gen3 waveform
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@ -7,7 +7,7 @@ from models.diffusion.unet_diffusion import TimestepEmbedSequential, \
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Downsample, Upsample, TimestepBlock
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from scripts.audio.gen.use_diffuse_tts import ceil_multiple
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from trainer.networks import register_model
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from utils.util import checkpoint
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from utils.util import checkpoint, print_network
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def is_sequence(t):
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@ -23,7 +23,7 @@ class ResBlock(TimestepBlock):
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out_channels=None,
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dims=2,
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kernel_size=3,
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efficient_config=True,
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efficient_config=False,
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use_scale_shift_norm=False,
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):
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super().__init__()
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@ -93,6 +93,8 @@ class ResBlock(TimestepBlock):
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class StackedResidualBlock(TimestepBlock):
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def __init__(self, channels, emb_channels, dropout):
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super().__init__()
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self.emb_layers = nn.Sequential(
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nn.SiLU(),
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linear(
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@ -102,29 +104,30 @@ class StackedResidualBlock(TimestepBlock):
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)
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gc = channels // 4
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super().__init__()
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self.initial_norm = nn.GroupNorm(num_groups=8, num_channels=channels)
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for i in range(5):
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out_channels = channels if i == 4 else gc
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self.add_module(
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f'conv{i + 1}',
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nn.Conv2d(channels + i * gc, out_channels, 3, 1, 1))
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self.add_module(f'gn{i+1}', nn.GroupNorm(num_groups=8, num_channels=channels))
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nn.Conv1d(channels + i * gc, out_channels, 3, 1, 1))
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self.add_module(f'gn{i+1}', nn.GroupNorm(num_groups=8, num_channels=out_channels))
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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zero_module(self.conv5)
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self.drop = nn.Dropout(p=dropout)
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def forawrd(self, x, emb):
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def forward(self, x, emb):
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return checkpoint(self.forward_, x, emb)
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def forward_(self, x, emb):
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emb_out = self.emb_layers(emb).type(h.dtype)
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emb_out = self.emb_layers(emb)
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scale, shift = torch.chunk(emb_out, 2, dim=1)
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x0 = self.initial_norm(x) * (1 + scale) + shift
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x0 = self.initial_norm(x) * (1 + scale.unsqueeze(-1)) + shift.unsqueeze(-1)
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x1 = self.lrelu(self.gn1(self.conv1(x0)))
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x2 = self.lrelu(self.gn2(self.conv2(torch.cat((x, x1), 1))))
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x3 = self.lrelu(self.gn3(self.conv3(torch.cat((x, x1, x2), 1))))
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x4 = self.lrelu(self.gn4(self.conv4(torch.cat((x, x1, x2, x3), 1))))
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x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
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x5 = self.drop(x5)
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return x5 + x
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@ -152,15 +155,14 @@ class DiffusionWaveformGen(nn.Module):
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def __init__(
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self,
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model_channels,
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model_channels=512,
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in_channels=64,
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in_mel_channels=256,
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conditioning_dim_factor=8,
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conditioning_expansion=4,
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conditioning_dim_factor=4,
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out_channels=128, # mean and variance
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dropout=0,
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channel_mult= (1,1.5,2),
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num_res_blocks=(1,1,1),
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num_res_blocks=(1,1,0),
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token_conditioning_resolutions=(1,4),
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mid_resnet_depth=10,
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conv_resample=True,
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@ -168,9 +170,8 @@ class DiffusionWaveformGen(nn.Module):
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use_fp16=False,
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kernel_size=3,
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scale_factor=2,
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time_embed_dim_multiplier=4,
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time_embed_dim_multiplier=1,
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freeze_main_net=False,
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efficient_convs=True, # Uses kernels with width of 1 in several places rather than 3.
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use_scale_shift_norm=True,
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# Parameters for regularization.
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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@ -197,7 +198,6 @@ class DiffusionWaveformGen(nn.Module):
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self.freeze_main_net = freeze_main_net
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self.in_mel_channels = in_mel_channels
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padding = 1 if kernel_size == 3 else 2
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down_kernel = 1 if efficient_convs else 3
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time_embed_dim = model_channels * time_embed_dim_multiplier
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self.time_embed = nn.Sequential(
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@ -213,12 +213,6 @@ class DiffusionWaveformGen(nn.Module):
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# transformer network.
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self.mel_converter = nn.Conv1d(in_mel_channels, conditioning_dim, 3, padding=1)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1))
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self.conditioning_timestep_integrator = TimestepEmbedSequential(
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ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
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ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
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ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, dims=dims, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm),
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)
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self.conditioning_expansion = conditioning_expansion
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self.input_blocks = nn.ModuleList(
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[
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@ -249,7 +243,6 @@ class DiffusionWaveformGen(nn.Module):
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out_channels=int(mult * model_channels),
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dims=dims,
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kernel_size=kernel_size,
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efficient_config=efficient_convs,
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use_scale_shift_norm=use_scale_shift_norm,
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)
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]
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@ -262,7 +255,7 @@ class DiffusionWaveformGen(nn.Module):
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self.input_blocks.append(
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TimestepEmbedSequential(
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Downsample(
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ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=down_kernel, pad=0 if down_kernel == 1 else 1
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ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=3, pad=1
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)
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)
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)
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@ -286,7 +279,6 @@ class DiffusionWaveformGen(nn.Module):
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out_channels=int(model_channels * mult),
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dims=dims,
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kernel_size=kernel_size,
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efficient_config=efficient_convs,
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use_scale_shift_norm=use_scale_shift_norm,
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)
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]
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@ -360,11 +352,6 @@ class DiffusionWaveformGen(nn.Module):
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else:
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code_emb = self.mel_converter(aligned_conditioning)
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# Everything after this comment is timestep dependent.
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code_emb = torch.repeat_interleave(code_emb, self.conditioning_expansion, dim=-1)
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code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
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first = True
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time_emb = time_emb.float()
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h = x
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for k, module in enumerate(self.input_blocks):
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@ -374,7 +361,6 @@ class DiffusionWaveformGen(nn.Module):
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else:
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h = module(h, time_emb)
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hs.append(h)
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first = False
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h = self.middle_block(h, time_emb)
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for module in self.output_blocks:
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h = torch.cat([h, hs.pop()], dim=1)
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@ -398,18 +384,11 @@ def register_unet_diffusion_waveform_gen3(opt_net, opt):
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if __name__ == '__main__':
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clip = torch.randn(2, 1, 32868)
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aligned_sequence = torch.randn(2,120,220)
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clip = torch.randn(2, 64, 880)
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aligned_sequence = torch.randn(2,256,220)
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ts = torch.LongTensor([600, 600])
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model = DiffusionWaveformGen(128,
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channel_mult=[1,1.5,2, 3, 4, 6, 8],
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num_res_blocks=[2, 2, 2, 2, 2, 2, 1],
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token_conditioning_resolutions=[1,4,16,64],
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kernel_size=3,
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scale_factor=2,
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time_embed_dim_multiplier=4,
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super_sampling=False,
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efficient_convs=False)
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model = DiffusionWaveformGen()
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# Test with sequence aligned conditioning
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o = model(clip, ts, aligned_sequence)
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print_network(model)
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