AI BriefingCombining Wav2Vec2 with an n-gram Language Model
Key point
This provides a technical guide on improving Wav2Vec2's speech recognition performance using an n-gram language model.
Details
Wav2Vec2 is a powerful speech recognition model, but combining it with an n-gram language model (LM) can further boost performance significantly. This combination with a language model is especially effective in environments with little training data.
Hugging Face Transformers, through its integration with the pyctcdecode library, provides an interface that lets users easily combine an n-gram model with a fine-tuned Wav2Vec2 checkpoint.
This guide includes the following key steps:
- Understanding decoding methods: the difference in decoding with and without a language model
- Data preparation: how to obtain data suitable for building a language model
- Building the model: the process of creating an n-gram model using KenLM
- Combining the models: integrating the built n-gram model with a fine-tuned Wav2Vec2 checkpoint to perform the final speech recognition
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