malar
/
speech-scoring
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

1. fixed pairing and data duplicates 

2. clean-up
master
Malar Kannan 2017-11-16 22:56:24 +05:30
parent 3d297f176f
commit 7fc89c0853
2 changed files with 9 additions and 46 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

13
speech_data.py
View File

@ -41,7 +41,7 @@ def create_spectrogram_tfrecords(audio_group='audio',sample_count=0,train_test_r
http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/ http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/
http://www.machinelearninguru.com/deep_learning/tensorflow/basics/tfrecord/tfrecord.html http://www.machinelearninguru.com/deep_learning/tensorflow/basics/tfrecord/tfrecord.html
''' '''
audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv',index_col=0) audio_samples = pd.read_csv( './outputs/' + audio_group + '.fixed.csv',index_col=0)
audio_samples['file_path'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x) audio_samples['file_path'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
n_records,n_spec,n_features = 0,0,0 n_records,n_spec,n_features = 0,0,0
@ -205,19 +205,19 @@ def record_generator_count(records_file):
return record_iterator,count return record_iterator,count
def fix_csv(audio_group='audio'): def fix_csv(audio_group='audio'):
audio_csv_lines = open('./outputs/' + audio_group + '.csv.orig','r').readlines() audio_csv_lines = open('./outputs/' + audio_group + '.csv','r').readlines()
audio_csv_data = [i.strip().split(',') for i in audio_csv_lines] audio_csv_data = [i.strip().split(',') for i in audio_csv_lines]
proper_rows = [i for i in audio_csv_data if len(i) == 7] proper_rows = [i for i in audio_csv_data if len(i) == 7]
with open('./outputs/' + audio_group + '.csv','w') as fixed_csv: with open('./outputs/' + audio_group + '.fixed.csv','w') as fixed_csv:
fixed_csv_w = csv.writer(fixed_csv, quoting=csv.QUOTE_MINIMAL) fixed_csv_w = csv.writer(fixed_csv, quoting=csv.QUOTE_MINIMAL)
fixed_csv_w.writerows(proper_rows) fixed_csv_w.writerows(proper_rows)
audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv' audio_samples = pd.read_csv( './outputs/' + audio_group + '.fixed.csv'
, names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']) , names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file'])
audio_samples['file_path'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x) audio_samples['file_path'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
audio_samples['file_exists'] = apply_by_multiprocessing(audio_samples['file_path'], os.path.exists) audio_samples['file_exists'] = apply_by_multiprocessing(audio_samples['file_path'], os.path.exists)
audio_samples = audio_samples[audio_samples['file_exists'] == True] audio_samples = audio_samples[audio_samples['file_exists'] == True]
audio_samples = audio_samples.drop(['file_path','file_exists'],axis=1).reset_index(drop=True) audio_samples = audio_samples.drop(['file_path','file_exists'],axis=1).reset_index(drop=True)
audio_samples.to_csv('./outputs/' + audio_group + '.csv') audio_samples.to_csv('./outputs/' + audio_group + '.fixed.csv')
def convert_old_audio(): def convert_old_audio():
audio_samples = pd.read_csv( './outputs/audio.csv.old' audio_samples = pd.read_csv( './outputs/audio.csv.old'
@ -243,7 +243,8 @@ if __name__ == '__main__':
# create_spectrogram_tfrecords('audio',sample_count=100) # create_spectrogram_tfrecords('audio',sample_count=100)
# create_spectrogram_tfrecords('story_all',sample_count=25) # create_spectrogram_tfrecords('story_all',sample_count=25)
# fix_csv('story_words_test') # fix_csv('story_words_test')
create_spectrogram_tfrecords('story_words_test',sample_count=100,train_test_ratio=0.0) fix_csv('story_phrases')
create_spectrogram_tfrecords('story_phrases',sample_count=100,train_test_ratio=0.3)
# create_spectrogram_tfrecords('audio',sample_count=50) # create_spectrogram_tfrecords('audio',sample_count=50)
# read_siamese_tfrecords_generator('audio') # read_siamese_tfrecords_generator('audio')
# padd_zeros_siamese_tfrecords('audio') # padd_zeros_siamese_tfrecords('audio')

42
speech_model.py
View File

@ -1,36 +1,16 @@
from __future__ import absolute_import from __future__ import absolute_import
from __future__ import print_function from __future__ import print_function
import numpy as np import numpy as np
# from speech_data import speech_model_data
from speech_data import read_siamese_tfrecords_generator from speech_data import read_siamese_tfrecords_generator
from keras.models import Model,load_model,model_from_yaml from keras.models import Model,load_model,model_from_yaml
from keras.layers import Input, Dense, Dropout, LSTM, Lambda, Concatenate from keras.layers import Input, Dense, Dropout, LSTM, Lambda, Concatenate
from keras.losses import categorical_crossentropy from keras.losses import categorical_crossentropy
# from keras.losses import binary_crossentropy
from keras.utils import to_categorical from keras.utils import to_categorical
# from keras.utils.np_utils import to_categorical
from keras.optimizers import RMSprop from keras.optimizers import RMSprop
from keras.callbacks import TensorBoard, ModelCheckpoint from keras.callbacks import TensorBoard, ModelCheckpoint
from keras import backend as K from keras import backend as K
from speech_tools import create_dir,step_count from speech_tools import create_dir,step_count
# def euclidean_distance(vects):
# x, y = vects
# return K.sqrt(
# K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
#
#
# def eucl_dist_output_shape(shapes):
# shape1, shape2 = shapes
# return (shape1[0], 1)
#
#
# def contrastive_loss(y_true, y_pred):
# '''Contrastive loss from Hadsell-et-al.'06
# http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
# '''
# return K.mean(y_true * K.square(y_pred) +
# (1 - y_true) * K.square(K.maximum(1 - y_pred, 0)))
def create_base_rnn_network(input_dim): def create_base_rnn_network(input_dim):
'''Base network to be shared (eq. to feature extraction). '''Base network to be shared (eq. to feature extraction).
@ -68,7 +48,6 @@ def dense_classifier(processed):
return Dense(2, activation='softmax')(d3) return Dense(2, activation='softmax')(d3)
def siamese_model(input_dim): def siamese_model(input_dim):
# input_dim = (15, 1654)
base_network = create_base_rnn_network(input_dim) base_network = create_base_rnn_network(input_dim)
input_a = Input(shape=input_dim) input_a = Input(shape=input_dim)
input_b = Input(shape=input_dim) input_b = Input(shape=input_dim)
@ -94,8 +73,6 @@ def load_model_arch(mod_file):
return mod return mod
def train_siamese(audio_group = 'audio'): def train_siamese(audio_group = 'audio'):
# the data, shuffled and split between train and test sets
# tr_pairs, te_pairs, tr_y_e, te_y_e = speech_model_data()
batch_size = 256 batch_size = 256
model_dir = './models/'+audio_group model_dir = './models/'+audio_group
create_dir(model_dir) create_dir(model_dir)
@ -103,8 +80,6 @@ def train_siamese(audio_group = 'audio'):
create_dir(log_dir) create_dir(log_dir)
tr_gen_fn,te_pairs,te_y,n_step,n_features,n_records = read_siamese_tfrecords_generator(audio_group,batch_size=batch_size,test_size=batch_size) tr_gen_fn,te_pairs,te_y,n_step,n_features,n_records = read_siamese_tfrecords_generator(audio_group,batch_size=batch_size,test_size=batch_size)
tr_gen = tr_gen_fn() tr_gen = tr_gen_fn()
# tr_y = to_categorical(tr_y_e, num_classes=2)
# te_y = to_categorical(te_y_e, num_classes=2)
input_dim = (n_step, n_features) input_dim = (n_step, n_features)
model = siamese_model(input_dim) model = siamese_model(input_dim)
@ -131,29 +106,17 @@ def train_siamese(audio_group = 'audio'):
mode='auto', mode='auto',
period=1) period=1)
# train # train
rms = RMSprop()#lr=0.001 rms = RMSprop()
model.compile(loss=categorical_crossentropy, optimizer=rms, metrics=[accuracy]) model.compile(loss=categorical_crossentropy, optimizer=rms, metrics=[accuracy])
write_model_arch(model,model_dir+'/siamese_speech_model_arch.yaml') write_model_arch(model,model_dir+'/siamese_speech_model_arch.yaml')
# model.fit(
# [tr_pairs[:, 0], tr_pairs[:, 1]],
# tr_y,
# batch_size=128,
# epochs=100,
# validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y),
# callbacks=[tb_cb, cp_cb])
epoch_n_steps = step_count(n_records,batch_size) epoch_n_steps = step_count(n_records,batch_size)
model.fit_generator(tr_gen model.fit_generator(tr_gen
, epochs=1000 , epochs=1000
, steps_per_epoch=epoch_n_steps , steps_per_epoch=epoch_n_steps
, validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y) , validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y)
# ,use_multiprocessing=True, workers=1
, max_queue_size=32 , max_queue_size=32
, callbacks=[tb_cb, cp_cb]) , callbacks=[tb_cb, cp_cb])
model.save(model_dir+'/siamese_speech_model-final.h5') model.save(model_dir+'/siamese_speech_model-final.h5')
# compute final accuracy on training and test sets
# y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
# tr_acc = compute_accuracy(tr_y, y_pred)
# print('* Accuracy on training set: %0.2f%%' % (100 * tr_acc))
y_pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]]) y_pred = model.predict([te_pairs[:, 0], te_pairs[:, 1]])
te_acc = compute_accuracy(te_y, y_pred) te_acc = compute_accuracy(te_y, y_pred)
@ -162,5 +125,4 @@ def train_siamese(audio_group = 'audio'):
if __name__ == '__main__': if __name__ == '__main__':
train_siamese('story_words_test') train_siamese('story_phrases')
# train_siamese('audio')