mirror of https://github.com/coqui-ai/TTS.git
61 lines
2.2 KiB
Python
61 lines
2.2 KiB
Python
import numpy as np
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import tensorflow as tf
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from scipy import signal as sig
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class PQMF(tf.keras.layers.Layer):
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def __init__(self, N=4, taps=62, cutoff=0.15, beta=9.0):
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super().__init__()
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# define filter coefficient
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self.N = N
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self.taps = taps
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self.cutoff = cutoff
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self.beta = beta
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QMF = sig.firwin(taps + 1, cutoff, window=("kaiser", beta))
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H = np.zeros((N, len(QMF)))
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G = np.zeros((N, len(QMF)))
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for k in range(N):
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constant_factor = (2 * k + 1) * (np.pi / (2 * N)) * (np.arange(taps + 1) - ((taps - 1) / 2))
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phase = (-1) ** k * np.pi / 4
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H[k] = 2 * QMF * np.cos(constant_factor + phase)
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G[k] = 2 * QMF * np.cos(constant_factor - phase)
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# [N, 1, taps + 1] == [filter_width, in_channels, out_channels]
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self.H = np.transpose(H[:, None, :], (2, 1, 0)).astype("float32")
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self.G = np.transpose(G[None, :, :], (2, 1, 0)).astype("float32")
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# filter for downsampling & upsampling
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updown_filter = np.zeros((N, N, N), dtype=np.float32)
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for k in range(N):
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updown_filter[0, k, k] = 1.0
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self.updown_filter = updown_filter.astype(np.float32)
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def analysis(self, x):
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"""
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x : :math:`[B, 1, T]`
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"""
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x = tf.transpose(x, perm=[0, 2, 1])
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x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]], constant_values=0.0)
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x = tf.nn.conv1d(x, self.H, stride=1, padding="VALID")
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x = tf.nn.conv1d(x, self.updown_filter, stride=self.N, padding="VALID")
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x = tf.transpose(x, perm=[0, 2, 1])
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return x
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def synthesis(self, x):
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"""
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x : B x D x T
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"""
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x = tf.transpose(x, perm=[0, 2, 1])
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x = tf.nn.conv1d_transpose(
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x,
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self.updown_filter * self.N,
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strides=self.N,
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output_shape=(tf.shape(x)[0], tf.shape(x)[1] * self.N, self.N),
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)
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x = tf.pad(x, [[0, 0], [self.taps // 2, self.taps // 2], [0, 0]], constant_values=0.0)
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x = tf.nn.conv1d(x, self.G, stride=1, padding="VALID")
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x = tf.transpose(x, perm=[0, 2, 1])
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return x
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