mirror of https://github.com/malarinv/tacotron2
compatibility to pretrained modesl
parent
5a30069f0a
commit
342b230b93
216
glow.py
216
glow.py
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@ -1,3 +1,4 @@
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# -*- coding: utf-8 -*-
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# *****************************************************************************
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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@ -12,19 +13,18 @@
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# names of its contributors may be used to endorse or promote products
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# derived from this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
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# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
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# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
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# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
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# THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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# *****************************************************************************
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import copy
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import torch
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from torch.autograd import Variable
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import torch.nn.functional as F
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@ -33,9 +33,9 @@ import torch.nn.functional as F
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@torch.jit.script
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def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
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n_channels_int = n_channels[0]
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in_act = input_a+input_b
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t_act = torch.tanh(in_act[:, :n_channels_int, :])
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s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
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in_act = input_a + input_b
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t_act = torch.nn.functional.tanh(in_act[:, :n_channels_int, :])
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s_act = torch.nn.functional.sigmoid(in_act[:, n_channels_int:, :])
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acts = t_act * s_act
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return acts
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@ -55,8 +55,12 @@ class WaveGlowLoss(torch.nn.Module):
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log_s_total = log_s_total + torch.sum(log_s)
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log_det_W_total += log_det_W_list[i]
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loss = torch.sum(z*z)/(2*self.sigma*self.sigma) - log_s_total - log_det_W_total
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return loss/(z.size(0)*z.size(1)*z.size(2))
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loss = (
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torch.sum(z * z) / (2 * self.sigma * self.sigma)
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- log_s_total
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- log_det_W_total
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)
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return loss / (z.size(0) * z.size(1) * z.size(2))
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class Invertible1x1Conv(torch.nn.Module):
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@ -65,17 +69,19 @@ class Invertible1x1Conv(torch.nn.Module):
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of its weight matrix. If reverse=True it does convolution with
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inverse
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"""
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def __init__(self, c):
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super(Invertible1x1Conv, self).__init__()
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self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=0,
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bias=False)
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self.conv = torch.nn.Conv1d(
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c, c, kernel_size=1, stride=1, padding=0, bias=False
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)
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# Sample a random orthonormal matrix to initialize weights
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W = torch.qr(torch.FloatTensor(c, c).normal_())[0]
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# Ensure determinant is 1.0 not -1.0
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if torch.det(W) < 0:
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W[:,0] = -1*W[:,0]
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W[:, 0] = -1 * W[:, 0]
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W = W.view(c, c, 1)
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self.conv.weight.data = W
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@ -86,11 +92,11 @@ class Invertible1x1Conv(torch.nn.Module):
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W = self.conv.weight.squeeze()
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if reverse:
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if not hasattr(self, 'W_inverse'):
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if not hasattr(self, "W_inverse"):
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# Reverse computation
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W_inverse = W.float().inverse()
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W_inverse = W.inverse()
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W_inverse = Variable(W_inverse[..., None])
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if z.type() == 'torch.HalfTensor':
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if z.type() == "torch.cuda.HalfTensor":
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W_inverse = W_inverse.half()
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self.W_inverse = W_inverse
