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Adjust code as per original iSTFTNet #1

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Adjust code as per original iSTFTNet #1

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4 changes: 2 additions & 2 deletions config_v1.json
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Original file line number Diff line number Diff line change
Expand Up @@ -8,8 +8,8 @@
"lr_decay": 0.999,
"seed": 1234,

"upsample_rates": [8,8,2],
"upsample_kernel_sizes": [16,16,4],
"upsample_rates": [8,8],
"upsample_kernel_sizes": [16,16],
"upsample_initial_channel": 512,
"resblock_kernel_sizes": [3,7,11],
"resblock_dilation_sizes": [[1,3,5], [1,3,5], [1,3,5]],
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43 changes: 20 additions & 23 deletions models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -101,7 +101,7 @@ def __init__(self, h):
self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))

self.out_proj_x1 = weight_norm(Conv1d(h.upsample_initial_channel // 4, 1, 7, 1, padding=3))
self.out_proj_x2 = weight_norm(Conv1d(h.upsample_initial_channel // 8, 1, 7, 1, padding=3))
#self.out_proj_x2 = weight_norm(Conv1d(h.upsample_initial_channel // 8, 1, 7, 1, padding=3))

def forward(self, x):
x = self.conv_pre(x)
Expand All @@ -117,15 +117,15 @@ def forward(self, x):
x = xs / self.num_kernels
if i == 1:
x1 = self.out_proj_x1(x)
elif i == 2:
x2 = self.out_proj_x2(x)
# elif i == 2:
# x2 = self.out_proj_x2(x)
x = F.leaky_relu(x)
x = self.reflection_pad(x)
x = self.conv_post(x)
spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :])
phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :])

return spec, phase, x1, x2
return spec, phase, x1 #, x2

def remove_weight_norm(self):
print('Removing weight norm...')
Expand Down Expand Up @@ -272,7 +272,7 @@ def __init__(self, kernels, channels, groups, strides):
self.pqmf_2 = PQMF(N=2, taps=256, cutoff=0.25, beta=10.0)
self.pqmf_4 = PQMF(N=4, taps=192, cutoff=0.13, beta=10.0)

def forward(self, x, x_hat, x2_hat, x1_hat):
def forward(self, x, x_hat, x1_hat):
y = []
y_hat = []
fmap = []
Expand All @@ -286,19 +286,19 @@ def forward(self, x, x_hat, x2_hat, x1_hat):
y_hat.append(p3_hat)
fmap_hat.append(p3_fmap_hat)

x2_ = self.pqmf_2(x)[:, :1, :] # Select first band
#x2_ = self.pqmf_2(x)[:, :1, :] # Select first band
x1_ = self.pqmf_4(x)[:, :1, :] # Select first band

x2_hat_ = self.pqmf_2(x_hat)[:, :1, :]
#x2_hat_ = self.pqmf_2(x_hat)[:, :1, :]
x1_hat_ = self.pqmf_4(x_hat)[:, :1, :]

p2_, p2_fmap_ = self.combd_2(x2_)
y.append(p2_)
fmap.append(p2_fmap_)
# p2_, p2_fmap_ = self.combd_2(x2_)
# y.append(p2_)
# fmap.append(p2_fmap_)

p2_hat_, p2_fmap_hat_ = self.combd_2(x2_hat)
y_hat.append(p2_hat_)
fmap_hat.append(p2_fmap_hat_)
# p2_hat_, p2_fmap_hat_ = self.combd_2(x2_hat)
# y_hat.append(p2_hat_)
# fmap_hat.append(p2_fmap_hat_)

p1_, p1_fmap_ = self.combd_1(x1_)
y.append(p1_)
Expand All @@ -309,16 +309,13 @@ def forward(self, x, x_hat, x2_hat, x1_hat):
fmap_hat.append(p1_fmap_hat_)





p2, p2_fmap = self.combd_2(x2_)
y.append(p2)
fmap.append(p2_fmap)

p2_hat, p2_fmap_hat = self.combd_2(x2_hat_)
y_hat.append(p2_hat)
fmap_hat.append(p2_fmap_hat)
# p2, p2_fmap = self.combd_2(x2_)
# y.append(p2)
# fmap.append(p2_fmap)
#
# p2_hat, p2_fmap_hat = self.combd_2(x2_hat_)
# y_hat.append(p2_hat)
# fmap_hat.append(p2_fmap_hat)

p1, p1_fmap = self.combd_1(x1_)
y.append(p1)
Expand Down
8 changes: 4 additions & 4 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -125,7 +125,7 @@ def train(rank, a, h):
y_mel = torch.autograd.Variable(y_mel.to(device, non_blocking=True))
y = y.unsqueeze(1)
# y_g_hat = generator(x)
spec, phase, x1, x2 = generator(x)
spec, phase, x1 = generator(x)

y_g_hat = stft.inverse(spec, phase)

Expand All @@ -135,7 +135,7 @@ def train(rank, a, h):
optim_d.zero_grad()

# MPD
y_df_hat_r, y_df_hat_g, _, _ = mcmbd(y, y_g_hat.detach(), x2.detach(), x1.detach())
y_df_hat_r, y_df_hat_g, _, _ = mcmbd(y, y_g_hat.detach(), x1.detach())
loss_disc_f, losses_disc_f_r, losses_disc_f_g = discriminator_loss(y_df_hat_r, y_df_hat_g)

# MSD
Expand All @@ -153,7 +153,7 @@ def train(rank, a, h):
# L1 Mel-Spectrogram Loss
loss_mel = F.l1_loss(y_mel, y_g_hat_mel) * 45

y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mcmbd(y, y_g_hat, x2, x1)
y_df_hat_r, y_df_hat_g, fmap_f_r, fmap_f_g = mcmbd(y, y_g_hat, x1)
y_ds_hat_r, y_ds_hat_g, fmap_s_r, fmap_s_g = msbd(y, y_g_hat)
loss_fm_f = 2 * feature_loss(fmap_f_r, fmap_f_g)
loss_fm_s = 2 * feature_loss(fmap_s_r, fmap_s_g)
Expand Down Expand Up @@ -201,7 +201,7 @@ def train(rank, a, h):
for j, batch in enumerate(validation_loader):
x, y, _, y_mel = batch
# y_g_hat = generator(x.to(device))
spec, phase, x1, x2 = generator(x.to(device))
spec, phase, x1 = generator(x.to(device))

y_g_hat = stft.inverse(spec, phase)

Expand Down