[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6Q4ZPQYJGZDKJC3DSWSJAPY":3,"skill-same:01M6Q4ZPQYJGZDKJC3DSWSJAPY":94},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6Q4ZPQYJGZDKJC3DSWSJAPY","voice-note-to-midi",null,"Convert voice notes, humming, and melodic audio recordings to quantized MIDI files using ML-based pitch detection and intelligent post-processing","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F语音与转录\u002Fvoice-note-to-midi\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"note","voice","midi","voice note to midi","voice notes","notes","clawd","post-processing","processing","machine","post","pitch","files","recordings","convert","music","learning","Audio","machine-learning","ml","2026-11-10 10:12:30",false,"---\nname: voice-note-to-midi\ndescription: Convert voice notes, humming, and melodic audio recordings to quantized MIDI files using ML-based pitch detection and intelligent post-processing\nauthor: Clawd\ntags: [audio, midi, music, transcription, machine-learning]\n---\n\n# ? Voice Note to MIDI\n\nTransform your voice memos, humming, and melodic recordings into clean, quantized MIDI files ready for your DAW.\n\n## What It Does\n\nThis skill provides a complete audio-to-MIDI conversion pipeline that:\n\n1. **Stem Separation** - Uses HPSS (Harmonic-Percussive Source Separation) to isolate melodic content from drums, noise, and background sounds\n2. **ML-Powered Pitch Detection** - Leverages Spotify's Basic Pitch model for accurate fundamental frequency extraction\n3. **Key Detection** - Automatically detects the musical key of your recording using Krumhansl-Kessler key profiles\n4. **Intelligent Quantization** - Snaps notes to a configurable timing grid with optional key-aware pitch correction\n5. **Post-Processing** - Applies octave pruning, overlap-based harmonic removal, and legato note merging for clean output\n\n### Pipeline Architecture\n\n```\nAudio Input (WAV\u002FM4A\u002FMP3)\n    ↓\n┌─────────────────────────────────────┐\n│ Step 1: Stem Separation (HPSS)     │\n│ - Isolate harmonic content          │\n│ - Remove drums\u002Fpercussion           │\n│ - Noise gating                      │\n└─────────────────────────────────────┘\n    ↓\n┌─────────────────────────────────────┐\n│ Step 2: Pitch Detection             │\n│ - Basic Pitch ML model (Spotify)    │\n│ - Polyphonic note detection         │\n│ - Onset\u002Foffset estimation           │\n└─────────────────────────────────────┘\n    ↓\n┌─────────────────────────────────────┐\n│ Step 3: Analysis                    │\n│ - Pitch class distribution          │\n│ - Key detection                     │\n│ - Dominant note identification      │\n└─────────────────────────────────────┘\n    ↓\n┌─────────────────────────────────────┐\n│ Step 4: Quantization & Cleanup      │\n│ - Timing grid snap                  │\n│ - Key-aware pitch correction        │\n│ - Octave pruning (harmonic removal) │\n│ - Overlap-based pruning             │\n│ - Note merging (legato)             │\n│ - Velocity normalization            │\n└─────────────────────────────────────┘\n    ↓\nMIDI Output (Standard MIDI File)\n```\n\n## Setup\n\n### Prerequisites\n\n- Python 3.11+ (Python 3.14+ recommended)\n- FFmpeg (for audio format support)\n- pip\n\n### Installation\n\n**Quick Install (Recommended):**\n\n```bash\ncd \u002Fpath\u002Fto\u002Fvoice-note-to-midi\n.\u002Fsetup.sh\n```\n\nThis automated script will:\n- Check Python 3.11+ is installed\n- Create the `~\u002Fmelody-pipeline` directory\n- Set up the virtual environment\n- Install all dependencies (basic-pitch, librosa, music21, etc.)\n- Download and configure the hum2midi script\n- Add melody-pipeline to your PATH\n\n**Manual Install:**\n\nIf you prefer manual setup:\n\n```bash\nmkdir -p ~\u002Fmelody-pipeline\ncd ~\u002Fmelody-pipeline\npython3 -m venv venv-bp\nsource venv-bp\u002Fbin\u002Factivate\npip install basic-pitch librosa soundfile mido music21\nchmod +x ~\u002Fmelody-pipeline\u002Fhum2midi\n```\n\n5. **Add to your PATH (optional):**\n\n```bash\necho 'export PATH=\"$HOME\u002Fmelody-pipeline:$PATH\"' >> ~\u002F.bashrc\nsource ~\u002F.bashrc\n```\n\n### Verify Installation\n\n```bash\ncd ~\u002Fmelody-pipeline\n.