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import logging
from typing import Union
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: Union[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: Union[Tensor, None] = None, z: Union[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
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