How to use tensorflow to conv_stft?

[panhu]

Hi,I use tensorflow to conv_stft like this:

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 tf.convert_to_tensor(kernel,tf.float32)

import torch.nn.functional as F

class ConvSTFT(tf.keras.layers.Layer):

def __init__(self, win_len=400, win_inc=200, fft_len=512, win_type='hanning', feature_type='real', fix=True):
    super(ConvSTFT, self).__init__() 
    
    self.fft_len = fft_len
    
    kernel= init_kernels(win_len, win_inc, self.fft_len, win_type)
    print('................',kernel.shape)
    self.weight = tf.Variable(kernel)
    self.feature_type = feature_type
    self.stride = win_inc
    self.win_len = win_len
    self.dim = self.fft_len

def call(self, inputs):

    outputs = F.conv1d(inputs, self.weight, stride=self.stride)
     
    output_list = []
    print("...............",outputs)
    dim = self.dim//2+1
    real = outputs[:, :dim, :]
    imag = outputs[:, dim:, :]
    output_list = [real,imag]
    return output_list

It is right?

[hieule88]

why we need to use conv1d instead of normal STFT, is it the same ?