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Remove no longer used files (old wavenet and LPCNet implementations)
This commit is contained in:
Jean-Marc Valin
parent
3122b6b3bc
commit
97dcf52a01
4 changed files with 0 additions and 308 deletions
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#!/usr/bin/python3
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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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from adadiff import Adadiff
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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 = 0.28
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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, enc, dec = lpcnet.new_wavernn_model()
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model.compile(optimizer=Adadiff(), 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 = 54
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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='int8')
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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]])/16.;
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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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out_data = np.reshape(data, (nb_frames*pcm_chunk_size, 1))
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model.load_weights('lpcnet3a_21.h5')
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order = 16
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pcm = 0.*out_data
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exc = out_data-0
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pitch = np.zeros((1, 1, 1), dtype='float32')
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fexc = np.zeros((1, 1, 1), 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])
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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_used_features:]
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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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pitch[0, 0, 0] = exc[f*frame_size + i - period, 0]
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fexc[0, 0, 0] = 2*exc[f*frame_size + i - 1]
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#fexc[0, 0, 0] = in_data[f*frame_size + i, 0]
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#print(cfeat.shape)
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p, state = dec.predict([fexc, cfeat[:, fr:fr+1, :], state])
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#p = np.maximum(p-0.003, 0)
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p = p/(1e-5 + np.sum(p))
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#print(np.sum(p))
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iexc[0, 0, 0] = np.argmax(np.random.multinomial(1, p[0,0,:], 1))-128
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exc[f*frame_size + i] = iexc[0, 0, 0]/16.
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#out_data[f*frame_size + i, 0] = iexc[0, 0, 0]
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pcm[f*frame_size + i, 0] = gain*iexc[0, 0, 0] - sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1, 0])
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print(iexc[0, 0, 0], out_data[f*frame_size + i, 0], pcm[f*frame_size + i, 0])
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@ -1,70 +0,0 @@
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#!/usr/bin/python3
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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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from adadiff import Adadiff
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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 = 0.44
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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, enc, dec = lpcnet.new_wavernn_model()
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model.compile(optimizer=Adadiff(), 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 = 54
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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='int8')
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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]])/16.;
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features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
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pitch = 1.*data
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pitch[:320] = 0
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for i in range(2, nb_frames*feature_chunk_size):
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period = int(50*features[i,36]+100)
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period = period - 4
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pitch[i*frame_size:(i+1)*frame_size] = data[i*frame_size-period:(i+1)*frame_size-period]
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in_pitch = np.reshape(pitch/16., (nb_frames, pcm_chunk_size, 1))
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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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out_data = (out_data.astype('int16')+128).astype('uint8')
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features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
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features = features[:, :, :nb_used_features]
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#in_data = np.concatenate([in_data, in_pitch], axis=-1)
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#with h5py.File('in_data.h5', 'w') as f:
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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('lpcnet3b_{epoch:02d}.h5')
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#model.load_weights('wavernn1c_01.h5')
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model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
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model.fit([in_data, features], out_data, batch_size=batch_size, epochs=30, validation_split=0.2, callbacks=[checkpoint])
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@ -1,69 +0,0 @@
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#!/usr/bin/python3
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import wavenet
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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 = 0.44
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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.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 = 54
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nb_used_features = wavenet.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='int8')
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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]])/16.;
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features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
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pitch = 1.*data
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pitch[:320] = 0
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for i in range(2, nb_frames*feature_chunk_size):
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period = int(50*features[i,36]+100)
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period = period - 4
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pitch[i*frame_size:(i+1)*frame_size] = data[i*frame_size-period:(i+1)*frame_size-period]
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in_pitch = np.reshape(pitch/16., (nb_frames, pcm_chunk_size, 1))
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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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out_data = (out_data.astype('int16')+128).astype('uint8')
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features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
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features = features[:, :, :nb_used_features]
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#in_data = np.concatenate([in_data, in_pitch], axis=-1)
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#with h5py.File('in_data.h5', 'w') as f:
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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('wavenet3c_{epoch:02d}.h5')
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#model.load_weights('wavernn1c_01.h5')
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model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
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model.fit([in_data, features], out_data, batch_size=batch_size, epochs=30, validation_split=0.2, callbacks=[checkpoint])
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#!/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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