audio-domain synthesis

dnn/lpcnet.py

@@ -4,6 +4,7 @@ import math
 from keras.models import Model
 from keras.layers import Input, LSTM, CuDNNGRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Bidirectional, MaxPooling1D, Activation
 from keras import backend as K
+from keras.initializers import Initializer
 from mdense import MDense
 import numpy as np
 import h5py
@@ -14,6 +15,30 @@ pcm_bits = 8
 pcm_levels = 2**pcm_bits
 nb_used_features = 38
 
+class PCMInit(Initializer):
+    def __init__(self, gain=.1, seed=None):
+        self.gain = gain
+        self.seed = seed
+
+    def __call__(self, shape, dtype=None):
+        num_rows = 1
+        for dim in shape[:-1]:
+            num_rows *= dim
+        num_cols = shape[-1]
+        flat_shape = (num_rows, num_cols)
+        if self.seed is not None:
+            np.random.seed(self.seed)
+        a = np.random.uniform(-1.7321, 1.7321, flat_shape)
+        #a[:,0] = math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows
+        #a[:,1] = .5*a[:,0]*a[:,0]*a[:,0]
+        a = a + np.reshape(math.sqrt(12)*np.arange(-.5*num_rows+.5,.5*num_rows-.4)/num_rows, (num_rows, 1))
+        return self.gain * a
+
+    def get_config(self):
+        return {
+            'gain': self.gain,
+            'seed': self.seed
+        }
 
 def new_wavernn_model():
     pcm = Input(shape=(None, 1))
@@ -35,6 +60,10 @@ def new_wavernn_model():
         cpcm = pcm
         cpitch = pitch
 
+    embed = Embedding(256, 128, embeddings_initializer=PCMInit())
+    cpcm = Reshape((-1, 128))(embed(pcm))
+
+
     cfeat = fconv2(fconv1(feat))
 
     rep = Lambda(lambda x: K.repeat_elements(x, 160, 1))

dnn/test_wavenet_audio.py

@@ -0,0 +1,103 @@
+#!/usr/bin/python3
+
+import wavenet
+import lpcnet
+import sys
+import numpy as np
+from keras.optimizers import Adam
+from keras.callbacks import ModelCheckpoint
+from ulaw import ulaw2lin, lin2ulaw
+import keras.backend as K
+import h5py
+
+#import tensorflow as tf
+#from keras.backend.tensorflow_backend import set_session
+#config = tf.ConfigProto()
+#config.gpu_options.per_process_gpu_memory_fraction = 0.44
+#set_session(tf.Session(config=config))
+
+nb_epochs = 40
+batch_size = 64
+
+#model = wavenet.new_wavenet_model(fftnet=True)
+model, enc, dec = lpcnet.new_wavernn_model()
+
+model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
+model.summary()
+
+pcmfile = sys.argv[1]
+feature_file = sys.argv[2]
+frame_size = 160
+nb_features = 54
+nb_used_features = wavenet.nb_used_features
+feature_chunk_size = 15
+pcm_chunk_size = frame_size*feature_chunk_size
+
+data = np.fromfile(pcmfile, dtype='int16')
+data = np.minimum(127, lin2ulaw(data[80:]/32768.))
+nb_frames = len(data)//pcm_chunk_size
+
+features = np.fromfile(feature_file, dtype='float32')
+
+data = data[:nb_frames*pcm_chunk_size]
+features = features[:nb_frames*feature_chunk_size*nb_features]
+
+in_data = np.concatenate([data[0:1], data[:-1]]);
+
+features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
+pitch = 1.*data
+pitch[:320] = 0
+for i in range(2, nb_frames*feature_chunk_size):
+    period = int(50*features[i,36]+100)
+    period = period - 4
+    pitch[i*frame_size:(i+1)*frame_size] = data[i*frame_size-period:(i+1)*frame_size-period]
+in_pitch = np.reshape(pitch/16., (nb_frames, pcm_chunk_size, 1))
+
+in_data = np.reshape(in_data, (nb_frames, pcm_chunk_size, 1))
+in_data = (in_data.astype('int16')+128).astype('uint8')
+out_data = np.reshape(data, (nb_frames, pcm_chunk_size, 1))
+out_data = (out_data.astype('int16')+128).astype('uint8')
+features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
+features = features[:, :, :nb_used_features]
+
+
+
+in_data = np.reshape(in_data, (nb_frames*pcm_chunk_size, 1))
+out_data = np.reshape(data, (nb_frames*pcm_chunk_size, 1))
+
+
+model.load_weights('wavenet3e_30.h5')
+
+order = 16
+
+pcm = 0.*out_data
+exc = out_data-0
+pitch = np.zeros((1, 1, 1), dtype='float32')
+fexc = np.zeros((1, 1, 1), dtype='float32')
+iexc = np.zeros((1, 1, 1), dtype='int16')
+state = np.zeros((1, lpcnet.rnn_units), dtype='float32')
+for c in range(1, nb_frames):
+    cfeat = enc.predict(features[c:c+1, :, :nb_used_features])
+    for fr in range(1, feature_chunk_size):
+        f = c*feature_chunk_size + fr
+        a = features[c, fr, nb_used_features:]
+        
+        #print(a)
+        gain = 1.;
+        period = int(50*features[c, fr, 36]+100)
+        period = period - 4
+        for i in range(frame_size):
+            pitch[0, 0, 0] = exc[f*frame_size + i - period, 0]
+            fexc[0, 0, 0] = iexc + 128
+            #fexc[0, 0, 0] = in_data[f*frame_size + i, 0]
+            #print(cfeat.shape)
+            p, state = dec.predict([fexc, cfeat[:, fr:fr+1, :], state])
+            p = p/(1e-5 + np.sum(p))
+            #print(np.sum(p))
+            iexc[0, 0, 0] = np.argmax(np.random.multinomial(1, p[0,0,:], 1))-128
+            exc[f*frame_size + i] = iexc[0, 0, 0]/16.
+            #out_data[f*frame_size + i, 0] = iexc[0, 0, 0]
+            pcm[f*frame_size + i, 0] = 32768*ulaw2lin(iexc[0, 0, 0]*1.0)
+            print(iexc[0, 0, 0], out_data[f*frame_size + i, 0], pcm[f*frame_size + i, 0])
+
+

dnn/train_wavenet_audio.py

@@ -1,6 +1,7 @@
 #!/usr/bin/python3
 
 import wavenet
+import lpcnet
 import sys
 import numpy as np
 from keras.optimizers import Adam
@@ -18,7 +19,9 @@ import h5py
 nb_epochs = 40
 batch_size = 64
 
-model = wavenet.new_wavenet_model(fftnet=True)
+#model = wavenet.new_wavenet_model(fftnet=True)
+model, _, _ = lpcnet.new_wavernn_model()
+
 model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
 model.summary()
 
@@ -64,7 +67,7 @@ features = features[:, :, :nb_used_features]
 # f.create_dataset('data', data=in_data[:50000, :, :])
 # f.create_dataset('feat', data=features[:50000, :, :])
 
-checkpoint = ModelCheckpoint('wavenet3c_{epoch:02d}.h5')
+checkpoint = ModelCheckpoint('wavenet3e_{epoch:02d}.h5')
 
 #model.load_weights('wavernn1c_01.h5')
 model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])

dnn/ulaw.py

@@ -5,7 +5,7 @@ import math
 def ulaw2lin(u):
     s = np.sign(u)
     u = np.abs(u)
-    return s*(np.exp(u/128*math.log(256))-1)/255
+    return s*(np.exp(u/128.*math.log(256))-1)/255
 
 
 def lin2ulaw(x):