Malar Kannan
parent
e865f17a0d
commit
6f3bca61cf
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@ -21,6 +21,9 @@ from keras.layers import Dense, Dropout, Input, Lambda
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from keras.optimizers import RMSprop
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from keras import backend as K
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%matplotlib inline
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import matplotlib.pyplot as plt
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num_classes = 10
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@ -104,8 +107,6 @@ tr_pairs, tr_y = create_pairs(x_train, digit_indices)
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digit_indices = [np.where(y_test == i)[0] for i in range(num_classes)]
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te_pairs, te_y = create_pairs(x_test, digit_indices)
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tr_pairs.shape
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# network definition
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base_network = create_base_network(input_dim)
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154
speech_data.py
154
speech_data.py
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@ -2,6 +2,7 @@ import pandas as pd
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import numpy as np
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from spectro_gen import generate_aiff_spectrogram
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from sklearn.model_selection import train_test_split
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import itertools
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import pickle,gc
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def sunflower_data():
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@ -18,43 +19,43 @@ def sunflower_data():
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return np.lib.pad(spgr,[(0, max_samples-orig), (0,0)],'median')
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pad_sun = sunflowers['file'].apply(append_zeros).values
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x_data = np.vstack(pad_sun).reshape((sample_count,max_samples,sample_size,))
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# x_data.shape
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# y_data.shape
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# train_test_split(x_data,y_data,test_size=0.33)[].shape
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# len(train_test_split(x_data,y_data,test_size=0.33))
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# sunflowers.loc[:,'file'][0]
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# generate_aiff_spectrogram('outputs/sunflowers-Alex-150-normal-589.aiff')
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# sunflowers[sunflowers['variant'] == 'phoneme']
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# sunflowers[sunflowers['variant'] == 'normal']
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# for s in sunflowers.values:
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# print(s)
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return train_test_split(x_data,y_data,test_size=0.33)
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def get_siamese_pairs(groupF1,groupF2):
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group1 = [r for (i,r) in groupF1.iterrows()]
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group2 = [r for (i,r) in groupF2.iterrows()]
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f = [(g1,g2) for g2 in group2 for g1 in group1]
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t = [i for i in itertools.combinations(group1,2)]+[i for i in itertools.combinations(group2,2)]
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return (t,f)
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def sunflower_pairs_data():
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audio_samples = pd.read_csv('./outputs/audio.csv',names=['word','voice','rate','variant','file'])
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sunflowers = audio_samples.loc[audio_samples['word'] == 'sunflowers'].reset_index(drop=True)
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sunflowers.loc[:,'file'] = sunflowers.loc[:,'file'].apply(lambda x:'outputs/'+x).apply(generate_aiff_spectrogram)
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y_data = sunflowers['variant'].apply(lambda x:x=='normal').values
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max_samples = sunflowers['file'].apply(lambda x:x.shape[0]).max()
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sample_size = sunflowers['file'][0].shape[1]
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sunflowers_pos = sunflowers[sunflowers['variant'] == 'normal'].reset_index(drop=True)
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sunflowers_neg = sunflowers[sunflowers['variant'] == 'phoneme'].reset_index(drop=True)
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audio_samples = audio_samples.loc[audio_samples['word'] == 'sunflowers'].reset_index(drop=True)
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audio_samples.loc[:,'spectrogram'] = audio_samples.loc[:,'file'].apply(lambda x:'outputs/audio/'+x).apply(generate_aiff_spectrogram)
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max_samples = audio_samples['spectrogram'].apply(lambda x:x.shape[0]).max()
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sample_size = audio_samples['spectrogram'][0].shape[1]
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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'] == '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(same)
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diff_data.extend(diff)
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Y = np.hstack([np.ones(len(same_data)),np.zeros(len(diff_data))])
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X_sample_pairs = same_data+diff_data
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def append_zeros(spgr):
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return np.lib.pad(spgr,[(0, max_samples-spgr.shape[0]), (0,0)],'median')
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def create_data(sf):
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sample_count = sf['file'].shape[0]
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pad_sun = sf['file'].apply(append_zeros).values
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x_data = np.vstack(pad_sun).reshape((sample_count,max_samples,sample_size))
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return x_data
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x_data_pos = create_data(sunflowers_pos)
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x_data_neg = create_data(sunflowers_neg)
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x_pos_train, x_pos_test, x_neg_train, x_neg_test =train_test_split(x_data_pos,x_data_neg,test_size=0.33)
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tr_y = np.array(x_pos_train.shape[0]*[[1,0]])
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te_y = np.array(x_pos_test.shape[0]*[[1,0]])
