wav2vec2 large

Introduction

The Wav2Vec2-Large model by Facebook is a powerful speech processing model designed for Automatic Speech Recognition (ASR). It is pretrained on 16kHz sampled speech audio, making it essential for users to input speech at the same sampling rate. The model demonstrates significant advancements in learning speech representations from raw audio, outperforming previous semi-supervised methods.

Architecture

Wav2Vec2-Large employs a novel approach by masking speech input in the latent space and addressing a contrastive task over quantized latent representations. This method allows for effective learning of speech features, which can be fine-tuned for downstream tasks like ASR.

Training

The model exhibits impressive results with varying amounts of labeled data. It achieves 1.8/3.3 Word Error Rate (WER) on Librispeech's clean/other test sets using all labeled data. With only one hour of labeled data, it surpasses earlier models using the 100-hour subset, utilizing 100 times less data. Remarkably, with just ten minutes of labeled data and pretraining on 53k hours of unlabeled data, it attains a WER of 4.8/8.2.

Guide: Running Locally

1. Environment Setup:

   • Install PyTorch and the Hugging Face Transformers library.
   • Clone the Wav2Vec2 repository from GitHub.

2. Model Loading:

   • Load the pretrained Wav2Vec2 model and tokenizer from the Hugging Face model hub.

3. Fine-Tuning:

   • Use the provided Colab notebook to fine-tune the model on your specific dataset.

4. Inference:

   • Ensure your input audio is sampled at 16kHz for optimal performance.

5. Cloud GPUs:

   • For efficient training and inference, consider using cloud GPU services like AWS, Google Cloud, or Azure.

License

The Wav2Vec2-Large model is licensed under the Apache 2.0 License, allowing for both commercial and non-commercial use with proper attribution.