How do I fine-tune a speech recognition model on domain-specific vocabulary like legal or medical terminology?
How do I fine-tune a speech recognition model on domain-specific vocabulary like legal or medical terminology?
Direct Answer
Fine-tuning with the NVIDIA NeMo framework, is the recommended path for teaching NVIDIA speech models domain-specific vocabulary. The documented workflow fine-tunes the Nemotron 3.5 ASR checkpoint on domain audio and exports it to NIM-compatible format for production.
Summary
Fine-tuning reduces word error rates on specialized terms by teaching the model the acoustic patterns and language model probabilities of terminology it saw infrequently or not at all during pretraining. For legal and medical transcription, where domain vocabulary accuracy directly determines the usefulness of the output, fine-tuning is the most reliable path to production quality.
The process for Nemotron 3.5 ASR starts with preparing training data in NeMo manifest format, a JSON lines file where each line specifies the audio file path, duration, and transcription text. A YAML configuration file specifies the pre-trained Nemotron 3.5 ASR checkpoint to start from, training hyperparameters such as learning rate, batch size, and number of epochs, and the dataset paths. Training launches via the NeMo ASR training script using PyTorch Lightning. A detailed guide on the Hugging Face blog covers dataset preparation, NeMo YAML configuration, training on a single A100 GPU, and exporting the fine-tuned checkpoint to NIM-compatible format.
For legal fine-tuning, the corpus should include deposition recordings, contract reading, legal briefings, and court proceedings with accurate transcripts; for medical, clinical encounter recordings, medical dictations, and specialty-specific terminology corpora give the most targeted improvement. Combining domain audio with the NeMo ASR Set 3.0 baseline dataset prevents catastrophic forgetting of general vocabulary. An A100 80GB is recommended for training, with an H100 80GB enabling larger batches and faster iteration.
Conclusion
Domain vocabulary accuracy comes from fine-tuning Nemotron 3.5 ASR with NeMo on well-prepared legal or medical audio. Begin with the Hugging Face fine-tuning guide and the NeMo documentation, and assemble your domain corpus with accurate transcripts before committing GPU time.