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speech-scoring
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implemented padding and pipeline is complete

master
Malar Kannan 2017-11-07 15:18:04 +05:30
parent 41b3f1a9fe
commit b8a9f87031
2 changed files with 42 additions and 11 deletions
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45
speech_data.py
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@ -95,17 +95,34 @@ def create_spectrogram_tfrecords(audio_group='audio'):
writer.write(example.SerializeToString()) writer.write(example.SerializeToString())
writer.close() writer.close()
def padd_zeros(spgr, max_samples):
return np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
'constant')
def find_max_n(trf):
max_n = 0
max_n_it = tf.python_io.tf_record_iterator(path=trf)
for string_record in max_n_it:
example = tf.train.Example()
example.ParseFromString(string_record)
spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
spec_n2 = example.features.feature['spec_n2'].int64_list.value[0]
max_n = max([max_n,spec_n1,spec_n2])
return max_n
def read_siamese_tfrecords(audio_group='audio'): def read_siamese_tfrecords(audio_group='audio'):
records_file = os.path.join('./outputs',audio_group+'.tfrecords') records_file = os.path.join('./outputs',audio_group+'.tfrecords')
record_iterator = tf.python_io.tf_record_iterator(path=records_file) record_iterator = tf.python_io.tf_record_iterator(path=records_file)
# input1,input2 = [],[]
input_pairs = [] input_pairs = []
output_class = [] output_class = []
input_words = [] max_n = find_max_n(records_file)
spec_w1 = 0
for string_record in record_iterator: for string_record in record_iterator:
example = tf.train.Example() example = tf.train.Example()
example.ParseFromString(string_record) example.ParseFromString(string_record)
word = example.features.feature['word'].bytes_list.value[0] # word = example.features.feature['word'].bytes_list.value[0]
input_words.append(word) # input_words.append(word)
example.features.feature['spec2'].float_list.value[0] example.features.feature['spec2'].float_list.value[0]
spec_n1 = example.features.feature['spec_n1'].int64_list.value[0] spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
spec_n2 = example.features.feature['spec_n2'].int64_list.value[0] spec_n2 = example.features.feature['spec_n2'].int64_list.value[0]
@ -113,10 +130,23 @@ def read_siamese_tfrecords(audio_group='audio'):
spec_w2 = example.features.feature['spec_w2'].int64_list.value[0] spec_w2 = example.features.feature['spec_w2'].int64_list.value[0]
spec1 = np.array(example.features.feature['spec1'].float_list.value).reshape(spec_n1,spec_w1) spec1 = np.array(example.features.feature['spec1'].float_list.value).reshape(spec_n1,spec_w1)
spec2 = np.array(example.features.feature['spec2'].float_list.value).reshape(spec_n2,spec_w2) spec2 = np.array(example.features.feature['spec2'].float_list.value).reshape(spec_n2,spec_w2)
input_pairs.append([spec1,spec2]) p_spec1,p_spec2 = padd_zeros(spec1,max_n),padd_zeros(spec2,max_n)
# input1.append(spec1)
# input2.append(spec2)
input_pairs.append(np.asarray([p_spec1,p_spec2]))
# input_pairs.append([spec1,spec2])
output = example.features.feature['output'].int64_list.value output = example.features.feature['output'].int64_list.value
output_class.append(output) output_class.append(np.asarray(output))
return input_pairs,output_class n_features = spec_w1
# if len(input_pairs) > 50:
# break
input_data,output_data = np.asarray(input_pairs),np.asarray(output_class)
# 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)
n_step,n_features = int(max_n),int(spec_w1)
return (tr_pairs,te_pairs,tr_y,te_y,n_step,n_features)
def audio_samples_word_count(audio_group='audio'): def audio_samples_word_count(audio_group='audio'):
audio_group = 'story_all' audio_group = 'story_all'
@ -157,7 +187,8 @@ if __name__ == '__main__':
# create_spectrogram_data() # create_spectrogram_data()
# create_spectrogram_data('story_words') # create_spectrogram_data('story_words')
# create_spectrogram_tfrecords('story_words') # create_spectrogram_tfrecords('story_words')
create_spectrogram_tfrecords('story_all') # create_spectrogram_tfrecords('story_all')
read_siamese_tfrecords('story_all')
# create_padded_spectrogram() # create_padded_spectrogram()
# create_speech_pairs_data() # create_speech_pairs_data()
# print(speech_model_data()) # print(speech_model_data())

8
speech_siamese.py
View File

@ -82,10 +82,10 @@ def siamese_model(input_dim):
def train_siamese(): def train_siamese():
# the data, shuffled and split between train and test sets # the data, shuffled and split between train and test sets
# tr_pairs, te_pairs, tr_y_e, te_y_e = speech_model_data() # tr_pairs, te_pairs, tr_y_e, te_y_e = speech_model_data()
pairs,y = read_siamese_tfrecords('story_words') (tr_pairs,te_pairs,tr_y,te_y,n_step,n_features) = read_siamese_tfrecords('story_words')
# tr_y = to_categorical(tr_y_e, num_classes=2) # tr_y = to_categorical(tr_y_e, num_classes=2)
# te_y = to_categorical(te_y_e, num_classes=2) # te_y = to_categorical(te_y_e, num_classes=2)
input_dim = (None, 1654) input_dim = (n_step, n_features)
model = siamese_model(input_dim) model = siamese_model(input_dim)
@ -114,11 +114,11 @@ def train_siamese():
rms = RMSprop(lr=0.001) rms = RMSprop(lr=0.001)
model.compile(loss=categorical_crossentropy, optimizer=rms, metrics=[accuracy]) model.compile(loss=categorical_crossentropy, optimizer=rms, metrics=[accuracy])
model.fit( model.fit(
[tr_pairs[:, 0], tr_pairs[:, 1]], [tr_x1, tr_x2],
tr_y, tr_y,
batch_size=128, batch_size=128,
epochs=50, epochs=50,
validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y), validation_data=([tr_pairs[:, 0], tr_pairs[:, 1]], te_y),
callbacks=[tb_cb, cp_cb]) callbacks=[tb_cb, cp_cb])
model.save('./models/siamese_speech_model-final.h5') model.save('./models/siamese_speech_model-final.h5')