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#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright (c) 2021 Kazuhiro KOBAYASHI <root.4mac@gmail.com>
#
# Distributed under terms of the MIT license.
"""
"""
import librosa
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class LogMelSpectrogram(nn.Module):
def __init__(
self,
fs=24000,
hop_size=128,
fft_size=1024,
win_length=None,
window="hann",
center=True,
pad_mode="reflect",
n_mels=80,
fmin=None,
fmax=None,
scaler_file=None,
):
super().__init__()
self.mag_layer = Magnitude(fs, hop_size, fft_size, win_length, window)
self.mel_layer = Magnitude2LogMelSpectrogram(fs, fft_size, n_mels, fmin, fmax)
if scaler_file is not None:
self.melspc_scaler = LogMelSpectrogramScaler(scaler_file)
else:
self.melspc_scaler = None
def forward(self, x):
mag = self.mag_layer(x)
log_melspc = self.mel_layer(mag)
if self.melspc_scaler is not None:
log_melspc = self.scaler_layer(log_melspc)
return log_melspc
class Magnitude(torch.nn.Module):
def __init__(
self,
fs=24000,
hop_size=128,
fft_size=1024,
win_length=None,
window="hann",
center=True,
pad_mode="reflect",
return_complex=True,
):
super().__init__()
self.hop_size = hop_size
self.fft_size = fft_size
self.win_length = fft_size if win_length is None else win_length
self.window = window
self.center = center
self.pad_mode = pad_mode
self.return_complex = return_complex
def forward(self, x):
"""
x: (B, 1, T)
ret: (B, T, fft_size // 2 + 1)
"""
f = getattr(torch, f"{self.window}_window")
window = f(self.win_length, dtype=x.dtype, device=x.device)
y = torch.stft(
x,
n_fft=self.fft_size,
win_length=self.win_length,
hop_length=self.hop_size,
window=window,
center=self.center,
pad_mode=self.pad_mode,
return_complex=self.return_complex,
)
return y.abs().transpose(1, 2)
class Magnitude2LogMelSpectrogram(torch.nn.Module):
def __init__(
self, fs=24000, fft_size=1024, n_mels=80, fmin=None, fmax=None, eps=1.0e-10
):
super().__init__()
self.eps = eps
fmin = 0 if fmin is None else fmin
fmax = fs / 2 if fmax is None else fmax
mel_basis = librosa.filters.mel(
fs, fft_size, n_mels=n_mels, fmin=fmin, fmax=fmax
)
self.register_buffer("mel_basis", torch.from_numpy(mel_basis.T).float())
def forward(self, x):
melspc = torch.matmul(x, self.mel_basis)
log_melspc = torch.clamp(melspc, min=self.eps).log10()
return log_melspc
class LogMelSpectrogram2LogMagnitude(nn.Module):
def __init__(
self,
fs,
fft_size,
n_mels=80,
fmin=None,
fmax=None,
eps=1.0e-10,
roll_size=24,
melspc_scaler_fn=None,
):
super().__init__()
self.eps = eps
self.roll_size = roll_size
self.melspc_scaler_fn = melspc_scaler_fn
fmin = 0 if fmin is None else fmin
fmax = fs / 2 if fmax is None else fmax
mel_basis = librosa.filters.mel(
fs, fft_size, n_mels=n_mels, fmin=fmin, fmax=fmax
)
inv_mel_basis = np.linalg.pinv(mel_basis)
self.register_buffer("inv_mel_basis", torch.from_numpy(inv_mel_basis.T).float())
def forward(self, x):
if self.melspc_scaler_fn is not None:
# denorm mlfb
x = self.melspc_scaler_fn.inverse_transform(x)
x = torch.pow(10.0, x)
spc = torch.matmul(x, self.inv_mel_basis)
log_spc = 10 * torch.clamp(spc, min=self.eps).log10()
z = F.pad(log_spc, (self.roll_size // 2 - 1, self.roll_size // 2))
z = z.unfold(-1, self.roll_size, step=1)
log_spc = torch.median(z, dim=-1)[0]
return log_spc
class CepstrumLiftering(nn.Module):
def __init__(self, lifter_size=None):
super().__init__()
if lifter_size <= 0:
raise ValueError("lifter_size must be > 0.")
else:
self.lifter_size = lifter_size
def forward(self, x):
cep = torch.fft.ifft(x, dim=-1)
cep[..., self.lifter_size : -self.lifter_size] = 0
x = torch.fft.fft(cep, dim=-1)
return x
class LogMelSpectrogramScaler(nn.Module):
def __init__(self, scaler):
super().__init__()
self.register_parameter(
"mean",
nn.Parameter(torch.from_numpy(scaler.mean_).float(), requires_grad=False),
)
self.register_parameter(
"scale",
nn.Parameter(
torch.from_numpy(scaler.var_).float().sqrt(), requires_grad=False
),
)
def forward(self, x):
return (x - self.mean) / self.scale
def inverse_transform(self, x):
return x * self.scale + self.mean
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