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speech-scoring
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completed the segmentation model

master
Malar Kannan 2017-12-07 15:17:59 +05:30
parent c8a07b3d7b
commit 91fde710f3
4 changed files with 51 additions and 33 deletions
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46
segment_data.py
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@ -7,9 +7,14 @@ from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
import tensorflow as tf
import shutil
from speech_pitch import *
from speech_tools import reservoir_sample
from speech_tools import reservoir_sample,padd_zeros
# import importlib
# import speech_tools
# importlib.reload(speech_tools)
# %matplotlib inline
SPEC_MAX_FREQUENCY = 8000
@ -99,7 +104,7 @@ def plot_segments(collection_name = 'story_test_segments'):
phon_stops.append((end_t,phon_ch))
phrase_spec = phrase_sample.to_spectrogram(window_length=0.03, maximum_frequency=8000)
sg_db = 10 * np.log10(phrase_spec.values)
result = np.zeros(sg_db.shape[0],dtype=np.int32)
result = np.zeros(sg_db.shape[0],dtype=np.int64)
ph_bounds = [t[0] for t in phon_stops[1:]]
b_frames = np.asarray([spec_frame(phrase_spec,b) for b in ph_bounds])
result[b_frames] = 1
@ -143,7 +148,7 @@ def create_segments_tfrecords(collection_name='story_test_segments',sample_count
end_t = phon['end_time']/1000
phon_ch = phon['start_phoneme']
phon_stops.append((end_t,phon_ch))
result = np.zeros(spec_n,dtype=np.int32)
result = np.zeros(spec_n,dtype=np.int64)
ph_bounds = [t[0] for t in phon_stops]
f_bounds = [spec_frame(phrase_spec,b) for b in ph_bounds]
valid_bounds = [i for i in f_bounds if 0 < i < spec_n]
@ -159,8 +164,8 @@ def create_segments_tfrecords(collection_name='story_test_segments',sample_count
'phrase': _bytes_feature([ph.encode('utf-8')]),
'file': _bytes_feature([fname.encode('utf-8')]),
'spec':_float_feature(spec),
'spec_n1':_int64_feature([spec_n]),
'spec_w1':_int64_feature([spec_w]),
'spec_n':_int64_feature([spec_n]),
'spec_w':_int64_feature([spec_w]),
'output':_int64_feature(result)
}
))
@ -185,10 +190,10 @@ def record_generator_count(records_file):
return record_iterator,count
def read_segments_tfrecords_generator(collection_name='audio',batch_size=32,test_size=0):
# collection_name = 'story_test'
records_file = './outputs/segments/'+collection_name+'/train.tfrecords'
const_file = './outputs/segments/'+collection_name+'/constants.pkl'
(n_spec,n_features,n_records) = pickle.load(open(const_file,'rb'))
def copy_read_consts(dest_dir):
shutil.copy2(const_file,dest_dir+'/constants.pkl')
return (n_spec,n_features,n_records)
@ -200,42 +205,48 @@ def read_segments_tfrecords_generator(collection_name='audio',batch_size=32,test
while True:
record_iterator,records_count = record_generator_count(records_file)
for (i,string_record) in enumerate(record_iterator):
# (i,string_record) = next(enumerate(record_iterator))
example = tf.train.Example()
example.ParseFromString(string_record)
spec_n = example.features.feature['spec_n'].int64_list.value[0]
spec_w = example.features.feature['spec_w'].int64_list.value[0]
spec = np.array(example.features.feature['spec'].float_list.value).reshape(spec_n,spec_w)
spec = np.array(example.features.feature['output'].int64_list.value)
p_spec = padd_zeros(spec,n_spec)
input_data.append(p_spec)
output = example.features.feature['output'].int64_list.value
output_data.append(np.asarray(output))
output = np.asarray(example.features.feature['output'].int64_list.value)
p_output = np.pad(output,(0,n_spec-output.shape[0]),'constant')
output_data.append(p_output)
if len(input_data) == batch_size or i == n_records-1:
input_arr = np.asarray(input_data)
output_arr = np.asarray(output_data)
input_arr.shape,output_arr.shape
yield (input_arr,output_arr)
input_data = []
output_data = []
# Read test in one-shot
# collection_name = 'story_test'
print('reading tfrecords({}-test)...'.format(collection_name))
te_records_file = './outputs/segments/'+collection_name+'/test.tfrecords'
te_re_iterator,te_n_records = record_generator_count(te_records_file)
# test_size = 10
test_size = min([test_size,te_n_records]) if test_size > 0 else te_n_records
input_data = np.zeros((test_size,2,n_spec,n_features))
output_data = np.zeros((test_size,2))
input_data = np.zeros((test_size,n_spec,n_features))
output_data = np.zeros((test_size,n_spec))
random_samples = enumerate(reservoir_sample(te_re_iterator,test_size))
for (i,string_record) in tqdm(random_samples,total=test_size):
# (i,string_record) = next(random_samples)
# string_record
example = tf.train.Example()
example.ParseFromString(string_record)
# example.features.feature['spec'].float_list.value
spec_n = example.features.feature['spec_n'].int64_list.value[0]
spec_w = example.features.feature['spec_w'].int64_list.value[0]
spec = np.array(example.features.feature['spec'].float_list.value).reshape(spec_n,spec_w)
p_spec = padd_zeros(spec,n_spec)
input_data[i] = p_spec
output = example.features.feature['output'].int64_list.value
output_data[i] = np.asarray(output)
output = np.asarray(example.features.feature['output'].int64_list.value)
p_output = np.pad(output,(0,n_spec-output.shape[0]),'constant')
output_data[i] = p_output
return record_generator,input_data,output_data,copy_read_consts
@ -244,4 +255,9 @@ if __name__ == '__main__':
# fix_csv('story_test_segments')
# plot_segments('story_test_segments')
# fix_csv('story_test')
create_segments_tfrecords('story_test')
pass
# create_segments_tfrecords('story_test')
# record_generator,input_data,output_data,copy_read_consts = read_segments_tfrecords_generator('story_test')
# tr_gen = record_generator()
# for i in tr_gen:
# print(i[0].shape,i[1].shape)

