updated README to include testing

2. updated to run on complete data

CLI.md

@@ -0,0 +1,2 @@
+### Convert audio files
+$ `for f in *.mp3; do ffmpeg -i "$f" "${f%.mp3}.aiff"; done`

README.md

@@ -0,0 +1,23 @@
+### Setup
+`. env/bin/activate` to activate the virtualenv.
+
+### Data Generation
+* update `OUTPUT_NAME` in *speech_samplegen.py* to create the dataset folder with the name
+* `python speech_samplegen.py` generates variants of audio samples
+
+### Data Preprocessing
+* `python speech_data.py` creates the training-testing data from the generated samples.
+* run `fix_csv(OUTPUT_NAME)` once to create the fixed index of the dataset generated
+* run `generate_sppas_trans(OUTPUT_NAME)` once to create the SPPAS transcription(wav+txt) data
+* run `$ (SPPAS_DIR)/bin/annotation.py -l eng -e csv --ipus --tok --phon --align --align -w ./outputs/OUTPUT_NAME/` once to create the phoneme alignment csv files for all variants.
+* `create_seg_phonpair_tfrecords(OUTPUT_NAME)` creates the tfrecords files
+ with the phoneme level pairs of right/wrong stresses
+
+### Training
+* `python speech_model.py` trains the model with the training data generated.
+* `train_siamese(OUTPUT_NAME)` trains the siamese model with the generated dataset.
+
+### Testing
+* `python speech_test.py` tests the trained model with the test dataset
+* `evaluate_siamese(TEST_RECORD_FILE,audio_group=OUTPUT_NAME,weights = WEIGHTS_FILE_NAME)`
+  the TEST_RECORD_FILE will be under outputs directory and WEIGHTS_FILE_NAME will be under the models directory, pick the most recent weights file.

segment_data.py

@@ -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
@@ -108,7 +113,9 @@ def plot_segments(collection_name = 'story_test_segments'):
 def generate_spec(aiff_file):
     phrase_sample = pm_snd(aiff_file)
     phrase_spec = phrase_sample.to_spectrogram(window_length=SPEC_WINDOW_SIZE, maximum_frequency=SPEC_MAX_FREQUENCY)
-    sg_db = 10 * np.log10(phrase_spec.values)
+    sshow_abs = np.abs(phrase_spec.values + np.finfo(phrase_spec.values.dtype).eps)
+    sg_db = 10 * np.log10(sshow_abs)
+    sg_db[sg_db < 0] = 0
     return sg_db,phrase_spec
 
 
@@ -137,19 +144,20 @@ def create_segments_tfrecords(collection_name='story_test_segments',sample_count
             fname = g.iloc[0]['filename']
             sg_db,phrase_spec = generate_spec(g.iloc[0]['file_path'])
             phon_stops = []
+            phrase_groups.set_postfix(phrase=ph)
             spec_n,spec_w = sg_db.shape
             spec = sg_db.reshape(-1)
             for (i,phon) in g.iterrows():
                 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]
             b_frames = np.asarray(valid_bounds)
-            # print(spec_n,b_frames)
+            if len(b_frames) > 0:
-            result[b_frames] = 1
+                result[b_frames] = 1
             nonlocal n_records,n_spec,n_features
             n_spec = max([n_spec,spec_n])
             n_features = spec_w
@@ -159,8 +167,8 @@ 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_n':_int64_feature([spec_n]),
-                'spec_w1':_int64_feature([spec_w]),
+                'spec_w':_int64_feature([spec_w]),
                 'output':_int64_feature(result)
               }
             ))
@@ -170,15 +178,88 @@ def create_segments_tfrecords(collection_name='story_test_segments',sample_count
 
     word_groups = [i for i in audio_samples.groupby('phrase')]
     wg_sampled = reservoir_sample(word_groups,sample_count) if sample_count > 0 else word_groups
+    # write_samples(word_groups,'all')
     tr_audio_samples,te_audio_samples = train_test_split(wg_sampled,test_size=train_test_ratio)
     write_samples(tr_audio_samples,'train')
     write_samples(te_audio_samples,'test')
     const_file = './outputs/segments/'+collection_name+'/constants.pkl'
     pickle.dump((n_spec,n_features,n_records),open(const_file,'wb'))
 
+def record_generator_count(records_file):
+    record_iterator = tf.python_io.tf_record_iterator(path=records_file)
+    count,spec_n = 0,0
+    for i in record_iterator:
+        count+=1
+    record_iterator = tf.python_io.tf_record_iterator(path=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)
+    # @threadsafe_iter
+    def record_generator():
+        print('reading tfrecords({}-train)...'.format(collection_name))
+        input_data = []
+        output_data = []
+        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)
+                p_spec = padd_zeros(spec,n_spec)
+                input_data.append(p_spec)
+                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
+    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,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)
+        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)
+        p_spec = padd_zeros(spec,n_spec)
+        input_data[i] = p_spec
+        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
+
 if __name__ == '__main__':
     # plot_random_phrases()
     # fix_csv('story_test_segments')
     # plot_segments('story_test_segments')
-    # fix_csv('story_test')
+    # fix_csv('story_words')
-    create_segments_tfrecords('story_test')
+    # pass
+    create_segments_tfrecords('story_words.30', sample_count=36,train_test_ratio=0.1)
+    # 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)

