L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Données et recherche · SKILL.md

deep-filter-net

deep-filter-net

Route DeepFilterNet speech enhancement, Python API/CLI, training data, model export/evaluation, and Rust/LADSPA realtime workflows.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: deep-filter-net description: "Route DeepFilterNet speech enhancement, Python API/CLI, training data, model export/evaluation, and Rust/LADSPA realtime workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

DeepFilterNet repo skill

Use this skill when a task involves DeepFilterNet, DeepFilterNet2/3, deep filtering for speech/audio enhancement, the Python deepFilter CLI, df.enhance APIs, HDF5 training data, ONNX export/evaluation, or the Rust deep-filter/LADSPA realtime runtime.

First route by task

  • Python enhancement and libdf primitives: use python-enhancement for deepFilter, deep-filter-py, df.enhance.init_df, df.enhance.enhance, df.io, model directories, pretrained names, CPU/CUDA selection, and libdf.DF STFT/ERB smoke checks.
  • Training data and training setup: use training-data for HDF5 speech/noise/RIR datasets, dataset.cfg, prepare_data, df.train, base_dir, checkpoints/resume, and host-specific batch sizes.
  • Model export and evaluation: use model-export-evaluation for ONNX export outputs, model summaries, VoiceBank/DNS2020 evaluation, DNSMOS, PESQ/STOI/composite metrics, and completed export directory validation.
  • Rust realtime deployment: use rust-realtime-deployment for the native Rust deep-filter binary, model archives, LADSPA plugin builds, PipeWire virtual source/sink templates, tract/WASM notes, and demo/UI prerequisites.

Confirm install and package surface

Read installation-and-package-map before choosing extras or backend packages. For a safe no-network check from this skill directory:

python scripts/check_deepfilternet_install.py

The checker verifies required imports, deepFilter/deep-filter-py --help, and a tiny libdf STFT/ERB smoke test without downloading models, running training, building Rust, or touching audio services.

DeepFilterNet essentials

  • Python package import root: df.
  • Main Python CLI entry points: deepFilter and deep-filter-py.
  • Required low-level Python extension for enhancement: libdf from deepfilterlib.
  • Optional training dataloader extension: libdfdata from deepfilterdataloader.
  • Pretrained Python model names used by the package: DeepFilterNet, DeepFilterNet2, DeepFilterNet3; the source default is DeepFilterNet3.
  • Python model audio is 48 kHz by default; the package can resample inputs but future agents should make sample-rate decisions explicit.
  • CUDA is optional acceleration. CPU is enough for import/API/helper correctness checks unless the user specifically asks to verify GPU performance.
  • Rust/LADSPA/PipeWire workflows are system-level: never install Rust packages, write /etc, restart PipeWire, or attach live audio devices without explicit user approval.

Troubleshooting entry point

For cross-cutting failures, start with references/troubleshooting.md. Then move to the nearest sub-skill troubleshooting file for concrete recovery commands and stop conditions.

Provenance and refresh

Read repo-provenance before deciding whether this skill matches a current checkout. If the commit, package metadata, dirty state, source layout, or public entry points differ materially, refresh the repo skill.

Structured router metadata for managed repo-skill import is in repo-routing-metadata.json. This production run intentionally leaves the skill as a self-contained repository artifact and does not import it into a live router.

Skills enregistrées