94 lines
3.6 KiB
Python
94 lines
3.6 KiB
Python
import paddle
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from paddle import nn
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from paddle.nn import functional as F
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from scipy import signal
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import numpy as np
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class STFT(nn.Layer):
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def __init__(self, n_fft, hop_length, win_length, window="hanning"):
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"""A module for computing differentiable stft transform. See `librosa.stft` for more details.
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Args:
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n_fft (int): number of samples in a frame.
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hop_length (int): number of samples shifted between adjacent frames.
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win_length (int): length of the window function.
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window (str, optional): name of window function, see `scipy.signal.get_window` for more details. Defaults to "hanning".
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"""
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super(STFT, self).__init__()
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self.hop_length = hop_length
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self.n_bin = 1 + n_fft // 2
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self.n_fft = n_fft
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# calculate window
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window = signal.get_window(window, win_length)
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if n_fft != win_length:
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pad = (n_fft - win_length) // 2
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window = np.pad(window, ((pad, pad), ), 'constant')
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# calculate weights
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r = np.arange(0, n_fft)
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M = np.expand_dims(r, -1) * np.expand_dims(r, 0)
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w_real = np.reshape(window *
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np.cos(2 * np.pi * M / n_fft)[:self.n_bin],
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(self.n_bin, 1, 1, self.n_fft))
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w_imag = np.reshape(window *
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np.sin(-2 * np.pi * M / n_fft)[:self.n_bin],
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(self.n_bin, 1, 1, self.n_fft))
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w = np.concatenate([w_real, w_imag], axis=0)
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self.weight = paddle.cast(paddle.to_tensor(w), paddle.get_default_dtype())
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def forward(self, x):
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"""Compute the stft transform.
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Args:
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x (Variable): shape(B, T), dtype flaot32, the input waveform.
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Returns:
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(real, imag)
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real (Variable): shape(B, C, 1, T), dtype flaot32, the real part of the spectrogram. (C = 1 + n_fft // 2)
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imag (Variable): shape(B, C, 1, T), dtype flaot32, the image part of the spectrogram. (C = 1 + n_fft // 2)
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"""
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# x(batch_size, time_steps)
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# pad it first with reflect mode
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# TODO(chenfeiyu): report an issue on paddle.flip
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pad_start = paddle.reverse(x[:, 1:1 + self.n_fft // 2], axis=[1])
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pad_stop = paddle.reverse(x[:, -(1 + self.n_fft // 2):-1], axis=[1])
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x = paddle.concat([pad_start, x, pad_stop], axis=-1)
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# to BC1T, C=1
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x = paddle.unsqueeze(x, axis=[1, 2])
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out = F.conv2d(x, self.weight, stride=(1, self.hop_length))
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real, imag = paddle.chunk(out, 2, axis=1) # BC1T
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return real, imag
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def power(self, x):
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"""Compute the power spectrogram.
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Args:
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(real, imag)
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real (Variable): shape(B, C, 1, T), dtype flaot32, the real part of the spectrogram.
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imag (Variable): shape(B, C, 1, T), dtype flaot32, the image part of the spectrogram.
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Returns:
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Variable: shape(B, C, 1, T), dtype flaot32, the power spectrogram.
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"""
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real, imag = self(x)
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power = real**2 + imag**2
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return power
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def magnitude(self, x):
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"""Compute the magnitude spectrogram.
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Args:
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(real, imag)
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real (Variable): shape(B, C, 1, T), dtype flaot32, the real part of the spectrogram.
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imag (Variable): shape(B, C, 1, T), dtype flaot32, the image part of the spectrogram.
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Returns:
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Variable: shape(B, C, 1, T), dtype flaot32, the magnitude spectrogram. It is the square root of the power spectrogram.
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"""
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power = self.power(x)
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magnitude = paddle.sqrt(power)
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return magnitude
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