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tr_pairs = np.array([x_pos_train,x_neg_train]).reshape(x_pos_train.shape[0],2,max_samples,sample_size)
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te_pairs = np.array([x_pos_test,x_neg_test]).reshape(x_pos_test.shape[0],2,max_samples,sample_size)
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return tr_pairs,te_pairs,tr_y,te_y
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sample = np.lib.pad(spgr,[(0, max_samples-spgr.shape[0]), (0,0)],'median')
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return np.expand_dims(sample,axis=0)
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def create_X(sp):
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# sample_count = sp[0]['file'].shape[0]
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l_sample = append_zeros(sp[0]['spectrogram'])
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r_sample = append_zeros(sp[1]['spectrogram'])#.apply(append_zeros).values
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# x_data = np.vstack(pad_sun).reshape((sample_count,max_samples,sample_size))
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return np.expand_dims(np.vstack([l_sample,r_sample]),axis=0)
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X_list = (create_X(sp) for sp in X_sample_pairs)
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X = np.vstack(X_list)
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tr_pairs,te_pairs,tr_y,te_y = train_test_split(X,Y,test_size=0.1)
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return train_test_split(X,Y,test_size=0.1)
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def create_spectrogram_data(audio_group='audio'):
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audio_samples = pd.read_csv('./outputs/'+audio_group+'.csv',names=['word','voice','rate','variant','file'])
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@ -64,58 +65,71 @@ def create_spectrogram_data(audio_group='audio'):
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def create_speech_pairs_data(audio_group='audio'):
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audio_samples = pd.read_pickle('outputs/spectrogram.pkl')
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y_data = audio_samples['variant'].apply(lambda x:x=='normal').values
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max_samples = audio_samples['spectrogram'].apply(lambda x:x.shape[0]).max()
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sample_size = audio_samples['spectrogram'][0].shape[1]
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pickle.dump((max_samples,sample_size),open('./spectrogram_vars.pkl','wb'))
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audio_samples_pos = audio_samples[audio_samples['variant'] == 'normal'].reset_index(drop=True)
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audio_samples_neg = audio_samples[audio_samples['variant'] == 'phoneme'].reset_index(drop=True)
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def append_zeros(spgr):
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return np.lib.pad(spgr,[(0, max_samples-spgr.shape[0]), (0,0)],'median')
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def create_data(sf):
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sample_count = sf['spectrogram'].shape[0]
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pad_sun = sf['spectrogram'].apply(append_zeros).values
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print('appended zeros')
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x_data = np.vstack(pad_sun).reshape((sample_count,max_samples,sample_size))
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print('reshaped')
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return x_data
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print('creating speech pair data')
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x_data_pos = create_data(audio_samples_pos)
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x_data_neg = create_data(audio_samples_neg)
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np.save('outputs/x_data_pos.npy',x_data_pos)
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np.save('outputs/x_data_neg.npy',x_data_neg)
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print('pickled speech pairs')
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def create_speech_model_data():
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(max_samples,sample_size) = pickle.load(open('./spectrogram_vars.pkl','rb'))
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x_data_pos = np.load('outputs/x_data_pos.npy')
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x_data_neg = np.load('outputs/x_data_neg.npy')
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x_pos_train, x_pos_test, x_neg_train, x_neg_test =train_test_split(x_data_pos,x_data_neg,test_size=0.33)
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del x_data_pos
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del x_data_neg
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def append_zeros(spgr):
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sample = np.lib.pad(spgr,[(0, max_samples-spgr.shape[0]), (0,0)],'median')
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return sample
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def create_X(sp):
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l_sample = append_zeros(sp[0]['spectrogram'])
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r_sample = append_zeros(sp[1]['spectrogram'])
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return np.asarray([l_sample,r_sample])
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print('generating siamese speech pairs')
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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'] == 'normal']#.reset_index(drop=True)
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sample_phon = g.loc[audio_samples['variant'] == 'phoneme']#.reset_index(drop=True)
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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[:10]])
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diff_data.extend([create_X(d) for d in diff[:10]])
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print('creating all speech pairs')
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Y = np.hstack([np.ones(len(same_data)),np.zeros(len(diff_data))])
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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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print('pickling X/Y')
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np.save('outputs/X.npy',X)
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np.save('outputs/Y.npy',Y)
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del X
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gc.collect()
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print('split train and test')
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tr_y = np.array(x_pos_train.shape[0]*[[1,0]])
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te_y = np.array(x_pos_test.shape[0]*[[1,0]])
