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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
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