\u002Fhum2midi --help\n```\n\n## Usage\n\n### Basic Usage\n\nConvert a voice memo to MIDI:\n\n```bash\n.\u002Fhum2midi my_humming.wav\n```\n\nThis creates `my_humming.mid` with 16th-note quantization.\n\n### Specify Output File\n\n```bash\n.\u002Fhum2midi input.wav output.mid\n```\n\n### Command-Line Options\n\n| Option | Description | Default |\n|--------|-------------|---------|\n| `--grid \u003Cvalue>` | Quantization grid: `1\u002F4`, `1\u002F8`, `1\u002F16`, `1\u002F32` | `1\u002F16` |\n| `--min-note \u003Cms>` | Minimum note duration in milliseconds | `50` |\n| `--no-quantize` | Skip quantization (output raw Basic Pitch MIDI) | disabled |\n| `--key-aware` | Enable key-aware pitch correction | disabled |\n| `--no-analysis` | Skip pitch analysis and key detection | disabled |\n\n### Usage Examples\n\n#### Quantize to eighth notes\n```bash\n.\u002Fhum2midi melody.wav --grid 1\u002F8\n```\n\n#### Key-aware quantization (recommended for tonal music)\n```bash\n.\u002Fhum2midi song.wav --key-aware\n```\n\n#### Require longer minimum notes\n```bash\n.\u002Fhum2midi humming.wav --min-note 100\n```\n\n#### Skip analysis for faster processing\n```bash\n.\u002Fhum2midi quick.wav --no-analysis\n```\n\n#### Combine options\n```bash\n.\u002Fhum2midi recording.wav output.mid --grid 1\u002F8 --key-aware --min-note 80\n```\n\n### Processing MIDI Input\n\nYou can also process existing MIDI files through the quantization pipeline:\n\n```bash\n.\u002Fhum2midi input.mid output.mid --grid 1\u002F16 --key-aware\n```\n\nThis skips the audio processing steps and goes directly to analysis and quantization.\n\n## Sample Output\n\n```\n═══════════════════════════════════════════════════════════════\n  hum2midi - Melody-to-MIDI Pipeline (Basic Pitch Edition)\n  [Key-Aware Mode Enabled]\n═══════════════════════════════════════════════════════════════\n\nInput:  my_humming.wav\nOutput: my_humming.mid\n\n→ Step 1: Stem Separation (HPSS)\n  Isolating melodic content...\n  Loaded: 5.23s @ 44100Hz\n  ✓ Melody stem extracted → 5.23s\n\n→ Step 2: Audio-to-MIDI Conversion (Basic Pitch)\n  Running Spotify's Basic Pitch ML model on melody stem...\n  ✓ Raw MIDI generated (Basic Pitch)\n\n→ Step 3: Pitch Analysis & Key Detection\n  Notes detected: 42 total, 7 unique\n  Note range: C3 - G4\n  Pitch classes: C3, E3, G3, A3, C4, D4, G4\n  Dominant note: G3 (23.8% of notes)\n  Detected key: G major\n\n→ Step 4: Quantization & Cleanup\n  Octave pruning: removed 3 harmonic notes above 67 (median+12)\n  Overlap pruning: removed 2 harmonic notes at overlapping positions\n  Note merging: merged 5 staccato chunks into legato notes (gap\u003C=60 ticks)\n  Grid:   240 ticks (1\u002F16)\n  Notes:  38 notes\n  Key:    G major\n  Key-aware: 2 notes corrected to scale\n  Tempo:  120 BPM\n  ✓ Quantized MIDI saved\n\n═══════════════════════════════════════════════════════════════\n  ✓ Done! Output: my_humming.mid\n═══════════════════════════════════════════════════════════════\n\n? ANALYSIS SUMMARY\n─────────────────────────────────────────────────────────────\n  Detected Notes: C3, E3, G3, A3, C4, D4, G4\n  Detected Key:   G major\n  Quantization:   Key-aware mode (notes snapped to scale)\n\nMIDI Info: 38 notes, 7 unique pitches, 120 BPM\nPitches: C3, E3, G3, A3, C4, D4, G4\n```\n\n## Notes & Limitations\n\n### Audio Quality Matters\n\n- **Clear, loud melody** produces the best results\n- **Background noise** can cause false note detection\n- **Reverb and effects** may confuse pitch detection\n- **Close-mic'd vocals** work significantly better than room recordings\n\n### Musical Considerations\n\n- **Monophonic sources** work best (single melody line)\n- **Polyphonic audio** (chords, multiple instruments) will produce messy results\n- **Vibrato