okwugbe 0.0.1

Minimalist Automatic Speech Recognition Library for African Languages

Okwugbe

Minimalist Automatic Speech Recognition Library for African Languages

Context

NB: This library follows our work Okwugbé on ASR for Fon and Igbo. Based on the architecture of the network described in our paper, it aims at easing the training process of ASR for other languages. The primary targets are African languages, but it supports other languages as well

Parameters

Parameter	Description	default
rnn_dim	RNN Dimension & Hidden Size	512
num_layers	Number of Layers	1
n_cnn	Number of CNN components	5
n_rnn	Number of RNN components	3
n_feats	Number of features for the ResCNN	128
in_channels	Number of input channels of the ResCNN	1
out_channels	Number of output channels of the ResCNN	32
kernel	Kernel Size for the ResCNN	3
stride	Stride Size for the ResCNN	2
padding	Padding Size for the ResCNN	1
dropout	Dropout (kept unique for all components)	0.1
with_attention	True to use attention mechanism, False else	False
batch_multiplier	Batch multiplier for Gradient Accumulation)	1 (no Gradient Accumulation)
grad_acc	Gradient Accumulation Option	False
model_path	Path for the saved model	'./okwugbe_model'
characters_set	Path to the .txt file containing unique characters	required
validation_set	Validation set size	0.2
train_path	Path to training set	required
test_path	Path to testing set	required
learning_rate	Learning rate	3e-5
batch_size	Batch Size	3e-5
patience	Early Stopping Patience	20
epochs	Training epochs	500
optimizer	Optimizer	'adamw'

Usage

• Import the trainer instance

  • from train_eval import Train_Okwugbe

    • train_path = '/path/to/training_file.csv'
    • test_path = '/path/to/testing_file.csv'
    • characters_set = '/path/to/character_set.txt'

  • /path/to/training_file.csv and /path/to/testing_file.csv are meant to be csv files with two columns:

  • the first one containing the full paths to audio wav files

  • the second one containing the textual transcription of audio contents

• Initialize the trainer instance

  • train = Train_Okwugbe(train_path, test_path, characters_set)

• Start the training

  • train.run()

TODO (as of now)

• Add automatic building of character set

Citation

Please cite our paper using the citation below if you use our work in anyway:

@article{2103.07762, Author = {Bonaventure F. P. Dossou and Chris C. Emezue}, Title = {OkwuGbé: End-to-End Speech Recognition for Fon and Igbo}, Year = {2021}, Eprint = {arXiv:2103.07762}, Howpublished = {African NLP, EACL 2021} }