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46 lines
1.2 KiB
Python
46 lines
1.2 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, Multiply, Bidirectional, MaxPooling1D, Activation
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from keras import backend as K
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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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rnn_units=64
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pcm_bits = 8
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pcm_levels = 2**pcm_bits
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nb_used_features = 37
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def new_wavernn_model():
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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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conv1 = Conv1D(16, 7, padding='causal')
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pconv1 = Conv1D(16, 5, padding='same')
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pconv2 = Conv1D(16, 5, padding='same')
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fconv1 = Conv1D(128, 3, padding='same')
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fconv2 = Conv1D(32, 3, padding='same')
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if True:
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cpcm = conv1(pcm)
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cpitch = pconv2(pconv1(pitch))
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else:
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cpcm = pcm
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cpitch = pitch
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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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rnn = CuDNNGRU(rnn_units, return_sequences=True)
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rnn_in = Concatenate()([cpcm, cpitch, rep(cfeat)])
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md = MDense(pcm_levels, activation='softmax')
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ulaw_prob = md(rnn(rnn_in))
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model = Model([pcm, pitch, feat], ulaw_prob)
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return model
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