and pitch bends** may be quantized to stepped pitches\n- **Rapid note passages** may be missed or merged\n\n### Technical Limitations\n\n- **Tempo is fixed** at 120 BPM in output (time positions are preserved, but tempo may need adjustment in your DAW)\n- **Note velocities** are normalized but may need manual adjustment\n- **Very short notes** (\u003C50ms) may be filtered out by default\n- **Extreme pitch ranges** may cause octave detection issues\n\n### Post-Processing Recommendations\n\nAfter generating MIDI, you may want to:\n\n1. **Import into your DAW** and adjust tempo to match your original recording\n2. **Quantize further** if stricter timing is needed\n3. **Adjust note velocities** for dynamics\n4. **Apply swing\u002Fgroove** templates if the rigid grid sounds too mechanical\n5. **Edit individual notes** that were misdetected (common with fast runs)\n\n### Supported Audio Formats\n\nInput formats supported via FFmpeg:\n- WAV, AIFF, FLAC (uncompressed, best quality)\n- MP3, M4A, AAC (compressed, acceptable)\n- OGG, OPUS (open source formats)\n- Most other formats FFmpeg supports\n\n## Troubleshooting\n\n### No notes detected\n- Check that input file isn't silent or corrupted\n- Try increasing `--min-note` threshold\n- Verify audio has clear melodic content (not just noise)\n\n### Too many notes \u002F messy output\n- Enable octave pruning and overlap pruning (on by default)\n- Use `--key-aware` to constrain to musical scale\n- Check for background noise in source audio\n\n### Wrong key detected\n- Key detection works best with at least 8-10 measures of music\n- Chromatic passages may confuse the detector\n- Manually review and adjust in your DAW if needed\n\n### Notes in wrong octave\n- Basic Pitch sometimes detects harmonics instead of fundamentals\n- The pipeline includes pruning, but some may slip through\n- Use your DAW's transpose function for simple octave shifts\n\n## References\n\n- [Basic Pitch](https:\u002F\u002Fgithub.com\u002Fspotify\u002Fbasic-pitch) - Spotify's polyphonic pitch detection model\n- [librosa HPSS](https:\u002F\u002Flibrosa.org\u002Fdoc\u002Flatest\u002Fgenerated\u002Flibrosa.decompose.hpss.html) - Harmonic-Percussive Source Separation\n- [Krumhansl-Kessler Key Profiles](https:\u002F\u002Frnhart.net\u002Farticles\u002Fkey-finding\u002F) - Key detection algorithm\n\n## License\n\nThis skill integrates Basic Pitch by Spotify, which is licensed under Apache 2.0. The pipeline script and documentation are provided under MIT license.\n","# ? Voice Note to MIDI\n\nTransform your voice memos, humming, and melodic recordings into clean, quantized MIDI files ready for your DAW.\n\n## What It Does\n\nThis skill provides a complete audio-to-MIDI conversion pipeline that:\n\n1. **Stem Separation** - Uses HPSS (Harmonic-Percussive Source Separation) to isolate melodic content from drums, noise, and background sounds\n2. **ML-Powered Pitch Detection** - Leverages Spotify's Basic Pitch model for accurate fundamental frequency extraction\n3. **Key Detection** - Automatically detects the musical key of your recording using Krumhansl-Kessler key profiles\n4. **Intelligent Quantization** - Snaps notes to a configurable timing grid with optional key-aware pitch correction\n5. **Post-Processing** - Applies octave pruning, overlap-based harmonic removal, and legato note merging for clean output\n\n### Pipeline Architecture\n\n```\nAudio Input (WAV\u002FM4A\u002FMP3)\n    ↓\n┌─────────────────────────────────────┐\n│ Step 1: Stem Separation (HPSS)     │\n│ - Isolate harmonic content          │\n│ - Remove drums\u002Fpercussion           │\n│ - Noise gating                      │\n└─────────────────────────────────────┘\n    ↓\n┌─────────────────────────────────────┐\n│ Step 2: Pitch Detection             │\n│ - Basic Pitch ML model (Spotify)    │\n│ - Polyphonic note detection         │\n│ - Onset\u002Foffset estimation           │\n└─────────────────────────────────────┘\n    ↓\n┌─────────────────────────────────────┐\n│ Step 3: Analysis                    │\n│ - Pitch class distribution          │\n│ - Key detection                     │\n│ - Dominant note identification      │\n└─────────────────────────────────────┘\n    ↓\n┌─────────────────────────────────────┐\n│ Step 4: Quantization & Cleanup      │\n│ - Timing grid snap                  │\n│ - Key-aware pitch correction        │\n│ - Octave pruning (harmonic removal) │\n│ - Overlap-based pruning             │\n│ - Note merging (legato)             │\n│ - Velocity normalization            │\n└─────────────────────────────────────┘\n    ↓\nMIDI Output (Standard MIDI File)\n```\n\n## Setup\n\n### Prerequisites\n\n- Python 3.11+ (Python 3.14+ recommended)\n- FFmpeg (for audio format support)\n- pip\n\n### Installation\n\n**Quick Install (Recommended):**\n\n```bash\ncd \u002Fpath\u002Fto\u002Fvoice-note-to-midi\n.\u002Fsetup.sh\n```\n\nThis automated script will:\n- Check Python 3.11+ is installed\n- Create the `~\u002Fmelody-pipeline` directory\n- Set up the virtual environment\n- Install all dependencies (basic-pitch, librosa, music21, etc.)\n- Download and configure the hum2midi script\n- Add melody-pipeline to your PATH\n\n**Manual Install:**\n\nIf you prefer manual setup:\n\n```bash\nmkdir -p ~\u002Fmelody-pipeline\ncd ~\u002Fmelody-pipeline\npython3 -m venv venv-bp\nsource venv-bp\u002Fbin\u002Factivate\npip install basic-pitch librosa soundfile mido music21\nchmod +x ~\u002Fmelody-pipeline\u002Fhum2midi\n```\n\n5. **Add to your PATH (optional):**\n\n```bash\necho 'export PATH=\"$HOME\u002Fmelody-pipeline:$PATH\"' >> ~\u002F.bashrc\nsource ~\u002F.bashrc\n```\n\n### Verify Installation\n\n```bash\ncd ~\u002Fmelody-pipeline\n.\u002Fhum2midi --help\n```\n\n## Usage\n\n### Basic Usage\n\nConvert a voice memo to MIDI:\n\n```bash\n.\u002Fhum2midi my_humming.wav\n```\n\nThis creates `my_humming.mid` with 16th-note quantization.\n\n### Specify Output File\n\n```bash\n.\u002Fhum2midi input.wav output.mid\n```\n\n### Command-Line Options\n\n| Option | Description | Default |\n|--------|-------------|---------|\n| `--grid \u003Cvalue>` | Quantization grid: `1\u002F4`, `1\u002F8`, `1\u002F16`, `1\u002F32` | `1\u002F16` |\n| `--min-note \u003Cms>` | Minimum note duration in milliseconds | `50` |\n| `--no-quantize` | Skip quantization (output raw Basic Pitch MIDI) | disabled |\n| `--key-aware` | Enable key-aware pitch correction | disabled |\n| `--no-analysis` | Skip pitch analysis and key detection | disabled |\n\n### Usage Examples\n\n#### Quantize to eighth notes\n```bash\n.\u002Fhum2midi melody.wav --grid 1\u002F8\n```\n\n#### Key-aware quantization (recommended for tonal music)\n```bash\n.\u002Fhum2midi song.wav --key-aware\n```\n\n#### Require longer minimum notes\n```bash\n.\u002Fhum2midi humming.wav --min-note 100\n```\n\n#### Skip analysis for faster processing\n```bash\n.\u002Fhum2midi quick.wav --no-analysis\n```\n\n#### Combine options\n```bash\n.\u002Fhum2midi recording.wav output.mid --grid 1\u002F8 --key-aware --min-note 80\n```\n\n### Processing MIDI Input\n\nYou can also process existing MIDI files through the quantization pipeline:\n\n```bash\n.\u002Fhum2midi input.mid output.mid --grid 1\u002F16 --key-aware\n```\n\nThis skips the audio processing steps and goes directly to analysis and quantization.\n\n## Sample Output\n\n```\n═══════════════════════════════════════════════════════════════\n  hum2midi - Melody-to-MIDI Pipeline (Basic Pitch Edition)\n  [Key-Aware Mode Enabled]\n═══════════════════════════════════════════════════════════════\n\nInput:  my_humming.wav\nOutput: my_humming.mid\n\n→ Step 1: Stem Separation (HPSS)\n  Isolating melodic content...\n  Loaded: 5.23s @ 44100Hz\n  ✓ Melody stem extracted → 5.23s\n\n→ Step 2: Audio-to-MIDI Conversion (Basic Pitch)\n  Running Spotify's Basic Pitch ML model on melody stem...