:
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raise ValueError("Minimum cutoff must be larger than zero.")
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if cutoff > 0.5:
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raise ValueError("A cutoff above 0.5 does not make sense.")
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self.kernel_size = kernel_size
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self.even = (kernel_size % 2 == 0)
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self.pad_left = kernel_size // 2 - int(self.even)
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self.pad_right = kernel_size // 2
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self.stride = stride
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self.padding = padding
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self.padding_mode = padding_mode
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filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
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self.register_buffer("filter", filter)
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#input [B, C, T]
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def forward(self, x):
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_, C, _ = x.shape
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if self.padding:
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x = F.pad(x, (self.pad_left, self.pad_right),
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mode=self.padding_mode)
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out = F.conv1d(x, self.filter.expand(C, -1, -1),
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stride=self.stride, groups=C)
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return out
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# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
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# LICENSE is in incl_licenses directory.
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import torch.nn as nn
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from torch.nn import functional as F
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from .filter import LowPassFilter1d
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from .filter import kaiser_sinc_filter1d
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class UpSample1d(nn.Module):
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def __init__(self, ratio=2, kernel_size=None):
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super().__init__()
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self.ratio = ratio
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self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
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self.stride = ratio
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self.pad = self.kernel_size // ratio - 1
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self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
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self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
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filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
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half_width=0.6 / ratio,
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kernel_size=self.kernel_size)
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self.register_buffer("filter", filter)
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# x: [B, C, T]
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def forward(self, x):
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_, C, _ = x.shape
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x = F.pad(x, (self.pad, self.pad), mode='replicate')
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x = self.ratio * F.conv_transpose1d(
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x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
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x = x[..., self.pad_left:-self.pad_right]
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return x
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class DownSample1d(nn.Module):
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def __init__(self, ratio=2, kernel_size=None):
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super().__init__()
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self.ratio = ratio
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self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
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self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
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half_width=0.6 / ratio,
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stride=ratio,
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kernel_size=self.kernel_size)
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def forward(self, x):
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xx = self.lowpass(x)
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return xx
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# 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):
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super(BigVGAN, self).__init__()
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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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global h
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jsonConfig = json.loads(data)
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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),
|
||||
padding=(get_padding(5, 1), 0))),
|
||||
norm_f(Conv2d(int(128 * self.d_mult), int(512 * self.d_mult), (kernel_size, 1), (stride, 1),
|
||||
padding=(get_padding(5, 1), 0))),
|
||||
norm_f(Conv2d(int(512 * self.d_mult), int(1024 * self.d_mult), (kernel_size, 1), (stride, 1),
|
||||
padding=(get_padding(5, 1), 0))),
|
||||
norm_f(Conv2d(int(1024 * self.d_mult), int(1024 * self.d_mult), (kernel_size, 1), 1, padding=(2, 0))),
|
||||
])
|
||||
self.conv_post = norm_f(Conv2d(int(1024 * self.d_mult), 1, (3, 1), 1, padding=(1, 0)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
n_pad = self.period - (t % self.period)
|
||||
x = F.pad(x, (0, n_pad), "reflect")
|
||||
t = t + n_pad
|
||||
x = x.view(b, c, t // self.period, self.period)
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiPeriodDiscriminator(nn.Module):
|
||||
def __init__(self, h):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
self.mpd_reshapes = h.mpd_reshapes
|
||||
print("mpd_reshapes: {}".format(self.mpd_reshapes))
