readme: 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)
   
  
   