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speech-scoring
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updated tested pickling

master
Malar Kannan 2017-10-17 19:17:44 +05:30
parent 88edcdd239
commit b3755ad80e
2 changed files with 6 additions and 4 deletions
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1
.gitignore vendored
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@ -139,3 +139,4 @@ Temporary Items
outputs/*
inputs/mnist
inputs/audio*

9
speech_data.py
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@ -56,13 +56,14 @@ def sunflower_pairs_data():
te_pairs = np.array([x_pos_test,x_neg_test]).reshape(x_pos_test.shape[0],2,max_samples,sample_size)
return tr_pairs,te_pairs,tr_y,te_y
def create_spectrogram_data():
def create_spectrogram_data(audio_group='audio'):
audio_samples = pd.read_csv('./outputs/'+audio_group+'.csv',names=['word','voice','rate','variant','file'])
# audio_samples = audio_samples.loc[audio_samples['word'] == 'sunflowers'].reset_index(drop=True)
audio_samples.loc[:,'spectrogram'] = audio_samples.loc[:,'file'].apply(lambda x:'outputs/'+audio_group+'/'+x).apply(generate_aiff_spectrogram)
audio_samples.to_pickle('spectrogram.pkl')
audio_samples.to_pickle('outputs/spectrogram.pkl')
def speech_pairs_data(audio_group):
audio_samples = pd.read_pickle('spectrogram.pkl')
def speech_pairs_data(audio_group='audio'):
audio_samples = pd.read_pickle('outputs/spectrogram.pkl')
y_data = audio_samples['variant'].apply(lambda x:x=='normal').values
max_samples = audio_samples['spectrogram'].apply(lambda x:x.shape[0]).max()
sample_size = audio_samples['spectrogram'][0].shape[1]