168 lines
7.0 KiB
Python
168 lines
7.0 KiB
Python
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# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Zhihao Du)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import torch.nn.functional as F
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from matcha.models.components.flow_matching import BASECFM
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import os, sys
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sys.path.insert(0, os.path.abspath(r'E:\2_PYTHON\Project\GPT\QWen\CosyVoice\third_party\Matcha-TTS'))
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class ConditionalCFM(BASECFM):
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def __init__(self, in_channels, cfm_params, n_spks=1, spk_emb_dim=64, estimator: torch.nn.Module = None):
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super().__init__(
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n_feats=in_channels,
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cfm_params=cfm_params,
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n_spks=n_spks,
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spk_emb_dim=spk_emb_dim,
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)
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self.t_scheduler = cfm_params.t_scheduler
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self.training_cfg_rate = cfm_params.training_cfg_rate
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self.inference_cfg_rate = cfm_params.inference_cfg_rate
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in_channels = in_channels + (spk_emb_dim if n_spks > 0 else 0)
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# Just change the architecture of the estimator here
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self.estimator = estimator
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@torch.inference_mode()
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def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, prompt_len=0, flow_cache=torch.zeros(1, 80, 0, 2)):
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"""Forward diffusion
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Args:
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mu (torch.Tensor): output of encoder
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shape: (batch_size, n_feats, mel_timesteps)
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mask (torch.Tensor): output_mask
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shape: (batch_size, 1, mel_timesteps)
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n_timesteps (int): number of diffusion steps
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temperature (float, optional): temperature for scaling noise. Defaults to 1.0.
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spks (torch.Tensor, optional): speaker ids. Defaults to None.
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shape: (batch_size, spk_emb_dim)
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cond: Not used but kept for future purposes
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Returns:
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sample: generated mel-spectrogram
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shape: (batch_size, n_feats, mel_timesteps)
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"""
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z = torch.randn_like(mu) * temperature
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cache_size = flow_cache.shape[2]
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# fix prompt and overlap part mu and z
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if cache_size != 0:
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z[:, :, :cache_size] = flow_cache[:, :, :, 0]
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mu[:, :, :cache_size] = flow_cache[:, :, :, 1]
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z_cache = torch.concat([z[:, :, :prompt_len], z[:, :, -34:]], dim=2)
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mu_cache = torch.concat([mu[:, :, :prompt_len], mu[:, :, -34:]], dim=2)
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flow_cache = torch.stack([z_cache, mu_cache], dim=-1)
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t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype)
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if self.t_scheduler == 'cosine':
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t_span = 1 - torch.cos(t_span * 0.5 * torch.pi)
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return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), flow_cache
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def solve_euler(self, x, t_span, mu, mask, spks, cond):
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"""
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Fixed euler solver for ODEs.
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Args:
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x (torch.Tensor): random noise
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t_span (torch.Tensor): n_timesteps interpolated
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shape: (n_timesteps + 1,)
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mu (torch.Tensor): output of encoder
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shape: (batch_size, n_feats, mel_timesteps)
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mask (torch.Tensor): output_mask
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shape: (batch_size, 1, mel_timesteps)
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spks (torch.Tensor, optional): speaker ids. Defaults to None.
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shape: (batch_size, spk_emb_dim)
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cond: Not used but kept for future purposes
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"""
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t, _, dt = t_span[0], t_span[-1], t_span[1] - t_span[0]
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t = t.unsqueeze(dim=0)
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# I am storing this because I can later plot it by putting a debugger here and saving it to a file
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# Or in future might add like a return_all_steps flag
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sol = []
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for step in range(1, len(t_span)):
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dphi_dt = self.forward_estimator(x, mask, mu, t, spks, cond)
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# Classifier-Free Guidance inference introduced in VoiceBox
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if self.inference_cfg_rate > 0:
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cfg_dphi_dt = self.forward_estimator(
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x, mask,
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torch.zeros_like(mu), t,
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torch.zeros_like(spks) if spks is not None else None,
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torch.zeros_like(cond)
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)
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dphi_dt = ((1.0 + self.inference_cfg_rate) * dphi_dt -
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self.inference_cfg_rate * cfg_dphi_dt)
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x = x + dt * dphi_dt
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t = t + dt
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sol.append(x)
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if step < len(t_span) - 1:
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dt = t_span[step + 1] - t
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return sol[-1]
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def forward_estimator(self, x, mask, mu, t, spks, cond):
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if isinstance(self.estimator, torch.nn.Module):
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return self.estimator.forward(x, mask, mu, t, spks, cond)
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else:
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ort_inputs = {
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'x': x.cpu().numpy(),
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'mask': mask.cpu().numpy(),
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'mu': mu.cpu().numpy(),
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't': t.cpu().numpy(),
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'spks': spks.cpu().numpy(),
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'cond': cond.cpu().numpy()
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}
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output = self.estimator.run(None, ort_inputs)[0]
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return torch.tensor(output, dtype=x.dtype, device=x.device)
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def compute_loss(self, x1, mask, mu, spks=None, cond=None):
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"""Computes diffusion loss
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Args:
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x1 (torch.Tensor): Target
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shape: (batch_size, n_feats, mel_timesteps)
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mask (torch.Tensor): target mask
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shape: (batch_size, 1, mel_timesteps)
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mu (torch.Tensor): output of encoder
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shape: (batch_size, n_feats, mel_timesteps)
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spks (torch.Tensor, optional): speaker embedding. Defaults to None.
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shape: (batch_size, spk_emb_dim)
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Returns:
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loss: conditional flow matching loss
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y: conditional flow
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shape: (batch_size, n_feats, mel_timesteps)
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"""
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b, _, t = mu.shape
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# random timestep
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t = torch.rand([b, 1, 1], device=mu.device, dtype=mu.dtype)
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if self.t_scheduler == 'cosine':
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t = 1 - torch.cos(t * 0.5 * torch.pi)
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# sample noise p(x_0)
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z = torch.randn_like(x1)
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y = (1 - (1 - self.sigma_min) * t) * z + t * x1
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u = x1 - (1 - self.sigma_min) * z
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# during training, we randomly drop condition to trade off mode coverage and sample fidelity
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if self.training_cfg_rate > 0:
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cfg_mask = torch.rand(b, device=x1.device) > self.training_cfg_rate
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mu = mu * cfg_mask.view(-1, 1, 1)
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spks = spks * cfg_mask.view(-1, 1)
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cond = cond * cfg_mask.view(-1, 1, 1)
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pred = self.estimator(y, mask, mu, t.squeeze(), spks, cond)
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loss = F.mse_loss(pred * mask, u * mask, reduction="sum") / (torch.sum(mask) * u.shape[1])
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return loss, y
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