|
||||
discriminators = [DiscriminatorP(h, rs, use_spectral_norm=h.use_spectral_norm) for rs in self.mpd_reshapes]
|
||||
self.discriminators = nn.ModuleList(discriminators)
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
fmap_rs.append(fmap_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class DiscriminatorR(nn.Module):
|
||||
def __init__(self, cfg, resolution):
|
||||
super().__init__()
|
||||
|
||||
self.resolution = resolution
|
||||
assert len(self.resolution) == 3, \
|
||||
"MRD layer requires list with len=3, got {}".format(self.resolution)
|
||||
self.lrelu_slope = LRELU_SLOPE
|
||||
|
||||
norm_f = weight_norm if cfg.use_spectral_norm == False else spectral_norm
|
||||
if hasattr(cfg, "mrd_use_spectral_norm"):
|
||||
print("INFO: overriding MRD use_spectral_norm as {}".format(cfg.mrd_use_spectral_norm))
|
||||
norm_f = weight_norm if cfg.mrd_use_spectral_norm == False else spectral_norm
|
||||
self.d_mult = cfg.discriminator_channel_mult
|
||||
if hasattr(cfg, "mrd_channel_mult"):
|
||||
print("INFO: overriding mrd channel multiplier as {}".format(cfg.mrd_channel_mult))
|
||||
self.d_mult = cfg.mrd_channel_mult
|
||||
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(nn.Conv2d(1, int(32 * self.d_mult), (3, 9), padding=(1, 4))),
|
||||
norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
|
||||
norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
|
||||
norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 9), stride=(1, 2), padding=(1, 4))),
|
||||
norm_f(nn.Conv2d(int(32 * self.d_mult), int(32 * self.d_mult), (3, 3), padding=(1, 1))),
|
||||
])
|
||||
self.conv_post = norm_f(nn.Conv2d(int(32 * self.d_mult), 1, (3, 3), padding=(1, 1)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
x = self.spectrogram(x)
|
||||
x = x.unsqueeze(1)
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, self.lrelu_slope)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
def spectrogram(self, x):
|
||||
n_fft, hop_length, win_length = self.resolution
|
||||
x = F.pad(x, (int((n_fft - hop_length) / 2), int((n_fft - hop_length) / 2)), mode='reflect')
|
||||
x = x.squeeze(1)
|
||||
x = torch.stft(x, n_fft=n_fft, hop_length=hop_length, win_length=win_length, center=False, return_complex=True)
|
||||
x = torch.view_as_real(x) # [B, F, TT, 2]
|
||||
mag = torch.norm(x, p=2, dim=-1) # [B, F, TT]
|
||||
|
||||
return mag
|
||||
|
||||
|
||||
class MultiResolutionDiscriminator(nn.Module):
|
||||
def __init__(self, cfg, debug=False):
|
||||
super().__init__()
|
||||
self.resolutions = cfg.resolutions
|
||||
assert len(self.resolutions) == 3, \
|
||||
"MRD requires list of list with len=3, each element having a list with len=3. got {}". \
|
||||
format(self.resolutions)
|
||||
self.discriminators = nn.ModuleList(
|
||||
[DiscriminatorR(cfg, resolution) for resolution in self.resolutions]
|
||||
)
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(x=y)
|
||||
y_d_g, fmap_g = d(x=y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
fmap_rs.append(fmap_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
def get_mel(x):
|
||||
return mel_spectrogram(x, h.n_fft, h.num_mels, h.sampling_rate, h.hop_size, h.win_size, h.fmin, h.fmax)
|
||||
|
||||
def mel_spectrogram(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
|
||||
if torch.min(y) < -1.:
|
||||
print('min value is ', torch.min(y))
|
||||
if torch.max(y) > 1.:
|
||||
print('max value is ', torch.max(y))
|
||||
|
||||
global mel_basis, hann_window
|
||||
if fmax not in mel_basis:
|
||||
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
|
||||
mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
|
||||
hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
|
||||
|
||||
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
||||
y = y.squeeze(1)
|
||||
|
||||
# complex tensor as default, then use view_as_real for future pytorch compatibility
|
||||
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
|
||||
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
|
||||
spec = torch.view_as_real(spec)
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
|
||||
|
||||
spec = torch.matmul(mel_basis[str(fmax)+'_'+str(y.device)], spec)
|
||||
spec = torch.nn.utils.spectral_normalize_torch(spec)
|
||||
|
||||
return spec
|
||||
|
||||
def feature_loss(fmap_r, fmap_g):
|
||||
loss = 0
|
||||
for dr, dg in zip(fmap_r, fmap_g):
|
||||
for rl, gl in zip(dr, dg):
|
||||
loss += torch.mean(torch.abs(rl - gl))
|
||||
|
||||
return loss * 2
|
||||
|
||||
|
||||
def init_weights(m, mean=0.0, std=0.01):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
m.weight.data.normal_(mean, std)
|
||||
|
||||
|
||||
def get_padding(kernel_size, dilation=1):
|
||||
return int((kernel_size * dilation - dilation) / 2)
|
||||
|
||||
|
||||
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
||||
loss = 0
|
||||
r_losses = []
|
||||
g_losses = []
|
||||
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
||||
r_loss = torch.mean((1 - dr) ** 2)
|
||||
g_loss = torch.mean(dg ** 2)
|
||||
loss += (r_loss + g_loss)
|
||||
r_losses.append(r_loss.item())
|
||||
g_losses.append(g_loss.item())
|
||||
|
||||
return loss, r_losses, g_losses
|
||||
|
||||
|
||||
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
|
||||
|
||||
return loss, gen_losses
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
model = BigVGAN()
|
||||
|
||||
c = torch.randn(3, 100, 10)
|
||||
z = torch.randn(3, 64, 10)
|
||||
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)
|
||||
@ -0,0 +1,46 @@
|
||||
{
|
||||
"resblock": "1",
|
||||
"num_gpus": 0,
|
||||
"batch_size": 32,
|
||||
"learning_rate": 0.0001,
|
||||
"adam_b1": 0.8,
|
||||
"adam_b2": 0.99,
|
||||
"lr_decay": 0.999,
|
||||
"seed": 1234,
|
||||
|
||||
"upsample_rates": [8,8,2,2],
|
||||
"upsample_kernel_sizes": [16,16,4,4],
|
||||
"upsample_initial_channel": 512,
|
||||
"resblock_kernel_sizes": [3,7,11],
|
||||
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
|
||||
|
||||
"activation": "snakebeta",
|
||||
"snake_logscale": true,
|
||||
|
||||
"discriminator": "mrd",
|
||||
"resolutions": [[1024, 120, 600], [2048, 240, 1200], [512, 50, 240]],
|
||||
"mpd_reshapes": [2, 3, 5, 7, 11],
|
||||
"use_spectral_norm": false,
|
||||
"discriminator_channel_mult": 1,
|
||||
|
||||
"segment_size": 8192,
|
||||
"num_mels": 100,
|
||||
"num_freq": 1025,
|
||||
"n_fft": 1024,
|
||||
"hop_size": 256,
|
||||
"win_size": 1024,
|
||||
|
||||
"sampling_rate": 24000,
|
||||
|
||||
"fmin": 0,
|
||||
"fmax": 12000,
|
||||
"fmax_for_loss": null,
|
||||
|
||||
"num_workers": 4,
|
||||
|
||||
"dist_config": {
|
||||
"dist_backend": "nccl",
|
||||
"dist_url": "tcp://localhost:54321",
|
||||
"world_size": 1
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue