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speech-scoring
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code cleanup

master
Malar Kannan 2017-10-26 12:48:31 +05:30
parent 5824158af2
commit 49e6a46efd
2 changed files with 17 additions and 11 deletions
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11
speech_data.py
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@ -31,11 +31,12 @@ def create_test_pair(l, r, max_samples):
r_sample = append_zeros(r, max_samples)
return np.asarray([[l_sample, r_sample]])
def create_X(sp, max_samples):
return create_pair(sp[0]['spectrogram'],sp[1]['spectrogram'],max_samples)
return create_pair(sp[0]['spectrogram'], sp[1]['spectrogram'], max_samples)
def get_word_pairs_data(word,max_samples):
def get_word_pairs_data(word, max_samples):
audio_samples = pd.read_csv(
'./outputs/audio.csv',
names=['word', 'voice', 'rate', 'variant', 'file'])
@ -55,7 +56,7 @@ def get_word_pairs_data(word,max_samples):
Y = np.hstack([np.ones(len(same_data)), np.zeros(len(diff_data))])
X = np.asarray(same_data + diff_data)
# tr_pairs, te_pairs, tr_y, te_y = train_test_split(X, Y, test_size=0.1)
return (X,Y)
return (X, Y)
def create_spectrogram_data(audio_group='audio'):
@ -106,8 +107,8 @@ def create_speech_pairs_data(audio_group='audio'):
def speech_model_data():
tr_pairs = np.load('outputs/tr_pairs.npy') / 255.0
te_pairs = np.load('outputs/te_pairs.npy') / 255.0
tr_pairs[tr_pairs < 0] = 0
te_pairs[te_pairs < 0] = 0
# tr_pairs[tr_pairs < 0] = 0
# te_pairs[te_pairs < 0] = 0
tr_y = np.load('outputs/tr_y.npy')
te_y = np.load('outputs/te_y.npy')
return tr_pairs, te_pairs, tr_y, te_y

17
test_siamese.py
View File

@ -6,12 +6,17 @@ reload(speech_data)
from speech_data import create_test_pair,get_word_pairs_data
import numpy as np
sunflower_data,sunflower_result = get_word_pairs_data('sunflowers',15)
sunflower_result
model = siamese_model((15, 1654))
model.load_weights('./models/siamese_speech_model-final.h5')
spec1 = record_spectrogram(n_sec=1.4)
spec2 = record_spectrogram(n_sec=1.4)
inp = create_test_pair(spec1,spec2,16)
model.predict([inp[:, 0], inp[:, 1]])
def predict_recording_with(m,sample_size=15):
spec1 = record_spectrogram(n_sec=1.4)
spec2 = record_spectrogram(n_sec=1.4)
inp = create_test_pair(spec1,spec2,sample_size)
return m.predict([inp[:, 0], inp[:, 1]])
predict_recording_with(model)
sunflower_data,sunflower_result = get_word_pairs_data('sunflowers',15)
sunflower_result
model.predict([sunflower_data[:, 0], sunflower_data[:, 1]]) < 0.5