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z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
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@ -104,86 +110,102 @@ class Invertible1x1Conv(torch.nn.Module):
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class WN(torch.nn.Module):
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"""
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This is the WaveNet like layer for the affine coupling. The primary difference
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from WaveNet is the convolutions need not be causal. There is also no dilation
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size reset. The dilation only doubles on each layer
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This is the WaveNet like layer for the affine coupling. The primary
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difference from WaveNet is the convolutions need not be causal. There is
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also no dilation size reset. The dilation only doubles on each layer
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"""
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def __init__(self, n_in_channels, n_mel_channels, n_layers, n_channels,
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kernel_size):
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def __init__(
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self, n_in_channels, n_mel_channels, n_layers, n_channels, kernel_size
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):
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super(WN, self).__init__()
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assert(kernel_size % 2 == 1)
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assert(n_channels % 2 == 0)
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assert kernel_size % 2 == 1
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assert n_channels % 2 == 0
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self.n_layers = n_layers
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self.n_channels = n_channels
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self.in_layers = torch.nn.ModuleList()
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self.res_skip_layers = torch.nn.ModuleList()
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self.cond_layers = torch.nn.ModuleList()
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start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
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start = torch.nn.utils.weight_norm(start, name='weight')
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start = torch.nn.utils.weight_norm(start, name="weight")
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self.start = start
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# Initializing last layer to 0 makes the affine coupling layers
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# do nothing at first. This helps with training stability
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end = torch.nn.Conv1d(n_channels, 2*n_in_channels, 1)
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end = torch.nn.Conv1d(n_channels, 2 * n_in_channels, 1)
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end.weight.data.zero_()
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end.bias.data.zero_()
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self.end = end
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cond_layer = torch.nn.Conv1d(n_mel_channels, 2*n_channels*n_layers, 1)
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self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
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for i in range(n_layers):
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dilation = 2 ** i
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padding = int((kernel_size*dilation - dilation)/2)
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in_layer = torch.nn.Conv1d(n_channels, 2*n_channels, kernel_size,
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dilation=dilation, padding=padding)
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in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
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padding = int((kernel_size * dilation - dilation) / 2)
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in_layer = torch.nn.Conv1d(
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n_channels,
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2 * n_channels,
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kernel_size,
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dilation=dilation,
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padding=padding,
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)
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in_layer = torch.nn.utils.weight_norm(in_layer, name="weight")
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self.in_layers.append(in_layer)
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cond_layer = torch.nn.Conv1d(n_mel_channels, 2 * n_channels, 1)
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cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
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self.cond_layers.append(cond_layer)
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# last one is not necessary
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if i < n_layers - 1:
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res_skip_channels = 2*n_channels
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res_skip_channels = 2 * n_channels
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else:
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res_skip_channels = n_channels
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res_skip_layer = torch.nn.Conv1d(n_channels, res_skip_channels, 1)
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res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
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res_skip_layer = torch.nn.utils.weight_norm(
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res_skip_layer, name="weight"
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)
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self.res_skip_layers.append(res_skip_layer)
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def forward(self, forward_input):
