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''' |
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https://github.com/cantabile-kwok/VoiceFlow-TTS/blob/main/model/diffsinger.py#L51 |
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This is the original implementation of the DiffSinger model. |
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It is a slightly modified WV which can be used for initial tests. |
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Will update this into original p-flow implementation later. |
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''' |
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import math |
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import torch.nn as nn |
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import torch |
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from torch.nn import Conv1d, Linear |
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import math |
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import torch.nn.functional as F |
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class Mish(nn.Module): |
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def forward(self, x): |
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return x * torch.tanh(F.softplus(x)) |
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class SinusoidalPosEmb(nn.Module): |
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def __init__(self, dim): |
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super(SinusoidalPosEmb, self).__init__() |
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self.dim = dim |
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def forward(self, x, scale=1000): |
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if x.ndim < 1: |
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x = x.unsqueeze(0) |
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device = x.device |
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half_dim = self.dim // 2 |
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emb = math.log(10000) / (half_dim - 1) |
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emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb) |
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emb = scale * x.unsqueeze(1) * emb.unsqueeze(0) |
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emb = torch.cat((emb.sin(), emb.cos()), dim=-1) |
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return emb |
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class ResidualBlock(nn.Module): |
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def __init__(self, encoder_hidden, residual_channels, dilation): |
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super().__init__() |
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self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation) |
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self.diffusion_projection = Linear(residual_channels, residual_channels) |
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self.conditioner_projection = Conv1d(encoder_hidden, 2 * residual_channels, 1) |
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self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1) |
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def forward(self, x, conditioner, diffusion_step): |
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diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1) |
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conditioner = self.conditioner_projection(conditioner) |
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y = x + diffusion_step |
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y = self.dilated_conv(y) + conditioner |
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gate, filter = torch.chunk(y, 2, dim=1) |
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y = torch.sigmoid(gate) * torch.tanh(filter) |
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y = self.output_projection(y) |
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residual, skip = torch.chunk(y, 2, dim=1) |
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return (x + residual) / math.sqrt(2.0), skip |
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class DiffSingerNet(nn.Module): |
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def __init__( |
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self, |
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in_dims=80, |
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residual_channels=256, |
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encoder_hidden=80, |
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dilation_cycle_length=1, |
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residual_layers=20, |
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pe_scale=1000 |
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): |
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super().__init__() |
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self.pe_scale = pe_scale |
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self.input_projection = Conv1d(in_dims, residual_channels, 1) |
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self.time_pos_emb = SinusoidalPosEmb(residual_channels) |
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dim = residual_channels |
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self.mlp = nn.Sequential( |
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nn.Linear(dim, dim * 4), |
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Mish(), |
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nn.Linear(dim * 4, dim) |
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) |
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self.residual_layers = nn.ModuleList([ |
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ResidualBlock(encoder_hidden, residual_channels, 2 ** (i % dilation_cycle_length)) |
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for i in range(residual_layers) |
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]) |
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self.skip_projection = Conv1d(residual_channels, residual_channels, 1) |
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self.output_projection = Conv1d(residual_channels, in_dims, 1) |
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nn.init.zeros_(self.output_projection.weight) |
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def forward(self, spec, spec_mask, mu, t, *args, **kwargs): |
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""" |
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:param spec: [B, M, T] |
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:param t: [B, ] |
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:param mu: [B, M, T] |
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:return: |
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""" |
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x = spec |
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x = self.input_projection(x) |
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x = F.relu(x) |
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t = self.time_pos_emb(t, scale=self.pe_scale) |
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t = self.mlp(t) |
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cond = mu |
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skip = [] |
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for layer_id, layer in enumerate(self.residual_layers): |
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x, skip_connection = layer(x, cond, t) |
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skip.append(skip_connection) |
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x = torch.sum(torch.stack(skip), dim=0) / math.sqrt(len(self.residual_layers)) |
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x = self.skip_projection(x) |
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x = F.relu(x) |
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x = self.output_projection(x) |
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return x * spec_mask |