segment_model.py

@@ -4,15 +4,21 @@ import numpy as np
 from keras.models import Model,load_model,model_from_yaml
 from keras.layers import Input,Concatenate,Lambda, Reshape, Dropout
 from keras.layers import Dense,Conv2D, LSTM, Bidirectional, GRU
-from keras.layers import BatchNormalization
+from keras.layers import BatchNormalization,Activation
 from keras.losses import categorical_crossentropy
 from keras.utils import to_categorical
-from keras.optimizers import RMSprop
+from keras.optimizers import RMSprop,Adadelta,Adagrad,Adam,Nadam
 from keras.callbacks import TensorBoard, ModelCheckpoint
 from keras import backend as K
 from keras.utils import plot_model
 from speech_tools import create_dir,step_count
-from segment_data import segment_data_gen
+from segment_data import read_segments_tfrecords_generator
+
+# import importlib
+# import segment_data
+# import speech_tools
+# importlib.reload(segment_data)
+# importlib.reload(speech_tools)
 
 
 # TODO implement ctc losses
@@ -30,39 +36,38 @@ def ctc_lambda_func(args):
     return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
 
 def segment_model(input_dim):
-    input_dim = (100,100,1)
     inp = Input(shape=input_dim)
     cnv1 = Conv2D(filters=32, kernel_size=(5,9))(inp)
     cnv2 = Conv2D(filters=1, kernel_size=(5,9))(cnv1)
     dr_cnv2 = Dropout(rate=0.95)(cnv2)
-    # dr_cnv2
     cn_rnn_dim = (dr_cnv2.shape[1].value,dr_cnv2.shape[2].value)
     r_dr_cnv2 = Reshape(target_shape=cn_rnn_dim)(dr_cnv2)
     b_gr1 = Bidirectional(GRU(512, return_sequences=True),merge_mode='sum')(r_dr_cnv2)
-    # b_gr1
     b_gr2 = Bidirectional(GRU(512, return_sequences=True),merge_mode='sum')(b_gr1)
     b_gr3 = Bidirectional(GRU(512, return_sequences=True),merge_mode='sum')(b_gr2)
-    # b_gr3
     oup = Dense(2, activation='softmax')(b_gr3)
-    # oup
     return Model(inp, oup)
 
 def simple_segment_model(input_dim):
-    input_dim = (100,100,1)
     inp = Input(shape=input_dim)
-    cnv1 = Conv2D(filters=32, kernel_size=(5,9))(inp)
+    b_gr1 = Bidirectional(LSTM(32, return_sequences=True))(inp)
-    cnv2 = Conv2D(filters=1, kernel_size=(5,9))(cnv1)
+    b_gr1 = Bidirectional(LSTM(16, return_sequences=True),merge_mode='sum')(b_gr1)
-    dr_cnv2 = Dropout(rate=0.95)(cnv2)
+    b_gr1 = LSTM(1, return_sequences=True,activation='softmax')(b_gr1)
-    # dr_cnv2
+    # b_gr1 = LSTM(4, return_sequences=True)(b_gr1)
-    cn_rnn_dim = (dr_cnv2.shape[1].value,dr_cnv2.shape[2].value)
+    # b_gr1 = LSTM(2, return_sequences=True)(b_gr1)
-    r_dr_cnv2 = Reshape(target_shape=cn_rnn_dim)(dr_cnv2)
+    # bn_b_gr1 = BatchNormalization(momentum=0.98)(b_gr1)
-    b_gr1 = Bidirectional(GRU(512, return_sequences=True),merge_mode='sum')(r_dr_cnv2)
+    # b_gr2 = GRU(64, return_sequences=True)(b_gr1)
-    # b_gr1
+    # bn_b_gr2 = BatchNormalization(momentum=0.98)(b_gr2)
-    b_gr2 = Bidirectional(GRU(512, return_sequences=True),merge_mode='sum')(b_gr1)
+    # d1 = Dense(32)(b_gr2)
-    b_gr3 = Bidirectional(GRU(512, return_sequences=True),merge_mode='sum')(b_gr2)
+    # bn_d1 = BatchNormalization(momentum=0.98)(d1)
-    # b_gr3
+    # bn_da1 = Activation('relu')(bn_d1)
-    oup = Dense(2, activation='softmax')(b_gr3)
+    # d2 = Dense(8)(bn_da1)
-    # oup
+    # bn_d2 = BatchNormalization(momentum=0.98)(d2)
+    # bn_da2 = Activation('relu')(bn_d2)
+    # d3 = Dense(1)(b_gr1)
+    # # bn_d3 = BatchNormalization(momentum=0.98)(d3)
+    # bn_da3 = Activation('softmax')(d3)
+    oup = Reshape(target_shape=(input_dim[0],))(b_gr1)
     return Model(inp, oup)
 
 def write_model_arch(mod,mod_file):
@@ -76,17 +81,20 @@ def load_model_arch(mod_file):
     model_f.close()
     return mod
 
