using csv writer instead as comma in phrases are mis-aligning columns

speech_data.py

@@ -11,17 +11,11 @@ import os
 import random
 import csv
 import gc
+import progressbar
 
-def get_siamese_pairs(groupF1, groupF2):
+def prog_bar(title):
-    group1 = [r for (i, r) in groupF1.iterrows()]
+    widgets = [title, progressbar.Counter(), ' [', progressbar.Bar(), '] - ', progressbar.ETA()]
-    group2 = [r for (i, r) in groupF2.iterrows()]
+    return progressbar.ProgressBar(widgets=widgets)
-    diff = [(g1, g2) for g2 in group2 for g1 in group1]
-    same = [i for i in itertools.combinations(group1, 2)
-         ] + [i for i in itertools.combinations(group2, 2)]
-    random.shuffle(same)
-    random.shuffle(diff)
-    # return (random.sample(same,10), random.sample(diff,10))
-    return same[:10],diff[:10]
 
 def siamese_pairs(rightGroup, wrongGroup):
     group1 = [r for (i, r) in rightGroup.iterrows()]
@@ -32,73 +26,8 @@ def siamese_pairs(rightGroup, wrongGroup):
     random.shuffle(rightRightPairs)
     # return (random.sample(same,10), random.sample(diff,10))
     # return rightRightPairs[:10],rightWrongPairs[:10]
-    return rightRightPairs,rightWrongPairs
+    return rightRightPairs[:32],rightWrongPairs[:32]
-
+    # return rightRightPairs,rightWrongPairs
-def append_zeros(spgr, max_samples):
-    return np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
-                      'median')
-
-def padd_zeros(spgr, max_samples):
-    return np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
-                      'constant')
-
-def to_onehot(a,class_count=2):
-    a_row_n = a.shape[0]
-    b = np.zeros((a_row_n, class_count))
-    b[np.arange(a_row_n), a] = 1
-    return b
-
-def create_pair(l, r, max_samples):
-    l_sample = padd_zeros(l, max_samples)
-    r_sample = padd_zeros(r, max_samples)
-    return np.asarray([l_sample, r_sample])
-
-
-def create_test_pair(l, r, max_samples):
-    l_sample = append_zeros(l, 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)
-
-
-# def get_word_pairs_data(word, max_samples):
-#     audio_samples = pd.read_csv(
-#         './outputs/audio.csv',
-#         names=['word', 'voice', 'rate', 'variant', 'file'])
-#     audio_samples = audio_samples.loc[audio_samples['word'] ==
-#                                       word].reset_index(drop=True)
-#     audio_samples.loc[:, 'spectrogram'] = audio_samples.loc[:, 'file'].apply(
-#         lambda x: 'outputs/audio/' + x).apply(generate_aiff_spectrogram)
-#     max_samples = audio_samples['spectrogram'].apply(
-#         lambda x: x.shape[0]).max()
-#     same_data, diff_data = [], []
-#     for (w, g) in audio_samples.groupby(audio_samples['word']):
-#         sample_norm = g.loc[audio_samples['variant'] == 'normal']
-#         sample_phon = g.loc[audio_samples['variant'] == 'phoneme']
-#         same, diff = get_siamese_pairs(sample_norm, sample_phon)
-#         same_data.extend([create_X(s, max_samples) for s in same])
-#         diff_data.extend([create_X(d, max_samples) for d in diff])
-#     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)
-
-
-def create_spectrogram_data(audio_group='audio'):
-    audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv'
-        , names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']
-        , quoting=csv.QUOTE_NONE)
-    # audio_samples = audio_samples.loc[audio_samples['word'] ==
-    #                                   'sunflowers'].reset_index(drop=True)
-    audio_samples['file_paths'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
-    audio_samples['file_exists'] = apply_by_multiprocessing(audio_samples['file_paths'], os.path.exists)
-    audio_samples = audio_samples[audio_samples['file_exists'] == True].reset_index()
-    audio_samples['spectrogram'] = apply_by_multiprocessing(audio_samples['file_paths'],generate_aiff_spectrogram)#.apply(
-    audio_samples['window_count'] = audio_samples.loc[:,'spectrogram'].apply(lambda x: x.shape[0])
-    audio_samples.to_pickle('outputs/{}-spectrogram.pkl'.format(audio_group))
 
 def create_spectrogram_tfrecords(audio_group='audio'):
     '''
@@ -113,7 +42,9 @@ def create_spectrogram_tfrecords(audio_group='audio'):
     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 = audio_samples[audio_samples['file_exists'] == True].reset_index()
-
+    audio_samples['rate_int'] = apply_by_multiprocessing(audio_samples['rate'], str.isdigit)
+    audio_samples = audio_samples[audio_samples['rate_int'] == True].reset_index().drop(['level_0'],axis=1)
+    audio_samples['rate'] = audio_samples['rate'].astype(int)
     def _float_feature(value):
       return tf.train.Feature(float_list=tf.train.FloatList(value=value))
 
