164 lines
8.3 KiB
Python
164 lines
8.3 KiB
Python
import pandas as pd
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from pandas_parallel import apply_by_multiprocessing
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# import dask as dd
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# import dask.dataframe as ddf
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import tensorflow as tf
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import numpy as np
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from spectro_gen import generate_aiff_spectrogram
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from sklearn.model_selection import train_test_split
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import itertools
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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 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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group2 = [r for (i, r) in wrongGroup.iterrows()]
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rightWrongPairs = [(g1, g2) for g2 in group2 for g1 in group1]
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rightRightPairs = [i for i in itertools.combinations(group1, 2)]
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random.shuffle(rightWrongPairs)
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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[:16],rightWrongPairs[:16]
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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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http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/
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http://www.machinelearninguru.com/deep_learning/tensorflow/basics/tfrecord/tfrecord.html
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'''
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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_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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def _int64_feature(value):
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return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
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def _bytes_feature(value):
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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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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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sample_wrong = g.loc[audio_samples['variant'] == 'medium']
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same, diff = siamese_pairs(sample_right, sample_wrong)
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groups = [([0,1],same),([1,0],diff)]
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for (output,group) in groups:
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for sample1,sample2 in group:
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spectro1,spectro2 = sample1['spectrogram'],sample2['spectrogram']
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spec_n1,spec_n2 = spectro1.shape[0],spectro2.shape[0]
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spec_w1,spec_w2 = spectro1.shape[1],spectro2.shape[1]
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spec1,spec2 = spectro1.reshape(-1),spectro2.reshape(-1)
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example = tf.train.Example(features=tf.train.Features(
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feature={
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'word': _bytes_feature([w.encode('utf-8')]),
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'phoneme1': _bytes_feature([sample1['phonemes'].encode('utf-8')]),
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'phoneme2': _bytes_feature([sample2['phonemes'].encode('utf-8')]),
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'voice1': _bytes_feature([sample1['voice'].encode('utf-8')]),
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'voice2': _bytes_feature([sample2['voice'].encode('utf-8')]),
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'language': _bytes_feature([sample1['language'].encode('utf-8')]),
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'rate1':_int64_feature([sample1['rate']]),
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'rate2':_int64_feature([sample2['rate']]),
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'variant1': _bytes_feature([sample1['variant'].encode('utf-8')]),
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'variant2': _bytes_feature([sample2['variant'].encode('utf-8')]),
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'file1': _bytes_feature([sample1['file'].encode('utf-8')]),
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'file2': _bytes_feature([sample2['file'].encode('utf-8')]),
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'spec1':_float_feature(spec1),
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'spec2':_float_feature(spec2),
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'spec_n1':_int64_feature([spec_n1]),
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'spec_w1':_int64_feature([spec_w1]),
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'spec_n2':_int64_feature([spec_n2]),
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'spec_w2':_int64_feature([spec_w2]),
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'output':_int64_feature(output)
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}
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))
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writer.write(example.SerializeToString())
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writer.close()
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def read_siamese_tfrecords(audio_group='audio'):
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records_file = os.path.join('./outputs',audio_group+'.tfrecords')
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record_iterator = tf.python_io.tf_record_iterator(path=records_file)
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input_pairs = []
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output_class = []
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input_words = []
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for string_record in record_iterator:
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example = tf.train.Example()
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example.ParseFromString(string_record)
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word = example.features.feature['word'].bytes_list.value[0]
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input_words.append(word)
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example.features.feature['spec2'].float_list.value[0]
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spec_n1 = example.features.feature['spec_n1'].int64_list.value[0]
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spec_n2 = example.features.feature['spec_n2'].int64_list.value[0]
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spec_w1 = example.features.feature['spec_w1'].int64_list.value[0]
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spec_w2 = example.features.feature['spec_w2'].int64_list.value[0]
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spec1 = np.array(example.features.feature['spec1'].float_list.value).reshape(spec_n1,spec_w1)
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spec2 = np.array(example.features.feature['spec2'].float_list.value).reshape(spec_n2,spec_w2)
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input_pairs.append([spec1,spec2])
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output = example.features.feature['output'].int64_list.value
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output_class.append(output)
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return input_pairs,output_class
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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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, 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_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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return len(audio_samples.groupby(audio_samples['word']))
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def fix_csv(audio_group='audio'):
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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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# 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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# 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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proper_rows = [i for i in audio_csv_data if len(i) == 7]
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with open('./outputs/' + audio_group + '-new.csv','w') as fixed_csv:
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fixed_csv_w = csv.writer(fixed_csv, quoting=csv.QUOTE_MINIMAL)
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fixed_csv_w.writerows(proper_rows)
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if __name__ == '__main__':
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# sunflower_pairs_data()
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# create_spectrogram_data()
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# create_spectrogram_data('story_words')
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# create_spectrogram_tfrecords('story_words')
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create_spectrogram_tfrecords('story_all')
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# create_padded_spectrogram()
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# create_speech_pairs_data()
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# print(speech_model_data())
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