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| from abc import ABC | |
| import torch | |
| import torch.nn.functional as F | |
| from matcha.models.components.decoder import Decoder | |
| from matcha.utils.pylogger import get_pylogger | |
| log = get_pylogger(__name__) | |
| class BASECFM(torch.nn.Module, ABC): | |
| def __init__( | |
| self, | |
| n_feats, | |
| cfm_params, | |
| n_spks=1, | |
| spk_emb_dim=128, | |
| ): | |
| super().__init__() | |
| self.n_feats = n_feats | |
| self.n_spks = n_spks | |
| self.spk_emb_dim = spk_emb_dim | |
| self.solver = cfm_params.solver | |
| if hasattr(cfm_params, "sigma_min"): | |
| self.sigma_min = cfm_params.sigma_min | |
| else: | |
| self.sigma_min = 1e-4 | |
| self.estimator = None | |
| def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=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. | |
| spks (torch.Tensor, optional): speaker ids. Defaults to None. | |
| shape: (batch_size, spk_emb_dim) | |
| cond: Not used but kept for future purposes | |
| 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, spks=spks, cond=cond) | |
| def solve_euler(self, x, t_span, mu, mask, spks, cond): | |
| """ | |
| 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) | |
| spks (torch.Tensor, optional): speaker ids. Defaults to None. | |
| shape: (batch_size, spk_emb_dim) | |
| cond: Not used but kept for future purposes | |
| """ | |
| 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, spks, cond) | |
| 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, spks=None, cond=None): | |
| """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) | |
| spks (torch.Tensor, optional): speaker embedding. Defaults to None. | |
| shape: (batch_size, spk_emb_dim) | |
| 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(), spks), u, reduction="sum") / ( | |
| torch.sum(mask) * u.shape[1] | |
| ) | |
| return loss, y | |
| class CFM(BASECFM): | |
| def __init__(self, in_channels, out_channel, cfm_params, decoder_params, n_spks=1, spk_emb_dim=64): | |
| super().__init__( | |
| n_feats=in_channels, | |
| cfm_params=cfm_params, | |
| n_spks=n_spks, | |
| spk_emb_dim=spk_emb_dim, | |
| ) | |
| in_channels = in_channels + (spk_emb_dim if n_spks > 1 else 0) | |
| # Just change the architecture of the estimator here | |
| self.estimator = Decoder(in_channels=in_channels, out_channels=out_channel, **decoder_params) | |