diff --git a/DeepSpeech.py b/DeepSpeech.py index b4e4b705..8ba9d02f 100755 --- a/DeepSpeech.py +++ b/DeepSpeech.py @@ -415,7 +415,8 @@ def train(): # Create training and validation datasets train_set = create_dataset(FLAGS.train_files.split(','), batch_size=FLAGS.train_batch_size, - cache_path=FLAGS.feature_cache) + cache_path=FLAGS.feature_cache, + train_phase=True) iterator = tfv1.data.Iterator.from_structure(tfv1.data.get_output_types(train_set), tfv1.data.get_output_shapes(train_set), @@ -426,7 +427,7 @@ def train(): if FLAGS.dev_files: dev_csvs = FLAGS.dev_files.split(',') - dev_sets = [create_dataset([csv], batch_size=FLAGS.dev_batch_size) for csv in dev_csvs] + dev_sets = [create_dataset([csv], batch_size=FLAGS.dev_batch_size, train_phase=False) for csv in dev_csvs] dev_init_ops = [iterator.make_initializer(dev_set) for dev_set in dev_sets] # Dropout diff --git a/evaluate.py b/evaluate.py index c86ebc1e..32c45367 100755 --- a/evaluate.py +++ b/evaluate.py @@ -47,7 +47,7 @@ def evaluate(test_csvs, create_model, try_loading): Config.alphabet) test_csvs = FLAGS.test_files.split(',') - test_sets = [create_dataset([csv], batch_size=FLAGS.test_batch_size) for csv in test_csvs] + test_sets = [create_dataset([csv], batch_size=FLAGS.test_batch_size, train_phase=False) for csv in test_csvs] iterator = tfv1.data.Iterator.from_structure(tfv1.data.get_output_types(test_sets[0]), tfv1.data.get_output_shapes(test_sets[0]), output_classes=tfv1.data.get_output_classes(test_sets[0])) diff --git a/util/feeding.py b/util/feeding.py index ba11ebb0..a041503a 100644 --- a/util/feeding.py +++ b/util/feeding.py @@ -15,7 +15,8 @@ from tensorflow.python.ops import gen_audio_ops as contrib_audio from util.config import Config from util.logging import log_error from util.text import text_to_char_array - +from util.flags import FLAGS +from util.spectrogram_augmentations import augment_freq_time_mask, augment_dropout, augment_pitch_and_tempo, augment_speed_up def read_csvs(csv_files): source_data = None @@ -31,28 +32,58 @@ def read_csvs(csv_files): return source_data -def samples_to_mfccs(samples, sample_rate): +def samples_to_mfccs(samples, sample_rate, train_phase=False): spectrogram = contrib_audio.audio_spectrogram(samples, window_size=Config.audio_window_samples, stride=Config.audio_step_samples, magnitude_squared=True) + + # Data Augmentations + if train_phase: + if FLAGS.augmentation_spec_dropout_keeprate < 1: + spectrogram = augment_dropout(spectrogram, + keep_prob=FLAGS.augmentation_spec_dropout_keeprate) + + if FLAGS.augmentation_freq_and_time_masking: + spectrogram = augment_freq_time_mask(spectrogram, + frequency_masking_para=FLAGS.augmentation_freq_and_time_masking_freq_mask_range, + time_masking_para=FLAGS.augmentation_freq_and_time_masking_time_mask_range, + frequency_mask_num=FLAGS.augmentation_freq_and_time_masking_number_freq_masks, + time_mask_num=FLAGS.augmentation_freq_and_time_masking_number_time_masks) + + if FLAGS.augmentation_pitch_and_tempo_scaling: + spectrogram = augment_pitch_and_tempo(spectrogram, + max_tempo=FLAGS.augmentation_pitch_and_tempo_scaling_max_tempo, + max_pitch=FLAGS.augmentation_pitch_and_tempo_scaling_max_pitch, + min_pitch=FLAGS.augmentation_pitch_and_tempo_scaling_min_pitch) + + if FLAGS.augmentation_speed_up_std > 0: + spectrogram = augment_speed_up(spectrogram, speed_std=FLAGS.augmentation_speed_up_std) + mfccs = contrib_audio.mfcc(spectrogram, sample_rate, dct_coefficient_count=Config.n_input) mfccs = tf.reshape(mfccs, [-1, Config.n_input]) return mfccs, tf.shape(input=mfccs)[0] -def audiofile_to_features(wav_filename): +def audiofile_to_features(wav_filename, train_phase=False): samples = tf.io.read_file(wav_filename) decoded = contrib_audio.decode_wav(samples, desired_channels=1) - features, features_len = samples_to_mfccs(decoded.audio, decoded.sample_rate) + features, features_len = samples_to_mfccs(decoded.audio, decoded.sample_rate, train_phase=train_phase) + + if train_phase: + if FLAGS.data_aug_features_multiplicative > 0: + features = features*tf.random.normal(mean=1, stddev=FLAGS.data_aug_features_multiplicative, shape=tf.shape(features)) + + if FLAGS.data_aug_features_additive > 0: + features = features+tf.random.normal(mean=0.0, stddev=FLAGS.data_aug_features_additive, shape=tf.shape(features)) return features, features_len -def entry_to_features(wav_filename, transcript): +def entry_to_features(wav_filename, transcript, train_phase): # https://bugs.python.org/issue32117 - features, features_len = audiofile_to_features(wav_filename) + features, features_len = audiofile_to_features(wav_filename, train_phase=train_phase) return wav_filename, features, features_len, tf.SparseTensor(*transcript) @@ -65,7 +96,7 @@ def to_sparse_tuple(sequence): return indices, sequence, shape -def create_dataset(csvs, batch_size, cache_path=''): +def create_dataset(csvs, batch_size, cache_path='', train_phase=False): df = read_csvs(csvs) df.sort_values(by='wav_filesize', inplace=True) @@ -97,10 +128,11 @@ def create_dataset(csvs, batch_size, cache_path=''): return tf.data.Dataset.zip((wav_filenames, features, transcripts)) num_gpus = len(Config.available_devices) + process_fn = partial(entry_to_features, train_phase=train_phase) dataset = (tf.data.Dataset.from_generator(generate_values, output_types=(tf.string, (tf.int64, tf.int32, tf.int64))) - .map(entry_to_features, num_parallel_calls=tf.data.experimental.AUTOTUNE) + .map(process_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE) .cache(cache_path) .window(batch_size, drop_remainder=True).flat_map(batch_fn) .prefetch(num_gpus)) diff --git a/util/flags.py b/util/flags.py index e1eb1788..faa59f7a 100644 --- a/util/flags.py +++ b/util/flags.py @@ -21,6 +21,28 @@ def create_flags(): f.DEFINE_integer('feature_win_step', 20, 'feature extraction window step length in milliseconds') f.DEFINE_integer('audio_sample_rate', 16000, 'sample rate value expected by model') + # Data Augmentation + # ================ + + f.DEFINE_float('data_aug_features_additive', 0, 'std of the Gaussian additive noise') + f.DEFINE_float('data_aug_features_multiplicative', 0, 'std of normal distribution around 1 for multiplicative noise') + + f.DEFINE_float('augmentation_spec_dropout_keeprate', 1, 'keep rate of dropout augmentation on spectrogram (if 1, no dropout will be performed on spectrogram)') + + f.DEFINE_boolean('augmentation_freq_and_time_masking', False, 'whether to use frequency and time masking augmentation') + f.DEFINE_integer('augmentation_freq_and_time_masking_freq_mask_range', 5, 'max range of masks in the frequency domain when performing freqtime-mask augmentation') + f.DEFINE_integer('augmentation_freq_and_time_masking_number_freq_masks', 3, 'number of masks in the frequency domain when performing freqtime-mask augmentation') + 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') + f.DEFINE_integer('augmentation_freq_and_time_masking_number_time_masks', 3, 'number of masks in the time domain when performing freqtime-mask augmentation') + + f.DEFINE_float('augmentation_speed_up_std', 0, 'std for speeding-up tempo. If std is 0, this augmentation is not performed') + + f.DEFINE_boolean('augmentation_pitch_and_tempo_scaling', False, 'whether to use spectrogram speed and tempo scaling') + f.DEFINE_float('augmentation_pitch_and_tempo_scaling_min_pitch', 0.95, 'min value of pitch scaling') + f.DEFINE_float('augmentation_pitch_and_tempo_scaling_max_pitch', 1.2, 'max value of pitch