-def train_segment(collection_name = 'test'):
+def train_segment(collection_name = 'test',resume_weights='',initial_epoch=0):
+    # 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 = segment_data_gen()
+    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(
@@ -99,35 +107,38 @@ def train_segment(collection_name = 'test'):
         embeddings_freq=0,
         embeddings_layer_names=None,
         embeddings_metadata=None)
-    cp_file_fmt = model_dir+'/siamese_speech_model-{epoch:02d}-epoch-{val_loss:0.2f}\
+    cp_file_fmt = model_dir+'/speech_segment_model-{epoch:02d}-epoch-{val_loss:0.2f}\
 -acc.h5'
 
     cp_cb = ModelCheckpoint(
         cp_file_fmt,
         monitor='val_loss',
         verbose=0,
-        save_best_only=True,
+        save_best_only=False,
         save_weights_only=True,
         mode='auto',
         period=1)
     # train
-    rms = RMSprop()
+    opt = RMSprop()
-    model.compile(loss=categorical_crossentropy, optimizer=rms, metrics=[accuracy])
+    model.compile(loss=categorical_crossentropy, optimizer=opt, metrics=[accuracy])
-    write_model_arch(model,model_dir+'/siamese_speech_model_arch.yaml')
+    write_model_arch(model,model_dir+'/speech_segment_model_arch.yaml')
     epoch_n_steps = step_count(n_records,batch_size)
+    if resume_weights != '':
+        model.load_weights(resume_weights)
     model.fit_generator(tr_gen
-                        , epochs=1000
+                        , epochs=10000
                         , 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])
+                        , callbacks=[tb_cb, cp_cb],initial_epoch=initial_epoch)
     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__':
-    train_segment('test')
+    # pass
+    train_segment('story_words')#,'./models/segment/story_phrases.1000/speech_segment_model-final.h5',1001)

speech_data.py

@@ -1,28 +1,28 @@
 import pandas as pd
-from speech_tools import apply_by_multiprocessing,threadsafe_iter,reservoir_sample
+from speech_tools import *
+from speech_pitch import *
 # import dask as dd
 # import dask.dataframe as ddf
 import tensorflow as tf
 from tensorflow.python.ops import data_flow_ops
 import numpy as np
-from speech_spectrum import generate_aiff_spectrogram
+from speech_spectrum import generate_aiff_spectrogram,generate_sample_spectrogram
-from speech_pitch import pitch_array
+from speech_similar import segmentable_phoneme
-from speech_pitch import compute_mfcc
 from sklearn.model_selection import train_test_split
-import itertools
 import os,shutil
 import random
 import csv
 import gc
 import pickle
+import itertools
 from tqdm import tqdm
 
 
 def siamese_pairs(rightGroup, wrongGroup):
     group1 = [r for (i, r) in rightGroup.iterrows()]
     group2 = [r for (i, r) in wrongGroup.iterrows()]
-    rightWrongPairs = [(g1, g2) for g2 in group2 for g1 in group1]+[(g2, g1) for g2 in group2 for g1 in group1]
+    rightWrongPairs = [(g1, g2) for g2 in group2 for g1 in group1]#+[(g2, g1) for g2 in group2 for g1 in group1]
-    rightRightPairs = [i for i in itertools.permutations(group1, 2)]#+[i for i in itertools.combinations(group2, 2)]
+    rightRightPairs = [i for i in itertools.combinations(group1, 2)]#+[i for i in itertools.combinations(group2, 2)]
     def filter_criteria(s1,s2):
         same = s1['variant'] == s2['variant']
         phon_same = s1['phonemes'] == s2['phonemes']
@@ -39,6 +39,58 @@ def siamese_pairs(rightGroup, wrongGroup):
     # return rightRightPairs[:10],rightWrongPairs[:10]
     return validRRPairs[:32],validRWPairs[:32]
 
+def seg_siamese_pairs(rightGroup, wrongGroup):
+    group1 = [r for (i, r) in rightGroup.iterrows()]
+    group2 = [r for (i, r) in wrongGroup.iterrows()]
+    rightWrongPairs = [(g1, g2) for g2 in group2 for g1 in group1]#+[(g2, g1) for g2 in group2 for g1 in group1]
+    rightRightPairs = [i for i in itertools.combinations(group1, 2)]#+[i for i in itertools.combinations(group2, 2)]
+    def filter_criteria(s1,s2):
+        same = s1['variant'] == s2['variant']
+        phon_same = s1['phonemes'] == s2['phonemes']
+        voice_diff = s1['voice'] != s2['voice']
+        if not same and phon_same:
+            return False
+        # if same and not voice_diff:
+        #     return False
+        return True
+    validRWPairs = [i for i in rightWrongPairs if filter_criteria(*i)]
+    validRRPairs = [i for i in rightRightPairs if filter_criteria(*i)]
+    random.shuffle(validRWPairs)
+    random.shuffle(validRRPairs)
+    rrPhonePairs = []
+    rwPhonePairs = []
+    def compute_seg_spec(s1,s2):
+        phon_count = len(s1['parsed_phoneme'])
+        seg1_count = len(s1['segments'].index)
+        seg2_count = len(s2['segments'].index)
+        if phon_count == seg1_count and seg2_count == phon_count:
+            s1nd,s2nd = pm_snd(s1['file_path']),pm_snd(s2['file_path'])
+            segs1 = [tuple(x) for x in s1['segments'][['start','end']].values]
+            segs2 = [tuple(x) for x in s2['segments'][['start','end']].values]
+            s1_cp = pd.Series(s1)
+            s2_cp = pd.Series(s2)
+            pp12 = zip(s1['parsed_phoneme'],s2['parsed_phoneme'],segs1,segs2)
+            for (p1,p2,(s1s,s1e),(s2s,s2e)) in pp12:
+                spc1 = generate_sample_spectrogram(s1nd.extract_part(s1s,s1e).values)
+                spc2 = generate_sample_spectrogram(s2nd.extract_part(s2s,s2e).values)
+                s1_cp['spectrogram'] = spc1
+                s2_cp['spectrogram'] = spc2
+                # import pdb; pdb.set_trace()
+                if repr(p1) == repr(p2):
+                    rrPhonePairs.append((s1_cp,s2_cp))
+                else:
+                    rwPhonePairs.append((s1_cp,s2_cp))
+    for (s1,s2) in validRRPairs:
+        compute_seg_spec(s1,s2)
+    for (s1,s2) in validRWPairs:
+        compute_seg_spec(s1,s2)
+    return rrPhonePairs[:32],rwPhonePairs[:32]
+    # return rightRightPairs[:10],rightWrongPairs[:10]
+    # return
+    # validRRPairs[:8],validRWPairs[:8]
+
+
+
 
