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name: python-enhancement description: "Use DeepFilterNet's installed Python CLI/API for enhancement, model selection, audio I/O, and libdf STFT/ERB primitives." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Python enhancement

Use this sub-skill when the task is to enhance audio with the installed DeepFilterNet Python package, verify the Python install, choose or load a pretrained/local model, use the df.enhance API, perform audio I/O/resampling, or smoke-test libdf STFT/ISTFT/ERB primitives.

Fast routing

• Use the installed Python CLI entry points deepFilter or deep-filter-py for ordinary file or directory enhancement. See CLI reference.
• Use df.enhance.init_df, df.enhance.enhance, df.io.load_audio, df.io.save_audio, and df.io.resample for Python scripts. See API reference.
• Use scripts/enhance_with_deepfilternet.py when you need an explicit no-network-by-default example that requires --input-file and --output-file.
• Use scripts/libdf_smoke.py to diagnose libdf install and STFT/ERB shape issues without loading a model or touching the network.
• For no-network model loading, local cache checks, batch/dir enhancement, package import checks, and API recipes, use workflows.
• For failures, start with troubleshooting.

Boundaries

This sub-skill owns Python enhancement and low-level libdf primitives only.

Route elsewhere:

• HDF5 dataset creation, dataset configs, and training loops: training-data.
• ONNX export, objective evaluation, DNSMOS, PESQ/STOI metrics, and exported artifact validation: model-export-evaluation.
• Rust deep-filter binary, LADSPA, PipeWire, native realtime audio, and demo/UI workflows: rust-realtime-deployment.

Required assumptions before enhancing

1. The package environment must import torch, torchaudio, df, and libdf.
2. DeepFilterNet model inference uses the model sample rate. The bundled pretrained models are configured for 48 kHz audio; load/resample inputs to the model sample rate before enhance.
3. Pretrained model names are DeepFilterNet, DeepFilterNet2, and DeepFilterNet3; package constants set the default model to DeepFilterNet3.
4. Passing a pretrained name to the package may download it when the cache is missing. For offline work, pass a local model directory containing config.ini and checkpoints/, or use the bundled helper script without --allow-download.
5. CPU is sufficient for correctness and small jobs. CUDA is optional acceleration; device selection defaults to CUDA when PyTorch reports it available unless the package config/environment forces CPU.

Completion checks

A Python enhancement task is complete when:

• deepFilter --help or deep-filter-py --help works, or the Python import checks in workflows succeed.
• The selected model directory/name is resolved and its config.ini plus checkpoint directory are available.
• Input audio is loaded as [channels, samples], resampled to the model sample rate if needed, enhanced, and saved to the requested output path or output directory.
• For libdf work, scripts/libdf_smoke.py reports finite STFT, synthesis, ERB, erb_norm, and unit_norm outputs with the expected shapes.