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conv_stft.py
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conv_stft.py
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import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
from scipy.signal import get_window
def init_kernels(win_len, win_inc, fft_len, win_type=None, invers=False):
if win_type == 'None' or win_type is None:
window = np.ones(win_len)
else:
window = get_window(win_type, win_len, fftbins=True)#**0.5
N = fft_len
fourier_basis = np.fft.rfft(np.eye(N))[:win_len]
real_kernel = np.real(fourier_basis)
imag_kernel = np.imag(fourier_basis)
kernel = np.concatenate([real_kernel, imag_kernel], 1).T
if invers :
kernel = np.linalg.pinv(kernel).T
kernel = kernel*window
kernel = kernel[:, None, :]
return torch.from_numpy(kernel.astype(np.float32)), torch.from_numpy(window[None,:,None].astype(np.float32))
class ConvSTFT(nn.Module):
def __init__(self, win_len, win_inc, fft_len=None, win_type='hamming', feature_type='real', fix=True):
super(ConvSTFT, self).__init__()
if fft_len == None:
self.fft_len = np.int(2**np.ceil(np.log2(win_len)))
else:
self.fft_len = fft_len
kernel, _ = init_kernels(win_len, win_inc, self.fft_len, win_type)
#self.weight = nn.Parameter(kernel, requires_grad=(not fix))
self.register_buffer('weight', kernel)
self.feature_type = feature_type
self.stride = win_inc
self.win_len = win_len
self.dim = self.fft_len
def forward(self, inputs):
if inputs.dim() == 2:
inputs = torch.unsqueeze(inputs, 1)
inputs = F.pad(inputs,[self.win_len-self.stride, self.win_len-self.stride])
outputs = F.conv1d(inputs, self.weight, stride=self.stride)
if self.feature_type == 'complex':
return outputs
else:
dim = self.dim//2+1
real = outputs[:, :dim, :]
imag = outputs[:, dim:, :]
mags = torch.sqrt(real**2+imag**2)
phase = torch.atan2(imag, real)
return mags, phase
class ConviSTFT(nn.Module):
def __init__(self, win_len, win_inc, fft_len=None, win_type='hamming', feature_type='real', fix=True):
super(ConviSTFT, self).__init__()
if fft_len == None:
self.fft_len = np.int(2**np.ceil(np.log2(win_len)))
else:
self.fft_len = fft_len
kernel, window = init_kernels(win_len, win_inc, self.fft_len, win_type, invers=True)
#self.weight = nn.Parameter(kernel, requires_grad=(not fix))
self.register_buffer('weight', kernel)
self.feature_type = feature_type
self.win_type = win_type
self.win_len = win_len
self.stride = win_inc
self.stride = win_inc
self.dim = self.fft_len
self.register_buffer('window', window)
self.register_buffer('enframe', torch.eye(win_len)[:,None,:])
def forward(self, inputs, phase=None):
"""
inputs : [B, N+2, T] (complex spec) or [B, N//2+1, T] (mags)
phase: [B, N//2+1, T] (if not none)
"""
if phase is not None:
real = inputs*torch.cos(phase)
imag = inputs*torch.sin(phase)
inputs = torch.cat([real, imag], 1)
outputs = F.conv_transpose1d(inputs, self.weight, stride=self.stride)
# this is from torch-stft: https://github.com/pseeth/torch-stft
t = self.window.repeat(1,1,inputs.size(-1))**2
coff = F.conv_transpose1d(t, self.enframe, stride=self.stride)
outputs = outputs/(coff+1e-8)
#outputs = torch.where(coff == 0, outputs, outputs/coff)
outputs = outputs[...,self.win_len-self.stride:-(self.win_len-self.stride)]
return outputs
def test_fft():
torch.manual_seed(20)
win_len = 320
win_inc = 160
fft_len = 512
inputs = torch.randn([1, 1, 16000*4])
fft = ConvSTFT(win_len, win_inc, fft_len, win_type='hanning', feature_type='real')
import librosa
outputs1 = fft(inputs)[0]
outputs1 = outputs1.numpy()[0]
np_inputs = inputs.numpy().reshape([-1])
librosa_stft = librosa.stft(np_inputs, win_length=win_len, n_fft=fft_len, hop_length=win_inc, center=False)
print(np.mean((outputs1 - np.abs(librosa_stft))**2))
def test_fft():
torch.manual_seed(20)
win_len = 320
win_inc = 160
fft_len = 512
inputs = torch.randn([1, 1, 16000*4])
fft = ConvSTFT(win_len, win_inc, fft_len, win_type='hanning', feature_type='real')
import librosa
outputs1 = fft(inputs)[0]
outputs1 = outputs1.numpy()[0]
np_inputs = inputs.numpy().reshape([-1])
librosa_stft = librosa.stft(np_inputs, win_length=win_len, n_fft=fft_len, hop_length=win_inc, center=False)
print(np.mean((outputs1 - np.abs(librosa_stft))**2))
def test_ifft1():
import soundfile as sf
N = 400
inc = 100
fft_len=512
torch.manual_seed(N)
data = np.random.randn(16000*8)[None,None,:]
# data = sf.read('../ori.wav')[0]
inputs = data.reshape([1,1,-1])
fft = ConvSTFT(N, inc, fft_len=fft_len, win_type='hanning', feature_type='complex')
ifft = ConviSTFT(N, inc, fft_len=fft_len, win_type='hanning', feature_type='complex')
inputs = torch.from_numpy(inputs.astype(np.float32))
outputs1 = fft(inputs)
print(outputs1.shape)
outputs2 = ifft(outputs1)
sf.write('conv_stft.wav', outputs2.numpy()[0,0,:],16000)
print('wav MSE', torch.mean(torch.abs(inputs[...,:outputs2.size(2)]-outputs2)**2))
def test_ifft2():
N = 400
inc = 100
fft_len=512
np.random.seed(20)
torch.manual_seed(20)
t = np.random.randn(16000*4)*0.001
t = np.clip(t, -1, 1)
#input = torch.randn([1,16000*4])
input = torch.from_numpy(t[None,None,:].astype(np.float32))
fft = ConvSTFT(N, inc, fft_len=fft_len, win_type='hanning', feature_type='complex')
ifft = ConviSTFT(N, inc, fft_len=fft_len, win_type='hanning', feature_type='complex')
out1 = fft(input)
output = ifft(out1)
print('random MSE', torch.mean(torch.abs(input-output)**2))
import soundfile as sf
sf.write('zero.wav', output[0,0].numpy(),16000)
if __name__ == '__main__':
#test_fft()
test_ifft1()
#test_ifft2()