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Remove the need for useless exc and pred files
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b05f950e38
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3 changed files with 11 additions and 21 deletions
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@ -56,10 +56,8 @@ model, _, _ = lpcnet.new_lpcnet_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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exc_file = sys.argv[1] # not used at present
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feature_file = sys.argv[2]
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pred_file = sys.argv[3] # LPC predictor samples. Not used at present, see below
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pcm_file = sys.argv[4] # 16 bit unsigned short PCM samples
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feature_file = sys.argv[1]
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pcm_file = sys.argv[2] # 16 bit unsigned short PCM samples
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frame_size = 160
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nb_features = 55
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nb_used_features = model.nb_used_features
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@ -96,8 +94,7 @@ features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
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# Note: the LPC predictor output is now calculated by the loop below, this code was
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# for an ealier version that implemented the prediction filter in C
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upred = np.fromfile(pred_file, dtype='int16')
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upred = upred[:nb_frames*pcm_chunk_size]
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upred = np.zeros((nb_frames*pcm_chunk_size,), dtype='int16')
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# Use 16th order LPC to generate LPC prediction output upred[] and (in
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# mu-law form) pred[]
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