Claude Video Enhance Audio

claude-video-enhance-audio — AI Audio Enhancement

This sub-skill handles AI-model-based audio processing. For FFmpeg-native audio operations (loudnorm, EQ, compression, silence removal, mixing), use claude-video-audio instead.

Pre-Flight

1. Activate venv: source ~/.video-skill/bin/activate
2. Check free VRAM: nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits
3. Verify required tools are installed for the requested operation

Source Separation (Demucs v4)

Isolate vocals, drums, bass, or other instruments from audio/video.

source ~/.video-skill/bin/activate
python3 scripts/audio_enhance.py separate "$INPUT" \
  --stems vocals \
  --model htdemucs_ft \
  --output vocals.wav

Options:

• --stems vocals — Extract vocals only (most common for video)
• --stems all — Separate into 4 stems: vocals, drums, bass, other
• --stems vocals,other — Extract specific combination
• --model htdemucs_ft — Best quality model (default, 7GB VRAM, SDR 9.20 dB)
• --model htdemucs — Faster, slightly lower quality (5GB VRAM)
• --output FILE — Output path (or directory for --stems all)

VRAM: 7GB for htdemucs_ft. Model is unloaded after processing.

Use cases:

• Extract clean dialogue from video with background music
• Remove background music while keeping speech
• Create music-only track for audio ducking
• Isolate instruments for remix

Speaker Diarization (pyannote-audio 4.0)

Identify who spoke when in a video.

source ~/.video-skill/bin/activate
python3 scripts/audio_enhance.py diarize "$INPUT" \
  --output speakers.json

Options:

• --hf-token TOKEN — HuggingFace token (required for pyannote models)
• --with-transcript — Combine with WhisperX transcript for speaker-labeled text
• --output speakers.json — Output path

VRAM: 2-4GB for pyannote, +6GB if --with-transcript (WhisperX runs sequentially).

Output format (speakers.json):

{
  "speakers": ["SPEAKER_0", "SPEAKER_1"],
  "segments": [
    {"speaker": "SPEAKER_0", "start": 0.5, "end": 5.2, "text": "Welcome to the show..."},
    {"speaker": "SPEAKER_1", "start": 5.8, "end": 12.1, "text": "Thanks for having me..."}
  ]
}

Requires: HuggingFace token with access to pyannote/speaker-diarization-3.1. Set via HF_TOKEN env var or --hf-token flag.

AI Noise Reduction (DeepFilterNet3)

Superior noise reduction compared to FFmpeg's afftdn filter.

source ~/.video-skill/bin/activate
python3 scripts/audio_enhance.py denoise "$INPUT" \
  --output clean.wav

VRAM: <1GB (very lightweight)

Quality comparison:

• DeepFilterNet3 PESQ score: 3.17-3.50
• FFmpeg afftdn PESQ score: ~2.50
• Dramatically better for speech with background noise

Fallback: If DeepFilterNet3 is not installed, falls back to FFmpeg afftdn:

ffmpeg -n -i "$INPUT" -af "afftdn=nf=-25:nt=w" -c:v copy "$OUTPUT"

Options:

• --compensate-delay — Compensate for processing delay (default: true)
• --atten-limit N — Maximum attenuation in dB (default: 100)

TTS Voiceover Generation

Generate voiceover narration from text.

source ~/.video-skill/bin/activate
python3 scripts/audio_enhance.py tts \
  --text "Welcome to this tutorial on video editing with Claude." \
  --provider elevenlabs \
  --voice "Rachel" \
  --output voiceover.wav

ElevenLabs (Best Quality — API)

python3 scripts/audio_enhance.py tts --text "..." --provider elevenlabs --voice "Rachel" --output vo.wav

• Quality: Best available
• Cost: $5/month starter (30 min), $22/month creator
• Requires: ELEVENLABS_API_KEY env var
• Voices: 10,000+ available

OpenAI TTS (Good Quality — API)

python3 scripts/audio_enhance.py tts --text "..." --provider openai --voice alloy --output vo.wav

• Quality: Very good
• Cost: $15/1M characters (tts-1), $30/1M (tts-1-hd)
• Requires: OPENAI_API_KEY env var
• Voices: alloy, echo, fable, onyx, nova, shimmer, ash, ballad, coral, sage, ember, vale, verse
• --model tts-1-hd for higher quality

Bark (Local — Free)

python3 scripts/audio_enhance.py tts --text "..." --provider bark --output vo.wav

• Quality: Good, expressive, supports non-verbal sounds
• Cost: Free
• VRAM: 12GB (exclusive use — unloads all other models)
• Supports: laughter, sighs, music (via text prompts like [laughs])

Cost confirmation: Always confirm before API-based TTS generation.

Audio Upsampling (AudioSR)

Upscale phone-quality audio to studio quality.

source ~/.video-skill/bin/activate
python3 scripts/audio_enhance.py upsample "$INPUT" \
  --output hd_audio.wav

VRAM: 6-8GB

What it does:

• Input: 8-16kHz bandwidth (phone recordings, compressed audio)
• Output: 24kHz bandwidth at 48kHz sample rate
• Dramatically improves clarity and presence

Options:

• --model speech — Optimized for speech content (default)
• --model music — Optimized for music content
• --model general — General purpose

Routing Guide: This Sub-Skill vs claude-video-audio

VRAM Management

All models are loaded on-demand and unloaded after processing with torch.cuda.empty_cache().

Safety Rules

1. Always confirm cost before API-based TTS generation
2. Run bash scripts/preflight.sh for output path validation
3. Warn about processing time for long audio files with heavy models
4. Never overwrite source audio files

Reference

Load references/audio-enhance.md for model details, API setup, and quality comparisons.