\n  ✓ Raw MIDI generated (Basic Pitch)\n\n→ Step 3: Pitch Analysis & Key Detection\n  Notes detected: 42 total, 7 unique\n  Note range: C3 - G4\n  Pitch classes: C3, E3, G3, A3, C4, D4, G4\n  Dominant note: G3 (23.8% of notes)\n  Detected key: G major\n\n→ Step 4: Quantization & Cleanup\n  Octave pruning: removed 3 harmonic notes above 67 (median+12)\n  Overlap pruning: removed 2 harmonic notes at overlapping positions\n  Note merging: merged 5 staccato chunks into legato notes (gap\u003C=60 ticks)\n  Grid:   240 ticks (1\u002F16)\n  Notes:  38 notes\n  Key:    G major\n  Key-aware: 2 notes corrected to scale\n  Tempo:  120 BPM\n  ✓ Quantized MIDI saved\n\n═══════════════════════════════════════════════════════════════\n  ✓ Done! Output: my_humming.mid\n═══════════════════════════════════════════════════════════════\n\n? ANALYSIS SUMMARY\n─────────────────────────────────────────────────────────────\n  Detected Notes: C3, E3, G3, A3, C4, D4, G4\n  Detected Key:   G major\n  Quantization:   Key-aware mode (notes snapped to scale)\n\nMIDI Info: 38 notes, 7 unique pitches, 120 BPM\nPitches: C3, E3, G3, A3, C4, D4, G4\n```\n\n## Notes & Limitations\n\n### Audio Quality Matters\n\n- **Clear, loud melody** produces the best results\n- **Background noise** can cause false note detection\n- **Reverb and effects** may confuse pitch detection\n- **Close-mic'd vocals** work significantly better than room recordings\n\n### Musical Considerations\n\n- **Monophonic sources** work best (single melody line)\n- **Polyphonic audio** (chords, multiple instruments) will produce messy results\n- **Vibrato and pitch bends** may be quantized to stepped pitches\n- **Rapid note passages** may be missed or merged\n\n### Technical Limitations\n\n- **Tempo is fixed** at 120 BPM in output (time positions are preserved, but tempo may need adjustment in your DAW)\n- **Note velocities** are normalized but may need manual adjustment\n- **Very short notes** (\u003C50ms) may be filtered out by default\n- **Extreme pitch ranges** may cause octave detection issues\n\n### Post-Processing Recommendations\n\nAfter generating MIDI, you may want to:\n\n1. **Import into your DAW** and adjust tempo to match your original recording\n2. **Quantize further** if stricter timing is needed\n3. **Adjust note velocities** for dynamics\n4. **Apply swing\u002Fgroove** templates if the rigid grid sounds too mechanical\n5. **Edit individual notes** that were misdetected (common with fast runs)\n\n### Supported Audio Formats\n\nInput formats supported via FFmpeg:\n- WAV, AIFF, FLAC (uncompressed, best quality)\n- MP3, M4A, AAC (compressed, acceptable)\n- OGG, OPUS (open source formats)\n- Most other formats FFmpeg supports\n\n## Troubleshooting\n\n### No notes detected\n- Check that input file isn't silent or corrupted\n- Try increasing `--min-note` threshold\n- Verify audio has clear melodic content (not just noise)\n\n### Too many notes \u002F messy output\n- Enable octave pruning and overlap pruning (on by default)\n- Use `--key-aware` to constrain to musical scale\n- Check for background noise in source audio\n\n### Wrong key detected\n- Key detection works best with at least 8-10 measures of music\n- Chromatic passages may confuse the detector\n- Manually review and adjust in your DAW if needed\n\n### Notes in wrong octave\n- Basic Pitch sometimes detects harmonics instead of fundamentals\n- The pipeline includes pruning, but some may slip through\n- Use your DAW's transpose function for simple octave shifts\n\n## References\n\n- [Basic Pitch](https:\u002F\u002Fgithub.com\u002Fspotify\u002Fbasic-pitch) - Spotify's polyphonic pitch detection model\n- [librosa HPSS](https:\u002F\u002Flibrosa.org\u002Fdoc\u002Flatest\u002Fgenerated\u002Flibrosa.decompose.hpss.html) - Harmonic-Percussive Source Separation\n- [Krumhansl-Kessler Key Profiles](https:\u002F\u002Frnhart.net\u002Farticles\u002Fkey-finding\u002F) - Key detection algorithm\n\n## License\n\nThis skill integrates Basic Pitch by Spotify, which is licensed under Apache 2.0. 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