models.py

import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from utils.tools import init_weights, get_padding
import numpy as np

LRELU_SLOPE = 0.1


class ResBlock1(torch.nn.Module):
    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
        super(ResBlock1, self).__init__()
        self.convs1 = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
                               padding=get_padding(kernel_size, dilation[0]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
                               padding=get_padding(kernel_size, dilation[1]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
                               padding=get_padding(kernel_size, dilation[2])))
        ])
        self.convs1.apply(init_weights)

        self.convs2 = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1)))
        ])
        self.convs2.apply(init_weights)

    def forward(self, x):
        for c1, c2 in zip(self.convs1, self.convs2):
            xt = F.leaky_relu(x, LRELU_SLOPE)
            xt = c1(xt)
            xt = F.leaky_relu(xt, LRELU_SLOPE)
            xt = c2(xt)
            x = xt + x
        return x

    def remove_weight_norm(self):
        for l in self.convs1:
            remove_weight_norm(l)
        for l in self.convs2:
            remove_weight_norm(l)


class ResBlock2(torch.nn.Module):
    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
        super(ResBlock2, self).__init__()
        self.convs = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
                               padding=get_padding(kernel_size, dilation[0]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
                               padding=get_padding(kernel_size, dilation[1])))
        ])
        self.convs.apply(init_weights)

    def forward(self, x):
        for c in self.convs:
            xt = F.leaky_relu(x, LRELU_SLOPE)
            xt = c(xt)
            x = xt + x
        return x

    def remove_weight_norm(self):
        for l in self.convs:
            remove_weight_norm(l)


class PitchEncoder(torch.nn.Module):
    def __init__(self, h):
        super(PitchEncoder, self).__init__()
        self.lin_pre = nn.Linear(h.hubert_dim, h.hifi_dim)
        self.pitch_emb = nn.Embedding(256, h.hifi_dim)
    
    def forward(self, x, pitch):
        x = self.lin_pre(x) + self.pitch_emb(pitch)
        return x


class SineGen(torch.nn.Module):
    """ Definition of sine generator
    SineGen(samp_rate, harmonic_num = 0, 
            sine_amp = 0.1, noise_std = 0.003,
            voiced_threshold = 0,
            flag_for_pulse=False)
    
    samp_rate: sampling rate in Hz
    harmonic_num: number of harmonic overtones (default 0)
    sine_amp: amplitude of sine-wavefrom (default 0.1)
    noise_std: std of Gaussian noise (default 0.003)
    voiced_thoreshold: F0 threshold for U/V classification (default 0)
    flag_for_pulse: this SinGen is used inside PulseGen (default False)
    
    Note: when flag_for_pulse is True, the first time step of a voiced
        segment is always sin(np.pi) or cos(0)
    """
    def __init__(self, samp_rate, harmonic_num = 0, 
                 sine_amp = 0.1, noise_std = 0.003,
                 voiced_threshold = 0,
                 flag_for_pulse=False):
        super(SineGen, self).__init__()
        self.sine_amp = sine_amp
        self.noise_std = noise_std
        self.harmonic_num = harmonic_num
        self.dim = self.harmonic_num + 1
        self.sampling_rate = samp_rate
        self.voiced_threshold = voiced_threshold
        self.flag_for_pulse = flag_for_pulse
    
    def _f02uv(self, f0):
        # generate uv signal
        uv = torch.ones_like(f0)
        uv = uv * (f0 > self.voiced_threshold)
        return uv
            
    def _f02sine(self, f0_values, upp):
        """ f0_values: (batchsize, length, dim)
            where dim indicates fundamental tone and overtones
        """
        # convert to F0 in rad. The interger part n can be ignored
        # because 2 * np.pi * n doesn't affect phase
        rad_values = (f0_values / self.sampling_rate) % 1
        
        # initial phase noise (no noise for fundamental component)
        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2],\
                              device = f0_values.device)
        rand_ini[:, 0] = 0
        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini        
        
