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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
import torch.nn.functional as F
from matcha.models.components.flow_matching import BASECFM
class ConditionalCFM(BASECFM):
def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None):
super().__init__(
n_feats=in_channels,
cfm_params=cfm_params,
n_spks=n_spks,
spk_emb_dim=spk_emb_dim,
)
self.t_scheduler = cfm_params.t_scheduler
self.training_cfg_rate = cfm_params.training_cfg_rate
self.inference_cfg_rate = cfm_params.inference_cfg_rate
in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0)
# Just change the architecture of the estimator here
self.estimator = estimator
@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)
"""
torch.manual_seed(42)
z = torch.randn_like(mu) * temperature
t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device)
if self.t_scheduler == 'cosine':
t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
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)
# Classifier-Free Guidance inference introduced in VoiceBox
if self.inference_cfg_rate > 0:
cfg_dphi_dt = self.estimator(
x, mask,
torch.zeros_like(mu), t,
torch.zeros_like(spks) if spks is not None else None,
torch.zeros_like(cond)
)
dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt -
self.inference_cfg_rate * cfg_dphi_dt)
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)
if self.t_scheduler == 'cosine':
t = 1 - torch.cos(t * 0.5 * torch.pi)
# 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
# during training, we randomly drop condition to trade off mode coverage and sample fidelity
if self.training_cfg_rate > 0:
cfg_mask = torch.rand(b, device=x1.device) > self.training_cfg_rate
mu = mu * cfg_mask.view(-1, 1, 1)
spks = spks * cfg_mask.view(-1, 1)
cond = cond * cfg_mask.view(-1, 1, 1)
pred = self.estimator(y, mask, mu, t.squeeze(), spks, cond)
loss = F.mse_loss(pred * mask, u * mask, reduction="sum") / (torch.sum(mask) * u.shape[1])
return loss, y
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