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import logging
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from ..melspec import MelSpectrogram
from .hparams import HParams
from .unet import UNet
logger = logging.getLogger(__name__)
def _normalize(x: Tensor) -> Tensor:
return x / (x.abs().max(dim=-1, keepdim=True).values + 1e-7)
class Denoiser(nn.Module):
@property
def stft_cfg(self) -> dict:
hop_size = self.hp.hop_size
return dict(hop_length=hop_size, n_fft=hop_size * 4, win_length=hop_size * 4)
@property
def n_fft(self):
return self.stft_cfg["n_fft"]
@property
def eps(self):
return 1e-7
def __init__(self, hp: HParams):
super().__init__()
self.hp = hp
self.net = UNet(input_dim=3, output_dim=3)
self.mel_fn = MelSpectrogram(hp)
self.dummy: Tensor
self.register_buffer("dummy", torch.zeros(1), persistent=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 _stft(self, x):
"""
Args:
x: (b t)
Returns:
mag: (b f t) in [0, inf)
cos: (b f t) in [-1, 1]
sin: (b f t) in [-1, 1]
"""
dtype = x.dtype
device = x.device
if x.is_mps:
x = x.cpu()
window = torch.hann_window(self.stft_cfg["win_length"], device=x.device)
s = torch.stft(x.float(), **self.stft_cfg, window=window, return_complex=True) # (b f t+1)
s = s[..., :-1] # (b f t)
mag = s.abs() # (b f t)
phi = s.angle() # (b f t)
cos = phi.cos() # (b f t)
sin = phi.sin() # (b f t)
mag = mag.to(dtype=dtype, device=device)
cos = cos.to(dtype=dtype, device=device)
sin = sin.to(dtype=dtype, device=device)
return mag, cos, sin
def _istft(self, mag: Tensor, cos: Tensor, sin: Tensor):
"""
Args:
mag: (b f t) in [0, inf)
cos: (b f t) in [-1, 1]
sin: (b f t) in [-1, 1]
Returns:
x: (b t)
"""
device = mag.device
dtype = mag.dtype
if mag.is_mps:
mag = mag.cpu()
cos = cos.cpu()
sin = sin.cpu()
real = mag * cos # (b f t)
imag = mag * sin # (b f t)
s = torch.complex(real, imag) # (b f t)
if s.isnan().any():
logger.warning("NaN detected in ISTFT input.")
s = F.pad(s, (0, 1), "replicate") # (b f t+1)
window = torch.hann_window(self.stft_cfg["win_length"], device=s.device)
x = torch.istft(s, **self.stft_cfg, window=window, return_complex=False)
if x.isnan().any():
logger.warning("NaN detected in ISTFT output, set to zero.")
x = torch.where(x.isnan(), torch.zeros_like(x), x)
x = x.to(dtype=dtype, device=device)
return x
def _magphase(self, real, imag):
mag = (real.pow(2) + imag.pow(2) + self.eps).sqrt()
cos = real / mag
sin = imag / mag
return mag, cos, sin
def _predict(self, mag: Tensor, cos: Tensor, sin: Tensor):
"""
Args:
mag: (b f t)
cos: (b f t)
sin: (b f t)
Returns:
mag_mask: (b f t) in [0, 1], magnitude mask
cos_res: (b f t) in [-1, 1], phase residual
sin_res: (b f t) in [-1, 1], phase residual
"""
x = torch.stack([mag, cos, sin], dim=1) # (b 3 f t)
mag_mask, real, imag = self.net(x).unbind(1) # (b 3 f t)
mag_mask = mag_mask.sigmoid() # (b f t)
real = real.tanh() # (b f t)
imag = imag.tanh() # (b f t)
_, cos_res, sin_res = self._magphase(real, imag) # (b f t)
return mag_mask, sin_res, cos_res
def _separate(self, mag, cos, sin, mag_mask, cos_res, sin_res):
"""Ref: https://audio-agi.github.io/Separate-Anything-You-Describe/AudioSep_arXiv.pdf"""
sep_mag = F.relu(mag * mag_mask)
sep_cos = cos * cos_res - sin * sin_res
sep_sin = sin * cos_res + cos * sin_res
return sep_mag, sep_cos, sep_sin
def forward(self, x: Tensor, y: Tensor | None = None):
"""
Args:
x: (b t), a mixed audio
y: (b t), a fg audio
"""
assert x.dim() == 2, f"Expected (b t), got {x.size()}"
x = x.to(self.dummy)
x = _normalize(x)
if y is not None:
assert y.dim() == 2, f"Expected (b t), got {y.size()}"
y = y.to(self.dummy)
y = _normalize(y)
mag, cos, sin = self._stft(x) # (b 2f t)
mag_mask, sin_res, cos_res = self._predict(mag, cos, sin)
sep_mag, sep_cos, sep_sin = self._separate(mag, cos, sin, mag_mask, cos_res, sin_res)
o = self._istft(sep_mag, sep_cos, sep_sin)
npad = x.shape[-1] - o.shape[-1]
o = F.pad(o, (0, npad))
if y is not None:
self.losses = dict(l1=F.l1_loss(o, y))
return o
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