504 lines
19 KiB
Python
504 lines
19 KiB
Python
import os
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import json
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from .env import AttrDict
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import numpy as np
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import torch
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import torch.nn.functional as F
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import torch.nn as nn
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from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
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from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
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from .utils import init_weights, get_padding
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LRELU_SLOPE = 0.1
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def load_model(model_path, device='cuda'):
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config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
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with open(config_file) as f:
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data = f.read()
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global h
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json_config = json.loads(data)
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h = AttrDict(json_config)
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generator = Generator(h).to(device)
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cp_dict = torch.load(model_path)
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generator.load_state_dict(cp_dict['generator'])
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generator.eval()
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generator.remove_weight_norm()
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del cp_dict
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return generator, h
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class ResBlock1(torch.nn.Module):
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def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
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super(ResBlock1, 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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def forward(self, x):
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for c1, c2 in zip(self.convs1, self.convs2):
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xt = F.leaky_relu(x, LRELU_SLOPE)
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xt = c1(xt)
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xt = F.leaky_relu(xt, LRELU_SLOPE)
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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 ResBlock2(torch.nn.Module):
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def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
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super(ResBlock2, 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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def forward(self, x):
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for c in self.convs:
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xt = F.leaky_relu(x, LRELU_SLOPE)
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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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def padDiff(x):
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return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0)
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class SineGen(torch.nn.Module):
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""" Definition of sine generator
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SineGen(samp_rate, harmonic_num = 0,
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sine_amp = 0.1, noise_std = 0.003,
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voiced_threshold = 0,
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flag_for_pulse=False)
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samp_rate: sampling rate in Hz
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harmonic_num: number of harmonic overtones (default 0)
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sine_amp: amplitude of sine-wavefrom (default 0.1)
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noise_std: std of Gaussian noise (default 0.003)
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voiced_thoreshold: F0 threshold for U/V classification (default 0)
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flag_for_pulse: this SinGen is used inside PulseGen (default False)
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Note: when flag_for_pulse is True, the first time step of a voiced
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segment is always sin(np.pi) or cos(0)
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"""
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def __init__(self, samp_rate, harmonic_num=0,
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sine_amp=0.1, noise_std=0.003,
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voiced_threshold=0,
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flag_for_pulse=False):
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super(SineGen, self).__init__()
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self.sine_amp = sine_amp
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self.noise_std = noise_std
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self.harmonic_num = harmonic_num
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self.dim = self.harmonic_num + 1
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self.sampling_rate = samp_rate
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self.voiced_threshold = voiced_threshold
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self.flag_for_pulse = flag_for_pulse
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def _f02uv(self, f0):
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# generate uv signal
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uv = (f0 > self.voiced_threshold).type(torch.float32)
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return uv
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def _f02sine(self, f0_values):
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""" f0_values: (batchsize, length, dim)
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where dim indicates fundamental tone and overtones
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"""
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# convert to F0 in rad. The interger part n can be ignored
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# because 2 * np.pi * n doesn't affect phase
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rad_values = (f0_values / self.sampling_rate) % 1
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# initial phase noise (no noise for fundamental component)
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rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
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device=f0_values.device)
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rand_ini[:, 0] = 0
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rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
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# instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
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if not self.flag_for_pulse:
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# for normal case
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# To prevent torch.cumsum numerical overflow,
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# it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
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# Buffer tmp_over_one_idx indicates the time step to add -1.
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# This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
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tmp_over_one = torch.cumsum(rad_values, 1) % 1
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tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
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cumsum_shift = torch.zeros_like(rad_values)
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cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
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sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
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* 2 * np.pi)
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else:
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# If necessary, make sure that the first time step of every
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# voiced segments is sin(pi) or cos(0)
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# This is used for pulse-train generation
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# identify the last time step in unvoiced segments
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uv = self._f02uv(f0_values)
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uv_1 = torch.roll(uv, shifts=-1, dims=1)
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uv_1[:, -1, :] = 1
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u_loc = (uv < 1) * (uv_1 > 0)
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# get the instantanouse phase
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tmp_cumsum = torch.cumsum(rad_values, dim=1)
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# different batch needs to be processed differently
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for idx in range(f0_values.shape[0]):
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temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
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temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
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# stores the accumulation of i.phase within
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# each voiced segments
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tmp_cumsum[idx, :, :] = 0
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tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
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# rad_values - tmp_cumsum: remove the accumulation of i.phase
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# within the previous voiced segment.
