---

name: "inference" description: "Routes HiFi-GAN wav-to-wav and mel-to-wav inference, checkpoint/config pairing, output naming, and smoke checks." disable-model-invocation: true metadata: disco-role: operating license: MIT

HiFi-GAN Inference

Use this sub-skill when you need to synthesize waveforms with a trained HiFi-GAN generator.

Use this route for

• Self-contained scripts/infer_hifigan.py --mode wav synthesis from a directory of input wavs.
• Self-contained scripts/infer_hifigan.py --mode mel synthesis from a directory of mel .npy files.
• Loading a generator checkpoint that sits beside its matching config.json.
• Choosing input and output directories and understanding generated filename suffixes.
• CPU/GPU selection, checkpoint/config mismatch recovery, and synthetic smoke checks.

Do not use this route for

• Training, fine-tuning, or checkpoint creation/resume. Use the training sub-skill instead.
• Upstream text-to-mel generation. Prepare those mel files first, then return here.

Read first

• references/root-material.md
• references/workflows.md
• references/troubleshooting.md

Bundled helpers

• scripts/infer_hifigan.py — self-contained wav/mel inference entrypoint using bundled HiFi-GAN runtime source.
• scripts/make_dummy_checkpoint.py — create a synthetic generator checkpoint from bundled source and copy or intentionally mismatch the paired config.json.
• scripts/make_tiny_inference_fixtures.py — create tiny wav and mel fixtures for smoke tests and negative cases.
• scripts/run_inference_smoke.py — build the synthetic inputs and run both inference modes end to end.

Key contracts

• scripts/infer_hifigan.py wraps the bundled inference.py and inference_e2e.py; both underlying paths read config.json from the checkpoint directory and do not accept a separate config flag.
• The checkpoint must be a generator checkpoint dict with a generator entry. Training checkpoints named do_######## are not valid inference inputs.
• scripts/infer_hifigan.py --mode wav expects mono wav files, computes mels with the loaded config, and writes <stem>_generated.wav.
• scripts/infer_hifigan.py --mode mel expects mel .npy files and writes <stem>_generated_e2e.wav.
• Both scripts auto-select CUDA when torch.cuda.is_available() is true and fall back to CPU otherwise.
• The bundled inference entrypoint ultimately runs code that iterates os.listdir(...) directly, so keep the input directory clean and populated only with the intended files.
• Use the matching config family for the checkpoint. The skill bundles config_v1.json, config_v2.json, and config_v3.json under ../../scripts/hifigan_runtime/configs/.

Known-good inspection environment

• Verified inspection facts: torch 2.3.1+cu121 imported on an NVIDIA A100-SXM4-40GB host; torch.cuda.is_available() returned true; torch.cuda.get_device_name(0) worked; torch.utils.tensorboard imported; librosa.util.normalize was present in librosa 0.10.2.post1.