 def _float_feature(value):
   return tf.train.Feature(float_list=tf.train.FloatList(value=value))
@@ -65,8 +117,8 @@ def create_spectrogram_tfrecords(audio_group='audio',sample_count=0,train_test_r
         for (w, word_group) in word_group_prog:
             word_group_prog.set_postfix(word=w,sample_name=sample_name)
             g = word_group.reset_index()
-            g['spectrogram'] = apply_by_multiprocessing(g['file_path'],pitch_array)
+            # g['spectrogram'] = apply_by_multiprocessing(g['file_path'],pitch_array)
-            # g['spectrogram'] = apply_by_multiprocessing(g['file_path'],generate_aiff_spectrogram)
+            g['spectrogram'] = apply_by_multiprocessing(g['file_path'],generate_aiff_spectrogram)
             # g['spectrogram'] = apply_by_multiprocessing(g['file_path'],compute_mfcc)
             sample_right = g.loc[g['variant'] == 'low']
             sample_wrong = g.loc[g['variant'] == 'medium']
@@ -122,10 +174,6 @@ 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 = []
@@ -168,7 +216,7 @@ def read_siamese_tfrecords_generator(audio_group='audio',batch_size=32,test_size
     # Read test in one-shot
     print('reading tfrecords({}-test)...'.format(audio_group))
     te_records_file  = os.path.join('./outputs',audio_group+'.test.tfrecords')
-    te_re_iterator,te_n_records = record_generator_count(records_file)
+    te_re_iterator,te_n_records = record_generator_count(te_records_file)
     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))
@@ -231,6 +279,94 @@ def convert_old_audio():
     audio_samples = audio_samples[['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']]
     audio_samples.to_csv('./outputs/audio_new.csv',index=False,header=False)
 
