glow.py

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import copy
import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable


def label_2_float(x, classes):
    return 2 * x / (classes - 1) - 1.

def float_2_label(x, classes) :
    assert abs(x).max() <= 1.0
    x = (x + 1.) * (classes - 1) / 2
    return x.clip(0, classes - 1)

def encode_mu_law(x, mu):
    mu = mu - 1
    fx = np.sign(x) * np.log(1 + mu * np.abs(x)) / np.log(1 + mu)
    return np.floor((fx + 1) / 2 * mu + 0.5)

def decode_mu_law(y, mu):
    mu = mu - 1
    x = np.sign(y) / mu * ((1 + mu) ** np.abs(y) - 1)
    return x

@torch.jit.script
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
    n_channels_int = n_channels[0]
    in_act = input_a+input_b
    t_act = torch.tanh(in_act[:, :n_channels_int, :])
    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
    acts = t_act * s_act
    return acts


class WaveGlowLoss(torch.nn.Module):
    def __init__(self, sigma=1.0):
        super(WaveGlowLoss, self).__init__()
        self.sigma = sigma

    def forward(self, model_output):
        z, log_s_list, log_det_W_list = model_output
        log_s_total, log_det_W_total = 0, 0
        for i, log_s in enumerate(log_s_list):
            log_s_total += torch.sum(log_s)
            log_det_W_total += log_det_W_list[i]

        loss = torch.sum(z*z)/(2*self.sigma*self.sigma) - log_s_total - log_det_W_total
        return loss/(z.size(0)*z.size(1)*z.size(2))


class Invertible1x1Conv(torch.nn.Module):
    """
    The layer outputs both the convolution, and the log determinant
    of its weight matrix.  If reverse=True it does convolution with
    inverse
    """
    def __init__(self, c):
        super(Invertible1x1Conv, self).__init__()
        self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=0, bias=True)

        # Sample a random orthonormal matrix to initialize weights
        W = torch.qr(torch.FloatTensor(c, c).normal_())[0]

        # Ensure determinant is 1.0 not -1.0
        if torch.det(W) < 0:
            W[:,0] = -1 * W[:, 0]
        W = W.view(c, c, 1)
        self.conv.weight.data = W

    def forward(self, z, reverse=False):
        # shape
        batch_size, group_size, n_of_groups = z.size()

        W = self.conv.weight.squeeze()

        if reverse:
            if not hasattr(self, 'W_inverse'):
                # Reverse computation
                W_inverse = W.float().inverse()
                W_inverse = Variable(W_inverse[..., None])
                if z.type() == 'torch.cuda.HalfTensor':
                    W_inverse = W_inverse.half()
                self.W_inverse = W_inverse
            z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
            return z
        else:
            # Forward computation
            log_det_W = batch_size * n_of_groups * torch.logdet(W)
            z = self.conv(z)
            return z, log_det_W


class WN(torch.nn.Module):
    """
    This is the WaveNet like layer for the affine coupling.  The primary difference
    from WaveNet is the convolutions need not be causal.  There is also no dilation
    size reset.  The dilation only doubles on each layer
    """
    def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels, kernel_size):
        super(WN, self).__init__()
        assert(kernel_size % 2 == 1)
        assert(n_channels % 2 == 0)
        self.n_layers = n_layers
        self.n_channels = n_channels
        self.in_layers = torch.nn.ModuleList()
        self.res_skip_layers = torch.nn.ModuleList()

        start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
        start = torch.nn.utils.weight_norm(start, name='weight')
        self.start = start

        # Initializing last layer to 0 makes the affine coupling layers
        # do nothing at first.  This helps with training stability
        end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1)
        end.weight.data.zero_()
        end.bias.data.zero_()
        self.end = end

        cond_layer = torch.nn.Conv1d(n_mel_channels, 2 * n_channels*n_layers, 1)
        self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')

        for i in range(n_layers):
            dilation = 2 ** i
            padding = int((kernel_size*dilation - dilation) / 2)

            in_layer = torch.nn.Conv1d(n_channels, 2 * n_channels, kernel_size,
                                       dilation=dilation, padding=padding)
            in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
            self.in_layers.append(in_layer)

            # last one is not necessary
            res_skip_channels = 2 * n_channels if i < n_layers - 1 else n_channels
            res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1)
            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
            self.res_skip_layers.append(res_skip_layer)

    def forward(self, forward_input):
        audio, spect = forward_input
        audio = self.start(audio)
        output = torch.zeros_like(audio)
        n_channels_tensor = torch.IntTensor([self.n_channels])

        spect = self.cond_layer(spect)

        for i in range(self.n_layers):
            spect_offset = 2 * i * self.n_channels
            acts = fused_add_tanh_sigmoid_multiply(
                self.in_layers[i](audio),
                spect[:, spect_offset:spect_offset + 2 * self.n_channels, :],
                n_channels_tensor)

            res_skip_acts = self.res_skip_layers[i](acts)
            if i < self.n_layers - 1:
                audio = audio + res_skip_acts[:, :self.n_channels, :]
                output = output + res_skip_acts[:, self.n_channels:, :]
            else:
                output = output + res_skip_acts

        return self.end(output)


class WaveGlow(torch.nn.Module):
    def __init__(self, n_mel_channels, n_flows, n_group, n_early_every,
                 n_early_size, n_classes, WN_config):
        super(WaveGlow, self).__init__()

        self.upsample = torch.nn.ConvTranspose1d(n_mel_channels,
                                                 n_mel_channels,
                                                 1024, stride=256)
        assert(n_group % 2 == 0)
        self.n_flows = n_flows
        self.n_group = n_group
        self.n_early_every = n_early_every
        self.n_early_size = n_early_size
        self.n_classes = n_classes
        self.WN = torch.nn.ModuleList()
        self.convinv = torch.nn.ModuleList()
        # self.fc = torch.nn.Linear(1, n_classes)

        n_half = int(n_group/2)

