diff --git a/dnn/dump_lpcnet.py b/dnn/dump_lpcnet.py index 8388fd7f..52e70f42 100755 --- a/dnn/dump_lpcnet.py +++ b/dnn/dump_lpcnet.py @@ -240,7 +240,6 @@ W = model.get_layer('gru_a').get_weights()[0][:embed_size,:] dump_embedding_layer_impl('gru_a_embed_sig', np.dot(E, W), f, hf) W = model.get_layer('gru_a').get_weights()[0][embed_size:2*embed_size,:] dump_embedding_layer_impl('gru_a_embed_pred', np.dot(E, W), f, hf) -E = model.get_layer('embed_exc').get_weights()[0] W = model.get_layer('gru_a').get_weights()[0][2*embed_size:3*embed_size,:] dump_embedding_layer_impl('gru_a_embed_exc', np.dot(E, W), f, hf) W = model.get_layer('gru_a').get_weights()[0][3*embed_size:,:] diff --git a/dnn/lpcnet.py b/dnn/lpcnet.py index 6b1adf64..fe56a9c9 100644 --- a/dnn/lpcnet.py +++ b/dnn/lpcnet.py @@ -114,8 +114,7 @@ class PCMInit(Initializer): } def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, use_gpu=True): - pcm = Input(shape=(None, 2)) - exc = Input(shape=(None, 1)) + pcm = Input(shape=(None, 3)) feat = Input(shape=(None, nb_used_features)) pitch = Input(shape=(None, 1)) dec_feat = Input(shape=(None, 128)) @@ -126,9 +125,7 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, use_g fconv2 = Conv1D(128, 3, padding='same', activation='tanh', name='feature_conv2') embed = Embedding(256, embed_size, embeddings_initializer=PCMInit(), name='embed_sig') - cpcm = Reshape((-1, embed_size*2))(embed(pcm)) - embed2 = Embedding(256, embed_size, embeddings_initializer=PCMInit(), name='embed_exc') - cexc = Reshape((-1, embed_size))(embed2(exc)) + cpcm = Reshape((-1, embed_size*3))(embed(pcm)) pembed = Embedding(256, 64, name='embed_pitch') cat_feat = Concatenate()([feat, Reshape((-1, 64))(pembed(pitch))]) @@ -149,13 +146,13 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, use_g rnn = GRU(rnn_units1, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_a') rnn2 = GRU(rnn_units2, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='gru_b') - rnn_in = Concatenate()([cpcm, cexc, rep(cfeat)]) + rnn_in = Concatenate()([cpcm, rep(cfeat)]) md = MDense(pcm_levels, activation='softmax', name='dual_fc') gru_out1, _ = rnn(rnn_in) gru_out2, _ = rnn2(Concatenate()([gru_out1, rep(cfeat)])) ulaw_prob = md(gru_out2) - model = Model([pcm, exc, feat, pitch], ulaw_prob) + model = Model([pcm, feat, pitch], ulaw_prob) model.rnn_units1 = rnn_units1 model.rnn_units2 = rnn_units2 model.nb_used_features = nb_used_features @@ -163,10 +160,10 @@ def new_lpcnet_model(rnn_units1=384, rnn_units2=16, nb_used_features = 38, use_g encoder = Model([feat, pitch], cfeat) - dec_rnn_in = Concatenate()([cpcm, cexc, dec_feat]) + dec_rnn_in = Concatenate()([cpcm, dec_feat]) dec_gru_out1, state1 = rnn(dec_rnn_in, initial_state=dec_state1) dec_gru_out2, state2 = rnn2(Concatenate()([dec_gru_out1, dec_feat]), initial_state=dec_state2) dec_ulaw_prob = md(dec_gru_out2) - decoder = Model([pcm, exc, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2]) + decoder = Model([pcm, dec_feat, dec_state1, dec_state2], [dec_ulaw_prob, state1, state2]) return model, encoder, decoder diff --git a/dnn/test_lpcnet.py b/dnn/test_lpcnet.py index b7a57c3e..2d0f3db0 100755 --- a/dnn/test_lpcnet.py +++ b/dnn/test_lpcnet.py @@ -63,13 +63,12 @@ periods = (.1 + 50*features[:,:,36:37]+100).astype('int16') -model.load_weights('lpcnet20c_384_10_G16_80.h5') +model.load_weights('lpcnet20g_384_10_G16_02.h5') order = 16 pcm = np.zeros((nb_frames*pcm_chunk_size, )) -fexc = np.zeros((1, 1, 2), dtype='float32') -iexc = np.zeros((1, 1, 1), dtype='int16') +fexc = np.zeros((1, 1, 3), dtype='int16') state1 = np.zeros((1, model.rnn_units1), dtype='float32') state2 = np.zeros((1, model.rnn_units2), dtype='float32') @@ -88,7 +87,7 @@ for c in range(0, nb_frames): pred = -sum(a*pcm[f*frame_size + i - 1:f*frame_size + i - order-1:-1]) fexc[0, 0, 1] = lin2ulaw(pred) - p, state1, state2 = dec.predict([fexc, iexc, cfeat[:, fr:fr+1, :], state1, state2]) + p, state1, state2 = dec.predict([fexc, cfeat[:, fr:fr+1, :], state1, state2]) #Lower the temperature for voiced frames to reduce noisiness p *= np.power(p, np.maximum(0, 1.5*features[c, fr, 37] - .5)) p = p/(1e-18 + np.sum(p)) @@ -96,8 +95,8 @@ for c in range(0, nb_frames): p = np.maximum(p-0.002, 0).astype('float64') 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] = pred + ulaw2lin(iexc[0, 0, 0]) + fexc[0, 0, 2] = np.argmax(np.random.multinomial(1, p[0,0,:], 1)) + pcm[f*frame_size + i] = pred + ulaw2lin(fexc[0, 0, 2]) fexc[0, 0, 0] = lin2ulaw(pcm[f*frame_size + i]) mem = coef*mem + pcm[f*frame_size + i] #print(mem) diff --git a/dnn/train_lpcnet.py b/dnn/train_lpcnet.py index 9d1fb181..3f0f2092 100755 --- a/dnn/train_lpcnet.py +++ b/dnn/train_lpcnet.py @@ -91,14 +91,15 @@ features[:,:,18:36] = 0 periods = (.1 + 50*features[:,:,36:37]+100).astype('int16') -in_data = np.concatenate([sig, pred], axis=-1) +in_data = np.concatenate([sig, pred, in_exc], axis=-1) del sig del pred +del in_exc # dump models to disk as we go checkpoint = ModelCheckpoint('lpcnet20g_384_10_G16_{epoch:02d}.h5') #model.load_weights('lpcnet9b_384_10_G16_01.h5') model.compile(optimizer=Adam(0.001, amsgrad=True, decay=5e-5), loss='sparse_categorical_crossentropy') -model.fit([in_data, in_exc, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2))]) +model.fit([in_data, features, periods], out_exc, batch_size=batch_size, epochs=nb_epochs, validation_split=0.0, callbacks=[checkpoint, lpcnet.Sparsify(2000, 40000, 400, (0.05, 0.05, 0.2))])