implemented tfrecord reader and model refactor wip
Malar Kannan
parent
5b682c78b8
commit
15f29895d4
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@ -31,7 +31,8 @@ def siamese_pairs(rightGroup, wrongGroup):
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random.shuffle(rightWrongPairs)
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random.shuffle(rightRightPairs)
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# return (random.sample(same,10), random.sample(diff,10))
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return rightRightPairs[:10],rightWrongPairs[:10]
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# return rightRightPairs[:10],rightWrongPairs[:10]
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return rightRightPairs,rightWrongPairs
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def append_zeros(spgr, max_samples):
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return np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
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@ -42,7 +43,6 @@ def padd_zeros(spgr, max_samples):
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'constant')
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def to_onehot(a,class_count=2):
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# >>> a = np.array([1, 0, 3])
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a_row_n = a.shape[0]
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b = np.zeros((a_row_n, class_count))
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b[np.arange(a_row_n), a] = 1
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@ -101,6 +101,10 @@ def create_spectrogram_data(audio_group='audio'):
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audio_samples.to_pickle('outputs/{}-spectrogram.pkl'.format(audio_group))
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def create_spectrogram_tfrecords(audio_group='audio'):
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'''
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http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/
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http://www.machinelearninguru.com/deep_learning/tensorflow/basics/tfrecord/tfrecord.html
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'''
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audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv'
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, names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']
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, quoting=csv.QUOTE_NONE)
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@ -120,7 +124,6 @@ def create_spectrogram_tfrecords(audio_group='audio'):
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return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
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writer = tf.python_io.TFRecordWriter('./outputs/' + audio_group + '.tfrecords')
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# audio_samples = audio_samples[:100]
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for (w, word_group) in audio_samples.groupby(audio_samples['word']):
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g = word_group.reset_index()
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g['spectrogram'] = apply_by_multiprocessing(g['file_path'],generate_aiff_spectrogram)
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@ -160,22 +163,28 @@ def create_spectrogram_tfrecords(audio_group='audio'):
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writer.write(example.SerializeToString())
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writer.close()
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def create_tagged_data(audio_samples):
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same_data, diff_data = [], []
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for (w, g) in audio_samples.groupby(audio_samples['word']):
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# sample_norm = g.loc[audio_samples['variant'] == 'low']
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# sample_phon = g.loc[audio_samples['variant'] == 'medium']
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sample_norm = g.loc[audio_samples['variant'] == 'normal']
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sample_phon = g.loc[audio_samples['variant'] == 'phoneme']
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same, diff = get_siamese_pairs(sample_norm, sample_phon)
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same_data.extend([create_X(s) for s in same])
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diff_data.extend([create_X(d) for d in diff])
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print('creating all speech pairs')
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Y_f = np.hstack([np.ones(len(same_data)), np.zeros(len(diff_data))])
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Y = to_onehot(Y_f.astype(np.int8))
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print('casting as array speech pairs')
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X = np.asarray(same_data + diff_data)
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return X,Y
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def read_siamese_tfrecords(audio_group='audio'):
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records_file = os.path.join('./outputs',audio_group+'.tfrecords')
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record_iterator = tf.python_io.tf_record_iterator(path=records_file)
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input_pairs = []
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output_class = []
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input_words = []
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for string_record in record_iterator:
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example = tf.train.Example()
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example.ParseFromString(string_record)
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word = example.features.feature['word'].bytes_list.value[0]
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input_words.append(word)
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example.features.feature['spec2'].float_list.value[0]
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spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
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spec_n2 = example.features.feature['spec_n2'].int64_list.value[0]
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spec_w1 = example.features.feature['spec_w1'].int64_list.value[0]
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spec_w2 = example.features.feature['spec_w2'].int64_list.value[0]
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spec1 = np.array(example.features.feature['spec1'].float_list.value).reshape(spec_n1,spec_w1)
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spec2 = np.array(example.features.feature['spec2'].float_list.value).reshape(spec_n2,spec_w2)
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input_pairs.append([spec1,spec2])
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output = example.features.feature['output'].int64_list.value
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output_class.append(output)
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return input_pairs,output_class
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def create_speech_pairs_data(audio_group='audio'):
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audio_samples = pd.read_pickle('outputs/{}-spectrogram.pkl'.format(audio_group))
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@ -195,10 +204,17 @@ def create_speech_pairs_data(audio_group='audio'):
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save_samples_for('train',tr_audio_samples)
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save_samples_for('test',te_audio_samples)
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def speech_data(audio_group='audio'):
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X = np.load('outputs/{}-X.npy'.format(audio_group)) / 255.0
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Y = np.load('outputs/{}-Y.npy'.format(audio_group))
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return (X,Y)
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def audio_samples_word_count(audio_group='audio'):
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audio_group = 'story_all'
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audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv'
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, names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']
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, quoting=csv.QUOTE_NONE)
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# audio_samples = audio_samples.loc[audio_samples['word'] ==
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# 'sunflowers'].reset_index(drop=True)
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audio_samples['file_path'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
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audio_samples['file_exists'] = apply_by_multiprocessing(audio_samples['file_path'], os.path.exists)
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audio_samples = audio_samples[audio_samples['file_exists'] == True].reset_index()
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return len(audio_samples.groupby(audio_samples['word']))
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def speech_model_data():
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tr_pairs = np.load('outputs/tr_pairs.npy') / 255.0
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@ -214,7 +230,8 @@ if __name__ == '__main__':
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# sunflower_pairs_data()
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# create_spectrogram_data()
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# create_spectrogram_data('story_words')
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create_spectrogram_tfrecords('story_words')
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# create_spectrogram_tfrecords('story_words')
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create_spectrogram_tfrecords('story_all')
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# create_padded_spectrogram()
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# create_speech_pairs_data()
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# print(speech_model_data())
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@ -1,7 +1,8 @@
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from __future__ import absolute_import
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from __future__ import print_function
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import numpy as np
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from speech_data import speech_model_data
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# from speech_data import speech_model_data
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from speech_data import read_siamese_tfrecords
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from keras.models import Model,load_model
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from keras.layers import Input, Dense, Dropout, LSTM, Lambda, Concatenate
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from keras.losses import categorical_crossentropy
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@ -80,10 +81,11 @@ def siamese_model(input_dim):
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def train_siamese():
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# the data, shuffled and split between train and test sets
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tr_pairs, te_pairs, tr_y_e, te_y_e = speech_model_data()
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tr_y = to_categorical(tr_y_e, num_classes=2)
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te_y = to_categorical(te_y_e, num_classes=2)
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input_dim = (tr_pairs.shape[2], tr_pairs.shape[3])
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# tr_pairs, te_pairs, tr_y_e, te_y_e = speech_model_data()
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pairs,y = read_siamese_tfrecords('story_words')
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# tr_y = to_categorical(tr_y_e, num_classes=2)
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# te_y = to_categorical(te_y_e, num_classes=2)
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input_dim = (None, 1654)
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model = siamese_model(input_dim)
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