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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from models.estimator import Decoder | |
| # copied from https://github.com/jaywalnut310/vits/blob/main/commons.py#L121 | |
| def sequence_mask(length: torch.Tensor, max_length: int = None) -> torch.Tensor: | |
| if max_length is None: | |
| max_length = length.max() | |
| x = torch.arange(max_length, dtype=length.dtype, device=length.device) | |
| return x.unsqueeze(0) < length.unsqueeze(1) | |
| # modified from https://github.com/shivammehta25/Matcha-TTS/blob/main/matcha/models/components/flow_matching.py | |
| class CFMDecoder(torch.nn.Module): | |
| def __init__(self, hidden_channels, out_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, gin_channels): | |
| super().__init__() | |
| self.hidden_channels = hidden_channels | |
| self.out_channels = out_channels | |
| self.filter_channels = filter_channels | |
| self.gin_channels = gin_channels | |
| self.sigma_min = 1e-4 | |
| self.estimator = Decoder(hidden_channels, out_channels, filter_channels, p_dropout, n_layers, n_heads, kernel_size, gin_channels) | |
| def forward(self, mu, mask, n_timesteps, temperature=1.0, c=None): | |
| """Forward diffusion | |
| Args: | |
| mu (torch.Tensor): output of encoder | |
| shape: (batch_size, n_feats, mel_timesteps) | |
| mask (torch.Tensor): output_mask | |
| shape: (batch_size, 1, mel_timesteps) | |
| n_timesteps (int): number of diffusion steps | |
| temperature (float, optional): temperature for scaling noise. Defaults to 1.0. | |
| c (torch.Tensor, optional): shape: (batch_size, gin_channels) | |
| Returns: | |
| sample: generated mel-spectrogram | |
| shape: (batch_size, n_feats, mel_timesteps) | |
| """ | |
| z = torch.randn_like(mu) * temperature | |
| t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device) | |
| return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, c=c) | |
| def solve_euler(self, x, t_span, mu, mask, c): | |
| """ | |
| Fixed euler solver for ODEs. | |
| Args: | |
| x (torch.Tensor): random noise | |
| t_span (torch.Tensor): n_timesteps interpolated | |
| shape: (n_timesteps + 1,) | |
| mu (torch.Tensor): output of encoder | |
| shape: (batch_size, n_feats, mel_timesteps) | |
| mask (torch.Tensor): output_mask | |
| shape: (batch_size, 1, mel_timesteps) | |
| c (torch.Tensor, optional): speaker condition. | |
| shape: (batch_size, gin_channels) | |
| """ | |
| t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0] | |
| # I am storing this because I can later plot it by putting a debugger here and saving it to a file | |
| # Or in future might add like a return_all_steps flag | |
| sol = [] | |
| for step in range(1, len(t_span)): | |
| dphi_dt = self.estimator(x, mask, mu, t, c) | |
| x = x + dt * dphi_dt | |
| t = t + dt | |
| sol.append(x) | |
| if step < len(t_span) - 1: | |
| dt = t_span[step + 1] - t | |
| return sol[-1] | |
| def compute_loss(self, x1, mask, mu, c): | |
| """Computes diffusion loss | |
| Args: | |
| x1 (torch.Tensor): Target | |
| shape: (batch_size, n_feats, mel_timesteps) | |
| mask (torch.Tensor): target mask | |
| shape: (batch_size, 1, mel_timesteps) | |
| mu (torch.Tensor): output of encoder | |
| shape: (batch_size, n_feats, mel_timesteps) | |
| c (torch.Tensor, optional): speaker condition. | |
| Returns: | |
| loss: conditional flow matching loss | |
| y: conditional flow | |
| shape: (batch_size, n_feats, mel_timesteps) | |
| """ | |
| b, _, t = mu.shape | |
| # random timestep | |
| t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype) | |
| # sample noise p(x_0) | |
| z = torch.randn_like(x1) | |
| y = (1 - (1 - self.sigma_min) * t) * z + t * x1 | |
| u = x1 - (1 - self.sigma_min) * z | |
| loss = F.mse_loss(self.estimator(y, mask, mu, t.squeeze(), c), u, reduction="sum") / ( | |
| torch.sum(mask) * u.shape[1] | |
| ) | |
| return loss, y | |