+def generate_sppas_trans(audio_group='story_words.all'):
+    # audio_group='story_words.all'
+    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 = audio_samples.head(5)
+    rows = tqdm(audio_samples.iterrows(),total = len(audio_samples.index)
+                , desc='Transcribing Words ')
+    for (i,row) in rows:
+    # len(audio_samples.iterrows())
+    # (i,row) = next(audio_samples.iterrows())
+        rows.set_postfix(word=row['word'])
+        transribe_audio_text(row['file_path'],row['word'])
+    rows.close()
+
+def create_seg_phonpair_tfrecords(audio_group='story_words.all',sample_count=0,train_test_ratio=0.1):
+    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 = audio_samples[(audio_samples['variant'] == 'low') | (audio_samples['variant'] == 'medium')]
+    audio_samples['parsed_phoneme'] = apply_by_multiprocessing(audio_samples['phonemes'],segmentable_phoneme)
+    # audio_samples['sound'] = apply_by_multiprocessing(audio_samples['file_path'],pm_snd)
+    # read_seg_file(audio_samples.iloc[0]['file_path'])
+    audio_samples['segments'] = apply_by_multiprocessing(audio_samples['file_path'],read_seg_file)
+    n_records,n_spec,n_features = 0,0,0
+
+    def write_samples(wg,sample_name):
+        word_group_prog = tqdm(wg,desc='Computing PhonPair spectrogram')
+        record_file = './outputs/{}.{}.tfrecords'.format(audio_group,sample_name)
+        writer = tf.python_io.TFRecordWriter(record_file)
+        for (w, word_group) in word_group_prog:
+            word_group_prog.set_postfix(word=w,sample_name=sample_name)
+            g = word_group.reset_index()
+            # g['spectrogram'] = apply_by_multiprocessing(g['file_path'],pitch_array)
+            # g['spectrogram'] = apply_by_multiprocessing(g['file_path'],generate_aiff_spectrogram)
+            # g['spectrogram'] = apply_by_multiprocessing(g['file_path'],compute_mfcc)
+            sample_right = g.loc[g['variant'] == 'low']
+            sample_wrong = g.loc[g['variant'] == 'medium']
+            same, diff = seg_siamese_pairs(sample_right, sample_wrong)
+            groups = [([0,1],same),([1,0],diff)]
+            for (output,group) in groups:
+                group_prog = tqdm(group,desc='Writing Spectrogram')
+                for sample1,sample2 in group_prog:
+                    group_prog.set_postfix(output=output
+                        ,var1=sample1['variant']
+                        ,var2=sample2['variant'])
+                    spectro1,spectro2 = sample1['spectrogram'],sample2['spectrogram']
+                    spec_n1,spec_n2 = spectro1.shape[0],spectro2.shape[0]
+                    spec_w1,spec_w2 = spectro1.shape[1],spectro2.shape[1]
+                    spec1,spec2 = spectro1.reshape(-1),spectro2.reshape(-1)
+                    nonlocal n_spec,n_records,n_features
+                    n_spec = max([n_spec,spec_n1,spec_n2])
+                    n_features = spec_w1
+                    n_records+=1
+                    example = tf.train.Example(features=tf.train.Features(
+                      feature={
+                        'word': _bytes_feature([w.encode('utf-8')]),
+                        'phoneme1': _bytes_feature([sample1['phonemes'].encode('utf-8')]),
+                        'phoneme2': _bytes_feature([sample2['phonemes'].encode('utf-8')]),
+                        'voice1': _bytes_feature([sample1['voice'].encode('utf-8')]),
+                        'voice2': _bytes_feature([sample2['voice'].encode('utf-8')]),
+                        'language': _bytes_feature([sample1['language'].encode('utf-8')]),
+                        'rate1':_int64_feature([sample1['rate']]),
+                        'rate2':_int64_feature([sample2['rate']]),
+                        'variant1': _bytes_feature([sample1['variant'].encode('utf-8')]),
+                        'variant2': _bytes_feature([sample2['variant'].encode('utf-8')]),
+                        'file1': _bytes_feature([sample1['file'].encode('utf-8')]),
+                        'file2': _bytes_feature([sample2['file'].encode('utf-8')]),
+                        'spec1':_float_feature(spec1),
+                        'spec2':_float_feature(spec2),
+                        'spec_n1':_int64_feature([spec_n1]),
+                        'spec_w1':_int64_feature([spec_w1]),
+                        'spec_n2':_int64_feature([spec_n2]),
+                        'spec_w2':_int64_feature([spec_w2]),
+                        'output':_int64_feature(output)
+                      }
+                    ))
+                    writer.write(example.SerializeToString())
+                group_prog.close()
+        word_group_prog.close()
+        writer.close()
+
+    word_groups = [i for i in audio_samples.groupby('word')]
+    wg_sampled = reservoir_sample(word_groups,sample_count) if sample_count > 0 else word_groups
+    tr_audio_samples,te_audio_samples = train_test_split(wg_sampled,test_size=train_test_ratio)
+    write_samples(tr_audio_samples,'train')
+    write_samples(te_audio_samples,'test')
+    const_file = os.path.join('./outputs',audio_group+'.constants')
+    pickle.dump((n_spec,n_features,n_records),open(const_file,'wb'))
+
 if __name__ == '__main__':
     # sunflower_pairs_data()
     # create_spectrogram_data()
@@ -245,8 +381,10 @@ if __name__ == '__main__':
     # create_spectrogram_tfrecords('audio',sample_count=100)
     # create_spectrogram_tfrecords('story_all',sample_count=25)
     # fix_csv('story_words_test')
-    #fix_csv('audio')
+    # fix_csv('test_5_words')
-    create_spectrogram_tfrecords('story_words_pitch',sample_count=0,train_test_ratio=0.1)
+    # generate_sppas_trans('test_5_words')
+    create_seg_phonpair_tfrecords('test_5_words')
+    # create_spectrogram_tfrecords('story_words.all',sample_count=0,train_test_ratio=0.1)
     #record_generator_count()
     # create_spectrogram_tfrecords('audio',sample_count=50)
     # read_siamese_tfrecords_generator('audio')

speech_model.py

@@ -74,7 +74,7 @@ def load_model_arch(mod_file):
     model_f.close()
     return mod
 
-def train_siamese(audio_group = 'audio'):
+def train_siamese(audio_group = 'audio',resume_weights='',initial_epoch=0):
     batch_size = 128
     model_dir = './models/'+audio_group
     create_dir(model_dir)
@@ -103,7 +103,7 @@ def train_siamese(audio_group = 'audio'):
 
     cp_cb = ModelCheckpoint(
         cp_file_fmt,
-        monitor='val_loss',
+        monitor='acc',
         verbose=0,
         save_best_only=True,
         save_weights_only=True,
@@ -114,19 +114,21 @@ def train_siamese(audio_group = 'audio'):
     model.compile(loss=categorical_crossentropy, optimizer=rms, metrics=[accuracy])
     write_model_arch(model,model_dir+'/siamese_speech_model_arch.yaml')
     epoch_n_steps = step_count(n_records,batch_size)
+    if resume_weights != '':
+        model.load_weights(resume_weights)
     model.fit_generator(tr_gen
-                        , epochs=1000
+                        , epochs=10000
                         , steps_per_epoch=epoch_n_steps
                         , validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y)
                         , max_queue_size=8
-                        , callbacks=[tb_cb, cp_cb])
+                        , callbacks=[tb_cb, cp_cb],initial_epoch=initial_epoch)
     model.save(model_dir+'/siamese_speech_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)
+    # te_acc = compute_accuracy(te_y, y_pred)
-    print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))
+    # print('* Accuracy on test set: %0.2f%%' % (100 * te_acc))
 
