HIYACCENT: An Improved Nigerian-Accented Speech Recognition System Based on Contrastive Learning

The global objective of this research was to develop a more robust model for the Nigerian English Speakers whose English pronunciations are heavily affected by their mother tongue. For this, the Wav2Vec-HIYACCENT model was proposed which introduced a new layer to the Novel Facebook Wav2vec to capture the disparity between the baseline model and Nigerian English Speeches. A CTC loss was also inserted on top of the model which adds flexibility to the speech-text alignment. This resulted in over 20% improvement in the performance for NAE.T

Fine-tuned facebook/wav2vec2-large on English using the UISpeech Corpus. When using this model, make sure that your speech input is sampled at 16kHz.

The script used for training can be found here: https://github.com/amceejay/HIYACCENT-NE-Speech-Recognition-System

##Usage: The model can be used directly (without a language model) as follows...

#Using the ASRecognition library: from asrecognition import ASREngine

asr = ASREngine("fr", model_path="codeceejay/HIYACCENT_Wav2Vec2")

audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = asr.transcribe(audio_paths)

##Writing your own inference speech: import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

LANG_ID = "en" MODEL_ID = "codeceejay/HIYACCENT_Wav2Vec2" SAMPLES = 10

#You can use common_voice/timit or Nigerian Accented Speeches can also be found here: https://openslr.org/70/ test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")

processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)

Preprocessing the datasets.

We need to read the audio files as arrays

def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch

test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)

with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

predicted_ids = torch.argmax(logits, dim=-1) predicted_sentences = processor.batch_decode(predicted_ids)

for i, predicted_sentence in enumerate(predicted_sentences): print("-" * 100) print("Reference:", test_dataset[i]["sentence"]) print("Prediction:", predicted_sentence)