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i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
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# get the sines
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sines = torch.cos(i_phase * 2 * np.pi)
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return sines
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def forward(self, f0):
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""" sine_tensor, uv = forward(f0)
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input F0: tensor(batchsize=1, length, dim=1)
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f0 for unvoiced steps should be 0
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output sine_tensor: tensor(batchsize=1, length, dim)
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output uv: tensor(batchsize=1, length, 1)
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"""
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with torch.no_grad():
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f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
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device=f0.device)
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# fundamental component
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fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
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# generate sine waveforms
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sine_waves = self._f02sine(fn) * self.sine_amp
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# generate uv signal
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# uv = torch.ones(f0.shape)
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# uv = uv * (f0 > self.voiced_threshold)
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uv = self._f02uv(f0)
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# noise: for unvoiced should be similar to sine_amp
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# std = self.sine_amp/3 -> max value ~ self.sine_amp
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# . for voiced regions is self.noise_std
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noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
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noise = noise_amp * torch.randn_like(sine_waves)
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# first: set the unvoiced part to 0 by uv
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# then: additive noise
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sine_waves = sine_waves * uv + noise
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return sine_waves, uv, noise
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class SourceModuleHnNSF(torch.nn.Module):
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""" SourceModule for hn-nsf
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SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
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add_noise_std=0.003, voiced_threshod=0)
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sampling_rate: sampling_rate in Hz
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harmonic_num: number of harmonic above F0 (default: 0)
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sine_amp: amplitude of sine source signal (default: 0.1)
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add_noise_std: std of additive Gaussian noise (default: 0.003)
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note that amplitude of noise in unvoiced is decided
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by sine_amp
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voiced_threshold: threhold to set U/V given F0 (default: 0)
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Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
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F0_sampled (batchsize, length, 1)
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Sine_source (batchsize, length, 1)
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noise_source (batchsize, length 1)
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uv (batchsize, length, 1)
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"""
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def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
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add_noise_std=0.003, voiced_threshod=0):
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super(SourceModuleHnNSF, self).__init__()
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self.sine_amp = sine_amp
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self.noise_std = add_noise_std
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# to produce sine waveforms
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self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
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sine_amp, add_noise_std, voiced_threshod)
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# to merge source harmonics into a single excitation
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self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
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self.l_tanh = torch.nn.Tanh()
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def forward(self, x):
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"""
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Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
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F0_sampled (batchsize, length, 1)
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Sine_source (batchsize, length, 1)
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noise_source (batchsize, length 1)
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"""
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# source for harmonic branch
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sine_wavs, uv, _ = self.l_sin_gen(x)
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sine_merge = self.l_tanh(self.l_linear(sine_wavs))
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# source for noise branch, in the same shape as uv
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noise = torch.randn_like(uv) * self.sine_amp / 3
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return sine_merge, noise, uv
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class Generator(torch.nn.Module):
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def __init__(self, h):
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super(Generator, self).__init__()
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self.h = h
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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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self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"]))
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self.m_source = SourceModuleHnNSF(
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sampling_rate=h["sampling_rate"],
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harmonic_num=8)
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self.noise_convs = nn.ModuleList()
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self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3))
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resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2
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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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c_cur = h["upsample_initial_channel"] // (2 ** (i + 1))
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self.ups.append(weight_norm(
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ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)),
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k, u, padding=(k - u) // 2)))
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if i + 1 < len(h["upsample_rates"]): #
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stride_f0 = np.prod(h["upsample_rates"][i + 1:])
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self.noise_convs.append(Conv1d(
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1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
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else:
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self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
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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))
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self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
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self.ups.apply(init_weights)
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self.conv_post.apply(init_weights)
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self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1)
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def forward(self, x, f0, g=None):
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# print(1,x.shape,f0.shape,f0[:, None].shape)
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f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
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# print(2,f0.shape)
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har_source, noi_source, uv = self.m_source(f0)
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har_source = har_source.transpose(1, 2)
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x = self.conv_pre(x)
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x = x + self.cond(g)
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# print(124,x.shape,har_source.shape)
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for i in range(self.num_upsamples):
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x = F.leaky_relu(x, LRELU_SLOPE)
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# print(3,x.shape)
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x = self.ups[i](x)
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x_source = self.noise_convs[i](har_source)
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# print(4,x_source.shape,har_source.shape,x.shape)
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x = x + x_source
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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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x = F.leaky_relu(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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remove_weight_norm(l)
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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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class DiscriminatorP(torch.nn.Module):
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def __init__(self, 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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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, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
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norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
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])
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self.conv_post = norm_f(Conv2d(1024, 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(torch.nn.Module):
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def __init__(self, periods=None):
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super(MultiPeriodDiscriminator, self).__init__()
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self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
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self.discriminators = nn.ModuleList()
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for period in self.periods:
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self.discriminators.append(DiscriminatorP(period))
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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 DiscriminatorS(torch.nn.Module):
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def __init__(self, use_spectral_norm=False):
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super(DiscriminatorS, self).__init__()
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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(Conv1d(1, 128, 15, 1, padding=7)),
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norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
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norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
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norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
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norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
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norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
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norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
|
])
|
|
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
|
|
|
def forward(self, x):
|
|
fmap = []
|
|
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 MultiScaleDiscriminator(torch.nn.Module):
|
|
def __init__(self):
|
|
super(MultiScaleDiscriminator, self).__init__()
|
|
self.discriminators = nn.ModuleList([
|
|
DiscriminatorS(use_spectral_norm=True),
|
|
DiscriminatorS(),
|
|
DiscriminatorS(),
|
|
])
|
|
self.meanpools = nn.ModuleList([
|
|
AvgPool1d(4, 2, padding=2),
|
|
AvgPool1d(4, 2, padding=2)
|
|
])
|
|
|
|
def forward(self, y, y_hat):
|
|
y_d_rs = []
|
|
y_d_gs = []
|
|
fmap_rs = []
|
|
fmap_gs = []
|
|
for i, d in enumerate(self.discriminators):
|
|
if i != 0:
|
|
y = self.meanpools[i - 1](y)
|
|
y_hat = self.meanpools[i - 1](y_hat)
|
|
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
|
|
|
|
|
|
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 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
|