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Zero
| # 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 threading | |
| import torch | |
| import torch.nn.functional as F | |
| from .matcha.flow_matching import BASECFM | |
| from .configs import CFM_PARAMS | |
| from tqdm import tqdm | |
| def cast_all(*args, dtype): | |
| return [a if (not a.dtype.is_floating_point) or a.dtype == dtype else a.to(dtype) for a in args] | |
| 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 | |
| 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)): | |
| """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) | |
| """ | |
| raise NotImplementedError("unused, needs updating for meanflow model") | |
| z = torch.randn_like(mu).to(mu.device).to(mu.dtype) * temperature | |
| cache_size = flow_cache.shape[2] | |
| # fix prompt and overlap part mu and z | |
| if cache_size != 0: | |
| z[:, :, :cache_size] = flow_cache[:, :, :, 0] | |
| mu[:, :, :cache_size] = flow_cache[:, :, :, 1] | |
| z_cache = torch.concat([z[:, :, :prompt_len], z[:, :, -34:]], dim=2) | |
| mu_cache = torch.concat([mu[:, :, :prompt_len], mu[:, :, -34:]], dim=2) | |
| flow_cache = torch.stack([z_cache, mu_cache], dim=-1) | |
| 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), flow_cache | |
| def solve_euler(self, x, t_span, mu, mask, spks, cond, meanflow=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) | |
| 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 | |
| meanflow: meanflow mode | |
| """ | |
| in_dtype = x.dtype | |
| x, t_span, mu, mask, spks, cond = cast_all(x, t_span, mu, mask, spks, cond, dtype=self.estimator.dtype) | |
| # Duplicated batch dims are for CFG | |
| # Do not use concat, it may cause memory format changed and trt infer with wrong results! | |
| B, T = mu.size(0), x.size(2) | |
| x_in = torch.zeros([2 * B, 80, T], device=x.device, dtype=x.dtype) | |
| mask_in = torch.zeros([2 * B, 1, T], device=x.device, dtype=x.dtype) | |
| mu_in = torch.zeros([2 * B, 80, T], device=x.device, dtype=x.dtype) | |
| t_in = torch.zeros([2 * B ], device=x.device, dtype=x.dtype) | |
| spks_in = torch.zeros([2 * B, 80 ], device=x.device, dtype=x.dtype) | |
| cond_in = torch.zeros([2 * B, 80, T], device=x.device, dtype=x.dtype) | |
| r_in = torch.zeros([2 * B ], device=x.device, dtype=x.dtype) # (only used for meanflow) | |
| for t, r in zip(t_span[:-1], t_span[1:]): | |
| t = t.unsqueeze(dim=0) | |
| r = r.unsqueeze(dim=0) | |
| # Shapes: | |
| # x_in ( 2B, 80, T ) | |
| # mask_in ( 2B, 1, T ) | |
| # mu_in ( 2B, 80, T ) | |
| # t_in ( 2B, ) | |
| # spks_in ( 2B, 80, ) | |
| # cond_in ( 2B, 80, T ) | |
| # r_in ( 2B, ) | |
| # x ( B, 80, T ) | |
| # mask ( B, 1, T ) | |
| # mu ( B, 80, T ) | |
| # t ( B, ) | |
| # spks ( B, 80, ) | |
| # cond ( B, 80, T ) | |
| # r ( B, ) | |
| x_in[:B] = x_in[B:] = x | |
| mask_in[:B] = mask_in[B:] = mask | |
| mu_in[:B] = mu | |
| t_in[:B] = t_in[B:] = t | |
| spks_in[:B] = spks | |
| cond_in[:B] = cond | |
| r_in[:B] = r_in[B:] = r # (only used for meanflow) | |
| dxdt = self.estimator.forward( | |
| x=x_in, mask=mask_in, mu=mu_in, t=t_in, spks=spks_in, cond=cond_in, | |
| r=r_in if meanflow else None, | |
| ) | |
| dxdt, cfg_dxdt = torch.split(dxdt, [B, B], dim=0) | |
| dxdt = ((1.0 + self.inference_cfg_rate) * dxdt - self.inference_cfg_rate * cfg_dxdt) | |
| dt = r - t | |
| x = x + dt * dxdt | |
| return x.to(in_dtype) | |
| 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 | |
| class CausalConditionalCFM(ConditionalCFM): | |
| def __init__(self, in_channels=240, cfm_params=CFM_PARAMS, n_spks=1, spk_emb_dim=80, estimator=None): | |
| super().__init__(in_channels, cfm_params, n_spks, spk_emb_dim, estimator) | |
| # TODO: BAD BAD IDEA - IT'LL MESS UP DISTILLATION - SETTING TO NONE | |
| self.rand_noise = None | |
| def forward(self, mu, mask, n_timesteps, temperature=1.0, spks=None, cond=None, noised_mels=None, meanflow=False): | |
| """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 | |
| noised_mels: gt mels noised a time t | |
| Returns: | |
| sample: generated mel-spectrogram | |
| shape: (batch_size, n_feats, mel_timesteps) | |
| """ | |
| B = mu.size(0) | |
| z = torch.randn_like(mu) | |
| if noised_mels is not None: | |
| prompt_len = mu.size(2) - noised_mels.size(2) | |
| z[..., prompt_len:] = noised_mels | |
| # time steps for reverse diffusion | |
| t_span = torch.linspace(0, 1, n_timesteps + 1, device=mu.device, dtype=mu.dtype) | |
| if (not meanflow) and (self.t_scheduler == 'cosine'): | |
| t_span = 1 - torch.cos(t_span * 0.5 * torch.pi) | |
| # NOTE: right now, the only meanflow models are also distilled models, which don't need CFG | |
| # because they were distilled with CFG outputs. We would need to add another hparam and | |
| # change the conditional logic here if we want to use CFG inference with a meanflow model. | |
| if meanflow: | |
| return self.basic_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond), None | |
| return self.solve_euler(z, t_span=t_span, mu=mu, mask=mask, spks=spks, cond=cond, meanflow=meanflow), None | |
| def basic_euler(self, x, t_span, mu, mask, spks, cond): | |
| in_dtype = x.dtype | |
| x, t_span, mu, mask, spks, cond = cast_all(x, t_span, mu, mask, spks, cond, dtype=self.estimator.dtype) | |
| print("S3 Token -> Mel Inference...") | |
| for t, r in tqdm(zip(t_span[..., :-1], t_span[..., 1:]), total=t_span.shape[-1] - 1): | |
| t, r = t[None], r[None] | |
| dxdt = self.estimator.forward(x, mask=mask, mu=mu, t=t, spks=spks, cond=cond, r=r) | |
| dt = r - t | |
| x = x + dt * dxdt | |
| return x.to(in_dtype) | |