        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
        # for normal case

        # To prevent torch.cumsum numerical overflow,
        # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
        # Buffer tmp_over_one_idx indicates the time step to add -1.
        # This will not change F0 of sine because (x-1) * 2*pi = x *2*pi
        tmp_over_one = torch.cumsum(rad_values, 1) % 1
        tmp_over_one *= upp
        tmp_over_one = F.interpolate(
            tmp_over_one.transpose(2, 1), scale_factor=upp,
            mode='linear', align_corners=True
        ).transpose(2, 1)
        rad_values = F.interpolate(rad_values.transpose(2, 1), scale_factor=upp, mode='nearest').transpose(2, 1)
        tmp_over_one %= 1
        tmp_over_one_idx = (tmp_over_one[:, 1:, :] - 
                            tmp_over_one[:, :-1, :]) < 0
        cumsum_shift = torch.zeros_like(rad_values)
        cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
        
        sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1) \
                            * 2 * np.pi)
        return  sines
    
    
    def forward(self, f0, upp):
        """ sine_tensor, uv = forward(f0)
        input F0: tensor(batchsize=1, length, dim=1)
                  f0 for unvoiced steps should be 0
        output sine_tensor: tensor(batchsize=1, length, dim)
        output uv: tensor(batchsize=1, length, 1)
        """
        with torch.no_grad():
            f0 = f0.unsqueeze(-1)
            f0_buf = torch.multiply(f0, torch.arange(1, self.dim + 1, device=f0.device).reshape((1, 1, -1)))
    
            # generate sine waveforms
            sine_waves = self._f02sine(f0_buf, upp) * self.sine_amp
            
            # generate uv signal
            #uv = torch.ones(f0.shape)
            #uv = uv * (f0 > self.voiced_threshold)
            uv = self._f02uv(f0)
            uv = F.interpolate(uv.transpose(2, 1), scale_factor=upp, mode='nearest').transpose(2, 1)
        
            # noise: for unvoiced should be similar to sine_amp
            #        std = self.sine_amp/3 -> max value ~ self.sine_amp
            #.       for voiced regions is self.noise_std
            noise_amp = uv * self.noise_std + (1-uv) * self.sine_amp / 3
            noise = noise_amp * torch.randn_like(sine_waves)
            
            # first: set the unvoiced part to 0 by uv
            # then: additive noise
            sine_waves = sine_waves * uv + noise
        return sine_waves, uv, noise


class SourceModuleHnNSF(torch.nn.Module):
    """ SourceModule for hn-nsf 
    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, 
                 add_noise_std=0.003, voiced_threshod=0)
    sampling_rate: sampling_rate in Hz
    harmonic_num: number of harmonic above F0 (default: 0)
    sine_amp: amplitude of sine source signal (default: 0.1)
    add_noise_std: std of additive Gaussian noise (default: 0.003)
        note that amplitude of noise in unvoiced is decided
        by sine_amp
    voiced_threshold: threhold to set U/V given F0 (default: 0)

    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
    F0_sampled (batchsize, length, 1)
    Sine_source (batchsize, length, 1)
    noise_source (batchsize, length 1)
    uv (batchsize, length, 1)
    """
    def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1, 
                 add_noise_std=0.003, voiced_threshod=0):
        super(SourceModuleHnNSF, self).__init__()
        
        self.sine_amp = sine_amp
        self.noise_std = add_noise_std

        # to produce sine waveforms
        self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
                                 sine_amp, add_noise_std, voiced_threshod)

        # to merge source harmonics into a single excitation
        self.l_linear = torch.nn.Linear(harmonic_num+1, 1)
        self.l_tanh = torch.nn.Tanh()

    def forward(self, x, upp):
        """
        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
        F0_sampled (batchsize, length, 1)
        Sine_source (batchsize, length, 1)
        noise_source (batchsize, length 1)
        """
        # source for harmonic branch
        sine_wavs, uv, _ = self.l_sin_gen(x, upp)
        sine_merge = self.l_tanh(self.l_linear(sine_wavs))