 
 
 if __name__ == '__main__':
-    train_siamese('story_words_pitch')
+    train_siamese('test_5_words')

speech_pitch.py

@@ -30,7 +30,7 @@ def compute_mfcc(sample_file='outputs/audio/sunflowers-Victoria-180-normal-870.a
     return sample_mfcc.to_array()
 
 def compute_formants(sample_file='outputs/audio/sunflowers-Victoria-180-normal-870.aiff'):
-    sample_file='outputs/audio/sunflowers-Victoria-180-normal-870.aiff'
+    # sample_file='outputs/audio/sunflowers-Victoria-180-normal-870.aiff'
     sample_sound = pm_snd(sample_file)
     sample_formant = sample_sound.to_formant_burg()
     # sample_formant.x_bins()

speech_samplegen.py

@@ -12,9 +12,9 @@ import time
 from tqdm import tqdm
 
 from generate_similar import similar_phoneme_phrase,similar_phrase
-from speech_tools import hms_string,create_dir,format_filename
+from speech_tools import hms_string,create_dir,format_filename,reservoir_sample
 
-OUTPUT_NAME = 'story_phrases'
+OUTPUT_NAME = 'test_5_words'
 dest_dir = os.path.abspath('.') + '/outputs/' + OUTPUT_NAME + '/'
 dest_file = './outputs/' + OUTPUT_NAME + '.csv'
 
@@ -216,6 +216,9 @@ def generate_audio_for_text_list(text_list):
     closer()
 
 def generate_audio_for_stories():
+    '''
+    Generates the audio sample variants for the list of words in the stories
+    '''
     # story_file = './inputs/all_stories_hs.json'
     story_file = './inputs/all_stories.json'
     stories_data = json.load(open(story_file))
@@ -224,7 +227,11 @@ def generate_audio_for_stories():
     text_list = sorted(list(set(text_list_dup)))
     generate_audio_for_text_list(text_list)
 
-def generate_test_audio_for_stories():
+def generate_test_audio_for_stories(sample_count=0):
+    '''
+    Picks a list of words from the wordlist that are not in story words
+    and generates the variants
+    '''
     story_file = './inputs/all_stories_hs.json'
     # story_file = './inputs/all_stories.json'
     stories_data = json.load(open(story_file))
@@ -234,11 +241,12 @@ def generate_test_audio_for_stories():
     word_list = [i.strip('\n_') for i in open('./inputs/wordlist.txt','r').readlines()]
     text_set = set(text_list)
     new_word_list = [i for i in word_list if i not in text_set and len(i) > 4]
-    test_words = new_word_list[:int(len(text_list)/5+1)]
+    # test_words = new_word_list[:int(len(text_list)/5+1)]
+    test_words = reservoir_sample(new_word_list,sample_count) if sample_count > 0 else new_word_list
     generate_audio_for_text_list(test_words)
 
 
 if __name__ == '__main__':
-    # generate_test_audio_for_stories()
+    generate_test_audio_for_stories(5)
     # generate_audio_for_text_list(['I want to go home','education'])
-    generate_audio_for_stories()
+    # generate_audio_for_stories()

speech_segmentgen.py

@@ -57,7 +57,8 @@ class Delegate (NSObject):
         '''Called automatically when the application has launched'''
         print("App Launched!")
         # phrases = story_texts()#random.sample(story_texts(), 100)  #
-        phrases = test_texts(30)
+        # phrases = test_texts(30)
+        phrases = story_words()
         # print(phrases)
         generate_audio(phrases)
 
@@ -174,14 +175,19 @@ class SynthesizerQueue(object):
 
 
 def story_texts():
-    # story_file = './inputs/all_stories_hs.json'
     story_file = './inputs/all_stories.json'
     stories_data = json.load(open(story_file))
-    # text_list_dup = [t[0] for i in stories_data.values() for t in i]
     text_list_dup = [t for i in stories_data.values() for t in i]
     text_list = sorted(list(set(text_list_dup)))
     return text_list
 
+def story_words():
+    story_file = './inputs/all_stories_hs.json'
+    stories_data = json.load(open(story_file))
+    text_list_dup = [t[0] for i in stories_data.values() for t in i]
+    text_list = sorted(list(set(text_list_dup)))
+    return text_list
+
 def test_texts(count=10):
     word_list = [i.strip('\n_') for i in open('./inputs/wordlist.txt','r').readlines()]
     text_list = sorted(random.sample(list(set(word_list)),count))

speech_similar.py

@@ -120,9 +120,11 @@ def parse_apple_phonemes(ph_str):
         elif pref[0].isdigit() and pref[1:] in apple_phonemes:
             return [ApplePhoneme(pref[1:], int(pref[0]) , True)] + parse_apple_phonemes(rest)
         elif not pref.isalnum():
-            return [ApplePhoneme(pref, 0, False)] + parse_apple_phonemes(rest)
+            return [ApplePhoneme(pref, -1, False)] + parse_apple_phonemes(rest)
     return []
 
+def segmentable_phoneme(ph_str):
+    return [p for p in parse_apple_phonemes(ph_str) if p.stress >=0]
 
 def similar_phoneme_word(ph_str):
     phons = parse_apple_phonemes(ph_str)

speech_spectrum.py

@@ -79,6 +79,9 @@ def generate_spec_frec(samples, samplerate):
     ims[ims < 0] = 0  #np.finfo(sshow.dtype).eps
     return ims, freq
 
+def generate_sample_spectrogram(samples):
+    ims, _ = generate_spec_frec(samples, 22050)
+    return ims
 
 def generate_aiff_spectrogram(audiopath):
     samples, samplerate, _ = snd.read(audiopath)

speech_test.py

@@ -1,5 +1,5 @@
 from speech_model import load_model_arch
-from speech_tools import record_spectrogram, file_player
+from speech_tools import record_spectrogram, file_player, padd_zeros, pair_for_word
 from speech_data import record_generator_count
 # from importlib import reload
 # import speech_data
@@ -20,6 +20,21 @@ def predict_recording_with(m,sample_size=15):
     inp = create_test_pair(spec1,spec2,sample_size)
     return m.predict([inp[:, 0], inp[:, 1]])
 
