NeMo Speech ASR Fine-Tuning

Use this skill when a user wants to fine-tune a NeMo Speech ASR model, choose a checkpoint, adapt a tokenizer, configure Lhotse dataloading, train, average checkpoints, or evaluate a fine-tuned ASR .nemo checkpoint. Also use it for post-run refinement planning after fine-tuning.

Default posture:

• Use the NeMo container unless the user explicitly asks for local execution.
• Prefer Lhotse for train and validation dataloaders.
• Use trainer.max_steps, not trainer.max_epochs.
• Use val_wer as the checkpoint monitor for validation.
• By default, evaluate WER without capitalization and punctuation effects. Change that only when the user explicitly asks for raw/cased/punctuated scoring.
• Report final quality from standalone evaluation, not only in-training validation logs.

Staged Workflow

Load only the reference file needed for the current stage: