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import torch
from tqdm import tqdm
class CFM(torch.nn.Module):
def __init__(
self,
estimator: torch.nn.Module,
):
super().__init__()
self.sigma_min = 1e-6
self.estimator = estimator
self.in_channels = estimator.in_channels
self.criterion = torch.nn.L1Loss()
@torch.inference_mode()
def inference(self,
mu: torch.Tensor,
x_lens: torch.Tensor,
prompt: torch.Tensor,
style: torch.Tensor,
n_timesteps=10,
temperature=1.0,
inference_cfg_rate=[0.5, 0.5],
random_voice=False,
):
"""Forward diffusion
Args:
mu (torch.Tensor): output of encoder
shape: (batch_size, n_feats, mel_timesteps)
x_lens (torch.Tensor): length of each mel-spectrogram
shape: (batch_size,)
prompt (torch.Tensor): prompt
shape: (batch_size, n_feats, prompt_len)
style (torch.Tensor): style
shape: (batch_size, style_dim)
n_timesteps (int): number of diffusion steps
temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
inference_cfg_rate (float, optional): Classifier-Free Guidance inference introduced in VoiceBox. Defaults to 0.5.
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, t_span, inference_cfg_rate, random_voice)
def solve_euler(self, x, x_lens, prompt, mu, style, t_span, inference_cfg_rate=[0.5, 0.5], random_voice=False,):
"""
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)
x_lens (torch.Tensor): length of each mel-spectrogram
shape: (batch_size,)
prompt (torch.Tensor): prompt
shape: (batch_size, n_feats, prompt_len)
style (torch.Tensor): style
shape: (batch_size, style_dim)
inference_cfg_rate (float, optional): Classifier-Free Guidance inference introduced in VoiceBox. Defaults to 0.5.
sway_sampling (bool, optional): Sway sampling. Defaults to False.
amo_sampling (bool, optional): AMO sampling. Defaults to False.
"""
t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
# apply prompt
prompt_len = prompt.size(-1)
prompt_x = torch.zeros_like(x)
prompt_x[..., :prompt_len] = prompt[..., :prompt_len]
x[..., :prompt_len] = 0
for step in tqdm(range(1, len(t_span))):
if random_voice:
cfg_dphi_dt = self.estimator(
torch.cat([x, x], dim=0),
torch.cat([torch.zeros_like(prompt_x), torch.zeros_like(prompt_x)], dim=0),
torch.cat([x_lens, x_lens], dim=0),
torch.cat([t.unsqueeze(0), t.unsqueeze(0)], dim=0),
torch.cat([torch.zeros_like(style), torch.zeros_like(style)], dim=0),
torch.cat([mu, torch.zeros_like(mu)], dim=0),
)
cond_txt, uncond = cfg_dphi_dt[0:1], cfg_dphi_dt[1:2]
dphi_dt = ((1.0 + inference_cfg_rate[0]) * cond_txt - inference_cfg_rate[0] * uncond)
elif all(i == 0 for i in inference_cfg_rate):
dphi_dt = self.estimator(x, prompt_x, x_lens, t.unsqueeze(0), style, mu)
elif inference_cfg_rate[0] == 0:
# Classifier-Free Guidance inference introduced in VoiceBox
cfg_dphi_dt = self.estimator(
torch.cat([x, x], dim=0),
torch.cat([prompt_x, torch.zeros_like(prompt_x)], dim=0),
torch.cat([x_lens, x_lens], dim=0),
torch.cat([t.unsqueeze(0), t.unsqueeze(0)], dim=0),
torch.cat([style, torch.zeros_like(style)], dim=0),
torch.cat([mu, mu], dim=0),
)
cond_txt_spk, cond_txt = cfg_dphi_dt[0:1], cfg_dphi_dt[1:2]
dphi_dt = ((1.0 + inference_cfg_rate[1]) * cond_txt_spk - inference_cfg_rate[1] * cond_txt)
elif inference_cfg_rate[1] == 0:
cfg_dphi_dt = self.estimator(
torch.cat([x, x], dim=0),
torch.cat([prompt_x, torch.zeros_like(prompt_x)], dim=0),
torch.cat([x_lens, x_lens], dim=0),
torch.cat([t.unsqueeze(0), t.unsqueeze(0)], dim=0),
torch.cat([style, torch.zeros_like(style)], dim=0),
torch.cat([mu, torch.zeros_like(mu)], dim=0),
)
cond_txt_spk, uncond = cfg_dphi_dt[0:1], cfg_dphi_dt[1:2]
dphi_dt = ((1.0 + inference_cfg_rate[0]) * cond_txt_spk - inference_cfg_rate[0] * uncond)
else:
# Multi-condition Classifier-Free Guidance inference introduced in MegaTTS3
cfg_dphi_dt = self.estimator(
torch.cat([x, x, x], dim=0),
torch.cat([prompt_x, torch.zeros_like(prompt_x), torch.zeros_like(prompt_x)], dim=0),
torch.cat([x_lens, x_lens, x_lens], dim=0),
torch.cat([t.unsqueeze(0), t.unsqueeze(0), t.unsqueeze(0)], dim=0),
torch.cat([style, torch.zeros_like(style), torch.zeros_like(style)], dim=0),
torch.cat([mu, mu, torch.zeros_like(mu)], dim=0),
)
cond_txt_spk, cond_txt, uncond = cfg_dphi_dt[0:1], cfg_dphi_dt[1:2], cfg_dphi_dt[2:3]
dphi_dt = (1.0 + inference_cfg_rate[0] + inference_cfg_rate[1]) * cond_txt_spk - \
inference_cfg_rate[0] * uncond - inference_cfg_rate[1] * cond_txt
x = x + dt * dphi_dt
t = t + dt
if step < len(t_span) - 1:
dt = t_span[step + 1] - t
x[:, :, :prompt_len] = 0
return x
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
estimator_out = self.estimator(y, prompt, x_lens, t.squeeze(), style, mu)
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
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