Optimizing OpusLM_7B_Anneal for a specific scenario requires model fine-tuning, which requires the preparation of a labeled dataset (with speech segments and corresponding text) that conforms to the structure of the Kaldi data catalog. The fine-tuning process is performed by modifying the config.yaml file to configure hyperparameters such as learning rate, batch size, etc., and calling espnet2/bin/train.py to initiate training. The completed model can be uploaded to the Hugging Face platform for sharing via the run.sh script. This feature enables the model to adapt to proprietary domain terminology (e.g., medical, legal) or dialect recognition, but note that the fine-tuning requires additional GPU computational resources and data cleaning efforts, which may otherwise lead to performance degradation.
This answer comes from the articleOpusLM_7B_Anneal: an efficient unified model for speech recognition and synthesisThe
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