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Add convolution
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2 changed files with 13 additions and 4 deletions
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@ -23,13 +23,22 @@ def new_wavernn_model():
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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(feat)])
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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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@ -62,7 +62,7 @@ features = features[:, :, :nb_used_features]
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# f.create_dataset('data', data=in_data[:50000, :, :])
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# f.create_dataset('feat', data=features[:50000, :, :])
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checkpoint = ModelCheckpoint('lpcnet1e_{epoch:02d}.h5')
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checkpoint = ModelCheckpoint('lpcnet1g_{epoch:02d}.h5')
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#model.load_weights('wavernn1c_01.h5')
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model.compile(optimizer=Adam(0.002, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
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