From aba9af8bdebbc9af0dbecc3060121d52d84cd557 Mon Sep 17 00:00:00 2001 From: Jean-Marc Valin Date: Tue, 9 Oct 2018 02:39:12 -0400 Subject: [PATCH] mu-law code cleanup --- dnn/test_wavenet_audio.py | 16 ++++++++-------- dnn/train_wavenet_audio.py | 23 +++++++++++------------ dnn/ulaw.py | 9 ++++++--- 3 files changed, 25 insertions(+), 23 deletions(-) diff --git a/dnn/test_wavenet_audio.py b/dnn/test_wavenet_audio.py index a68707e1..44b7dc75 100755 --- a/dnn/test_wavenet_audio.py +++ b/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) - fexc[0, 0, 0] = lin2ulaw(pcm[f*frame_size + i, 0]/32768) + 128 - print(iexc[0, 0, 0], 32768*ulaw2lin(out_data[f*frame_size + i, 0]), pcm[f*frame_size + i, 0], pred) + pcm[f*frame_size + i, 0] = pred + ulaw2lin(iexc[0, 0, 0]) + fexc[0, 0, 0] = lin2ulaw(pcm[f*frame_size + i, 0]) + print(iexc[0, 0, 0], ulaw2lin(out_data[f*frame_size + i, 0]), pcm[f*frame_size + i, 0], pred) diff --git a/dnn/train_wavenet_audio.py b/dnn/train_wavenet_audio.py index 7fcc83ae..970fa7f3 100755 --- a/dnn/train_wavenet_audio.py +++ b/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') -out_data = lin2ulaw((udata-upred)/32768) +in_data = in_data.astype('uint8') +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('int16')+128).astype('uint8') +out_data = out_data.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('int16')+128).astype('uint8') +in_exc = in_exc.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']) diff --git a/dnn/ulaw.py b/dnn/ulaw.py index 9d5532cf..b79d4315 100644 --- a/dnn/ulaw.py +++ b/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 = np.round(u) + u = (s*(128*np.log(1+scale*x)/math.log(256))) + u = np.clip(128 + np.round(u), 0, 255) return u.astype('int16')