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name: nemo description: "Use for NVIDIA NeMo Speech ASR, TTS, audio processing, SpeechLM2 and voice-agent workflows, speaker diarization, data tooling, and repository development." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

NeMo

Use this repo skill for NVIDIA NeMo Speech tasks involving automatic speech recognition, text-to-speech, audio-to-audio processing, SpeechLM2 and voice-agent workflows, speaker diarization, data preparation/tools, or maintaining the NeMo Speech repository.

NeMo Speech is a PyTorch speech AI toolkit. The active user-facing areas in this skill are ASR, TTS, audio, SpeechLM2, speaker tasks, shared data/tools, and repo development. The generated skill is self-contained: use the bundled sub-skill references and helper scripts instead of relying on the original checkout's docs, examples, tests, or scripts being present.

Install And Import Check

Prefer the current public install guidance when preparing a NeMo runtime environment:

# Source checkout, reproducible supported stack.
uv sync --extra all --extra cu13

# Bring-your-own Python/PyTorch/CUDA stack.
uv pip install 'nemo-toolkit[asr,tts,audio,speechlm2]'

# Minimal import check for an installed environment.
python -c "import nemo; print(nemo.__version__)"

Use Python 3.12+, PyTorch 2.7+, and a CUDA-capable NVIDIA GPU for training. CPU-only inference may work for small checks but is slow and not representative for production speech workloads. Avoid uv sync --locked when the user explicitly wants to keep an existing PyTorch/CUDA stack.

Read references/repo-provenance.md before deciding whether this skill matches a current checkout. If the commit, dirty state, package version, active collections, examples, or docs have changed, refresh the skill before relying on version-sensitive commands.

Route By Task

• sub-skills/asr/SKILL.md: ASR model selection, .nemo/pretrained checkpoint loading, transcription, timestamps, streaming/cache-aware inference, fine-tuning, Lhotse batching, tokenizer changes, decoding customization, ASR evaluation, and ASR troubleshooting.
• sub-skills/tts/SKILL.md: TTS synthesis, FastPitch/HiFi-GAN cascades, MagpieTTS/EasyMagpie, G2P, audio codecs, TTS manifests, text/phoneme preprocessing, finetuning, evaluation, and TTS checkpoint troubleshooting.
• sub-skills/audio/SKILL.md: Audio enhancement, restoration, denoising, separation, beamforming, predictive/generative audio-to-audio models, audio manifests, evaluation, augmentation export, and multi-channel audio handling.
• sub-skills/speechlm2/SKILL.md: SpeechLM2/SALM/SALMAutomodel, duplex STT/S2S/EAR-TTS, Nemotron VoiceChat, HuggingFace conversion, vLLM plugin integration, voice-agent configuration, and Automodel backend troubleshooting.
• sub-skills/speaker-diarization/SKILL.md: Speaker recognition, embeddings, diarization, VAD, Sortformer diarization, ASR+diarization scoring, forced alignment, RTTM/CTM manifests, and speaker workflow troubleshooting.
• sub-skills/data-tools/SKILL.md: Shared NeMo JSON manifests, Lhotse and tarred datasets, duration bins, dataset weights, tokenizer utilities, ASR evaluator, CTC segmentation, speech data explorer/simulator, checkpoint utilities, and safe data validation.
• sub-skills/repo-development/SKILL.md: Repository maintenance, install variants, code style, focused tests, docs builds, bug reproduction, optional backend decisions, native verification, and PR/CI guidance.

Common Decisions

• Start with the task's collection sub-skill, then route to data-tools only for shared data validation, conversion, sharding, tokenizer, or evaluation utilities.
• Keep ASR transcription/fine-tuning separate from speaker diarization and forced alignment; use speaker-diarization for RTTM, CTM, VAD, speaker embeddings, and NFA-style alignment tasks.
• Keep classic TTS synthesis and G2P in tts; use speechlm2 only when the task involves SALM, duplex speech-to-speech, Nemotron VoiceChat, vLLM, HuggingFace export, or voice-agent services.
• Use bundled helper scripts for safe preflight validation before launching long GPU, model-download, training, checkpoint-conversion, or dataset-conversion jobs.
• Treat original NeMo examples and scripts as evidence. If a user wants to run source checkout examples, first verify the repo checkout, dependencies, hardware, data paths, and command safety with the relevant sub-skill.

Bundled Helper Scripts

Each helper is safe by default and standard-library-only unless the sub-skill says otherwise:

• ASR: sub-skills/asr/scripts/check_asr_manifest.py
• TTS: sub-skills/tts/scripts/check_tts_manifest.py
• Audio: sub-skills/audio/scripts/check_audio_manifest.py
• SpeechLM2: sub-skills/speechlm2/scripts/check_speechlm2_config.py
• Speaker/diarization: sub-skills/speaker-diarization/scripts/check_speaker_manifest.py
• Data/tools: sub-skills/data-tools/scripts/validate_manifest.py
• Repo development: sub-skills/repo-development/scripts/select_tests.py

Run helpers from the nearest sub-skill directory, for example:

cd sub-skills/asr
python scripts/check_asr_manifest.py train.jsonl --canary --min-duration 0.1 --max-duration 30

Troubleshooting First Steps

1. Confirm the correct collection extra is installed: asr, tts, audio, speechlm2, or a deliberate all install.
2. Confirm the user's PyTorch/CUDA stack before changing NeMo dependencies; do not replace a bring-your-own PyTorch install accidentally.
3. Validate manifests/configs with the bundled helper in the owning sub-skill before running long jobs.
4. For Hydra overrides, quote strings/lists deliberately and null conflicting settings when switching batch modes.
5. For checkpoint workflows, distinguish .nemo, PyTorch Lightning .ckpt, HuggingFace/safetensors, and distributed checkpoint directories before loading or converting.
6. For repository changes, reproduce the issue with a minimal test, add the test, fix the root cause, and run focused tests plus isort --check and black --check on changed Python paths.