wav2vec2 large 960h

Introduction

Wav2Vec2.0 is a model developed by Facebook AI for automatic speech recognition. It is trained on 960 hours of Librispeech data at 16kHz and has demonstrated superior performance in recognizing speech with limited labeled data. The model achieves state-of-the-art results by learning representations from raw audio and fine-tuning on transcribed speech.

Architecture

Wav2Vec2.0 operates by masking the speech input in the latent space and solving a contrastive task over a quantization of the latent representations. This approach allows for effective learning from large amounts of unlabeled data, significantly reducing the need for labeled data in training.

Training

The model is pretrained on extensive unlabeled speech data (53k hours) and fine-tuned on smaller labeled datasets. It achieves a Word Error Rate (WER) of 1.8/3.3 on clean/other test sets of Librispeech using all labeled data and maintains competitive WERs with significantly less labeled data.

Guide: Running Locally

1. Install Dependencies: Ensure you have Python and install the required libraries such as transformers, datasets, and torch.
2. Load the Model: Use the Wav2Vec2Processor and Wav2Vec2ForCTC classes from the transformers library to load the pretrained model and processor.
3. Transcribe Audio: Load your audio data, preprocess it using the processor, and feed it into the model to obtain transcriptions.
4. Evaluate: Use the provided evaluation script to check the model's performance on test datasets like LibriSpeech.

Suggested Cloud GPUs

For optimal performance, it is recommended to use cloud GPU services such as AWS EC2 with NVIDIA GPUs, Google Cloud Platform's AI Platform, or Azure's Machine Learning services.

License

The Wav2Vec2.0 model is released under the Apache 2.0 license, allowing for wide usage and modification in both personal and commercial projects.