---

name: training-data description: "Prepare and validate DeepFilterNet training datasets, dataset configuration, training launch setup, checkpoints/resume behavior, and host batch-size settings." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

DeepFilterNet training data and training setup

Use this sub-skill when the task is to prepare DeepFilterNet HDF5 datasets, validate a dataset.cfg, set up a training command, reason about base_dir outputs/checkpoints/resume, or apply host-specific batch-size settings.

Do not use this sub-skill for:

• Enhancing audio or loading models for inference: use ../python-enhancement/SKILL.md.
• ONNX export, benchmark metrics, DNSMOS, VoiceBank, or post-training evaluation: use ../model-export-evaluation/SKILL.md.
• Network dataset downloads, cluster submission, or large shared-filesystem copy jobs. Those source workflows are reference-only; validate local files first and require explicit user approval before any large data movement.

Fast route

1. Confirm the environment has the training stack: DeepFilterNet Python package, PyTorch/torchaudio, and libdfdata/deepfilterdataloader; HDF5/audio conversion also needs h5py and audio codec support.

2. Read references/data-formats.md before creating HDF5 datasets or editing dataset.cfg.

3. Validate the dataset config before training:

   python scripts/validate_dataset_config.py --config dataset.cfg --data-dir data --require-files --check-hdf5
   

4. Follow references/workflows.md for the safe prepare-data → validate → train sequence, checkpoint/resume behavior, and host batch-size workflow.

5. Use references/configuration.md when creating or modifying base_dir/config.ini.

6. If anything fails, check references/troubleshooting.md before retrying long-running commands.

Bundled helpers

• scripts/validate_dataset_config.py: self-contained JSON/HDF5 dataset config validator. It works without h5py by warning and skipping HDF5 internals.
• scripts/set_batch_size.py: self-contained host-batch-size helper adapted from the DeepFilterNet training workflow. It updates only train.batch_size, train.batch_size_eval, and the host batch-size config keys it owns.

Stop conditions

Stop and ask for user input before:

• Downloading external datasets or running any script that fetches, extracts, copies, deletes, or rsyncs large data.
• Starting training when the validator reports missing required splits, missing HDF5 files, malformed sampling factors, or no usable speech/noise datasets.
• Mutating an existing run directory whose checkpoints/config do not match the intended model, sample rate, or FFT settings.
• Continuing after repeated NaN/non-finite losses unless the user accepts a reduced/debug run and the troubleshooting checks have been applied.