From 0b1152b5c317f29d275188f842e2a552d7ae2e7a Mon Sep 17 00:00:00 2001 From: Malar Kannan Date: Tue, 28 Nov 2017 19:10:19 +0530 Subject: [PATCH] implemented the model, todo implement ctc and training queueing logic --- requirements-linux.txt | 1 - segment_data.py | 3 ++- segment_model.py | 40 ++++++++++++++++++---------------------- speech_model.py | 6 ++++-- 4 files changed, 24 insertions(+), 26 deletions(-) diff --git a/requirements-linux.txt b/requirements-linux.txt index 025282d..e7f19ea 100644 --- a/requirements-linux.txt +++ b/requirements-linux.txt @@ -41,7 +41,6 @@ parso==0.1.0 partd==0.3.8 pexpect==4.2.1 pickleshare==0.7.4 -pkg-resources==0.0.0 praat-parselmouth==0.2.0 progressbar2==3.34.3 prompt-toolkit==1.0.15 diff --git a/segment_data.py b/segment_data.py index 77aba57..1e94655 100644 --- a/segment_data.py +++ b/segment_data.py @@ -7,5 +7,6 @@ def fix_csv(collection_name = 'test'): def segment_data_gen(collection_name = 'test'): - # collection_name = 'test' + collection_name = 'test' seg_data = pd.read_csv('./outputs/'+collection_name+'.fixed.csv',index_col=0) + diff --git a/segment_model.py b/segment_model.py index 683cdd6..acb4eec 100644 --- a/segment_model.py +++ b/segment_model.py @@ -2,8 +2,9 @@ from __future__ import absolute_import from __future__ import print_function import numpy as np from keras.models import Model,load_model,model_from_yaml -from keras.layers import Input,Concatenate,Lambda, BatchNormalization, Dropout -from keras.layers import Dense, LSTM, Bidirectional, GRU +from keras.layers import Input,Concatenate,Lambda, Reshape, Dropout +from keras.layers import Dense,Conv2D, LSTM, Bidirectional, GRU +from keras.layers import BatchNormalization from keras.losses import categorical_crossentropy from keras.utils import to_categorical from keras.optimizers import RMSprop @@ -11,34 +12,28 @@ from keras.callbacks import TensorBoard, ModelCheckpoint from keras import backend as K from keras.utils import plot_model from speech_tools import create_dir,step_count -from speech_data import segment_data_gen +from segment_data import segment_data_gen +# TODO implement ctc losses +# https://github.com/fchollet/keras/blob/master/examples/image_ocr.py def accuracy(y_true, y_pred): '''Compute classification accuracy with a fixed threshold on distances. ''' return K.mean(K.equal(y_true, K.cast(y_pred > 0.5, y_true.dtype))) -def dense_classifier(processed): - conc_proc = Concatenate()(processed) - d1 = Dense(64, activation='relu')(conc_proc) - # dr1 = Dropout(0.1)(d1) - # d2 = Dense(128, activation='relu')(d1) - d3 = Dense(8, activation='relu')(d1) - # dr2 = Dropout(0.1)(d2) - return Dense(2, activation='softmax')(d3) - def segment_model(input_dim): inp = Input(shape=input_dim) # ls0 = LSTM(512, return_sequences=True)(inp) - ls1 = LSTM(128, return_sequences=True)(inp) - ls2 = LSTM(64, return_sequences=True)(ls1) - # ls3 = LSTM(32, return_sequences=True)(ls2) - ls4 = LSTM(32)(ls2) - d1 = Dense(64, activation='relu')(ls4) - d3 = Dense(8, activation='relu')(d1) - oup = Dense(2, activation='softmax')(d3) - return Model(inp, oup) + cnv1 = Conv2D(filters=512, kernel_size=(5,9))(inp) + cnv2 = Conv2D(filters=1, kernel_size=(5,9))(cnv1) + dr_cnv2 = Dropout(rate=0.95)(cnv2) + cn_rnn_dim = (dr_cnv2.shape[1].value,dr_cnv2.shape[2].value) + r_dr_cnv2 = Reshape(target_shape=cn_rnn_dim)(dr_cnv2) + b_gr1 = Bidirectional(GRU(512, return_sequences=True))(r_dr_cnv2) + b_gr2 = Bidirectional(GRU(512, return_sequences=True))(b_gr1) + b_gr3 = Bidirectional(GRU(512))(b_gr2) + return Model(inp, b_gr3) def write_model_arch(mod,mod_file): model_f = open(mod_file,'w') @@ -99,10 +94,11 @@ def train_segment(collection_name = 'test'): model.save(model_dir+'/speech_segment_model-final.h5') y_pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]]) - te_acc = compute_accuracy(te_y, y_pred) - print('* Accuracy on test set: %0.2f%%' % (100 * te_acc)) + # te_acc = compute_accuracy(te_y, y_pred) + # print('* Accuracy on test set: %0.2f%%' % (100 * te_acc)) if __name__ == '__main__': + import pdb; pdb.set_trace() train_segment('test') diff --git a/speech_model.py b/speech_model.py index 820bf20..23168cb 100644 --- a/speech_model.py +++ b/speech_model.py @@ -20,9 +20,11 @@ def create_base_rnn_network(input_dim): inp = Input(shape=input_dim) # ls0 = LSTM(512, return_sequences=True)(inp) ls1 = LSTM(128, return_sequences=True)(inp) - ls2 = LSTM(64, return_sequences=True)(ls1) + bn_ls1 = BatchNormalization(momentum=0.98)(ls1) + ls2 = LSTM(64, return_sequences=True)(bn_ls1) + bn_ls2 = BatchNormalization(momentum=0.98)(ls2) # ls3 = LSTM(32, return_sequences=True)(ls2) - ls4 = LSTM(32)(ls2) + ls4 = LSTM(32)(bn_ls2) # d1 = Dense(128, activation='relu')(ls4) #d2 = Dense(64, activation='relu')(ls2) return Model(inp, ls4)