1. fixed missing wrong pairs

2.using different progress bakend

speech_data.py

@@ -11,11 +11,12 @@ import os
 import random
 import csv
 import gc
-import progressbar
+# import progressbar
+from tqdm import tqdm
 
-def prog_bar(title):
-    widgets = [title, progressbar.Counter(), ' [', progressbar.Bar(), '] - ', progressbar.ETA()]
-    return progressbar.ProgressBar(widgets=widgets)
+# def prog_bar(title):
+#     widgets = [title, progressbar.Counter(), ' [', progressbar.Bar(), '] - ', progressbar.ETA()]
+#     return progressbar.ProgressBar(widgets=widgets)
 
 def siamese_pairs(rightGroup, wrongGroup):
     group1 = [r for (i, r) in rightGroup.iterrows()]
@@ -26,7 +27,7 @@ def siamese_pairs(rightGroup, wrongGroup):
     random.shuffle(rightRightPairs)
     # return (random.sample(same,10), random.sample(diff,10))
     # return rightRightPairs[:10],rightWrongPairs[:10]
-    return rightRightPairs[:16],rightWrongPairs[:16]
+    return rightRightPairs[:32],rightWrongPairs[:32]
     # return rightRightPairs,rightWrongPairs
 
 def create_spectrogram_tfrecords(audio_group='audio'):
@@ -55,16 +56,21 @@ def create_spectrogram_tfrecords(audio_group='audio'):
       return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
 
     writer = tf.python_io.TFRecordWriter('./outputs/' + audio_group + '.tfrecords')
-    prog = prog_bar('Generating siamese pairs : ')
-    for (w, word_group) in prog(audio_samples.groupby(audio_samples['word'])):
+    prog = tqdm(audio_samples.groupby(audio_samples['word']),desc='Computing spectrogram')
+    for (w, word_group) in prog:
+        prog.set_postfix(word=w)
         g = word_group.reset_index()
         g['spectrogram'] = apply_by_multiprocessing(g['file_path'],generate_aiff_spectrogram)
-        sample_right = g.loc[audio_samples['variant'] == 'low']
-        sample_wrong = g.loc[audio_samples['variant'] == 'medium']
+        sample_right = g.loc[g['variant'] == 'low']
+        sample_wrong = g.loc[g['variant'] == 'medium']
         same, diff = siamese_pairs(sample_right, sample_wrong)
         groups = [([0,1],same),([1,0],diff)]
         for (output,group) in groups:
-            for sample1,sample2 in group:
+            group_prog = tqdm(group,desc='Writing Spectrogram')
+            for sample1,sample2 in group_prog:
+                group_prog.set_postfix(output=output
+                    ,var1=sample1['variant']
+                    ,var2=sample2['variant'])
                 spectro1,spectro2 = sample1['spectrogram'],sample2['spectrogram']
                 spec_n1,spec_n2 = spectro1.shape[0],spectro2.shape[0]
                 spec_w1,spec_w2 = spectro1.shape[1],spectro2.shape[1]
@@ -93,6 +99,7 @@ def create_spectrogram_tfrecords(audio_group='audio'):
                   }
                 ))
                 writer.write(example.SerializeToString())
+    prog.close()
     writer.close()
 
 def padd_zeros(spgr, max_samples):
@@ -141,7 +148,7 @@ def read_siamese_tfrecords(audio_group='audio'):
         # if len(input_pairs) > 50:
         #     break
     input_data,output_data = np.asarray(input_pairs),np.asarray(output_class)
-    # import pdb; pdb.set_trace()
+    import pdb; pdb.set_trace()
     # tr_x1,te_x1,tr_x2,te_x2,tr_y,te_y = train_test_split(input1,input2,output_class)
     tr_pairs,te_pairs,tr_y,te_y = train_test_split(input_data,output_data)
     # return (tr_x1,te_x1,tr_x2,te_x2,tr_y,te_y)
@@ -186,9 +193,9 @@ if __name__ == '__main__':
     # sunflower_pairs_data()
     # create_spectrogram_data()
     # create_spectrogram_data('story_words')
-    # create_spectrogram_tfrecords('story_words')
-    # create_spectrogram_tfrecords('story_all')
-    read_siamese_tfrecords('story_all')
+    create_spectrogram_tfrecords('story_words')
+    # create_spectrogram_tfrecords('story_words_test')
+    # read_siamese_tfrecords('story_all')
     # create_padded_spectrogram()
     # create_speech_pairs_data()
     # print(speech_model_data())

speech_siamese.py

@@ -114,11 +114,11 @@ def train_siamese():
     rms = RMSprop(lr=0.001)
     model.compile(loss=categorical_crossentropy, optimizer=rms, metrics=[accuracy])
     model.fit(
-        [tr_x1, tr_x2],
+        [tr_pairs[:, 0], tr_pairs[:, 1]],
         tr_y,
         batch_size=128,
-        epochs=50,
-        validation_data=([tr_pairs[:, 0], tr_pairs[:, 1]], te_y),
+        epochs=100,
+        validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),
         callbacks=[tb_cb, cp_cb])
 
     model.save('./models/siamese_speech_model-final.h5')