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name: training description: "Routes HiFi-GAN training, fine-tuning, dataset layout, checkpointing, validation, and distributed GPU training workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

training

Use this sub-skill when the user needs to train or fine-tune HiFi-GAN with the skill's self-contained bundled train_hifigan.py entrypoint, choose a V1/V2/V3 config, prepare LJSpeech-style filelists, manage checkpoints/logs, or debug CUDA/distributed training failures.

Use this route for

• Launching bundled scripts/train_hifigan.py for full training with v1, v2, v3, or a custom JSON config.
• Preparing LJSpeech-1.1/wavs, LJSpeech-1.1/training.txt, and LJSpeech-1.1/validation.txt style inputs.
• Fine-tuning with teacher-forced mel .npy files in ft_dataset or a custom --input_mels_dir.
• Understanding checkpoint save/resume behavior under --checkpoint_path.
• Debugging validation/TensorBoard output and multi-GPU torch.multiprocessing/NCCL training.
• Running or adapting the bundled tiny-fixture smoke helpers before attempting an expensive run.
• Training without depending on an external HiFi-GAN checkout; the root skill bundles the needed source/config files under ../../scripts/hifigan_runtime/.

Do not use this route for

• Checkpoint-based waveform generation from wav inputs. Use the sibling inference sub-skill.
• Mel-to-wav or end-to-end synthesis from .npy mel files. Use the sibling inference sub-skill.
• Editing the HiFi-GAN model architecture beyond config-level training choices; use the root shared model/config references under ../../references/ when available.

Read first

• references/training-workflows.md — training/fine-tuning commands, checkpoint/log behavior, validation, and distributed notes.
• references/config-and-data.md — V1/V2/V3 config differences, LJSpeech filelist schema, mel .npy layout, and environment facts.
• references/troubleshooting.md — concrete recovery guidance for missing wavs, sample-rate mismatches, fine-tuning mel errors, CUDA, TensorBoard, checkpoint collisions, and DDP mistakes.
• Root shared references under ../../references/ — model/config internals and provenance shared with other HiFi-GAN sub-skills.

Skill-owned scripts

• scripts/train_hifigan.py — self-contained training/fine-tuning entrypoint that runs bundled HiFi-GAN source and resolves --config v1|v2|v3 to bundled configs.
• scripts/make_ljspeech_fixture.py — creates a tiny LJSpeech-style wav/filelist fixture, optionally with mel .npy files or intentionally bad rows for negative tests.
• scripts/smoke_train_tiny.py — creates a temporary fixture and tiny config, then runs a short GPU training pass through scripts/train_hifigan.py with safe compatibility shims for modern PyTorch/librosa stacks.

Fast command patterns

From this sub-skill's scripts/ directory or by using the script path from anywhere with a CUDA-capable PyTorch environment:

python scripts/train_hifigan.py --config v1
python scripts/train_hifigan.py --config v2 --checkpoint_path cp_hifigan_v2
python scripts/train_hifigan.py --config v3 --checkpoint_path cp_hifigan_v3

For fine-tuning, provide basename-matched mel files and pass the flag exactly as a truthy value:

python scripts/train_hifigan.py --fine_tuning True --config v1 --input_mels_dir ft_dataset

For a cheap local sanity check before a real run:

python scripts/smoke_train_tiny.py --dry-run
python scripts/smoke_train_tiny.py

Verified environment baseline

The inspection run verified that torch 2.3.1+cu121 imports on an NVIDIA A100-SXM4-40GB host, CUDA is available, torch.cuda.get_device_name(0) works, torch.utils.tensorboard imports, and librosa.util.normalize is present in librosa 0.10.2.post1. The repository requirements are older; read references/troubleshooting.md before assuming modern library compatibility.

Routing notes

• If the task says "train", "fine-tune", "resume", "checkpoint", "validation loss", "TensorBoard", "LJSpeech filelist", "config_v1/config_v2/config_v3", "train_hifigan.py", "multi-GPU", or "NCCL", stay here.
• If the task asks to synthesize wavs or run inference.py / inference_e2e.py, route to inference instead.
• If a workflow needs shared architecture facts, keep this route active for launch/debugging and use root shared references under ../../references/ only for model/config background.