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time_frequence.py
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# -*- coding: utf-8 -*-
from __future__ import absolute_import
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import scipy.signal
class ifft(nn.Module):
def __init__(self, nfft=1024):
super(ifft, self).__init__()
assert nfft % 2 == 0
self.nfft = int(nfft)
self.n_freq = n_freq = int(nfft / 2)
real_kernels, imag_kernels, self.ac_cof = _get_ifft_kernels(nfft)
self.real_conv = nn.Conv2d(1, nfft, (n_freq, 1), stride=1, padding=0, bias=False)
self.imag_conv = nn.Conv2d(1, nfft, (n_freq, 1), stride=1, padding=0, bias=False)
self.real_conv.weight.data.copy_(real_kernels)
self.imag_conv.weight.data.copy_(imag_kernels)
self.real_model = nn.Sequential(self.real_conv)
self.imag_model = nn.Sequential(self.imag_conv)
def forward(self, magn, phase, ac=None):
assert magn.size()[2] == phase.size()[2] == self.n_freq
output = self.real_model(magn) - self.imag_model(phase)
if ac is not None:
output = output + ac * self.ac_cof
return output / self.nfft
def _get_ifft_kernels(nfft):
nfft = int(nfft)
assert nfft % 2 == 0
def kernel_fn(time, freq):
return np.exp(1j * (2 * np.pi * time * freq) / 1024.)
kernels = np.fromfunction(kernel_fn, (int(nfft), int(nfft/2+1)), dtype=np.float64)
kernels = np.zeros((1024, 513)) * 1j
'''
for i in range(1024):
for j in range(513):
kernels[i, j] = kernel_fn(i, j)
'''
ac_cof = float(np.real(kernels[0, 0]))
kernels = 2 * kernels[:, 1:]
kernels[:, -1] = kernels[:, -1] / 2.0
real_kernels = np.real(kernels)
imag_kernels = np.imag(kernels)
real_kernels = torch.from_numpy(real_kernels[:, np.newaxis, :, np.newaxis])
imag_kernels = torch.from_numpy(imag_kernels[:, np.newaxis, :, np.newaxis])
return real_kernels, imag_kernels, ac_cof
class istft(nn.Module):
def __init__(self, nfft=1024, hop_length=512):
super(istft, self).__init__()
assert nfft % 2 == 0
assert hop_length <= nfft
self.hop_length = hop_length
self.nfft = int(nfft)
self.n_freq = n_freq = int(nfft / 2)
self.real_kernels, self.imag_kernels, self.ac_cof = _get_istft_kernels(nfft)
trans_kernels = np.zeros((nfft, nfft), np.float64)
np.fill_diagonal(trans_kernels, np.ones((nfft, ), dtype=np.float64))
self.trans_kernels = nn.Parameter(torch.from_numpy(trans_kernels[:, np.newaxis, np.newaxis, :]).float())
def forward(self, magn, phase, ac):
'''
batch None frequency frame
'''
assert magn.size()[2] == phase.size()[2] == self.n_freq
nfft = self.nfft
hop = self.hop_length
# complex conjugate
phase = -1. * phase
real_part = F.conv2d(magn, self.real_kernels)
imag_part = F.conv2d(phase, self.imag_kernels)
output = real_part - imag_part
ac = ac.unsqueeze(1)
ac = float(self.ac_cof) * ac.expand_as(output)
output = output + ac
output = output / float(self.nfft)
output = F.conv_transpose2d(output, self.trans_kernels, stride=self.hop_length)
output = output.squeeze(1)
output = output.squeeze(1)
return output
def _get_istft_kernels(nfft):
nfft = int(nfft)
assert nfft % 2 == 0
def kernel_fn(time, freq):
return np.exp(1j * (2 * np.pi * time * freq) / nfft)
kernels = np.fromfunction(kernel_fn, (int(nfft), int(nfft/2+1)), dtype=np.float64)
ac_cof = float(np.real(kernels[0, 0]))
kernels = 2 * kernels[:, 1:]
kernels[:, -1] = kernels[:, -1] / 2.0
real_kernels = np.real(kernels)
imag_kernels = np.imag(kernels)
real_kernels = nn.Parameter(torch.from_numpy(real_kernels[:, np.newaxis, :, np.newaxis]).float())
imag_kernels = nn.Parameter(torch.from_numpy(imag_kernels[:, np.newaxis, :, np.newaxis]).float())
return real_kernels, imag_kernels, ac_cof
class stft(nn.Module):
def __init__(self, nfft=1024, hop_length=512, window="hanning"):
super(stft, self).__init__()
assert nfft % 2 == 0
self.hop_length = hop_length
self.n_freq = n_freq = nfft//2 + 1
self.real_kernels, self.imag_kernels = _get_stft_kernels(nfft, window)
def forward(self, sample):
sample = sample.unsqueeze(1)
sample = sample.unsqueeze(1)
magn = F.conv2d(sample, self.real_kernels, stride=self.hop_length)
phase = F.conv2d(sample, self.imag_kernels, stride=self.hop_length)
magn = magn.permute(0, 2, 1, 3)
phase = phase.permute(0, 2, 1, 3)
# complex conjugate
phase = -1 * phase[:,:,1:,:]
ac = magn[:,:,0,:]
magn = magn[:,:,1:,:]
return magn, phase, ac
def _get_stft_kernels(nfft, window):
nfft = int(nfft)
assert nfft % 2 == 0
def kernel_fn(freq, time):
return np.exp(-1j * (2 * np.pi * time * freq) / float(nfft))
kernels = np.fromfunction(kernel_fn, (nfft//2+1, nfft), dtype=np.float64)
if window == "hanning":
win_cof = scipy.signal.get_window("hanning", nfft)[np.newaxis, :]
else:
win_cof = np.ones((1, nfft), dtype=np.float64)
kernels = kernels[:, np.newaxis, np.newaxis, :] * win_cof
real_kernels = nn.Parameter(torch.from_numpy(np.real(kernels)).float())
imag_kernels = nn.Parameter(torch.from_numpy(np.imag(kernels)).float())
return real_kernels, imag_kernels
if __name__ == "__main__":
signal = np.random.rand(1024 * 10)
signal = signal - np.mean(signal)
signal = signal[np.newaxis, :]
model = stft(window="retangle")
real, imag, ac = model.forward(Variable(torch.from_numpy(signal).float()))
real = real.data.numpy()
imag = imag.data.numpy()
ac = ac.data.numpy()
print(ac)