Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,569 Bytes
076829a 90433f5 076829a 5f21aef 076829a 5f21aef 076829a 2ce7240 076829a 2ce7240 076829a 5f21aef 2ce7240 6e7f5b9 fadb220 2ce7240 fadb220 2ce7240 fadb220 076829a 44aea80 076829a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 |
# 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)
"""
z = torch.randn_like(mu) * temperature
t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
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]
t = t.unsqueeze(dim=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.forward_estimator(x, mask, mu, t, spks, cond)
# Classifier-Free Guidance inference introduced in VoiceBox
if self.inference_cfg_rate > 0:
cfg_dphi_dt = self.forward_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 forward_estimator(self, x, mask, mu, t, spks, cond):
if isinstance(self.estimator, torch.nn.Module):
return self.estimator.forward(x, mask, mu, t, spks, cond)
else:
ort_inputs = {
'x': x.cpu().numpy(),
'mask': mask.cpu().numpy(),
'mu': mu.cpu().numpy(),
't': t.cpu().numpy(),
'spks': spks.cpu().numpy(),
'cond': cond.cpu().numpy()
}
output = self.estimator.run(None, ort_inputs)[0]
return torch.tensor(output, dtype=x.dtype, device=x.device)
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
|