wavlm large

Introduction

WavLM-Large is a speech model developed by Microsoft for large-scale self-supervised pre-training aimed at full-stack speech processing tasks. It is tailored for tasks such as speech recognition and audio classification, with a focus on spoken content modeling and speaker identity preservation.

Architecture

WavLM builds upon the HuBERT framework, enhancing the Transformer structure with gated relative position bias to improve recognition capabilities and introducing an utterance mixing training strategy to enhance speaker discrimination. The training dataset was expanded to 94k hours, and the model achieves state-of-the-art performance on the SUPERB benchmark.

Training

The model was pre-trained on 60,000 hours of Libri-Light, 10,000 hours of GigaSpeech, and 24,000 hours of VoxPopuli, totaling 94,000 hours. It learns universal representations for various speech processing tasks and needs to be fine-tuned for specific applications such as speech recognition and audio classification.

Guide: Running Locally

1. Install Dependencies: Ensure you have Python and PyTorch installed. Use the Transformers library by Hugging Face.
2. Download the Model: Clone the model repository from Hugging Face or GitHub.
3. Fine-tune the Model: Follow the official examples for speech recognition or audio classification.
4. Inference: Once fine-tuned, use the model for inference tasks.

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

License

The model is licensed under the terms specified in the official license.