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speech-scoring
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tfrecords wip

master
Malar Kannan 2017-11-06 12:36:20 +05:30
parent 5ff437b095
commit fabd882664
1 changed files with 33 additions and 0 deletions
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speech_data.py
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@ -90,6 +90,39 @@ def create_spectrogram_data(audio_group='audio'):
audio_samples['window_count'] = audio_samples.loc[:,'spectrogram'].apply(lambda x: x.shape[0]) audio_samples['window_count'] = audio_samples.loc[:,'spectrogram'].apply(lambda x: x.shape[0])
audio_samples.to_pickle('outputs/{}-spectrogram.pkl'.format(audio_group)) audio_samples.to_pickle('outputs/{}-spectrogram.pkl'.format(audio_group))
def create_spectrogram_tfrecords(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 _float_feature(value):
return tf.train.Feature(float_list=tf.train.FloatList(value=value))
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=value))
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
writer = tf.python_io.TFRecordWriter(output_path)
for sample in audio_samples:
example = tf.train.Example(features=tf.train.Features(
feature={
'label': _int64_feature([label]),
'path': _bytes_feature([image_path]),
'instance' : _bytes_feature([instance_id])
}
))
writer.write(example.SerializeToString())
writer.close()
def create_tagged_data(audio_samples): def create_tagged_data(audio_samples):
same_data, diff_data = [], [] same_data, diff_data = [], []