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tr_pairs = np.array([x_pos_train,x_neg_train]).reshape(x_pos_train.shape[0],2,max_samples,sample_size)
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te_pairs = np.array([x_pos_test,x_neg_test]).reshape(x_pos_test.shape[0],2,max_samples,sample_size)
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print('reshaped to input dim')
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print('train/test splitting speech pairs')
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tr_pairs,te_pairs,tr_y,te_y = train_test_split(X,Y,test_size=0.1)
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print('pickling train/test')
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np.save('outputs/tr_pairs.npy',tr_pairs)
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np.save('outputs/te_pairs.npy',te_pairs)
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np.save('outputs/tr_y.npy',tr_y)
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np.save('outputs/te_y.npy',te_y)
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print('pickled speech model data')
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# return tr_pairs,te_pairs,tr_y,te_y
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# def create_speech_model_data():
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# (max_samples,sample_size) = pickle.load(open('./spectrogram_vars.pkl','rb'))
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# x_data_pos = np.load('outputs/x_data_pos.npy')
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# x_data_neg = np.load('outputs/x_data_neg.npy')
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# x_pos_train, x_pos_test, x_neg_train, x_neg_test =train_test_split(x_data_pos,x_data_neg,test_size=0.1)
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# del x_data_pos
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# del x_data_neg
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# gc.collect()
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# print('split train and test')
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# tr_y = np.array(x_pos_train.shape[0]*[1])
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# te_y = np.array(x_pos_test.shape[0]*[[1,0]])
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# tr_pairs = np.array([x_pos_train,x_neg_train]).reshape(x_pos_train.shape[0],2,max_samples,sample_size)
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# te_pairs = np.array([x_pos_test,x_neg_test]).reshape(x_pos_test.shape[0],2,max_samples,sample_size)
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# print('reshaped to input dim')
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# np.save('outputs/tr_pairs.npy',tr_pairs)
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# np.save('outputs/te_pairs.npy',te_pairs)
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# np.save('outputs/tr_y.npy',tr_y)
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# np.save('outputs/te_y.npy',te_y)
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# print('pickled speech model data')
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def speech_model_data():
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tr_pairs = np.load('outputs/tr_pairs.npy')
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te_pairs = np.load('outputs/te_pairs.npy')
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tr_pairs = np.load('outputs/tr_pairs.npy').astype(np.float32)/255.0
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te_pairs = np.load('outputs/te_pairs.npy').astype(np.float32)/255.0
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tr_y = np.load('outputs/tr_y.npy')
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te_y = np.load('outputs/te_y.npy')
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return tr_pairs,te_pairs,tr_y,te_y
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if __name__ == '__main__':
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# sunflower_pairs_data()
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#create_spectrogram_data()
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# create_speech_pairs_data()
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# create_speech_model_data()
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print(speech_model_data())
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create_speech_pairs_data()
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# print(speech_model_data())
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@ -14,17 +14,21 @@ 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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import random
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# import random
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# from keras.datasets import mnist
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from speech_data import speech_model_data
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from keras.models import Model
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from keras.layers import Dense, Dropout, Input, Lambda, LSTM, SimpleRNN
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from keras.optimizers import RMSprop
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from keras.layers import Input, Dense, Dropout, SimpleRNN, LSTM, Lambda
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# Dense, Dropout, Input, Lambda, LSTM, SimpleRNN
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from keras.optimizers import RMSprop, SGD
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from keras.callbacks import TensorBoard
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from keras import backend as K
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def euclidean_distance(vects):
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x, y = vects
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return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
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return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True),
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K.epsilon()))
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def eucl_dist_output_shape(shapes):
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@ -37,20 +41,35 @@ def contrastive_loss(y_true, y_pred):
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http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
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'''
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margin = 1
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# print(y_true, y_pred)
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return K.mean(y_true * K.square(y_pred) +
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(1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))
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def create_base_network(input_dim):
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def create_base_rnn_network(input_dim):
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'''Base network to be shared (eq. to feature extraction).