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audio, spect = forward_input
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audio = self.start(audio)
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output = torch.zeros_like(audio)
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n_channels_tensor = torch.IntTensor([self.n_channels])
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spect = self.cond_layer(spect)
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for i in range(self.n_layers):
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spect_offset = i*2*self.n_channels
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acts = fused_add_tanh_sigmoid_multiply(
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self.in_layers[i](audio),
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spect[:,spect_offset:spect_offset+2*self.n_channels,:],
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n_channels_tensor)
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self.cond_layers[i](spect),
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torch.IntTensor([self.n_channels]),
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)
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res_skip_acts = self.res_skip_layers[i](acts)
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if i < self.n_layers - 1:
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audio = audio + res_skip_acts[:,:self.n_channels,:]
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output = output + res_skip_acts[:,self.n_channels:,:]
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audio = res_skip_acts[:, : self.n_channels, :] + audio
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skip_acts = res_skip_acts[:, self.n_channels :, :]
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else:
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output = output + res_skip_acts
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skip_acts = res_skip_acts
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if i == 0:
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output = skip_acts
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else:
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output = skip_acts + output
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return self.end(output)
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class WaveGlow(torch.nn.Module):
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def __init__(self, n_mel_channels, n_flows, n_group, n_early_every,
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n_early_size, WN_config):
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def __init__(
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self,
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n_mel_channels,
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n_flows,
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n_group,
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n_early_every,
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n_early_size,
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WN_config,
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):
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super(WaveGlow, self).__init__()
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self.upsample = torch.nn.ConvTranspose1d(n_mel_channels,
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n_mel_channels,
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1024, stride=256)
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assert(n_group % 2 == 0)
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self.upsample = torch.nn.ConvTranspose1d(
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n_mel_channels, n_mel_channels, 1024, stride=256
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)
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assert n_group % 2 == 0
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self.n_flows = n_flows
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self.n_group = n_group
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self.n_early_every = n_early_every
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@ -191,18 +213,19 @@ class WaveGlow(torch.nn.Module):
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self.WN = torch.nn.ModuleList()
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self.convinv = torch.nn.ModuleList()
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n_half = int(n_group/2)
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n_half = int(n_group / 2)
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# Set up layers with the right sizes based on how many dimensions
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# have been output already
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n_remaining_channels = n_group
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for k in range(n_flows):
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if k % self.n_early_every == 0 and k > 0:
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n_half = n_half - int(self.n_early_size/2)
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n_half = n_half - int(self.n_early_size / 2)
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n_remaining_channels = n_remaining_channels - self.n_early_size
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self.convinv.append(Invertible1x1Conv(n_remaining_channels))
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self.WN.append(WN(n_half, n_mel_channels*n_group, **WN_config))
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self.n_remaining_channels = n_remaining_channels # Useful during inference
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self.WN.append(WN(n_half, n_mel_channels * n_group, **WN_config))
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self.n_remaining_channels = n_remaining_channels
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# Useful during inference
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def forward(self, forward_input):
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"""
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@ -213,12 +236,16 @@ class WaveGlow(torch.nn.Module):
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# Upsample spectrogram to size of audio
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spect = self.upsample(spect)
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assert(spect.size(2) >= audio.size(1))
