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データ・研究 · SKILL.md

data-tools

data-tools

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.

Independent GitHub snapshot · Automated classification · Original documentation governs

スキルの説明

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.

保存したスキル