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from abc import ABC
import torch
import torch.nn.functional as F
from modules.diffusion_transformer import DiT
from modules.commons import sequence_mask
from tqdm import tqdm
class BASECFM(torch.nn.Module, ABC):
def __init__(
self,
args,
):
super().__init__()
self.sigma_min = 1e-6
self.estimator = None
self.in_channels = args.DiT.in_channels
self.criterion = torch.nn.MSELoss() if args.reg_loss_type == "l2" else torch.nn.L1Loss()
if hasattr(args.DiT, 'zero_prompt_speech_token'):
self.zero_prompt_speech_token = args.DiT.zero_prompt_speech_token
else:
self.zero_prompt_speech_token = False
@torch.inference_mode()
def inference(self, mu, x_lens, prompt, style, f0, n_timesteps, temperature=1.0, inference_cfg_rate=0.5):
"""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)
"""
B, T = mu.size(0), mu.size(1)
z = torch.randn([B, self.in_channels, T], device=mu.device) * temperature
t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device)
# t_span = t_span + (-1) * (torch.cos(torch.pi / 2 * t_span) - 1 + t_span)
return self.solve_euler(z, x_lens, prompt, mu, style, f0, t_span, inference_cfg_rate)
def solve_euler(self, x, x_lens, prompt, mu, style, f0, t_span, inference_cfg_rate=0.5):
"""
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, _, _ = 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 = []
# apply prompt
prompt_len = prompt.size(-1)
prompt_x = torch.zeros_like(x)
prompt_x[..., :prompt_len] = prompt[..., :prompt_len]
x[..., :prompt_len] = 0
if self.zero_prompt_speech_token:
mu[..., :prompt_len] = 0
for step in tqdm(range(1, len(t_span))):
dt = t_span[step] - t_span[step - 1]
if inference_cfg_rate > 0:
# Stack original and CFG (null) inputs for batched processing
stacked_prompt_x = torch.cat([prompt_x, torch.zeros_like(prompt_x)], dim=0)
stacked_style = torch.cat([style, torch.zeros_like(style)], dim=0)
stacked_mu = torch.cat([mu, torch.zeros_like(mu)], dim=0)
stacked_x = torch.cat([x, x], dim=0)
stacked_t = torch.cat([t.unsqueeze(0), t.unsqueeze(0)], dim=0)
# Perform a single forward pass for both original and CFG inputs
stacked_dphi_dt = self.estimator(
stacked_x, stacked_prompt_x, x_lens, stacked_t, stacked_style, stacked_mu,
)
# Split the output back into the original and CFG components
dphi_dt, cfg_dphi_dt = stacked_dphi_dt.chunk(2, dim=0)
# Apply CFG formula
dphi_dt = (1.0 + inference_cfg_rate) * dphi_dt - inference_cfg_rate * cfg_dphi_dt
else:
dphi_dt = self.estimator(x, prompt_x, x_lens, t.unsqueeze(0), style, mu)
x = x + dt * dphi_dt
t = t + dt
sol.append(x)
if step < len(t_span) - 1:
dt = t_span[step + 1] - t
x[:, :, :prompt_len] = 0
return sol[-1]
def forward(self, x1, x_lens, prompt_lens, mu, style):
"""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 = x1.shape
# random timestep
t = torch.rand([b, 1, 1], device=mu.device, dtype=x1.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
prompt = torch.zeros_like(x1)
for bib in range(b):
prompt[bib, :, :prompt_lens[bib]] = x1[bib, :, :prompt_lens[bib]]
# range covered by prompt are set to 0
y[bib, :, :prompt_lens[bib]] = 0
if self.zero_prompt_speech_token:
mu[bib, :, :prompt_lens[bib]] = 0
estimator_out = self.estimator(y, prompt, x_lens, t.squeeze(1).squeeze(1), style, mu, prompt_lens)
loss = 0
for bib in range(b):
loss += self.criterion(estimator_out[bib, :, prompt_lens[bib]:x_lens[bib]], u[bib, :, prompt_lens[bib]:x_lens[bib]])
loss /= b
return loss, estimator_out + (1 - self.sigma_min) * z
class CFM(BASECFM):
def __init__(self, args):
super().__init__(
args
)
if args.dit_type == "DiT":
self.estimator = DiT(args)
else:
raise NotImplementedError(f"Unknown diffusion type {args.dit_type}")
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