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assert spect.size(2) >= audio.size(1)
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if spect.size(2) > audio.size(1):
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spect = spect[:, :, :audio.size(1)]
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spect = spect[:, :, : audio.size(1)]
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spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
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spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
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spect = (
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spect.contiguous()
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.view(spect.size(0), spect.size(1), -1)
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.permute(0, 2, 1)
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)
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audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
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output_audio = []
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@ -227,26 +254,26 @@ class WaveGlow(torch.nn.Module):
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for k in range(self.n_flows):
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if k % self.n_early_every == 0 and k > 0:
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output_audio.append(audio[:,:self.n_early_size,:])
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audio = audio[:,self.n_early_size:,:]
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output_audio.append(audio[:, : self.n_early_size, :])
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audio = audio[:, self.n_early_size :, :]
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audio, log_det_W = self.convinv[k](audio)
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log_det_W_list.append(log_det_W)
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n_half = int(audio.size(1)/2)
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audio_0 = audio[:,:n_half,:]
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audio_1 = audio[:,n_half:,:]
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n_half = int(audio.size(1) / 2)
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audio_0 = audio[:, :n_half, :]
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audio_1 = audio[:, n_half:, :]
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output = self.WN[k]((audio_0, spect))
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log_s = output[:, n_half:, :]
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b = output[:, :n_half, :]
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audio_1 = torch.exp(log_s)*audio_1 + b
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audio_1 = torch.exp(log_s) * audio_1 + b
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log_s_list.append(log_s)
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audio = torch.cat([audio_0, audio_1],1)
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audio = torch.cat([audio_0, audio_1], 1)
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output_audio.append(audio)
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return torch.cat(output_audio,1), log_s_list, log_det_W_list
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return torch.cat(output_audio, 1), log_s_list, log_det_W_list
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def infer(self, spect, sigma=1.0):
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spect = self.upsample(spect)
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@ -255,41 +282,52 @@ class WaveGlow(torch.nn.Module):
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spect = spect[:, :, :-time_cutoff]
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spect = spect.unfold(2, self.n_group, self.n_group).permute(0, 2, 1, 3)
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spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
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spect = (
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spect.contiguous()
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.view(spect.size(0), spect.size(1), -1)
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.permute(0, 2, 1)
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)
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if spect.type() == 'torch.HalfTensor':
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audio = torch.HalfTensor(spect.size(0),
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self.n_remaining_channels,
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spect.size(2)).normal_()
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if spect.type() == "torch.cuda.HalfTensor":
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audio = torch.cuda.HalfTensor(
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spect.size(0), self.n_remaining_channels, spect.size(2)
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).normal_()
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else:
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audio = torch.FloatTensor(spect.size(0),
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self.n_remaining_channels,
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spect.size(2)).normal_()
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# cuda.FloatTensor -> FloatTensor
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audio = torch.FloatTensor(
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spect.size(0), self.n_remaining_channels, spect.size(2)
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).normal_()
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audio = torch.autograd.Variable(sigma*audio)
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audio = torch.autograd.Variable(sigma * audio)
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for k in reversed(range(self.n_flows)):
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n_half = int(audio.size(1)/2)
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audio_0 = audio[:,:n_half,:]
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audio_1 = audio[:,n_half:,:]
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n_half = int(audio.size(1) / 2)
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audio_0 = audio[:, :n_half, :]
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audio_1 = audio[:, n_half:, :]