+def predict_tts_sample(sample_word = 'able',audio_group='story_words',weights = 'siamese_speech_model-153-epoch-0.55-acc.h5'):
+    # sample_word = 'able';audio_group='story_words';weights = 'siamese_speech_model-153-epoch-0.55-acc.h5'
+    const_file = './models/'+audio_group+'/constants.pkl'
+    arch_file='./models/'+audio_group+'/siamese_speech_model_arch.yaml'
+    weight_file='./models/'+audio_group+'/'+weights
+    (sample_size,n_features,n_records) = pickle.load(open(const_file,'rb'))
+    model = load_model_arch(arch_file)
+    model.load_weights(weight_file)
+    spec1,spec2 = pair_for_word(sample_word)
+    p_spec1 = padd_zeros(spec1,sample_size)
+    p_spec2 = padd_zeros(spec2,sample_size)
+    inp = np.array([[p_spec1,p_spec2]])
+    result = model.predict([inp[:, 0], inp[:, 1]])[0]
+    res_str = 'same' if result[0] < result[1] else 'diff'
+    return res_str
 
 def test_with(audio_group):
     X,Y = speech_data(audio_group)
@@ -177,7 +192,7 @@ def visualize_results(audio_group='audio'):
 if __name__ == '__main__':
     # evaluate_siamese('./outputs/story_words_test.train.tfrecords',audio_group='story_words.gpu',weights ='siamese_speech_model-58-epoch-0.00-acc.h5')
     # evaluate_siamese('./outputs/story_words.test.tfrecords',audio_group='story_words',weights ='siamese_speech_model-675-epoch-0.00-acc.h5')
-    evaluate_siamese('./outputs/story_words_pitch.test.tfrecords',audio_group='story_words_pitch',weights ='siamese_speech_model-867-epoch-0.12-acc.h5')
+    evaluate_siamese('./outputs/story_words.test.tfrecords',audio_group='story_words',weights ='siamese_speech_model-153-epoch-0.55-acc.h5')
     # play_results('story_words')
     #inspect_tfrecord('./outputs/story_phrases.test.tfrecords',audio_group='story_phrases')
     # visualize_results('story_words.gpu')

speech_testgen.py

@@ -0,0 +1,50 @@
+import voicerss_tts
+import json
+from speech_tools import format_filename
+
+def generate_voice(phrase):
+    voice = voicerss_tts.speech({
+        'key': '0ae89d82aa78460691c99a4ac8c0f9ec',
+        'hl': 'en-us',
+        'src': phrase,
+        'r': '0',
+        'c': 'mp3',
+        'f': '22khz_16bit_mono',
+        'ssml': 'false',
+        'b64': 'false'
+    })
+    if not voice['error']:
+        return voice[b'response']
+    return None
+
+
+def generate_test_audio_for_stories():
+    story_file = './inputs/all_stories_hs.json'
+    # story_file = './inputs/all_stories.json'
+    stories_data = json.load(open(story_file))
+    text_list_dup = [t[0] for i in stories_data.values() for t in i]
+    text_list = sorted(list(set(text_list_dup)))[:10]
+    for t in text_list:
+        v = generate_voice(t)
+        if v:
+            f_name = format_filename(t)
+            tf = open('inputs/voicerss/'+f_name+'.mp3','wb')
+            tf.write(v)
+            tf.close()
+
+# def generate_test_audio_for(records_file,audio_group='audio'):
+#     # audio_group='audio';model_file = 'siamese_speech_model-305-epoch-0.20-acc.h5'
+#     # records_file  = os.path.join('./outputs',eval_group+'.train.tfrecords')
+#     const_file = os.path.join('./models/'+audio_group+'/','constants.pkl')
+#     (n_spec,n_features,n_records) = pickle.load(open(const_file,'rb'))
+#     print('evaluating {}...'.format(records_file))
+#     record_iterator,records_count = record_generator_count(records_file)
+#     all_results = []
+#     for (i,string_record) in tqdm(enumerate(record_iterator),total=records_count):
+#         total+=1
+#         example = tf.train.Example()
+#         example.ParseFromString(string_record)
+#         word = example.features.feature['word'].bytes_list.value[0].decode()
+
+# audio = generate_voice('hello world')
+# audio

speech_tools.py

@@ -2,17 +2,22 @@ import os
 import math
 import string
 import threading
+import itertools
+import random
 import multiprocessing
+import subprocess
 import pandas as pd
 import numpy as np
 import pyaudio
 from pysndfile import sndio as snd
 # from matplotlib import pyplot as plt
-from speech_spectrum import plot_stft, generate_spec_frec
+from speech_spectrum import plot_stft, generate_spec_frec,generate_aiff_spectrogram
 
 SAMPLE_RATE = 22050
 N_CHANNELS = 2
 
+devnull = open(os.devnull, 'w')
+
 def step_count(n_records,batch_size):
     return int(math.ceil(n_records*1.0/batch_size))
 
