wav2vec2 xls r 2b

Introduction

Wav2Vec2 XLS-R-2B is a large-scale multilingual model developed by Facebook AI for speech processing. It is designed to handle 128 languages and is pretrained on 436,000 hours of unlabeled speech data. The model aims to support tasks such as Automatic Speech Recognition (ASR), translation, and classification by utilizing the wav2vec 2.0 framework. It is noted for its significant parameter size of 2 billion, which facilitates cross-lingual representation learning.

Architecture

XLS-R is an extension of wav2vec 2.0, leveraging a large corpus of multilingual speech data. The model is constructed with up to 2 billion parameters, allowing it to learn complex and generalized speech representations. It has been pretrained on diverse datasets including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107, all sampled at 16kHz.

Training

The model training involves self-supervised learning on 436K hours of speech audio. XLS-R's training setup covers a wide range of tasks and languages, demonstrating improvements in tasks like speech translation, recognition, and language identification. It has set new benchmarks in various datasets by lowering error rates significantly compared to previous models.

Guide: Running Locally

To run XLS-R locally, follow these steps:

1. Install Dependencies: Ensure you have Python and PyTorch installed.
2. Clone Repository: Get the model code from github.com/pytorch/fairseq.
3. Load Model: Use the Hugging Face Transformers library to load the model.
4. Fine-tune: Follow instructions from this Google Colab notebook to fine-tune the model on specific tasks.

For optimal performance, consider using cloud GPUs like AWS EC2, Google Cloud, or Azure.

License

Wav2Vec2 XLS-R-2B is released under the Apache-2.0 license, allowing for both personal and commercial use with attribution.