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output = self.WN[k]((audio_0, spect))
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s = output[:, n_half:, :]
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b = output[:, :n_half, :]
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audio_1 = (audio_1 - b)/torch.exp(s)
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audio = torch.cat([audio_0, audio_1],1)
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audio_1 = (audio_1 - b) / torch.exp(s)
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audio = torch.cat([audio_0, audio_1], 1)
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audio = self.convinv[k](audio, reverse=True)
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if k % self.n_early_every == 0 and k > 0:
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if spect.type() == 'torch.HalfTensor':
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z = torch.HalfTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
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if spect.type() == "torch.cuda.HalfTensor":
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z = torch.cuda.HalfTensor(
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spect.size(0), self.n_early_size, spect.size(2)
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).normal_()
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else:
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z = torch.FloatTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
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audio = torch.cat((sigma*z, audio),1)
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# cuda.FloatTensor -> FloatTensor
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z = torch.FloatTensor(
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spect.size(0), self.n_early_size, spect.size(2)
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).normal_()
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audio = torch.cat((sigma * z, audio), 1)
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audio = audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data
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audio = (
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audio.permute(0, 2, 1).contiguous().view(audio.size(0), -1).data
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)
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return audio
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@staticmethod
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@ -298,7 +336,7 @@ class WaveGlow(torch.nn.Module):
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for WN in waveglow.WN:
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WN.start = torch.nn.utils.remove_weight_norm(WN.start)
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WN.in_layers = remove(WN.in_layers)
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WN.cond_layer = torch.nn.utils.remove_weight_norm(WN.cond_layer)
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WN.cond_layers = remove(WN.cond_layers)
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WN.res_skip_layers = remove(WN.res_skip_layers)
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return waveglow
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@ -1,4 +1,3 @@
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# -*- coding: utf-8 -*-
|
||||
# *****************************************************************************
|
||||
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
|
||||
#
|
||||
|
|
@ -13,18 +12,19 @@
|
|||
# names of its contributors may be used to endorse or promote products
|
||||
# derived from this software without specific prior written permission.
|
||||
#
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
|
||||
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
||||
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
||||
# ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
|
||||
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
|
||||
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
|
||||
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
|
||||
# DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
|
||||
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
|
||||
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
|
||||
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
|
||||
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
|
||||
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
|
||||
# THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
||||
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
||||
#
|
||||
# *****************************************************************************
|
||||
import copy
|
||||
import torch
|
||||
from torch.autograd import Variable
|
||||
import torch.nn.functional as F
|
||||
|
|
@ -33,9 +33,9 @@ import torch.nn.functional as F
|
|||
@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.nn.functional.tanh(in_act[:, :n_channels_int, :])
|
||||
s_act = torch.nn.functional.sigmoid(in_act[:, n_channels_int:, :])
|
||||
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
|
||||
|
||||
|
|
@ -55,12 +55,8 @@ class WaveGlowLoss(torch.nn.Module):
|
|||
log_s_total = 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))
|
||||
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):
|
||||
|
|
@ -69,19 +65,17 @@ class Invertible1x1Conv(torch.nn.Module):
|
|||
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=False
|
||||
)
|
||||
self.conv = torch.nn.Conv1d(c, c, kernel_size=1, stride=1, padding=0,
|
||||
bias=False)
|
||||
|
||||
# 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[:,0] = -1*W[:,0]
|
||||
W = W.view(c, c, 1)
|
||||
self.conv.weight.data = W
|
||||
|
||||
|
|
@ -92,11 +86,11 @@ class Invertible1x1Conv(torch.nn.Module):
|
|||
W = self.conv.weight.squeeze()
|
||||
|
||||
if reverse:
|
||||
if not hasattr(self, "W_inverse"):
|
||||
if not hasattr(self, 'W_inverse'):
|
||||
# Reverse computation
|
||||
W_inverse = W.inverse()
|
||||
W_inverse = W.float().inverse()
|
||||
W_inverse = Variable(W_inverse[..., None])
|
||||
if z.type() == "torch.cuda.HalfTensor":
|
||||
if z.type() == 'torch.HalfTensor':
|
||||
W_inverse = W_inverse.half()
|
||||
self.W_inverse = W_inverse
|
||||
z = F.conv1d(z, self.W_inverse, bias=None, stride=1, padding=0)
|
||||
|
|
@ -110,102 +104,86 @@ class Invertible1x1Conv(torch.nn.Module):
|
|||
|
||||
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
