spec-augment 0.0.3

Tensorflow Layer that implements the SpecAugment technique

Theory

This library contains an implementation of SpecAugment, a simple data augmentation technique for speech recognition. Following the kapre philosophy, the implementation of this method has been encapsulated in a custom layer of Tensorflow, so that it can be incorporated into our neural network architectures directly.

Install Package

To install the package execute the following command.

pip install spec-augment

Usage

SpecAugment(freq_mask_param=5, time_mask_param=10)

SpecAugment(freq_mask_param=5, time_mask_param=10, n_freq_mask=5, n_time_mask=3)

SpecAugment(freq_mask_param=5, time_mask_param=10, n_freq_mask=5, n_time_mask=3,
            mask_value=0)

Arguments

• freq_mask_param - Frequency Mask Parameter (F in the paper)
• time_mask_param - Time Mask Parameter (T in the paper)
• n_freq_mask - Number of frequency masks to apply (mF in the paper). By default is 1.
• n_time_mask - Number of time masks to apply (mT in the paper). By default is 1.
• mask_value - Imputation value. By default is zero.

Example

SpecAugment is a technique applicable to spectrograms. In the following example, kapre is used to compute the Mel Spectrogram of a sample audio from Librosa.

import tensorflow as tf
from tensorflow.keras.models import Sequential
import librosa
import kapre
from spec_augment import SpecAugment

filename = librosa.ex('trumpet')
y, sr = librosa.load(filename)

audio_tensor = tf.reshape(tf.cast(y, tf.float32), (1, -1, 1))
input_shape = y.reshape(-1, 1).shape

melgram = kapre.composed.get_melspectrogram_layer(input_shape=input_shape,
                                                  n_fft=1024,
                                                  return_decibel=True,
                                                  n_mels=256,
                                                  input_data_format='channels_last',
                                                  output_data_format='channels_last')

Now we instantiate the SpecAugment layer. We are using an F of 5 (freq_mask_param), a T of 10 (time_mask_param), 5 frequency masks (n_freq_mask) and 3 time masks (n_time_mask). We will use a mask_value of -100.

# Now we define the SpecAugment layer
spec_augment = SpecAugment(freq_mask_param=5,
                           time_mask_param=10,
                           n_freq_mask=5,
                           n_time_mask=3,
                           mask_value=-100)                 
model = Sequential()
model.add(melgram)
model.add(spec_augment)

model.summary()

References

https://arxiv.org/abs/1904.08779