okwugbe 0.1.8

Automatic Speech Recognition Library for African Languages

Okwugbe

Automatic Speech Recognition Library for (low-resource) African Languages

Motivation

Our aim is to foster ASR for African languages by making the whole process--from dataset gathering and preprocessing to training--as easy as possible. 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

Usage

pip install okwugbe

#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()

Parameters

Here are the parameters for the package, as well as their default values.

The default values have been chosen so that you only have to make minimal changes to get a good ASR model going.

Parameter	Description	default
use_common_voice	Whether or not to use common voice	False
lang	language to use from Common Voice. Must be specified if use_common_voice is set to True.	None
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	20
patience	Early Stopping Patience	20
epochs	Training epochs	500
optimizer	Optimizer	'adamw'
freq_mask	frequency masking (for speech augmentation)	30
time_mask	time masking (for speech augmentation)	100
display_plot	whether or not to plot metrics during training	True

Integration with Common Voicee

You easily train on Common Voice data set with Okwugbe by specifying use_common_voice=True and setting lang to the language code of your choice. This language must be hosted on Common Voice.

#Initialize the trainer instance
train = Train_Okwugbe(use_common_voice=True, lang='mn') # for mongolian

#Start the training
train.run()

Here is the list of our current supported languages in Common Voice.

tt	en	de	fr	cy	br	cv	tr	ky	ga-IE	kab	ca	zh-TW	sl	it	nl	cnh	eo	et	fa	pt	eu	es	zh-CN	mn	sah	dv	rw	sv-SE	ru	id	ar	ta	ia	lv	ja	vot	ab	zh-HK	rm-sursilv
tatar	english	german	french	welsh	breton	chuvash	turkish	kyrgyz	irish	kabyle	catalan	taiwanese	slovenian	italian	dutch	hakha chin	esperanto	estonian	persian	portuguese	basque	spanish	chinese	mongolian	sakha	dhivehi	kinyarwanda	swedish	russian	indonesian	arabic	tamil	interlingua	latvian	japanese	votic	abkhaz	cantonese	romansh sursilvan

Tutorials

• on using OkwuGbe
• on using OkwuGbe with Common Voice

ASR Data for African languages

Wondering where to find dataset for your African language? Here are some resources to check:

• OpenSLR
• Mozilla Common Voice
• AfricaNLP-Public-Datasets

Debugging

is strictly for debugging!

Citation

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

@inproceedings{dossou-emezue-2021-okwugbe,
    title = "{O}kwu{G}b{\'e}: End-to-End Speech Recognition for {F}on and {I}gbo",
    author = "Dossou, Bonaventure F. P.  and
      Emezue, Chris Chinenye",
    booktitle = "Proceedings of the Fifth Workshop on Widening Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.winlp-1.1",
    pages = "1--4",
    abstract = "Language is a fundamental component of human communication. African low-resourced languages have recently been a major subject of research in machine translation, and other text-based areas of NLP. However, there is still very little comparable research in speech recognition for African languages. OkwuGb{\'e} is a step towards building speech recognition systems for African low-resourced languages. Using Fon and Igbo as our case study, we build two end-to-end deep neural network-based speech recognition models. We present a state-of-the-art automatic speech recognition (ASR) model for Fon, and a benchmark ASR model result for Igbo. Our findings serve both as a guide for future NLP research for Fon and Igbo in particular, and the creation of speech recognition models for other African low-resourced languages in general. The Fon and Igbo models source code have been made publicly available. Moreover, Okwugbe, a python library has been created to make easier the process of ASR model building and training.",
}```