nemo-speech-asr-finetune

name: nemo-speech-asr-finetune description: Guide NeMo Speech users through ASR fine-tuning with container setup and Lhotse training.

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:

1. Setup and checkpoint selection: read references/setup-checkpoints.md.
2. Data prep, transcript-style preflight, Lhotse, bucketing, validation dataloader, and blends: read references/data-lhotse.md.
3. Architecture detection, tokenizer changes, and AED/Canary multitask metrics: read references/architecture-tokenizer-metrics.md.
4. Training, checkpoint averaging, and evaluation: read references/training-evaluation.md and, when reporting WER, references/evaluation-style-contract.md.
5. Post-run refinement, error analysis, curriculum, and general-vs-domain evaluation: read references/refinement-iteration.md.

If the user explicitly asks for parallel/sub-agent work, split the work by these same stages. Keep each agent scoped to one stage and have the main agent integrate the final command/config.

Core Commands

Generic fine-tuning uses examples/asr/speech_to_text_finetune.py. For architecture-specific recipes, route to:

• CTC: examples/asr/asr_ctc/speech_to_text_ctc_bpe.py
• RNNT: examples/asr/asr_transducer/speech_to_text_rnnt_bpe.py
• Hybrid RNNT/CTC or TDT/CTC: examples/asr/asr_hybrid_transducer_ctc/speech_to_text_hybrid_rnnt_ctc_bpe.py
• AED/Canary: examples/asr/speech_multitask/speech_to_text_aed.py

Always check the current repo docs before giving version-sensitive claims:

• README.md
• docs/source/asr/fine_tuning.rst
• docs/source/asr/datasets.rst
• docs/source/dataloaders.rst
• docs/source/asr/featured_models.rst
• docs/source/asr/asr_checkpoints.rst
• nemo/collections/common/data/lhotse/dataloader.py

Non-Negotiable Pitfalls

• When changing Lhotse batch modes, explicitly null conflicting options. For OOMptimizer profiles, set batch_size=null, batch_duration=null, and quadratic_duration=null when adding bucket_batch_size.
• Set model.validation_ds.use_lhotse=true, but prefer static validation batch_size with bucketing disabled.
• Do not use fused loss/WER or tune fused_batch_size for RNNT/TDT fine-tuning guidance from this skill.
• Run the first OOMptimizer pass with default CLI settings; lower --memory-fraction only after a real training OOM.
• Run preflight checks before long jobs: disk space, free GPUs, manifest validity, and duration/text sanity.
• Before any fine-tuning, audit transcript style within and across all fine-tuning/validation/test sources. Do not train on mixed casing, punctuation, inverse-text-normalization, or symbol conventions; choose and fix one target style first, and compare it with the original checkpoint's prediction style when applicable.
• For small domain adaptation, start with a lower LR than large-data fine-tuning; do not blindly use 1e-4.
• Do not train a tokenizer on validation or test transcripts.
• Do not ignore silent Lhotse filtering from min_duration, max_duration, min_tps, and max_tps.
• Do not use amp=true for inference/evaluation; use amp=false compute_dtype=bfloat16.
• Unless the user asks otherwise, report the default WER with capitalization and punctuation removed, and record any raw WER separately when it helps diagnose transcript-style mismatch.
• For AED/Canary, configure multitask_metrics_cfg so ASR and translation/task-specific samples are evaluated with the right constrained metrics.
• If checkpoint averaging is used, evaluate the averaged checkpoint and keep it only if it beats the best individual checkpoint.