implemented train/test split at word-level and generator returns one-shot validation data
Malar Kannan
parent
ab452494b3
commit
e9b18921ee
206
speech_data.py
206
speech_data.py
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@ -35,72 +35,74 @@ def _int64_feature(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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def create_spectrogram_tfrecords(audio_group='audio'):
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def create_spectrogram_tfrecords(audio_group='audio',sample_count=0):
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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 = pd.read_csv( './outputs/' + audio_group + '.csv',index_col=0)
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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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n_records,n_spec,n_features = 0,0,0
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n_records = n_spec = n_features = 0
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def write_samples(wg,sample_name):
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wg_sampled = reservoir_sample(wg,sample_count) if sample_count > 0 else wg
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word_group_prog = tqdm(wg_sampled,desc='Computing spectrogram')
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record_file = './outputs/{}.{}.tfrecords'.format(audio_group,sample_name)
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writer = tf.python_io.TFRecordWriter(record_file)
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for (w, word_group) in word_group_prog:
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word_group_prog.set_postfix(word=w,sample_name=sample_name)
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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[g['variant'] == 'low']
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sample_wrong = g.loc[g['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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group_prog = tqdm(group,desc='Writing Spectrogram')
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for sample1,sample2 in group_prog:
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group_prog.set_postfix(output=output
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,var1=sample1['variant']
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,var2=sample2['variant'])
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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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nonlocal n_spec,n_records,n_features
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n_spec = max([n_spec,spec_n1,spec_n2])
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n_features = spec_w1
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n_records+=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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group_prog.close()
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word_group_prog.close()
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writer.close()
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writer = tf.python_io.TFRecordWriter('./outputs/' + audio_group + '.tfrecords')
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prog = tqdm(audio_samples.groupby(audio_samples['word']),desc='Computing spectrogram')
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for (w, word_group) in prog:
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prog.set_postfix(word=w)
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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[g['variant'] == 'low']
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sample_wrong = g.loc[g['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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group_prog = tqdm(group,desc='Writing Spectrogram')
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for sample1,sample2 in group_prog:
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group_prog.set_postfix(output=output
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,var1=sample1['variant']
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,var2=sample2['variant'])
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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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n_spec = max([n_spec,spec_n1,spec_n2])
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n_features = spec_w1
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n_records+=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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group_prog.close()
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prog.close()
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writer.close()
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word_groups = [i for i in audio_samples.groupby('word')]
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tr_audio_samples,te_audio_samples = train_test_split(word_groups,test_size=0.1)
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write_samples(tr_audio_samples,'train')
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write_samples(te_audio_samples,'test')
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const_file = os.path.join('./outputs',audio_group+'.constants')
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pickle.dump((n_spec,n_features,n_records),open(const_file,'wb'))
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@ -196,12 +198,15 @@ def read_siamese_tfrecords_oneshot(audio_group='audio',sample_size=3000):
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output_data = np.zeros((samples,2))
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random_samples = enumerate(reservoir_sample(record_iterator,samples))
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for (i,string_record) in tqdm(random_samples,total=samples):
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# if i == samples:
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# break
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example = tf.train.Example()
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example.ParseFromString(string_record)
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spec1 = np.array(example.features.feature['spec1'].float_list.value).reshape(n_spec,n_features)
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spec2 = np.array(example.features.feature['spec2'].float_list.value).reshape(n_spec,n_features)
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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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p_spec1,p_spec2 = padd_zeros(spec1,n_spec),padd_zeros(spec2,n_spec)
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input_data[i] = np.asarray([spec1,spec2])
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output = example.features.feature['output'].int64_list.value
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output_data[i] = np.asarray(output)
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@ -210,7 +215,65 @@ def read_siamese_tfrecords_oneshot(audio_group='audio',sample_size=3000):
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# result = (tr_pairs,te_pairs,tr_y,te_y,n_spec,n_features)
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return input_data,output_data
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def read_siamese_tfrecords_generator(audio_group='audio',batch_size=32):
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def read_siamese_tfrecords_generator(audio_group='audio',batch_size=32,sample_size=100):
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records_file = os.path.join('./outputs',audio_group+'.train.tfrecords')
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input_pairs = []
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output_class = []
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const_file = os.path.join('./outputs',audio_group+'.constants')
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(n_spec,n_features,n_records) = pickle.load(open(const_file,'rb'))
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print('reading tfrecords({}-train)...'.format(audio_group))
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def record_generator():
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input_data = []
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output_data = []
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while True:
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record_iterator = tf.python_io.tf_record_iterator(path=records_file)
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#tqdm(enumerate(record_iterator),total=n_records)
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for (i,string_record) in enumerate(record_iterator):
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example = tf.train.Example()
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example.ParseFromString(string_record)
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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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p_spec1,p_spec2 = padd_zeros(spec1,n_spec),padd_zeros(spec2,n_spec)
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input_data.append(np.asarray([p_spec1,p_spec2]))
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output = example.features.feature['output'].int64_list.value
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output_data.append(np.asarray(output))
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if len(input_data) == batch_size:
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input_arr = np.asarray(input_data)
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output_arr = np.asarray(output_data)
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yield ([input_arr[:, 0], input_arr[:, 1]],output_arr)
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input_data = []
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output_data = []
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# Read test in one-shot
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te_records_file = os.path.join('./outputs',audio_group+'.test.tfrecords')
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te_re_iterator = tf.python_io.tf_record_iterator(path=records_file)
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print('reading tfrecords({}-test)...'.format(audio_group))
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samples = min([sample_size,n_records])
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# samples = n_records
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input_data = np.zeros((samples,2,n_spec,n_features))
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output_data = np.zeros((samples,2))
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random_samples = enumerate(reservoir_sample(te_re_iterator,samples))
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for (i,string_record) in tqdm(random_samples,total=samples):
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example = tf.train.Example()
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example.ParseFromString(string_record)
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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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p_spec1,p_spec2 = padd_zeros(spec1,n_spec),padd_zeros(spec2,n_spec)
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input_data[i] = np.asarray([p_spec1,p_spec2])
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output = example.features.feature['output'].int64_list.value
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output_data[i] = np.asarray(output)
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return record_generator,input_data,output_data,n_spec,n_features,n_records
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def read_siamese_tfrecords_generator_old(audio_group='audio',batch_size=32):
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records_file = os.path.join('./outputs',audio_group+'_padded.tfrecords')
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input_pairs = []
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output_class = []
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@ -330,9 +393,16 @@ 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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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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with open('./outputs/' + audio_group + '.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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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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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]
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audio_samples = audio_samples.drop(['file_path','file_exists'],axis=1).reset_index(drop=True)
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audio_samples.to_csv('./outputs/' + audio_group + '.csv')
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def convert_old_audio():
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audio_samples = pd.read_csv( './outputs/audio.csv.old'
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@ -352,9 +422,11 @@ if __name__ == '__main__':
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# create_spectrogram_tfrecords('story_words_test')
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# read_siamese_tfrecords('story_all')
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# read_siamese_tfrecords('story_words_test')
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padd_zeros_siamese_tfrecords('story_words')
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# padd_zeros_siamese_tfrecords('story_words')
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# fix_csv()
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# pickle_constants('story_words')
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# create_spectrogram_tfrecords('audio')
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# create_spectrogram_tfrecords('audio',sample_count=100)
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read_siamese_tfrecords_generator('audio')
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# padd_zeros_siamese_tfrecords('audio')
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# create_padded_spectrogram()
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# create_speech_pairs_data()
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@ -13,6 +13,9 @@ from keras.optimizers import RMSprop
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from keras.callbacks import TensorBoard, ModelCheckpoint
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from keras import backend as K
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def create_dir(direc):
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if not os.path.exists(direc):
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os.makedirs(direc)
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def euclidean_distance(vects):
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x, y = vects
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@ -79,13 +82,14 @@ def siamese_model(input_dim):
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return model
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def train_siamese():
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def train_siamese(audio_group = 'audio'):
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# the data, shuffled and split between train and test sets
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# tr_pairs, te_pairs, tr_y_e, te_y_e = speech_model_data()
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batch_size = 512
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tr_gen_fn,n_step,n_features,n_records = read_siamese_tfrecords_generator('audio',batch_size)
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model_dir = './models/'+audio_group
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create_dir(model_dir)
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tr_gen_fn,te_pairs,te_y,n_step,n_features,n_records = read_siamese_tfrecords_generator(audio_group,batch_size,300)
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tr_gen = tr_gen_fn()
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(te_pairs,te_y) = read_siamese_tfrecords_oneshot('audio',1000)
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# tr_y = to_categorical(tr_y_e, num_classes=2)
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# te_y = to_categorical(te_y_e, num_classes=2)
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input_dim = (n_step, n_features)
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@ -102,7 +106,7 @@ def train_siamese():
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embeddings_freq=0,
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embeddings_layer_names=None,
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embeddings_metadata=None)
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cp_file_fmt = './models/siamese_speech_model-{epoch:02d}-epoch-{val_loss:0.2f}\
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cp_file_fmt = model_dir+'/siamese_speech_model-{epoch:02d}-epoch-{val_loss:0.2f}\
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-acc.h5'
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cp_cb = ModelCheckpoint(
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@ -126,9 +130,10 @@ def train_siamese():
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model.fit_generator(tr_gen
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,epochs=100
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,steps_per_epoch=n_records//batch_size
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,validation_data=([te_pairs[:, 0], te_pairs[:, 1]], te_y)
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,use_multiprocessing=True)
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model.save('./models/siamese_speech_model-final.h5')
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model.save(model_dir+'/siamese_speech_model-final.h5')
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# compute final accuracy on training and test sets
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# y_pred = model.predict([tr_pairs[:, 0], tr_pairs[:, 1]])
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# tr_acc = compute_accuracy(tr_y, y_pred)
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