        # source for noise branch, in the same shape as uv
        # noise = torch.randn_like(uv) * self.sine_amp / 3
        return sine_merge


class GeneratorNSF(torch.nn.Module):
    def __init__(self, h):
        super(GeneratorNSF, self).__init__()
        self.num_kernels = len(h.resblock_kernel_sizes)
        self.num_upsamples = len(h.upsample_rates)
        self.m_source = SourceModuleHnNSF(
            sampling_rate=h.sampling_rate,
            harmonic_num=8
        )
        self.noise_convs = nn.ModuleList()

        self.conv_pre = weight_norm(Conv1d(h.hifi_dim, h.upsample_initial_channel, 7, 1, padding=3))
        resblock = ResBlock1 if h.resblock == '1' else ResBlock2

        self.ups = nn.ModuleList()
        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):

            c_cur = h.upsample_initial_channel // (2 ** (i + 1))
            self.ups.append(weight_norm(
                ConvTranspose1d(h.upsample_initial_channel//(2**i), h.upsample_initial_channel//(2**(i+1)),
                                k, u, padding=(k-u)//2)))
            if i + 1 < len(h.upsample_rates):  #
                stride_f0 = int(np.prod(h.upsample_rates[i + 1:]))
                self.noise_convs.append(Conv1d(
                    1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
            else:
                self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))

        self.resblocks = nn.ModuleList()
        for i in range(len(self.ups)):
            ch = h.upsample_initial_channel//(2**(i+1))
            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
                self.resblocks.append(resblock(h, ch, k, d))

        self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
        self.ups.apply(init_weights)
        self.conv_post.apply(init_weights)
        self.upp = int(np.prod(h.upsample_rates))

    def forward(self, x, f0):
        """ `x` as (bs, seq_len, dim), regular hifi assumes input of shape (bs, n_mels, seq_len) """
        x = x.permute(0, 2, 1) # (bs, seq_len, dim) --> (bs, dim, seq_len)
        har_source = self.m_source(f0, self.upp).transpose(1, 2)

        x = self.conv_pre(x)
        for i in range(self.num_upsamples):
            x = F.leaky_relu(x, LRELU_SLOPE)
            x = self.ups[i](x)
            x_source = self.noise_convs[i](har_source)
            x = x + x_source
            xs = None
            for j in range(self.num_kernels):
                if xs is None:
                    xs = self.resblocks[i*self.num_kernels+j](x)
                else:
                    xs += self.resblocks[i*self.num_kernels+j](x)
            x = xs / self.num_kernels
        x = F.leaky_relu(x)
        x = self.conv_post(x)
        x = torch.tanh(x)

        return x

    def remove_weight_norm(self):
        print('Removing weight norm...')
        for l in self.ups:
            remove_weight_norm(l)
        for l in self.resblocks:
            l.remove_weight_norm()
        remove_weight_norm(self.conv_pre)
        remove_weight_norm(self.conv_post)


class DiscriminatorP(torch.nn.Module):
    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
        super(DiscriminatorP, self).__init__()
        self.period = period
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList([
            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
        ])
        self.conv_post = norm_f(Conv2d(1024, 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(torch.nn.Module):
    def __init__(self):
        super(MultiPeriodDiscriminator, self).__init__()
        self.discriminators = nn.ModuleList([
            DiscriminatorP(2),
            DiscriminatorP(3),
            DiscriminatorP(5),
            DiscriminatorP(7),
            DiscriminatorP(11),
        ])

    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 DiscriminatorS(torch.nn.Module):
    def __init__(self, use_spectral_norm=False):
        super(DiscriminatorS, self).__init__()
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList([
            norm_f(Conv1d(1, 128, 15, 1, padding=7)),
            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
            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