using csv writer instead as comma in phrases are mis-aligning columns
Malar Kannan
parent
15f29895d4
commit
55e2de2f04
134
speech_data.py
134
speech_data.py
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@ -11,17 +11,11 @@ import os
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import random
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import csv
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import gc
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import progressbar
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def get_siamese_pairs(groupF1, groupF2):
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group1 = [r for (i, r) in groupF1.iterrows()]
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group2 = [r for (i, r) in groupF2.iterrows()]
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diff = [(g1, g2) for g2 in group2 for g1 in group1]
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same = [i for i in itertools.combinations(group1, 2)
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] + [i for i in itertools.combinations(group2, 2)]
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random.shuffle(same)
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random.shuffle(diff)
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# return (random.sample(same,10), random.sample(diff,10))
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return same[:10],diff[:10]
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def prog_bar(title):
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widgets = [title, progressbar.Counter(), ' [', progressbar.Bar(), '] - ', progressbar.ETA()]
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return progressbar.ProgressBar(widgets=widgets)
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def siamese_pairs(rightGroup, wrongGroup):
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group1 = [r for (i, r) in rightGroup.iterrows()]
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@ -32,73 +26,8 @@ def siamese_pairs(rightGroup, wrongGroup):
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random.shuffle(rightRightPairs)
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# return (random.sample(same,10), random.sample(diff,10))
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# return rightRightPairs[:10],rightWrongPairs[:10]
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return rightRightPairs,rightWrongPairs
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def append_zeros(spgr, max_samples):
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return np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
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'median')
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def padd_zeros(spgr, max_samples):
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return np.lib.pad(spgr, [(0, max_samples - spgr.shape[0]), (0, 0)],
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'constant')
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def to_onehot(a,class_count=2):
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a_row_n = a.shape[0]
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b = np.zeros((a_row_n, class_count))
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b[np.arange(a_row_n), a] = 1
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return b
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def create_pair(l, r, max_samples):
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l_sample = padd_zeros(l, max_samples)
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r_sample = padd_zeros(r, max_samples)
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return np.asarray([l_sample, r_sample])
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def create_test_pair(l, r, max_samples):
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l_sample = append_zeros(l, max_samples)
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r_sample = append_zeros(r, max_samples)
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return np.asarray([[l_sample, r_sample]])
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def create_X(sp, max_samples):
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return create_pair(sp[0]['spectrogram'], sp[1]['spectrogram'], max_samples)
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# def get_word_pairs_data(word, max_samples):
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# audio_samples = pd.read_csv(
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# './outputs/audio.csv',
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# names=['word', 'voice', 'rate', 'variant', 'file'])
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# audio_samples = audio_samples.loc[audio_samples['word'] ==
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# word].reset_index(drop=True)
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# audio_samples.loc[:, 'spectrogram'] = audio_samples.loc[:, 'file'].apply(
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# lambda x: 'outputs/audio/' + x).apply(generate_aiff_spectrogram)
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# max_samples = audio_samples['spectrogram'].apply(
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# lambda x: x.shape[0]).max()
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# same_data, diff_data = [], []
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# for (w, g) in audio_samples.groupby(audio_samples['word']):
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# sample_norm = g.loc[audio_samples['variant'] == 'normal']
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# sample_phon = g.loc[audio_samples['variant'] == 'phoneme']
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# same, diff = get_siamese_pairs(sample_norm, sample_phon)
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# same_data.extend([create_X(s, max_samples) for s in same])
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# diff_data.extend([create_X(d, max_samples) for d in diff])
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# Y = np.hstack([np.ones(len(same_data)), np.zeros(len(diff_data))])
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# X = np.asarray(same_data + diff_data)
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# # tr_pairs, te_pairs, tr_y, te_y = train_test_split(X, Y, test_size=0.1)
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# return (X, Y)
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def create_spectrogram_data(audio_group='audio'):
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audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv'
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, names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']
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, quoting=csv.QUOTE_NONE)
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# audio_samples = audio_samples.loc[audio_samples['word'] ==
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# 'sunflowers'].reset_index(drop=True)
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audio_samples['file_paths'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
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audio_samples['file_exists'] = apply_by_multiprocessing(audio_samples['file_paths'], os.path.exists)
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audio_samples = audio_samples[audio_samples['file_exists'] == True].reset_index()
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audio_samples['spectrogram'] = apply_by_multiprocessing(audio_samples['file_paths'],generate_aiff_spectrogram)#.apply(
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audio_samples['window_count'] = audio_samples.loc[:,'spectrogram'].apply(lambda x: x.shape[0])
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audio_samples.to_pickle('outputs/{}-spectrogram.pkl'.format(audio_group))
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return rightRightPairs[:32],rightWrongPairs[:32]
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# return rightRightPairs,rightWrongPairs
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def create_spectrogram_tfrecords(audio_group='audio'):
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'''
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@ -113,7 +42,9 @@ def create_spectrogram_tfrecords(audio_group='audio'):
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audio_samples['file_path'] = audio_samples.loc[:, 'file'].apply(lambda x: 'outputs/' + audio_group + '/' + x)
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audio_samples['file_exists'] = apply_by_multiprocessing(audio_samples['file_path'], os.path.exists)
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audio_samples = audio_samples[audio_samples['file_exists'] == True].reset_index()
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audio_samples['rate_int'] = apply_by_multiprocessing(audio_samples['rate'], str.isdigit)
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audio_samples = audio_samples[audio_samples['rate_int'] == True].reset_index().drop(['level_0'],axis=1)
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audio_samples['rate'] = audio_samples['rate'].astype(int)
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def _float_feature(value):
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return tf.train.Feature(float_list=tf.train.FloatList(value=value))
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@ -124,7 +55,8 @@ def create_spectrogram_tfrecords(audio_group='audio'):
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return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
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writer = tf.python_io.TFRecordWriter('./outputs/' + audio_group + '.tfrecords')
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for (w, word_group) in audio_samples.groupby(audio_samples['word']):
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prog = prog_bar('Generating siamese pairs : ')
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for (w, word_group) in prog(audio_samples.groupby(audio_samples['word'])):
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g = word_group.reset_index()
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g['spectrogram'] = apply_by_multiprocessing(g['file_path'],generate_aiff_spectrogram)
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sample_right = g.loc[audio_samples['variant'] == 'low']
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@ -186,24 +118,6 @@ def read_siamese_tfrecords(audio_group='audio'):
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output_class.append(output)
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return input_pairs,output_class
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def create_speech_pairs_data(audio_group='audio'):
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audio_samples = pd.read_pickle('outputs/{}-spectrogram.pkl'.format(audio_group))
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# sample_size = audio_samples['spectrogram'][0].shape[1]
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tr_audio_samples,te_audio_samples = train_test_split(audio_samples, test_size=0.1)
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def save_samples_for(sample_name,samples):
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print('generating {} siamese speech pairs'.format(sample_name))
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X,Y = create_tagged_data(samples)
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print('shuffling array speech pairs')
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rng_state = np.random.get_state()
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np.random.shuffle(X)
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np.random.set_state(rng_state)
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np.random.shuffle(Y)
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print('pickling X/Y')
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np.save('outputs/{}-train-X.npy'.format(audio_group), X)
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np.save('outputs/{}-train-Y.npy'.format(audio_group), Y)
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save_samples_for('train',tr_audio_samples)
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save_samples_for('test',te_audio_samples)
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def audio_samples_word_count(audio_group='audio'):
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audio_group = 'story_all'
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audio_samples = pd.read_csv( './outputs/' + audio_group + '.csv'
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@ -216,15 +130,23 @@ def audio_samples_word_count(audio_group='audio'):
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audio_samples = audio_samples[audio_samples['file_exists'] == True].reset_index()
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return len(audio_samples.groupby(audio_samples['word']))
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def speech_model_data():
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tr_pairs = np.load('outputs/tr_pairs.npy') / 255.0
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te_pairs = np.load('outputs/te_pairs.npy') / 255.0
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tr_pairs[tr_pairs < 0] = 0
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te_pairs[te_pairs < 0] = 0
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tr_y = np.load('outputs/tr_y.npy')
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te_y = np.load('outputs/te_y.npy')
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return tr_pairs, te_pairs, tr_y, te_y
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def fix_csv(audio_group='audio'):
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audio_group = 'story_all'
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audio_samples = pd.read_csv( './outputs/story_words.csv'
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, names=['word','phonemes', 'voice', 'language', 'rate', 'variant', 'file']
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, quoting=csv.QUOTE_NONE)
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voice_set = set(audio_samples['voice'].unique().tolist())
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audio_csv_lines = open('./outputs/' + audio_group + '.csv','r').readlines()
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audio_csv_data = [i.strip().split(',') for i in audio_csv_lines]
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to_be_fixed = [i for i in audio_csv_data if len(i) > 7]
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def unite_words(entries):
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entries = to_be_fixed[0]
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word_entries = next(((entries[:i],entries[i:]) for (i,e) in enumerate(entries) if e in voice_set),'')
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word_entries[1]
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return
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to_be_fixed[0]
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entries = [unite_words for e in to_be_fixed]
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[i for i in entries if len(i) % 2 != 0]
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if __name__ == '__main__':
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# sunflower_pairs_data()
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@ -3,6 +3,7 @@ from AppKit import NSSpeechSynthesizer, NSSpeechInputModeProperty
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from AppKit import NSSpeechModePhoneme
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from Foundation import NSURL
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import json
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import csv
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import random
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import os
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import re
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@ -81,6 +82,11 @@ class SynthFile(object):
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return ','.join([str(c) for c in cols])+'\n'
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def get_values(self):
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cols = [self.word, self.phoneme, self.voice,
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self.voice_lang, self.rate, self.variant,
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self.filename]
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return [str(c) for c in cols]
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class SynthVariant(object):
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"""docstring for SynthVariant."""
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@ -191,22 +197,11 @@ def synth_generator():
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print("It took {} to synthsize all variants.".format(time_str))
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return synth_for_words
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def write_synths(synth_list, fname, csv=False):
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f = open(fname, 'w')
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if csv:
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for s in synth_list:
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f.write(s.get_csv())
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else:
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json.dump([s.get_json() for s in synth_list], f)
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f.close()
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def synth_logger(fname, csv=False):
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f = open(fname, 'w')
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s_csv_w = csv.writer(f, quoting=csv.QUOTE_MINIMAL)
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def csv_writer(s):
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f.write(s.get_csv())
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s_csv_w.writerow(s.get_values())
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synth_list = []
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def json_writer(s):
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