@@ -124,7 +55,8 @@ def create_spectrogram_tfrecords(audio_group='audio'):
       return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
 
     writer = tf.python_io.TFRecordWriter('./outputs/' + audio_group + '.tfrecords')
-    for (w, word_group) in audio_samples.groupby(audio_samples['word']):
+    prog = prog_bar('Generating siamese pairs : ')
+    for (w, word_group) in prog(audio_samples.groupby(audio_samples['word'])):
         g = word_group.reset_index()
         g['spectrogram'] = apply_by_multiprocessing(g['file_path'],generate_aiff_spectrogram)
         sample_right = g.loc[audio_samples['variant'] == 'low']
@@ -186,24 +118,6 @@ def read_siamese_tfrecords(audio_group='audio'):
         output_class.append(output)
     return input_pairs,output_class
 
-def create_speech_pairs_data(audio_group='audio'):
-    audio_samples = pd.read_pickle('outputs/{}-spectrogram.pkl'.format(audio_group))
-    # sample_size = audio_samples['spectrogram'][0].shape[1]
-    tr_audio_samples,te_audio_samples = train_test_split(audio_samples, test_size=0.1)
-    def save_samples_for(sample_name,samples):
-        print('generating {} siamese speech pairs'.format(sample_name))
-        X,Y = create_tagged_data(samples)
-        print('shuffling array speech pairs')
-        rng_state = np.random.get_state()
-        np.random.shuffle(X)
-        np.random.set_state(rng_state)
-        np.random.shuffle(Y)
-        print('pickling X/Y')
-        np.save('outputs/{}-train-X.npy'.format(audio_group), X)
-        np.save('outputs/{}-train-Y.npy'.format(audio_group), Y)
-    save_samples_for('train',tr_audio_samples)
-    save_samples_for('test',te_audio_samples)
-
 def audio_samples_word_count(audio_group='audio'):
     audio_group = 'story_all'
     audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv'
@@ -216,15 +130,23 @@ def audio_samples_word_count(audio_group='audio'):
     audio_samples = audio_samples[audio_samples['file_exists'] == True].reset_index()
     return len(audio_samples.groupby(audio_samples['word']))
 
-def speech_model_data():
+def fix_csv(audio_group='audio'):
-    tr_pairs = np.load('outputs/tr_pairs.npy') / 255.0
+    audio_group = 'story_all'
-    te_pairs = np.load('outputs/te_pairs.npy') / 255.0
+    audio_samples = pd.read_csv( './outputs/story_words.csv'
-    tr_pairs[tr_pairs < 0] = 0
+        , names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']
-    te_pairs[te_pairs < 0] = 0
+        , quoting=csv.QUOTE_NONE)
-    tr_y = np.load('outputs/tr_y.npy')
+    voice_set = set(audio_samples['voice'].unique().tolist())
-    te_y = np.load('outputs/te_y.npy')
+    audio_csv_lines = open('./outputs/' + audio_group + '.csv','r').readlines()
-    return tr_pairs, te_pairs, tr_y, te_y
+    audio_csv_data = [i.strip().split(',') for i in audio_csv_lines]
-
+    to_be_fixed = [i for i in audio_csv_data if len(i) > 7]
+    def unite_words(entries):
+        entries = to_be_fixed[0]
+        word_entries = next(((entries[:i],entries[i:])  for (i,e) in enumerate(entries) if e in voice_set),'')
+        word_entries[1]
+        return
+    to_be_fixed[0]
+    entries = [unite_words for e in to_be_fixed]
+    [i for i in entries if len(i) % 2 != 0]
 
 if __name__ == '__main__':
     # sunflower_pairs_data()

tts_samplegen.py

@@ -3,6 +3,7 @@ from AppKit import NSSpeechSynthesizer, NSSpeechInputModeProperty
 from AppKit import NSSpeechModePhoneme
 from Foundation import NSURL
 import json
+import csv
 import random
 import os
 import re
@@ -81,6 +82,11 @@ class SynthFile(object):
 
         return ','.join([str(c) for c in cols])+'\n'
 
+    def get_values(self):
+        cols = [self.word, self.phoneme, self.voice,
+                self.voice_lang, self.rate, self.variant,
+                self.filename]
+        return [str(c) for c in cols]
 
 class SynthVariant(object):
     """docstring for SynthVariant."""
@@ -191,22 +197,11 @@ def synth_generator():
         print("It took {} to synthsize all variants.".format(time_str))
     return synth_for_words
 
-
-def write_synths(synth_list, fname, csv=False):
-    f = open(fname, 'w')
-    if csv:
-        for s in synth_list:
-            f.write(s.get_csv())
-    else:
-        json.dump([s.get_json() for s in synth_list], f)
-    f.close()
-
-
 def synth_logger(fname, csv=False):
     f = open(fname, 'w')
-
+    s_csv_w = csv.writer(f, quoting=csv.QUOTE_MINIMAL)
     def csv_writer(s):
-        f.write(s.get_csv())
+        s_csv_w.writerow(s.get_values())
     synth_list = []
 
     def json_writer(s):