@@ -50,6 +55,17 @@ 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 read_seg_file(aiff_name):
+    base_name = aiff_name.rsplit('.aiff',1)[0]
+    seg_file = base_name+'-palign.csv'
+    seg_data = pd.read_csv(seg_file,names=['action','start','end','phoneme'])
+    seg_data = seg_data[(seg_data['action'] == 'PhonAlign') & (seg_data['phoneme'] != '#')]
+    return seg_data
+
 def record_spectrogram(n_sec, plot=False, playback=False):
     # show_record_prompt()
     N_SEC = n_sec
@@ -85,6 +101,20 @@ def record_spectrogram(n_sec, plot=False, playback=False):
     ims, _ = generate_spec_frec(one_channel, SAMPLE_RATE)
     return ims
 
+def pair_for_word(phrase='able'):
+    spec1 = generate_aiff_spectrogram('./inputs/pairs/good/'+phrase+'.aiff')
+    spec2 = generate_aiff_spectrogram('./inputs/pairs/test/'+phrase+'.aiff')
+    return spec1,spec2
+
+def transribe_audio_text(aiff_name,phrase):
+    base_name = aiff_name.rsplit('.aiff',1)[0]
+    wav_name = base_name+'.wav'
+    txt_name = base_name+'.txt'
+    params = ['ffmpeg', '-y', '-i',aiff_name,wav_name]
+    subprocess.call(params,stdout=devnull,stderr=devnull)
+    trcr_f = open(txt_name,'w')
+    trcr_f.write(phrase)
+    trcr_f.close()
 
 def _apply_df(args):
     df, func, num, kwargs = args

voicerss_tts.py

@@ -0,0 +1,52 @@
+import http.client, urllib.request, urllib.parse, urllib.error
+
+def speech(settings):
+	__validate(settings)
+	return __request(settings)
+
+def __validate(settings):
+	if not settings: raise RuntimeError('The settings are undefined')
+	if 'key' not in settings or not settings['key']: raise RuntimeError('The API key is undefined')
+	if 'src' not in settings or not settings['src']: raise RuntimeError('The text is undefined')
+	if 'hl' not in settings or not settings['hl']: raise RuntimeError('The language is undefined')
+
+def __request(settings):
+	result = {'error': None, 'response': None}
+
+	headers = {'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8'}
+	params = urllib.parse.urlencode(__buildRequest(settings))
+
+	if 'ssl' in settings and settings['ssl']:
+		conn = http.client.HTTPSConnection('api.voicerss.org:443')
+	else:
+		conn = http.client.HTTPConnection('api.voicerss.org:80')
+
+	conn.request('POST', '/', params, headers)
+
+	response = conn.getresponse()
+	content = response.read()
+
+	if response.status != 200:
+		result[b'error'] = response.reason
+	elif content.find(b'ERROR') == 0:
+		result[b'error'] = content
+	else:
+		result[b'response'] = content
+
+	conn.close()
+
+	return result
+
+def __buildRequest(settings):
+	params = {'key': '', 'src': '', 'hl': '', 'r': '', 'c': '', 'f': '', 'ssml': '', 'b64': ''}
+
+	if 'key' in settings: params['key'] = settings['key']
+	if 'src' in settings: params['src'] = settings['src']
+	if 'hl' in settings: params['hl'] = settings['hl']
+	if 'r' in settings: params['r'] = settings['r']
+	if 'c' in settings: params['c'] = settings['c']
+	if 'f' in settings: params['f'] = settings['f']
+	if 'ssml' in settings: params['ssml'] = settings['ssml']
+	if 'b64' in settings: params['b64'] = settings['b64']
+
+	return params

voicerss_tts.py.bak

@@ -0,0 +1,52 @@
+import httplib, urllib
+
+def speech(settings):
+	__validate(settings)
+	return __request(settings)
+
+def __validate(settings):
+	if not settings: raise RuntimeError('The settings are undefined')
+	if 'key' not in settings or not settings['key']: raise RuntimeError('The API key is undefined')
+	if 'src' not in settings or not settings['src']: raise RuntimeError('The text is undefined')
+	if 'hl' not in settings or not settings['hl']: raise RuntimeError('The language is undefined')
+
+def __request(settings):
+	result = {'error': None, 'response': None}
+
+	headers = {'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8'}
+	params = urllib.urlencode(__buildRequest(settings))
+	
+	if 'ssl' in settings and settings['ssl']:
+		conn = httplib.HTTPSConnection('api.voicerss.org:443')
+	else:
+		conn = httplib.HTTPConnection('api.voicerss.org:80')
+		
+	conn.request('POST', '/', params, headers)
+	
+	response = conn.getresponse()
+	content = response.read()
+	
+	if response.status != 200:
+		result['error'] = response.reason
+	elif content.find('ERROR') == 0:
+		result['error'] = content
+	else:
+		result['response'] = content
+		
+	conn.close()
+
+	return result
+
+def __buildRequest(settings):
+	params = {'key': '', 'src': '', 'hl': '', 'r': '', 'c': '', 'f': '', 'ssml': '', 'b64': ''}
+	
+	if 'key' in settings: params['key'] = settings['key']
+	if 'src' in settings: params['src'] = settings['src']
+	if 'hl' in settings: params['hl'] = settings['hl']
+	if 'r' in settings: params['r'] = settings['r']
+	if 'c' in settings: params['c'] = settings['c']
+	if 'f' in settings: params['f'] = settings['f']
+	if 'ssml' in settings: params['ssml'] = settings['ssml']
+	if 'b64' in settings: params['b64'] = settings['b64']
+	
+	return params