denoise-deepfilter

name	denoise-deepfilter
description	Use when the user wants to denoise audio using modern ML-based filtering. DeepFilterNet produces cleaner speech than traditional FFT/rnnoise methods — no watery/hollow artifacts. Wraps `deepFilter` binary for podcast/voice cleanup.

Denoise (DeepFilterNet)

Remove background noise from audio using DeepFilterNet 3, a state-of-the-art ML-based denoiser. Preserves natural voice timbre better than rnnoise or ffmpeg's afftdn — ideal for podcast, interview, and voice-memo cleanup.

When to use

• The user has a noisy podcast recording or interview.
• The source is speech-heavy (podcast, voice memo, audiobook); DeepFilterNet is tuned for voice.
• Existing denoise (rnnoise, afftdn) leaves artifacts (hollowness, over-suppression) → try DeepFilterNet for comparison.
• The user wants a single-pass, set-and-forget denoise without threshold tweaking.

Do not use this skill for:

• Music with noise — DeepFilterNet is speech-optimized; may remove subtle instruments.
• Surgical noise reduction needing manual control → use ffmpeg afftdn or rnnoise with threshold tuning.

Inputs

1. Input file — required. WAV, MP3, FLAC supported.
2. Output path — defaults to <input-stem>.deepfilter.<ext>.
3. Attenuation limit — optional, in dB. Controls how aggressively the model suppresses noise. Default: unlimited (let model decide). Range: 1–40 dB. Lower = more aggressive. Common: 10–20 dB for heavy noise.
4. Model version — optional. Default: latest (v3). Can specify deepfilter-v3 or deepfilter-v2 if needed for compatibility.

Procedure

1. Check if deepFilter is on PATH (which deepFilter). If missing, provide install instructions:

   • Option A (binary): Download from GitHub releases: https://github.com/Rikorose/DeepFilterNet/releases. Unzip and add to PATH.
   • Option B (Rust): cargo install deep_filter.
   • Or run install-deps if the plugin includes it.

2. Build the command:

   deepFilter \
     "<input-file>" \
     -o "<output-file>" \
     [--attenuation-limit <dB>]
   

   If --attenuation-limit is provided, insert it. Example: --attenuation-limit 15.

3. Run the command and capture stdout/stderr. DeepFilterNet reports progress (model loading, processing).

4. Verify output: ffprobe -i "<output-file>" -hide_banner to confirm the file is valid and report duration (should match input).

Output

• Denoised WAV file at the resolved output path, same sample rate and bit depth as input.
• Summary: <input> → <output> (denoised, attenuation-limit <limit|unlimited>, duration <N>s).
• If the output is noticeably quieter (not the expected outcome), suggest checking ffmpeg -i <output> -filter:a "loudnorm" to re-normalize.

Notes

• First run may download model weights (~100 MB); subsequent runs are fast.
• DeepFilterNet preserves sample rate and bit depth — no re-encoding. Output is WAV.
• Model is tuned for speech; best results on voice/podcast. Not recommended for music or highly dynamic sources.
• If the user wants more/less aggressive filtering, suggest adjusting --attenuation-limit or trying denoise-rnnoise as an alternative.
• Output duration may be slightly different from input due to internal buffering; report actual output duration to confirm.

Dependencies

Install deepFilter binary from GitHub releases or via cargo install deep_filter. Requires no Python or special runtime — it's a standalone Rust binary. Audio format support via ffmpeg or embedded codecs.