adding 'train_phase' to create_dataset. Now we can augment only the training-set.
This commit is contained in:
Bernardo Henz
Reuben Morais
parent
0cc5ff230f
commit
49c6a9c973
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@ -415,7 +415,8 @@ def train():
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# Create training and validation datasets
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train_set = create_dataset(FLAGS.train_files.split(','),
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batch_size=FLAGS.train_batch_size,
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cache_path=FLAGS.feature_cache)
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cache_path=FLAGS.feature_cache,
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train_phase=True)
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iterator = tfv1.data.Iterator.from_structure(tfv1.data.get_output_types(train_set),
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tfv1.data.get_output_shapes(train_set),
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@ -426,7 +427,7 @@ def train():
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if FLAGS.dev_files:
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dev_csvs = FLAGS.dev_files.split(',')
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dev_sets = [create_dataset([csv], batch_size=FLAGS.dev_batch_size) for csv in dev_csvs]
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dev_sets = [create_dataset([csv], batch_size=FLAGS.dev_batch_size, train_phase=False) for csv in dev_csvs]
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dev_init_ops = [iterator.make_initializer(dev_set) for dev_set in dev_sets]
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# Dropout
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@ -47,7 +47,7 @@ def evaluate(test_csvs, create_model, try_loading):
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Config.alphabet)
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test_csvs = FLAGS.test_files.split(',')
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test_sets = [create_dataset([csv], batch_size=FLAGS.test_batch_size) for csv in test_csvs]
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test_sets = [create_dataset([csv], batch_size=FLAGS.test_batch_size, train_phase=False) for csv in test_csvs]
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iterator = tfv1.data.Iterator.from_structure(tfv1.data.get_output_types(test_sets[0]),
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tfv1.data.get_output_shapes(test_sets[0]),
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output_classes=tfv1.data.get_output_classes(test_sets[0]))
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@ -32,36 +32,38 @@ def read_csvs(csv_files):
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return source_data
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def samples_to_mfccs(samples, sample_rate):
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def samples_to_mfccs(samples, sample_rate, train_phase=False):
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spectrogram = contrib_audio.audio_spectrogram(samples,
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window_size=Config.audio_window_samples,
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stride=Config.audio_step_samples,
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magnitude_squared=True)
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if FLAGS.augmention_sparse_deform:
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spectrogram = augment_sparse_deform(spectrogram,
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time_warping_para=FLAGS.augmentation_time_warp_max_warping,
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normal_around_warping_std=FLAGS.augmentation_sparse_deform_std_warp)
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# Data Augmentations
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if train_phase:
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if FLAGS.augmention_sparse_deform:
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spectrogram = augment_sparse_deform(spectrogram,
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time_warping_para=FLAGS.augmentation_time_warp_max_warping,
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normal_around_warping_std=FLAGS.augmentation_sparse_deform_std_warp)
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if FLAGS.augmentation_spec_dropout_keeprate < 1:
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spectrogram = augment_dropout(spectrogram,
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keep_prob=FLAGS.augmentation_spec_dropout_keeprate)
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if FLAGS.augmentation_spec_dropout_keeprate < 1:
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spectrogram = augment_dropout(spectrogram,
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keep_prob=FLAGS.augmentation_spec_dropout_keeprate)
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if FLAGS.augmentation_freq_and_time_masking:
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spectrogram = augment_freq_time_mask(spectrogram,
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frequency_masking_para=FLAGS.augmentation_freq_and_time_masking_freq_mask_range,
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time_masking_para=FLAGS.augmentation_freq_and_time_masking_time_mask_range,
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frequency_mask_num=FLAGS.augmentation_freq_and_time_masking_number_freq_masks,
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time_mask_num=FLAGS.augmentation_freq_and_time_masking_number_time_masks)
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if FLAGS.augmentation_freq_and_time_masking:
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spectrogram = augment_freq_time_mask(spectrogram,
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frequency_masking_para=FLAGS.augmentation_freq_and_time_masking_freq_mask_range,
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time_masking_para=FLAGS.augmentation_freq_and_time_masking_time_mask_range,
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frequency_mask_num=FLAGS.augmentation_freq_and_time_masking_number_freq_masks,
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time_mask_num=FLAGS.augmentation_freq_and_time_masking_number_time_masks)
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if FLAGS.augmentation_pitch_and_tempo_scaling:
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spectrogram = augment_pitch_and_tempo(spectrogram,
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max_tempo=FLAGS.augmentation_pitch_and_tempo_scaling_max_tempo,
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max_pitch=FLAGS.augmentation_pitch_and_tempo_scaling_max_pitch,
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min_pitch=FLAGS.augmentation_pitch_and_tempo_scaling_min_pitch)
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if FLAGS.augmentation_pitch_and_tempo_scaling:
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spectrogram = augment_pitch_and_tempo(spectrogram,
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max_tempo=FLAGS.augmentation_pitch_and_tempo_scaling_max_tempo,
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max_pitch=FLAGS.augmentation_pitch_and_tempo_scaling_max_pitch,
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min_pitch=FLAGS.augmentation_pitch_and_tempo_scaling_min_pitch)
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if FLAGS.augmentation_speed_up_std > 0:
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spectrogram = augment_speed_up(spectrogram, speed_std=FLAGS.augmentation_speed_up_std)