|
||||
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
|
||||
):
|
||||
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
|
||||
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()
|
||||
self.cond_layers = torch.nn.ModuleList()
|
||||
|
||||
start = torch.nn.Conv1d(n_in_channels, n_channels, 1)
|
||||
start = torch.nn.utils.weight_norm(start, name="weight")
|
||||
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 = 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")
|
||||
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)
|
||||
|
||||
cond_layer = torch.nn.Conv1d(n_mel_channels, 2 * n_channels, 1)
|
||||
cond_layer = torch.nn.utils.weight_norm(cond_layer, name="weight")
|
||||
self.cond_layers.append(cond_layer)
|
||||
|
||||
# last one is not necessary
|
||||
if i < n_layers - 1:
|
||||
res_skip_channels = 2 * n_channels
|
||||
res_skip_channels = 2*n_channels
|
||||
else:
|
||||
res_skip_channels = 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"
|
||||
)
|
||||
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 = i*2*self.n_channels
|
||||
acts = fused_add_tanh_sigmoid_multiply(
|
||||
self.in_layers[i](audio),
|
||||
self.cond_layers[i](spect),
|
||||
torch.IntTensor([self.n_channels]),
|
||||
)
|
||||
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 = res_skip_acts[:, : self.n_channels, :] + audio
|
||||
skip_acts = res_skip_acts[:, self.n_channels :, :]
|
||||
audio = audio + res_skip_acts[:,:self.n_channels,:]
|
||||
output = output + res_skip_acts[:,self.n_channels:,:]
|
||||
else:
|
||||
skip_acts = res_skip_acts
|
||||
output = output + res_skip_acts
|
||||
|
||||
if i == 0:
|
||||
output = skip_acts
|
||||
else:
|
||||
output = skip_acts + output
|
||||
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,
|
||||
WN_config,
|
||||
):
|
||||
def __init__(self, n_mel_channels, n_flows, n_group, n_early_every,
|
||||
n_early_size, 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.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
|
||||
|
|
@ -213,19 +191,18 @@ class WaveGlow(torch.nn.Module):
|
|||
self.WN = torch.nn.ModuleList()
|
||||
self.convinv = torch.nn.ModuleList()
|
||||
|
||||
n_half = int(n_group / 2)
|
||||
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_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
|
||||
self.WN.append(WN(n_half, n_mel_channels*n_group, **WN_config))
|
||||
self.n_remaining_channels = n_remaining_channels # Useful during inference
|
||||
|
||||
def forward(self, forward_input):
|
||||
"""
|
||||
|
|
@ -236,16 +213,12 @@ class WaveGlow(torch.nn.Module):
|
|||
|
||||
# Upsample spectrogram to size of audio
|
||||
spect = self.upsample(spect)
|
||||
assert spect.size(2) >= audio.size(1)
|
||||
assert(spect.size(2) >= audio.size(1))
|
||||
if spect.size(2) > audio.size(1):
|
||||
spect = spect[:, :, : audio.size(1)]
|
||||
spect = spect[:, :, :audio.size(1)]
|
||||
|
||||
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)
|
||||
)
|
||||
spect = spect.contiguous().view(spect.size(0), spect.size(1), -1).permute(0, 2, 1)
|
||||
|
||||
audio = audio.unfold(1, self.n_group, self.n_group).permute(0, 2, 1)
|
||||
output_audio = []
|
||||
|
|
@ -254,26 +227,26 @@ class WaveGlow(torch.nn.Module):
|
|||
|
||||
for k in range(self.n_flows):
|
||||
if k % self.n_early_every == 0 and k > 0:
|
||||
output_audio.append(audio[:, : self.n_early_size, :])
|
||||
audio = audio[:, self.n_early_size :, :]
|
||||
output_audio.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:, :]
|
||||
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
|
||||
audio_1 = torch.exp(log_s)*audio_1 + b
|
||||
log_s_list.append(log_s)
|
||||
|
||||
audio = torch.cat([audio_0, audio_1], 1)
|
||||
audio = torch.cat([audio_0, audio_1],1)
|
||||
|
||||
output_audio.append(audio)
|
||||
return torch.cat(output_audio, 1), log_s_list, log_det_W_list
|
||||
return torch.cat(output_audio,1), log_s_list, log_det_W_list
|
||||
|
||||
def infer(self, spect, sigma=1.0):
|
||||
spect = self.upsample(spect)
|
||||
|
|
@ -282,52 +255,41 @@ class WaveGlow(torch.nn.Module):
|
|||
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)
|
||||
)
|
||||
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_()
|
||||
if spect.type() == 'torch.HalfTensor':
|
||||
audio = torch.HalfTensor(spect.size(0),
|
||||
self.n_remaining_channels,
|
||||
spect.size(2)).normal_()
|
||||
else:
|
||||
# cuda.FloatTensor -> FloatTensor
|
||||
audio = torch.FloatTensor(
|
||||
spect.size(0), self.n_remaining_channels, spect.size(2)
|
||||
).normal_()
|
||||
audio = torch.FloatTensor(spect.size(0),
|
||||
self.n_remaining_channels,
|
||||
spect.size(2)).normal_()
|
||||
|
||||
audio = torch.autograd.Variable(sigma * audio)
|
||||
audio = torch.autograd.Variable(sigma*audio)
|
||||
|
||||
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:, :]
|
||||
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_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_()
|
||||
if spect.type() == 'torch.HalfTensor':
|
||||
z = torch.HalfTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
|
||||
else:
|
||||
# cuda.FloatTensor -> FloatTensor
|
||||
z = torch.FloatTensor(
|
||||
spect.size(0), self.n_early_size, spect.size(2)
|
||||
).normal_()
|
||||
audio = torch.cat((sigma * z, audio), 1)
|
||||
z = torch.FloatTensor(spect.size(0), self.n_early_size, spect.size(2)).normal_()
|
||||
audio = torch.cat((sigma*z, audio),1)
|
||||
|
||||
audio = (
|
||||
audio.permute(0, 2, 1).contiguous().view(audio.size(0), -1).data
|
||||
)
|
||||
audio = audio.permute(0,2,1).contiguous().view(audio.size(0), -1).data
|
||||
return audio
|
||||
|
||||
@staticmethod
|
||||
|
|
@ -336,7 +298,7 @@ class WaveGlow(torch.nn.Module):
|
|||
for WN in waveglow.WN:
|
||||
WN.start = torch.nn.utils.remove_weight_norm(WN.start)
|
||||
WN.in_layers = remove(WN.in_layers)
|
||||
WN.cond_layers = remove(WN.cond_layers)
|
||||
WN.cond_layer = torch.nn.utils.remove_weight_norm(WN.cond_layer)
|
||||
WN.res_skip_layers = remove(WN.res_skip_layers)
|
||||
return waveglow
|
||||
|
||||
|
|
@ -39,9 +39,9 @@ class HParams(object):
|
|||
filter_length = 1024
|
||||
hop_length = 256
|
||||
win_length = 1024
|
||||
n_mel_channels: int = 40
|
||||
n_mel_channels: int = 80
|
||||
mel_fmin: float = 0.0
|
||||
mel_fmax: float = 4000.0
|
||||
mel_fmax: float = 8000.0
|
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################################
|
||||
# Model Parameters #
|
||||
################################
|
||||
|
|
|
|||
Loading…
Reference in New Issue