scaling') + f.DEFINE_float('augmentation_pitch_and_tempo_scaling_max_tempo', 1.2, 'max vlaue of tempo scaling') + + # Global Constants # ================ diff --git a/util/spectrogram_augmentations.py b/util/spectrogram_augmentations.py new file mode 100644 index 00000000..9cf36a24 --- /dev/null +++ b/util/spectrogram_augmentations.py @@ -0,0 +1,66 @@ +import tensorflow as tf + +def augment_freq_time_mask(mel_spectrogram, + frequency_masking_para=30, + time_masking_para=10, + frequency_mask_num=3, + time_mask_num=3): + freq_max = tf.shape(mel_spectrogram)[1] + time_max = tf.shape(mel_spectrogram)[2] + # Frequency masking + for _ in range(frequency_mask_num): + f = tf.random.uniform(shape=(), minval=0, maxval=frequency_masking_para, dtype=tf.dtypes.int32) + f0 = tf.random.uniform(shape=(), minval=0, maxval=freq_max - f, dtype=tf.dtypes.int32) + value_ones_freq_prev = tf.ones(shape=[1, f0, time_max]) + value_zeros_freq = tf.zeros(shape=[1, f, time_max]) + value_ones_freq_next = tf.ones(shape=[1, freq_max-(f0+f), time_max]) + freq_mask = tf.concat([value_ones_freq_prev, value_zeros_freq, value_ones_freq_next], axis=1) + #mel_spectrogram[:, f0:f0 + f, :] = 0 #can't assign to tensor + #mel_spectrogram[:, f0:f0 + f, :] = value_zeros_freq #can't assign to tensor + mel_spectrogram = mel_spectrogram*freq_mask + + # Time masking + for _ in range(time_mask_num): + t = tf.random.uniform(shape=(), minval=0, maxval=time_masking_para, dtype=tf.dtypes.int32) + t0 = tf.random.uniform(shape=(), minval=0, maxval=time_max - t, dtype=tf.dtypes.int32) + value_zeros_time_prev = tf.ones(shape=[1, freq_max, t0]) + value_zeros_time = tf.zeros(shape=[1, freq_max, t]) + value_zeros_time_next = tf.ones(shape=[1, freq_max, time_max-(t0+t)]) + time_mask = tf.concat([value_zeros_time_prev, value_zeros_time, value_zeros_time_next], axis=2) + #mel_spectrogram[:, :, t0:t0 + t] = 0 #can't assign to tensor + #mel_spectrogram[:, :, t0:t0 + t] = value_zeros_time #can't assign to tensor + mel_spectrogram = mel_spectrogram*time_mask + + return mel_spectrogram + +def augment_pitch_and_tempo(spectrogram, + max_tempo=1.2, + max_pitch=1.1, + min_pitch=0.95): + original_shape = tf.shape(spectrogram) + choosen_pitch = tf.random.uniform(shape=(), minval=min_pitch, maxval=max_pitch) + choosen_tempo = tf.random.uniform(shape=(), minval=1, maxval=max_tempo) + new_height = tf.cast(tf.cast(original_shape[1], tf.float32)*choosen_pitch, tf.int32) + new_width = tf.cast(tf.cast(original_shape[2], tf.float32)/(choosen_tempo), tf.int32) + spectrogram_aug = tf.image.resize_bilinear(tf.expand_dims(spectrogram, -1), [new_height, new_width]) + spectrogram_aug = tf.image.crop_to_bounding_box(spectrogram_aug, offset_height=0, offset_width=0, target_height=tf.minimum(original_shape[1], new_height), target_width=tf.shape(spectrogram_aug)[2]) + spectrogram_aug = tf.cond(choosen_pitch < 1, + lambda: tf.image.pad_to_bounding_box(spectrogram_aug, offset_height=0, offset_width=0, + target_height=original_shape[1], target_width=tf.shape(spectrogram_aug)[2]), + lambda: spectrogram_aug) + return spectrogram_aug[:, :, :, 0] + + +def augment_speed_up(spectrogram, + speed_std=0.1): + original_shape = tf.shape(spectrogram) + choosen_speed = tf.math.abs(tf.random.normal(shape=(), stddev=speed_std)) # abs makes sure the augmention will only speed up + choosen_speed = 1 + choosen_speed + new_height = tf.cast(tf.cast(original_shape[1], tf.float32), tf.int32) + new_width = tf.cast(tf.cast(original_shape[2], tf.float32)/(choosen_speed), tf.int32) + spectrogram_aug = tf.image.resize_bilinear(tf.expand_dims(spectrogram, -1), [new_height, new_width]) + return spectrogram_aug[:, :, :, 0] + +def augment_dropout(spectrogram, + keep_prob=0.95): + return tf.nn.dropout(spectrogram, rate=1-keep_prob)