In this KZfaq tutorial, we'll explore the Wav2Vec2 model, a powerful tool for speech recognition and representation learning. If you're in the field of speech recognition or interested in top-notch models, you've likely heard of Wav2Vec2. This video focuses on practical steps, guiding you through fine-tuning Wav2Vec2 with your own speech data without delving deep into technicalities.
Wav2Vec2 is designed for Connectionist Temporal Classification (CTC) loss, and we'll show you how to use it effectively for your tasks. You can leverage pre-trained models and adapt them to your needs, saving you from starting from scratch.
We'll walk you through the code, ensuring you have the necessary requirements like PyTorch and Transformers. You'll also learn how to apply audio augmentations to enhance data quality.
Throughout the tutorial, you'll discover how to monitor your model's progress with TensorBoard, implement early stopping, and save the best checkpoints. We'll also cover converting your PyTorch model to ONNX for easier deployment on various platforms.
To validate the model's performance, we'll run inference on a test dataset, checking character and word error rates to showcase the model's accuracy.
This tutorial aims to empower you to use Wav2Vec2 effectively for speech recognition tasks, whether you're a beginner or an experienced practitioner.
GitHub link: github.com/pythonlessons/mltu...
Trained model: drive.google.com/drive/folder...
#transformers #nlp #wav2vec #tensorflow #pytorch