        # Set up layers with the right sizes based on how many dimensions
        # have been output already
        n_remaining_channels = n_group
        for k in range(n_flows):
            if k % self.n_early_every == 0 and k > 0:
                n_half = n_half - int(self.n_early_size / 2)
                n_remaining_channels = n_remaining_channels - self.n_early_size
            self.convinv.append(Invertible1x1Conv(n_remaining_channels))
            self.WN.append(WN(n_half, n_mel_channels*n_group, **WN_config))
        self.n_remaining_channels = n_remaining_channels  # Useful during inference

    def pre_process(self, inputs):
        spect, audio = inputs
        audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
        # audio = encode_mu_law(audio, self.n_classes)
        # audio = label_2_float(audio, self.n_classes)
        return spect.cuda(), audio.cuda()

    def forward(self, forward_input):
        """
        forward_input[0] = mel_spectrogram:  batch x n_mel_channels x frames
        forward_input[1] = audio: batch x time
        """
        spect, audio = forward_input
        spect = self.upsample(spect)

        #  Upsample spectrogram to size of audio
        seg_len = audio.size(1) * audio.size(2)
        assert(spect.size(2) >= seg_len)
        if spect.size(2) > seg_len:
            spect = spect[:, :, :seg_len]

        spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
        spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)

        outputs = []
        log_s_list = []
        log_det_W_list = []

        for k in range(self.n_flows):
            if k % self.n_early_every == 0 and k > 0:
                outputs.append(audio[:, :self.n_early_size, :])
                audio = audio[:, self.n_early_size:, :]

            audio, log_det_W = self.convinv[k](audio)
            log_det_W_list.append(log_det_W)

            n_half = int(audio.size(1) / 2)
            audio_0 = audio[:, :n_half, :]
            audio_1 = audio[:, n_half:, :]

            output = self.WN[k]((audio_0, spect))
            log_s = output[:, n_half:, :]
            b = output[:, :n_half, :]
            audio_1 = torch.exp(log_s)*audio_1 + b
            log_s_list.append(log_s)

            audio = torch.cat([audio_0, audio_1], 1)

        outputs.append(audio)
        outputs = torch.cat(outputs, 1)
        # outputs = outputs.permute(0, 2, 1).contiguous().view(outputs.size(0), -1).unsqueeze(-1)
        # logits = self.fc(outputs)
        return outputs, log_s_list, log_det_W_list

    def post_process(self, audio):
        posterior = torch.nn.functional.softmax(logits, dim=-1)
        distrib = torch.distributions.Categorical(posterior)
        # label -> float
        audio = label_2_float(distrib.sample().squeeze(), self.n_classes)
        return decode_mu_law(audio.cpu().numpy(), self.n_classes)

    def infer(self, spect, sigma=1.0):
        spect = self.upsample(spect)
        # trim conv artifacts. maybe pad spec to kernel multiple
        time_cutoff = self.upsample.kernel_size[0] - self.upsample.stride[0]
        spect = spect[:, :, :-time_cutoff]

        spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
        spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)

        if spect.type() == 'torch.cuda.HalfTensor':
            audio = torch.cuda.HalfTensor(spect.size(0),
                                          self.n_remaining_channels,
                                          spect.size(2)).normal_()
        else:
            audio = torch.cuda.FloatTensor(spect.size(0),
                                           self.n_remaining_channels,
                                           spect.size(2)).normal_()

        audio *= sigma

        for k in reversed(range(self.n_flows)):
            n_half = int(audio.size(1) / 2)
            audio_0 = audio[:, :n_half, :]
            audio_1 = audio[:, n_half:, :]
            output = self.WN[k]((audio_0, spect))

            s = output[:, n_half:, :]
            b = output[:, :n_half, :]
            audio_1 = (audio_1 - b) / torch.exp(s)
            audio = torch.cat([audio_0, audio_1], 1)

            audio = self.convinv[k](audio, reverse=True)

            if k % self.n_early_every == 0 and k > 0:
                if spect.type() == 'torch.cuda.HalfTensor':
                    z = torch.cuda.HalfTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
                else:
                    z = torch.cuda.FloatTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
                audio = torch.cat((sigma*z, audio), 1)

        return audio.permute(0, 2, 1).contiguous().view(audio.size(0), -1).data
        # logits = self.fc(audio)
        # return self.post_process(logits)

    @staticmethod
    def remove_weightnorm(model):
        waveglow = model
        for WN in waveglow.WN:
            WN.start = torch.nn.utils.remove_weight_norm(WN.start)
            WN.in_layers = remove(WN.in_layers)
            WN.cond_layer = torch.nn.utils.remove_weight_norm(WN.cond_layer)
            WN.res_skip_layers = remove(WN.res_skip_layers)
        return waveglow


def remove(conv_list):
    new_conv_list = torch.nn.ModuleList()
    for old_conv in conv_list:
        old_conv = torch.nn.utils.remove_weight_norm(old_conv)
        new_conv_list.append(old_conv)
    return new_conv_list