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| import logging | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| import torch | |
| from torch import Tensor, nn | |
| from torch.distributions import Beta | |
| from ..common import Normalizer | |
| from ..denoiser.inference import load_denoiser | |
| from ..melspec import MelSpectrogram | |
| from .hparams import HParams | |
| from .lcfm import CFM, IRMAE, LCFM | |
| from .univnet import UnivNet | |
| logger = logging.getLogger(__name__) | |
| def _maybe(fn): | |
| def _fn(*args): | |
| if args[0] is None: | |
| return None | |
| return fn(*args) | |
| return _fn | |
| def _normalize_wav(x: Tensor): | |
| return x / (x.abs().max(dim=-1, keepdim=True).values + 1e-7) | |
| class Enhancer(nn.Module): | |
| def __init__(self, hp: HParams): | |
| super().__init__() | |
| self.hp = hp | |
| n_mels = self.hp.num_mels | |
| vocoder_input_dim = n_mels + self.hp.vocoder_extra_dim | |
| latent_dim = self.hp.lcfm_latent_dim | |
| self.lcfm = LCFM( | |
| IRMAE( | |
| input_dim=n_mels, | |
| output_dim=vocoder_input_dim, | |
| latent_dim=latent_dim, | |
| ), | |
| CFM( | |
| cond_dim=n_mels, | |
| output_dim=self.hp.lcfm_latent_dim, | |
| solver_nfe=self.hp.cfm_solver_nfe, | |
| solver_method=self.hp.cfm_solver_method, | |
| time_mapping_divisor=self.hp.cfm_time_mapping_divisor, | |
| ), | |
| z_scale=self.hp.lcfm_z_scale, | |
| ) | |
| self.lcfm.set_mode_(self.hp.lcfm_training_mode) | |
| self.mel_fn = MelSpectrogram(hp) | |
| self.vocoder = UnivNet(self.hp, vocoder_input_dim) | |
| self.denoiser = load_denoiser(self.hp.denoiser_run_dir, "cpu") | |
| self.normalizer = Normalizer() | |
| self._eval_lambd = 0.0 | |
| self.dummy: Tensor | |
| self.register_buffer("dummy", torch.zeros(1)) | |
| if self.hp.enhancer_stage1_run_dir is not None: | |
| pretrained_path = ( | |
| self.hp.enhancer_stage1_run_dir | |
| / "ds/G/default/mp_rank_00_model_states.pt" | |
| ) | |
| self._load_pretrained(pretrained_path) | |
| logger.info(f"{self.__class__.__name__} summary") | |
| logger.info(f"{self.summarize()}") | |
| def _load_pretrained(self, path): | |
| # Clone is necessary as otherwise it holds a reference to the original model | |
| cfm_state_dict = {k: v.clone() for k, v in self.lcfm.cfm.state_dict().items()} | |
| denoiser_state_dict = { | |
| k: v.clone() for k, v in self.denoiser.state_dict().items() | |
| } | |
| state_dict = torch.load(path, map_location="cpu")["module"] | |
| self.load_state_dict(state_dict, strict=False) | |
| self.lcfm.cfm.load_state_dict(cfm_state_dict) # Reset cfm | |
| self.denoiser.load_state_dict(denoiser_state_dict) # Reset denoiser | |
| logger.info(f"Loaded pretrained model from {path}") | |
| def summarize(self): | |
| npa_train = lambda m: sum(p.numel() for p in m.parameters() if p.requires_grad) | |
| npa = lambda m: sum(p.numel() for p in m.parameters()) | |
| rows = [] | |
| for name, module in self.named_children(): | |
| rows.append(dict(name=name, trainable=npa_train(module), total=npa(module))) | |
| rows.append(dict(name="total", trainable=npa_train(self), total=npa(self))) | |
| df = pd.DataFrame(rows) | |
| return df.to_markdown(index=False) | |
| def to_mel(self, x: Tensor, drop_last=True): | |
| """ | |
| Args: | |
| x: (b t), wavs | |
| Returns: | |
| o: (b c t), mels | |
| """ | |
| if drop_last: | |
| return self.mel_fn(x)[..., :-1] # (b d t) | |
| return self.mel_fn(x) | |
| def _may_denoise(self, x: Tensor, y: Tensor | None = None): | |
| if self.hp.lcfm_training_mode == "cfm": | |
| return self.denoiser(x, y) | |
| return x | |
| def configurate_(self, nfe, solver, lambd, tau): | |
| """ | |
| Args: | |
| nfe: number of function evaluations | |
| solver: solver method | |
| lambd: denoiser strength [0, 1] | |
| tau: prior temperature [0, 1] | |
| """ | |
| self.lcfm.cfm.solver.configurate_(nfe, solver) | |
| self.lcfm.eval_tau_(tau) | |
| self._eval_lambd = lambd | |
| def forward(self, x: Tensor, y: Tensor | None = None, z: Tensor | None = None): | |
| """ | |
| Args: | |
| x: (b t), mix wavs (fg + bg) | |
| y: (b t), fg clean wavs | |
| z: (b t), fg distorted wavs | |
| Returns: | |
| o: (b t), reconstructed wavs | |
| """ | |
| assert x.dim() == 2, f"Expected (b t), got {x.size()}" | |
| assert y is None or y.dim() == 2, f"Expected (b t), got {y.size()}" | |
| if self.hp.lcfm_training_mode == "cfm": | |
| self.normalizer.eval() | |
| x = _normalize_wav(x) | |
| y = _maybe(_normalize_wav)(y) | |
| z = _maybe(_normalize_wav)(z) | |
| x_mel_original = self.normalizer(self.to_mel(x), update=False) # (b d t) | |
| if self.hp.lcfm_training_mode == "cfm": | |
| if self.training: | |
| lambd = Beta(0.2, 0.2).sample(x.shape[:1]).to(x.device) | |
| lambd = lambd[:, None, None] | |
| x_mel_denoised = self.normalizer( | |
| self.to_mel(self._may_denoise(x, z)), update=False | |
| ) | |
| x_mel_denoised = x_mel_denoised.detach() | |
| x_mel_denoised = lambd * x_mel_denoised + (1 - lambd) * x_mel_original | |
| self._visualize(x_mel_original, x_mel_denoised) | |
| else: | |
| lambd = self._eval_lambd | |
| if lambd == 0: | |
| x_mel_denoised = x_mel_original | |
| else: | |
| x_mel_denoised = self.normalizer( | |
| self.to_mel(self._may_denoise(x, z)), update=False | |
| ) | |
| x_mel_denoised = x_mel_denoised.detach() | |
| x_mel_denoised = ( | |
| lambd * x_mel_denoised + (1 - lambd) * x_mel_original | |
| ) | |
| else: | |
| x_mel_denoised = x_mel_original | |
| y_mel = _maybe(self.to_mel)(y) # (b d t) | |
| y_mel = _maybe(self.normalizer)(y_mel) | |
| lcfm_decoded = self.lcfm(x_mel_denoised, y_mel, ψ0=x_mel_original) # (b d t) | |
| if lcfm_decoded is None: | |
| o = None | |
| else: | |
| o = self.vocoder(lcfm_decoded, y) | |
| return o | |