133 lines
4.5 KiB
Python
133 lines
4.5 KiB
Python
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
|
|
|
|
@torch.inference_mode()
|
|
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)
|