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85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
#!/usr/bin/python3
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import math
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from keras.models import Model
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from keras.layers import Input, LSTM, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Add, Multiply, Bidirectional, MaxPooling1D, Activation
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from keras import backend as K
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from keras.initializers import Initializer
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from keras.initializers import VarianceScaling
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from mdense import MDense
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import numpy as np
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import h5py
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import sys
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from causalconv import CausalConv
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from gatedconv import GatedConv
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units=128
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pcm_bits = 8
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pcm_levels = 2**pcm_bits
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nb_used_features = 38
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class PCMInit(Initializer):
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def __init__(self, gain=.1, seed=None):
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self.gain = gain
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self.seed = seed
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def __call__(self, shape, dtype=None):
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num_rows = 1
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for dim in shape[:-1]:
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num_rows *= dim
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num_cols = shape[-1]
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flat_shape = (num_rows, num_cols)
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if self.seed is not None:
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np.random.seed(self.seed)
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a = np.random.uniform(-1.7321, 1.7321, flat_shape)
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#a[:,0] = math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows
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#a[:,1] = .5*a[:,0]*a[:,0]*a[:,0]
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a = a + np.reshape(math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows, (num_rows, 1))
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return self.gain * a
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def get_config(self):
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return {
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'gain': self.gain,
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'seed': self.seed
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}
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def new_wavenet_model(fftnet=False):
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pcm = Input(shape=(None, 1))
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pitch = Input(shape=(None, 1))
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feat = Input(shape=(None, nb_used_features))
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dec_feat = Input(shape=(None, 32))
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fconv1 = Conv1D(128, 3, padding='same', activation='tanh')
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fconv2 = Conv1D(32, 3, padding='same', activation='tanh')
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cfeat = fconv2(fconv1(feat))
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rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))
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activation='tanh'
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rfeat = rep(cfeat)
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#tmp = Concatenate()([pcm, rfeat])
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embed = Embedding(256, units, embeddings_initializer=PCMInit())
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tmp = Reshape((-1, units))(embed(pcm))
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init = VarianceScaling(scale=1.5,mode='fan_avg',distribution='uniform')
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for k in range(10):
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res = tmp
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dilation = 9-k if fftnet else k
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tmp = Concatenate()([tmp, rfeat])
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c = GatedConv(units, 2, dilation_rate=2**dilation, activation='tanh', kernel_initializer=init)
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tmp = Dense(units, activation='relu')(c(tmp))
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'''tmp = Concatenate()([tmp, rfeat])
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c1 = CausalConv(units, 2, dilation_rate=2**dilation, activation='tanh')
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c2 = CausalConv(units, 2, dilation_rate=2**dilation, activation='sigmoid')
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tmp = Multiply()([c1(tmp), c2(tmp)])
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tmp = Dense(units, activation='relu')(tmp)'''
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if k != 0:
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tmp = Add()([tmp, res])
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md = MDense(pcm_levels, activation='softmax')
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ulaw_prob = md(tmp)
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model = Model([pcm, feat], ulaw_prob)
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return model
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