mu-law code cleanup

dnn/test_wavenet_audio.py

@@ -34,7 +34,7 @@ feature_chunk_size = 15
 pcm_chunk_size = frame_size*feature_chunk_size
 
 data = np.fromfile(pcmfile, dtype='int16')
-data = np.minimum(127, lin2ulaw(data/32768.))
+data = lin2ulaw(data)
 nb_frames = len(data)//pcm_chunk_size
 
 features = np.fromfile(feature_file, dtype='float32')
@@ -54,9 +54,9 @@ for i in range(2, nb_frames*feature_chunk_size):
 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')
+in_data = in_data.astype('uint8')
 out_data = np.reshape(data, (nb_frames, pcm_chunk_size, 1))
-out_data = (out_data.astype('int16')+128).astype('uint8')
+out_data = out_data.astype('uint8')
 features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
 features = features[:, :, :]
 
@@ -66,7 +66,7 @@ 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('wavenet4f3_30.h5')
+model.load_weights('wavenet4f2_30.h5')
 
 order = 16
 
@@ -87,7 +87,7 @@ for c in range(1, nb_frames):
         for i in range(frame_size):
             #fexc[0, 0, 0] = iexc + 128
             pred = -sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1, 0])
-            fexc[0, 0, 1] = np.minimum(127, lin2ulaw(pred/32768.)) + 128
+            fexc[0, 0, 1] = lin2ulaw(pred)
 
             p, state = dec.predict([fexc, iexc, cfeat[:, fr:fr+1, :], state])
             #p = p*p
@@ -96,8 +96,8 @@ for c in range(1, nb_frames):
             p = p/(1e-8 + np.sum(p))
 
             iexc[0, 0, 0] = np.argmax(np.random.multinomial(1, p[0,0,:], 1))
-            pcm[f*frame_size + i, 0] = pred + 32768*ulaw2lin(iexc[0, 0, 0]-128)
+            pcm[f*frame_size + i, 0] = pred + ulaw2lin(iexc[0, 0, 0])
-            fexc[0, 0, 0] = lin2ulaw(pcm[f*frame_size + i, 0]/32768) + 128
+            fexc[0, 0, 0] = lin2ulaw(pcm[f*frame_size + i, 0])
-            print(iexc[0, 0, 0], 32768*ulaw2lin(out_data[f*frame_size + i, 0]), pcm[f*frame_size + i, 0], pred)
+            print(iexc[0, 0, 0], ulaw2lin(out_data[f*frame_size + i, 0]), pcm[f*frame_size + i, 0], pred)
 
 

dnn/train_wavenet_audio.py

@@ -36,7 +36,7 @@ feature_chunk_size = 15
 pcm_chunk_size = frame_size*feature_chunk_size
 
 udata = np.fromfile(pcm_file, dtype='int16')
-data = np.minimum(127, lin2ulaw(udata/32768.))
+data = lin2ulaw(udata)
 nb_frames = len(data)//pcm_chunk_size
 
 features = np.fromfile(feature_file, dtype='float32')
@@ -48,14 +48,14 @@ features = features[:nb_frames*feature_chunk_size*nb_features]
 in_data = np.concatenate([data[0:1], data[:-1]]);
 noise = np.concatenate([np.zeros((len(data)*1//5)), np.random.randint(-3, 3, len(data)*1//5), np.random.randint(-2, 2, len(data)*1//5), np.random.randint(-1, 1, len(data)*2//5)])
 in_data = in_data + noise
-in_data = np.maximum(-127, np.minimum(127, in_data))
+in_data = np.clip(in_data, 0, 255)
 
 features = np.reshape(features, (nb_frames*feature_chunk_size, nb_features))
 
 upred = np.fromfile(pred_file, dtype='int16')
 upred = upred[:nb_frames*pcm_chunk_size]
 
-pred_in = 32768.*ulaw2lin(in_data)
+pred_in = ulaw2lin(in_data)
 for i in range(2, nb_frames*feature_chunk_size):
     upred[i*frame_size:(i+1)*frame_size] = 0
     #if i % 100000 == 0:
@@ -64,7 +64,7 @@ for i in range(2, nb_frames*feature_chunk_size):
         upred[i*frame_size:(i+1)*frame_size] = upred[i*frame_size:(i+1)*frame_size] - \
             pred_in[i*frame_size-k:(i+1)*frame_size-k]*features[i, nb_features-16+k]
 
-pred = np.minimum(127, lin2ulaw(upred/32768.))
+pred = lin2ulaw(upred)
 #pred = pred + np.random.randint(-1, 1, len(data))
 
 
@@ -77,23 +77,22 @@ for i in range(2, nb_frames*feature_chunk_size):
 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')
+in_data = in_data.astype('uint8')
-out_data = lin2ulaw((udata-upred)/32768)
+out_data = lin2ulaw(udata-upred)
 in_exc = np.concatenate([out_data[0:1], out_data[:-1]]);
 
 out_data = np.reshape(out_data, (nb_frames, pcm_chunk_size, 1))
-out_data = np.maximum(-127, np.minimum(127, out_data))
+out_data = out_data.astype('uint8')
-out_data = (out_data.astype('int16')+128).astype('uint8')
 
 in_exc = np.reshape(in_exc, (nb_frames, pcm_chunk_size, 1))
-in_exc = np.maximum(-127, np.minimum(127, in_exc))
+in_exc = in_exc.astype('uint8')
-in_exc = (in_exc.astype('int16')+128).astype('uint8')
 
 
 features = np.reshape(features, (nb_frames, feature_chunk_size, nb_features))
 features = features[:, :, :nb_used_features]
 pred = np.reshape(pred, (nb_frames, pcm_chunk_size, 1))
-pred = (pred.astype('int16')+128).astype('uint8')
+pred = pred.astype('uint8')
+
 periods = (50*features[:,:,36:37]+100).astype('int16')
 
 in_data = np.concatenate([in_data, pred], axis=-1)
@@ -104,7 +103,7 @@ in_data = np.concatenate([in_data, pred], axis=-1)
 # f.create_dataset('data', data=in_data[:50000, :, :])
 # f.create_dataset('feat', data=features[:50000, :, :])
 
-checkpoint = ModelCheckpoint('wavenet4f3_{epoch:02d}.h5')
+checkpoint = ModelCheckpoint('wavenet5b_{epoch:02d}.h5')
 
 #model.load_weights('wavenet4f2_30.h5')
 model.compile(optimizer=Adam(0.001, amsgrad=True, decay=2e-4), loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])

dnn/ulaw.py

@@ -2,15 +2,18 @@
 import numpy as np
 import math
 
+scale = 255.0/32768.0
+scale_1 = 32768.0/255.0
 def ulaw2lin(u):
+    u = u - 128
     s = np.sign(u)
     u = np.abs(u)
-    return s*(np.exp(u/128.*math.log(256))-1)/255
+    return s*scale_1*(np.exp(u/128.*math.log(256))-1)
 
 
 def lin2ulaw(x):
     s = np.sign(x)
     x = np.abs(x)
-    u = (s*(128*np.log(1+255*x)/math.log(256)))
+    u = (s*(128*np.log(1+scale*x)/math.log(256)))
-    u = np.round(u)
+    u = np.clip(128 + np.round(u), 0, 255)
     return u.astype('int16')