---

name: "hifi-gan" description: "Routes HiFi-GAN training, fine-tuning, waveform inference, and checkpoint/config troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

HiFi-GAN

HiFi-GAN is a speech vocoder repository with two user-facing workflows: training/fine-tuning from LJSpeech-style data and inference from trained generator checkpoints.

Use this skill for

• choosing between config_v1.json, config_v2.json, and config_v3.json
• training or fine-tuning HiFi-GAN with the repository's data layout
• wav-to-wav inference or mel-to-wav inference from generator checkpoints
• checkpoint/config pairing, output layout, and filename conventions
• CUDA, TensorBoard, librosa, mel-channel, and data-layout troubleshooting
• quick smoke checks with bundled helpers before expensive runs
• self-contained bundled runtime source/configs when the original checkout is not available

Do not use this skill for

• ASR, diarization, forced alignment, upstream text-to-mel generation, or other speech tasks that are not HiFi-GAN vocoder training/inference
• unrelated audio repos or generic deep-learning engines

Read first

• references/model-overview.md
• references/configuration.md
• references/troubleshooting.md
• references/repo-provenance.md
• scripts/hifigan_runtime/ for copied core source and bundled configs
• sub-skills/training/SKILL.md
• sub-skills/inference/SKILL.md

Route map

• training — launch or resume training, fine-tuning, dataset/filelist preparation, checkpoint and TensorBoard behavior, and DDP/CUDA troubleshooting.
• inference — synthesize wavs from wav or mel inputs, pair checkpoints with configs, and handle output directories and smoke checks.

Shared facts

• The generator consumes 80 mel channels and emits one waveform channel.
• The bundled configs keep num_mels = 80, sampling_rate = 22050, and hop_size = 256.
• The runtime skill includes copied HiFi-GAN source under scripts/hifigan_runtime/, including train.py, inference.py, inference_e2e.py, models.py, meldataset.py, env.py, utils.py, LICENSE, and bundled V1/V2/V3 configs.
• Use sub-skills/training/scripts/train_hifigan.py as the self-contained training/fine-tuning entrypoint.
• Use sub-skills/inference/scripts/infer_hifigan.py as the self-contained wav/mel inference entrypoint.
• Training writes config.json into the checkpoint directory and saves generator checkpoints as g_########.
• Inference reads config.json from the checkpoint directory and writes _generated.wav or _generated_e2e.wav files.
• The bundled smoke helpers create tiny synthetic fixtures only; they are for wiring checks, not quality evaluation.

Safe setup

• Use a Python environment that can import torch, librosa, scipy, tensorboard, and matplotlib.
• Prefer the sub-skill smoke helpers before long GPU runs or after dependency changes.
• Do not require an external source checkout for core HiFi-GAN code; the generated skill bundles the needed runtime source and configs.
• Keep the input directory clean: the bundled inference entrypoint ultimately runs code that iterates os.listdir(...) directly.
• Keep training filelists basename-only in the first column; do not append .wav.

Handoff pattern

1. Start at this root skill to choose the right route.
2. If the request is about training or fine-tuning, open sub-skills/training/SKILL.md.
3. If the request is about generation, open sub-skills/inference/SKILL.md.
4. If the request involves model/config details or failure analysis, read the shared references/ files first.