86 lines
4.7 KiB
Python
86 lines
4.7 KiB
Python
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 tensorflow as tf
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def sunflower_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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sample_count = sunflowers['file'].shape[0]
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sunflowers['file'][0].shape[0]
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def append_zeros(spgr):
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orig = spgr.shape[0]
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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 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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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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def speech_pairs_data(audio_group):
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audio_samples = pd.read_csv('./outputs/'+audio_group+'.csv',names=['word','voice','rate','variant','file'])
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audio_samples.loc[:,'file'] = audio_samples.loc[:,'file'].apply(lambda x:'outputs/'+audio_group+'/'+x).apply(generate_aiff_spectrogram)
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y_data = audio_samples['variant'].apply(lambda x:x=='normal').values
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max_samples = audio_samples['file'].apply(lambda x:x.shape[0]).max()
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sample_size = audio_samples['file'][0].shape[1]
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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['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(audio_samples_pos)
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x_data_neg = create_data(audio_samples_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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if __name__ == '__main__':
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print(speech_pairs_data())
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