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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 | |
| import onnxruntime as ort | |
| import numpy as np | |
| 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 | |
| self.estimator_context = None # for tensorrt | |
| self.session = None # for onnx | |
| 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.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 forward_estimator(self, x, mask, mu, t, spks, cond): | |
| if self.estimator is not None: | |
| return self.estimator.forward(x, mask, mu, t, spks, cond) | |
| # elif self.estimator_context is not None: | |
| # assert self.training is False, 'tensorrt cannot be used in training' | |
| # bs = x.shape[0] | |
| # hs = x.shape[1] | |
| # seq_len = x.shape[2] | |
| # # assert bs == 1 and hs == 80 | |
| # ret = torch.empty_like(x) | |
| # self.estimator_context.set_input_shape("x", x.shape) | |
| # self.estimator_context.set_input_shape("mask", mask.shape) | |
| # self.estimator_context.set_input_shape("mu", mu.shape) | |
| # self.estimator_context.set_input_shape("t", t.shape) | |
| # self.estimator_context.set_input_shape("spks", spks.shape) | |
| # self.estimator_context.set_input_shape("cond", cond.shape) | |
| # # Create a list of bindings | |
| # bindings = [int(x.data_ptr()), int(mask.data_ptr()), int(mu.data_ptr()), int(t.data_ptr()), int(spks.data_ptr()), int(cond.data_ptr()), int(ret.data_ptr())] | |
| # # Execute the inference | |
| # self.estimator_context.execute_v2(bindings=bindings) | |
| # return ret | |
| else: | |
| x_np = x.cpu().numpy() | |
| mask_np = mask.cpu().numpy() | |
| mu_np = mu.cpu().numpy() | |
| t_np = t.cpu().numpy() | |
| spks_np = spks.cpu().numpy() | |
| cond_np = cond.cpu().numpy() | |
| ort_inputs = { | |
| 'x': x_np, | |
| 'mask': mask_np, | |
| 'mu': mu_np, | |
| 't': t_np, | |
| 'spks': spks_np, | |
| 'cond': cond_np | |
| } | |
| output = self.session.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 | |