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'''
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inp = Input(shape=input_dim)
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sr1 = SimpleRNN(128)(inp)
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# sr2 = LSTM(128)(sr1)
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# sr2 = SimpleRNN(128)(sr)
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x = Dense(128, activation='relu')(sr1)
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return Model(inp, x)
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# d1 = Dense(1024, activation='sigmoid')(inp)
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# # d2 = Dense(2, activation='sigmoid')(d1)
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ls1 = LSTM(1024, return_sequences=True)(inp)
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ls2 = LSTM(512, return_sequences=True)(ls1)
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ls3 = LSTM(32)(ls2) # , return_sequences=True
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# sr2 = SimpleRNN(128, return_sequences=True)(sr1)
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# sr3 = SimpleRNN(32)(sr2)
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# x = Dense(128, activation='relu')(sr1)
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return Model(inp, ls3)
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def create_base_network(input_dim):
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'''Base network to be shared (eq. to feature extraction).
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'''
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input = Input(shape=input_dim)
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x = Dense(128, activation='relu')(input)
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x = Dropout(0.1)(x)
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x = Dense(128, activation='relu')(x)
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x = Dropout(0.1)(x)
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x = Dense(128, activation='relu')(x)
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return Model(input, x)
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def compute_accuracy(y_true, y_pred):
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'''Compute classification accuracy with a fixed threshold on distances.
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@ -66,8 +85,8 @@ def accuracy(y_true, y_pred):
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# the data, shuffled and split between train and test sets
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tr_pairs,te_pairs,tr_y,te_y = speech_model_data()
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# y_train.shape,y_test.shape
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tr_pairs, te_pairs, tr_y, te_y = speech_model_data()
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# y_train.shape,y_test.shape
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# x_train.shape,x_test.shape
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# x_train = x_train.reshape(60000, 784)
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# x_test = x_test.reshape(10000, 784)
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@ -75,11 +94,11 @@ tr_pairs,te_pairs,tr_y,te_y = speech_model_data()
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# x_test = x_test.astype('float32')
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# x_train /= 255
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# x_test /= 255
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input_dim = tr_pairs.shape[2:]
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input_dim = (tr_pairs.shape[2], tr_pairs.shape[3])
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epochs = 20
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# network definition
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base_network = create_base_network(input_dim)
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base_network = create_base_rnn_network(input_dim)
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input_a = Input(shape=input_dim)
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input_b = Input(shape=input_dim)
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@ -90,17 +109,25 @@ processed_a = base_network(input_a)
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processed_b = base_network(input_b)
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distance = Lambda(euclidean_distance,
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output_shape=eucl_dist_output_shape)([processed_a, processed_b])
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output_shape=eucl_dist_output_shape)(
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[processed_a, processed_b]
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)
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model = Model([input_a, input_b], distance)
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tb_cb = TensorBoard(log_dir='./siamese_logs', histogram_freq=1, batch_size=32,
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write_graph=True, write_grads=True, write_images=True,
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embeddings_freq=0, embeddings_layer_names=None,
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embeddings_metadata=None)
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# train
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rms = RMSprop()
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rms = RMSprop(lr=0.00001) # lr=0.001)
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sgd = SGD(lr=0.001)
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model.compile(loss=contrastive_loss, optimizer=rms, metrics=[accuracy])
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model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,
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batch_size=128,
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epochs=epochs,
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validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y))
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validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),
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callbacks=[tb_cb])
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# compute final accuracy on training and test sets
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y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
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