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96 lines
3 KiB
Python
Executable file
96 lines
3 KiB
Python
Executable file
#!/usr/bin/python3
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import wavenet
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import lpcnet
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import sys
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import numpy as np
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from keras.optimizers import Adam
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from keras.callbacks import ModelCheckpoint
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from ulaw import ulaw2lin, lin2ulaw
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import keras.backend as K
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import h5py
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import tensorflow as tf
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from keras.backend.tensorflow_backend import set_session
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config = tf.ConfigProto()
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config.gpu_options.per_process_gpu_memory_fraction = 0.2
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set_session(tf.Session(config=config))
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nb_epochs = 40
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batch_size = 64
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#model = wavenet.new_wavenet_model(fftnet=True)
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model, enc, dec = lpcnet.new_wavernn_model()
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
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#model.summary()
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pcmfile = sys.argv[1]
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feature_file = sys.argv[2]
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frame_size = 160
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nb_features = 55
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nb_used_features = lpcnet.nb_used_features
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feature_chunk_size = 15
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pcm_chunk_size = frame_size*feature_chunk_size
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data = np.fromfile(pcmfile, dtype='int16')
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data = lin2ulaw(data)
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nb_frames = len(data)//pcm_chunk_size
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features = np.fromfile(feature_file, dtype='float32')
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data = data[:nb_frames*pcm_chunk_size]
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features = features[:nb_frames*feature_chunk_size*nb_features]
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in_data = np.concatenate([data[0:1], data[:-1]]);
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features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
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in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
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in_data = in_data.astype('uint8')
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out_data = np.reshape(data, (nb_frames, pcm_chunk_size, 1))
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out_data = out_data.astype('uint8')
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features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
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features = features[:, :, :]
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periods = (50*features[:,:,36:37]+100).astype('int16')
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in_data = np.reshape(in_data, (nb_frames*pcm_chunk_size, 1))
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out_data = np.reshape(data, (nb_frames*pcm_chunk_size, 1))
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model.load_weights('wavenet4f2_30.h5')
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order = 16
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pcm = 0.*out_data
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fexc = np.zeros((1, 1, 2), dtype='float32')
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iexc = np.zeros((1, 1, 1), dtype='int16')
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state = np.zeros((1, lpcnet.rnn_units), dtype='float32')
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for c in range(1, nb_frames):
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cfeat = enc.predict([features[c:c+1, :, :nb_used_features], periods[c:c+1, :, :]])
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for fr in range(1, feature_chunk_size):
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f = c*feature_chunk_size + fr
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a = features[c, fr, nb_features-order:]
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#print(a)
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gain = 1.;
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period = int(50*features[c, fr, 36]+100)
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period = period - 4
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for i in range(frame_size):
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#fexc[0, 0, 0] = iexc + 128
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pred = -sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1, 0])
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fexc[0, 0, 1] = lin2ulaw(pred)
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p, state = dec.predict([fexc, iexc, cfeat[:, fr:fr+1, :], state])
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#p = p*p
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#p = p/(1e-18 + np.sum(p))
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p = np.maximum(p-0.001, 0).astype('float64')
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p = p/(1e-8 + np.sum(p))
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iexc[0, 0, 0] = np.argmax(np.random.multinomial(1, p[0,0,:], 1))
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pcm[f*frame_size + i, 0] = pred + ulaw2lin(iexc[0, 0, 0])
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fexc[0, 0, 0] = lin2ulaw(pcm[f*frame_size + i, 0])
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print(iexc[0, 0, 0], ulaw2lin(out_data[f*frame_size + i, 0]), pcm[f*frame_size + i, 0], pred)
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