wav2vec2 large xlsr 53

Introduction

The WAV2VEC2-LARGE-XLSR-53 model is developed by Facebook AI, designed for cross-lingual speech recognition. It is built on the wav2vec 2.0 framework and pre-trained on multilingual speech data. This model is advantageous for tasks like Automatic Speech Recognition (ASR) and is particularly effective in low-resource languages, as it enables a single multilingual model to perform competitively against monolingual systems.

Architecture

The model employs a contrastive task over masked latent speech representations, learning a quantization of the latents shared across languages. This architecture allows the model to capture cross-lingual speech representations effectively, with increased sharing observed among related languages. The XLSR-53 model is pretrained with speech data in 53 languages, enhancing its ability to generalize across diverse linguistic contexts.

Training

XLSR-53 is pretrained on 16kHz sampled speech audio. The training involved solving contrastive tasks over masked latent speech representations. This approach facilitates effective cross-lingual pretraining, which outperforms monolingual pretraining. The model has shown significant improvements in phoneme and word error rates on various benchmarks like CommonVoice and BABEL.

Guide: Running Locally

To run the model locally, follow these steps:

1. Install Dependencies: Ensure you have Python and the Hugging Face Transformers library installed.
2. Download the Model: Use the Transformers library to load the wav2vec2-large-xlsr-53 model.
3. Prepare Data: Ensure your speech input data is sampled at 16kHz.
4. Fine-Tuning: Fine-tune the model on your specific task using labeled data. Refer to this notebook for a detailed guide.
5. Cloud GPUs: To expedite training, consider using cloud GPU services like Google Colab, AWS, or Azure.

License

The model is released under the Apache 2.0 License, permitting open and flexible use across various applications.