---

name: audio description: "Use for NeMo Speech audio-to-audio enhancement, restoration, separation, denoising, beamforming, generation, Lhotse audio manifests, evaluation, augmentation saving, multi-channel handling, and audio troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Audio

Use this sub-skill for NeMo Speech audio-to-audio work: enhancement, restoration, denoising, source separation, beamforming, background-noise removal, predictive/generative audio models, audio model processing APIs, Lhotse audio manifests, objective evaluation, online augmentation export, and multi-channel audio handling.

Route classic speech transcription, timestamps, ASR decoding, and ASR fine-tuning to ../asr/SKILL.md. Route text-to-speech waveform synthesis to the TTS sub-skill when available. Route generic manifest sharding, tokenizer building, and dataset conversion utilities to the data-tools sub-skill when available.

Start Here

1. Read references/workflows.md for audio processing, evaluation, training, fine-tuning, Lhotse, augmentation-saving, and multi-channel workflows.
2. Read references/api-reference.md before using AudioToAudioModel, model subclasses, dataset factories, Lhotse conversion helpers, metrics, losses, transforms, masking, or Maxine BNR classes.
3. Read references/configuration.md before editing Hydra YAML, dataset manifests, Lhotse CutSet/Shar settings, model-family configs, sampler overrides, metrics, or output paths.
4. Read references/troubleshooting.md when imports, optional dependencies, CUDA/backends, manifest schemas, Hydra overrides, model loading, processing, evaluation, Lhotse, augmentation, or multi-channel workflows fail.

Safe Bundled Tools

• scripts/check_audio_manifest.py validates audio-to-audio JSONL manifests without importing NeMo, downloading models, opening audio contents, training, or writing outputs.
• Run it before long processing, training, or evaluation jobs: python scripts/check_audio_manifest.py data.jsonl --input-key noisy_filepath --target-key clean_filepath --check-files --output-dir outputs/enhanced --output-manifest outputs/enhanced_manifest.json.
• Use --require-target for training/evaluation manifests and omit it for inference-only manifests where target audio is intentionally absent.

Evidence Base

This sub-skill distills repository evidence from docs/source/audio/intro.rst, docs/source/audio/models.rst, docs/source/audio/datasets.rst, docs/source/audio/configs.rst, docs/source/audio/api.rst, examples/audio/*.py, examples/audio/conf/*.yaml, nemo/collections/audio/models/*.py, nemo/collections/audio/data/**, and tests/collections/audio/**. Runtime guidance is self-contained; do not require future agents to reopen those source files or run the original example scripts.