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数据与研究 · SKILL.md

data-tools

data-tools

【数据与研究】Use for NeMo Speech data preparation, JSON manifests, Lhotse and tarred datasets, duration bins, dataset weights, tokenizers, evaluation utilities, CTC segmentation, checkpoint utilities, and safe data validation.

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技能说明

GitHub

name: data-tools description: "Use for NeMo Speech data preparation, JSON manifests, Lhotse and tarred datasets, duration bins, dataset weights, tokenizers, evaluation utilities, CTC segmentation, checkpoint utilities, and safe data validation." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Data Tools

Use this sub-skill for shared NeMo Speech data preparation and utility work: JSONL manifests, Lhotse input_cfg planning, CutSet/Shar inputs, tarred dataset conversion, duration-bin and dataset-weight estimation, tokenizer preparation, ASR evaluator data contracts, CTC segmentation catalogs, speech data explorer/simulator planning, checkpoint utilities, and customization dataset preparation.

Route model-specific training, decoding, inference, or architecture work to sibling sub-skills such as ../asr/SKILL.md, ../audio/SKILL.md, or ../speechlm2/SKILL.md when those are the primary goal. Route repository contribution policy, linting, and test strategy to a repo-development sub-skill when present.

Start Here

  1. Read references/data-formats.md before creating or validating NeMo manifests, Lhotse input_cfg, tokenizer corpora, ASR evaluator inputs, or customization JSONL.
  2. Read references/lhotse-and-tarred-data.md before enabling Lhotse, estimating duration bins, mixing datasets, converting to tarred data, or planning Shar/CutSet inputs.
  3. Read references/tool-catalog.md before using NeMo data utility scripts, tokenizers, checkpoint averaging, ASR evaluator, CTC segmentation, Speech Data Explorer, Speech Data Simulator, or customization dataset prep.
  4. Read references/troubleshooting.md when imports, optional dependencies, CUDA/backends, Hydra overrides, manifest schemas, tar shard alignment, tokenizer generation, or long data workflows fail.

Safe Bundled Tool

  • scripts/validate_manifest.py validates JSONL manifests without importing NeMo, downloading data, opening audio contents by default, training, starting servers, or writing outputs.
  • From this sub-skill directory, run it before long data jobs: python scripts/validate_manifest.py manifest.jsonl --required audio_filepath duration text --min-duration 0.1 --max-duration 30 --summary.
  • Use --check-files only when local audio paths are expected to exist and file-existence checks are safe; omit it for tarred manifests, remote URIs, object-store paths, and manifests with audio filenames inside tar archives.

Evidence Base

This sub-skill distills repository evidence from docs/source/dataloaders.rst, docs/source/tools/*.rst, docs/source/common/data.rst, scripts/speech_recognition/*.py, scripts/tokenizers/*.py, scripts/dataset_processing/**, scripts/checkpoint_averaging/**, tools/asr_evaluator/**, tools/ctc_segmentation/**, tools/speech_data_explorer/**, tools/speech_data_simulator/**, tools/customization_dataset_preparation/**, tests/collections/common/test_lhotse_*, and customization dataset preparation tests. Runtime guidance is self-contained; do not require future agents to reopen those source files or run original checkout scripts.

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