28
segment_model.py
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@ -14,11 +14,11 @@ from keras.utils import plot_model
from speech_tools import create_dir,step_count
from segment_data import read_segments_tfrecords_generator
import importlib
import segment_data
import speech_tools
importlib.reload(segment_data)
importlib.reload(speech_tools)
# import importlib
# import segment_data
# import speech_tools
# importlib.reload(segment_data)
# importlib.reload(speech_tools)
# TODO implement ctc losses
@ -55,15 +55,14 @@ def segment_model(input_dim):
def simple_segment_model(input_dim):
# input_dim = (100,100)
input_dim = (506,743)
inp = Input(shape=input_dim)
b_gr1 = Bidirectional(GRU(256, return_sequences=True),merge_mode='sum')(inp)
# b_gr1
b_gr2 = Bidirectional(GRU(64, return_sequences=True),merge_mode='sum')(b_gr1)
b_gr3 = Bidirectional(GRU(1, return_sequences=True),merge_mode='sum')(b_gr2)
# b_gr3
# oup = Dense(2, activation='softmax')(b_gr3)
# oup
return Model(inp, b_gr3)
oup = Reshape(target_shape=(input_dim[0],))(b_gr3)
return Model(inp, oup)
def write_model_arch(mod,mod_file):
model_f = open(mod_file,'w')
@ -77,17 +76,19 @@ def load_model_arch(mod_file):
return mod
def train_segment(collection_name = 'test'):
collection_name = 'story_test'
# collection_name = 'story_test'
batch_size = 128
# batch_size = 4
model_dir = './models/segment/'+collection_name
create_dir(model_dir)
log_dir = './logs/segment/'+collection_name
create_dir(log_dir)
tr_gen_fn,inp,oup,copy_read_consts = read_segments_tfrecords_generator(collection_name,batch_size,2*batch_size)
tr_gen_fn,te_x,te_y,copy_read_consts = read_segments_tfrecords_generator(collection_name,batch_size,2*batch_size)
tr_gen = tr_gen_fn()
n_step,n_features,n_records = copy_read_consts(model_dir)
input_dim = (n_step, n_features)
model = simple_segment_model(input_dim)
# model.output_shape,model.input_shape
plot_model(model,show_shapes=True, to_file=model_dir+'/model.png')
# loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred, labels, input_length, label_length])
tb_cb = TensorBoard(
@ -119,16 +120,17 @@ def train_segment(collection_name = 'test'):
model.fit_generator(tr_gen
, epochs=1000
, steps_per_epoch=epoch_n_steps
, validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y)
, validation_data=(te_x, te_y)
, max_queue_size=32
, callbacks=[tb_cb, cp_cb])
model.save(model_dir+'/speech_segment_model-final.h5')
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)
# print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))
if __name__ == '__main__':
# pass
train_segment('test')

6
speech_data.py
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@ -1,5 +1,5 @@
import pandas as pd
from speech_tools import apply_by_multiprocessing,threadsafe_iter,reservoir_sample
from speech_tools import apply_by_multiprocessing,threadsafe_iter,reservoir_sample,padd_zeros
# import dask as dd
# import dask.dataframe as ddf
import tensorflow as tf
@ -121,10 +121,6 @@ def create_spectrogram_tfrecords(audio_group='audio',sample_count=0,train_test_r
const_file = os.path.join('./outputs',audio_group+'.constants')
pickle.dump((n_spec,n_features,n_records),open(const_file,'wb'))
def padd_zeros(spgr, max_samples):
return np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
'constant')
def read_siamese_tfrecords_generator(audio_group='audio',batch_size=32,test_size=0):
records_file = os.path.join('./outputs',audio_group+'.train.tfrecords')
input_pairs = []

4
speech_tools.py
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@ -52,6 +52,10 @@ def reservoir_sample(iterable, k):
sample[j] = item # replace item with gradually decreasing probability
return sample
def padd_zeros(spgr, max_samples):
return np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
'constant')
def record_spectrogram(n_sec, plot=False, playback=False):
# show_record_prompt()
N_SEC = n_sec