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if FLAGS.augmentation_speed_up_std > 0:
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spectrogram = augment_speed_up(spectrogram, speed_std=FLAGS.augmentation_speed_up_std)
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mfccs = contrib_audio.mfcc(spectrogram, sample_rate, dct_coefficient_count=Config.n_input)
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mfccs = tf.reshape(mfccs, [-1, Config.n_input])
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@ -69,25 +71,29 @@ def samples_to_mfccs(samples, sample_rate):
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return mfccs, tf.shape(input=mfccs)[0]
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def audiofile_to_features(wav_filename):
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def audiofile_to_features(wav_filename, train_phase=False):
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samples = tf.io.read_file(wav_filename)
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decoded = contrib_audio.decode_wav(samples, desired_channels=1)
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features, features_len = samples_to_mfccs(decoded.audio, decoded.sample_rate)
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features, features_len = samples_to_mfccs(decoded.audio, decoded.sample_rate, train_phase=train_phase)
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if train_phase:
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if FLAGS.data_aug_features_multiplicative > 0:
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features = features*tf.random.normal(mean=1, stddev=FLAGS.data_aug_features_multiplicative, shape=tf.shape(features))
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if FLAGS.data_aug_features_multiplicative > 0:
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features = features*tf.random.normal(mean=1, stddev=FLAGS.data_aug_features_multiplicative, shape=tf.shape(features))
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if FLAGS.data_aug_features_additive > 0:
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features = features+tf.random.normal(mean=0.0, stddev=FLAGS.data_aug_features_additive, shape=tf.shape(features))
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if FLAGS.data_aug_features_additive > 0:
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features = features+tf.random.normal(mean=0.0, stddev=FLAGS.data_aug_features_additive, shape=tf.shape(features))
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return features, features_len
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def entry_to_features(wav_filename, transcript):
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def entry_to_features_not_augmented(wav_filename, transcript):
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# https://bugs.python.org/issue32117
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features, features_len = audiofile_to_features(wav_filename)
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features, features_len = audiofile_to_features(wav_filename, train_phase=False)
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return wav_filename, features, features_len, tf.SparseTensor(*transcript)
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def entry_to_features_augmented(wav_filename, transcript):
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# https://bugs.python.org/issue32117
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features, features_len = audiofile_to_features(wav_filename, train_phase=True)
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return wav_filename, features, features_len, tf.SparseTensor(*transcript)
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@ -100,7 +106,7 @@ def to_sparse_tuple(sequence):
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return indices, sequence, shape
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def create_dataset(csvs, batch_size, cache_path=''):
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def create_dataset(csvs, batch_size, cache_path='', train_phase=True):
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df = read_csvs(csvs)
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df.sort_values(by='wav_filesize', inplace=True)
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@ -112,6 +118,7 @@ def create_dataset(csvs, batch_size, cache_path=''):
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log_error('While processing {}:\n {}'.format(series['wav_filename'], error_message))
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exit(1)
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entry_to_features = entry_to_features_augmented if train_phase else entry_to_features_not_augmented
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def generate_values():
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for _, row in df.iterrows():
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yield row.wav_filename, to_sparse_tuple(row.transcript)
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@ -39,7 +39,7 @@ def create_flags():
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f.DEFINE_integer('augmentation_freq_and_time_masking_time_mask_range', 2, 'max range of masks in the time domain when performing freqtime-mask augmentation')
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f.DEFINE_integer('augmentation_freq_and_time_masking_number_time_masks', 3, 'number of masks in the time domain when performing freqtime-mask augmentation')
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f.DEFINE_float('augmentation_speed_up_std', 0.5, 'std for speeding-up tempo. If std is 0, this augmentation is not performed')
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f.DEFINE_float('augmentation_speed_up_std', 0, 'std for speeding-up tempo. If std is 0, this augmentation is not performed')
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f.DEFINE_integer('augmentation_pitch_and_tempo_scaling', 0, 'whether to use spectrogram speed and tempo scaling')
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f.DEFINE_float('augmentation_pitch_and_tempo_scaling_min_pitch', 0.95, 'min value of pitch scaling')
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@ -37,7 +37,6 @@ def augment_freq_time_mask(mel_spectrogram,
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freq_max = tf.shape(mel_spectrogram)[1]
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time_max = tf.shape(mel_spectrogram)[2]
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# Frequency masking
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# Testing without loop
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for _ in range(frequency_mask_num):
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f = tf.random.uniform(shape=(), minval=0, maxval=frequency_masking_para, dtype=tf.dtypes.int32)
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f0 = tf.random.uniform(shape=(), minval=0, maxval=freq_max - f, dtype=tf.dtypes.int32)
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@ -50,7 +49,6 @@ def augment_freq_time_mask(mel_spectrogram,
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mel_spectrogram = mel_spectrogram*freq_mask
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# Time masking
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# Testing without loop
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for _ in range(time_mask_num):
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t = tf.random.uniform(shape=(), minval=0, maxval=time_masking_para, dtype=tf.dtypes.int32)
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t0 = tf.random.uniform(shape=(), minval=0, maxval=time_max - t, dtype=tf.dtypes.int32)
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