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d98780c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 26 15:36:16 2024
@author: louis
"""
from torchaudio.transforms import Spectrogram as OriginalSpectrogram, InverseSpectrogram as OriginalInverseSpectrogram
import torch
from model.utils.tensor_ops import zero_pad
default_stft_parameters = dict(
n_fft=512,
hop_length=256,
win_length=512,
window_fn=torch.hann_window,
center=True,
)
class Spectrogram(OriginalSpectrogram):
def forward(self, waveform):
if not self.center:
raise NotImplementedError()
waveform_padded = torch.nn.functional.pad(waveform, (self.n_fft // 2, self.n_fft // 2))
X = super().forward(waveform_padded)
return X[..., 1:-1]
class InverseSpectrogramCOLA(OriginalInverseSpectrogram):
def forward(self, spectrogram, length=None):
if not self.center:
raise NotImplementedError()
# pack batch as in original
# spectrogram = torch.nn.functional.pad(spectrogram, (0, 1))
shape = spectrogram.size()
spectrogram = spectrogram.reshape(-1, shape[-2], shape[-1])
expected_waveform_length = self.n_fft + self.hop_length * (shape[-1] - 1)
c = torch.fft.irfft(spectrogram, dim=-2)
waveform = torch.nn.functional.fold(
c,
output_size=(1, expected_waveform_length),
kernel_size=(1, self.n_fft),
dilation=1,
padding=0,
stride=(1, self.hop_length),
)
waveform = waveform[..., self.n_fft // 2 :].squeeze(-3, -2)
if length is not None:
waveform = zero_pad(waveform, length)
# unpack batch
waveform = waveform.reshape(shape[:-2] + waveform.shape[-1:])
return waveform
default_stft_module = Spectrogram(**default_stft_parameters, power=None)
default_istft_module = InverseSpectrogramCOLA(**default_stft_parameters)
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