audio enhancement expert

📁 willsigmon/sigstack 📅 Jan 1, 1970
4
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0
周安装量
#50201
全站排名
安装命令
npx skills add https://github.com/willsigmon/sigstack --skill Audio Enhancement Expert

Skill 文档

Audio Enhancement Expert

Polish podcast and voice audio with AI enhancement tools.

Top Tools

Auphonic (API Available)

  • Loudness normalization (LUFS)
  • Noise/hum reduction
  • Leveling between speakers
  • API + Zapier integration
  • Pricing: 2 hours free/mo, then pay-per-use

Adobe Speech Enhancer

  • Free (1 hour/day limit)
  • Web-based at podcast.adobe.com/enhance
  • Premium: 4 hours, video support
  • Great for quick cleanup

Cleanvoice AI

  • Removes filler words (um, uh)
  • Background noise removal
  • Mouth sounds/clicks removal
  • Pricing: Pay-per-minute

Dolby.io

  • Enterprise audio APIs
  • Real-time enhancement
  • SDK integration
  • Developer-focused

Auphonic API

Single Production

curl -X POST https://auphonic.com/api/simple/productions.json \
  -u "user:pass" \
  -F "input_file=@episode.mp3" \
  -F "output_basename=episode-enhanced" \
  -F "algorithms=loudness,leveler,denoise"

Python SDK

import auphonic

client = auphonic.Client(username="...", password="...")

production = client.create_production(
    input_file="episode.mp3",
    algorithms=["loudness", "leveler", "denoise", "filtering"],
    output_files=[{
        "format": "mp3",
        "bitrate": 128
    }]
)

production.start()

Enhancement Pipeline

Recommended Order

1. Noise reduction (remove constant noise)
2. De-reverb (reduce room echo)
3. Leveling (balance speakers)
4. Compression (even out volume)
5. Loudness normalization (hit target LUFS)

Target Levels

  • Podcasts: -16 LUFS (Spotify), -14 LUFS (Apple)
  • Audiobooks: -18 to -20 LUFS
  • Video: -24 LUFS (broadcast)

Local Processing

FFmpeg Noise Reduction

# Get noise profile
ffmpeg -i input.mp3 -af "silencedetect=n=-30dB:d=0.5" -f null -

# Apply noise reduction
ffmpeg -i input.mp3 \
  -af "highpass=f=80,lowpass=f=12000,afftdn=nf=-20" \
  output.mp3

SoX Enhancement

# Normalize + noise reduce
sox input.mp3 output.mp3 \
  norm -1 \
  noisered noise.prof 0.21

Batch Processing

# Auphonic batch
for file in audio_files:
    production = client.create_production(
        input_file=file,
        algorithms=["loudness", "leveler"]
    )
    production.start()

Use when: Podcast polish, voice clarity, noise removal, loudness normalization