什么是 语音笔记转 MIDI？

voice-note-to-midi 技能在 Openclaw Skills 框架内提供了一个先进的音频转 MIDI 转换流水线。它采用多阶段处理方法，旨在处理人类人声录音中的细微差别，如音高偏移和背景噪音。这包括用于隔离旋律内容的音轨分离，以及用于多音符音高提取的 Spotify Basic Pitch 模型。

通过将此工具集成到您的音乐工作流程中，您可以弥补自发的灵感创意与结构化的数字作品之间的差距。该技能通过提供智能量化和调式感知修正，超越了简单的音高检测，确保生成的 MIDI 文件在音乐上准确无误，并可在任何数字音频工作站 (DAW) 中立即使用。它是 Openclaw Skills 如何简化创意技术任务的完美示例。

下载入口:https://github.com/openclaw/skills/tree/main/skills/danbennettuk/voice-note-to-midi

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

语音笔记转 MIDI 应用场景

• 使用语音备忘录随时随地捕捉旋律灵感，以便后期制作。
• 为非乐器演奏者的词曲作者将哼唱的旋律转换为 MIDI 轨道。
• 从旧的录音中提取主旋律线条以创建新的编曲。
• 在录音室期间，使用人声引导快速制作合成器线条的原型。

1. 该流水线使用 HPSS 进行音轨分离，从噪音或打击乐元素中隔离旋律谐波内容。
2. 它通过 Spotify 的 Basic Pitch 机器学习模型运行处理后的音频，以提取原始基频和音符起始时间。
3. 系统进行详细的音高分析，以检测音乐调式并识别主要的音符分布。
4. 它应用智能量化，将音符吸附到时间网格上，并执行可选的调式感知音高修正。
5. 最终的后期处理会移除谐波重叠（八度修剪）并合并连奏音符，以获得干净、专业的 MIDI 输出。

语音笔记转 MIDI 配置指南

要开始使用 Openclaw Skills 的此组件，请确保您已安装 Python 3.11+ 和 FFmpeg。使用自动设置脚本进行快速安装：

cd /path/to/voice-note-to-midi
./setup.sh

对于手动安装和依赖管理，您可以设置虚拟环境并通过 pip 安装所需的库：

mkdir -p ~/melody-pipeline
cd ~/melody-pipeline
python3 -m venv venv-bp
source venv-bp/bin/activate
pip install basic-pitch librosa soundfile mido music21

语音笔记转 MIDI 数据架构与分类体系

该技能处理各种音频输入并生成结构化的 MIDI 数据。下表概述了数据处理情况：

该工具为输出文件保持 120 BPM 的参考速度，同时保留原始演奏的相对时值。

name: voice-note-to-midi
description: Convert voice notes, humming, and melodic audio recordings to quantized MIDI files using ML-based pitch detection and intelligent post-processing
author: Clawd
tags: [audio, midi, music, transcription, machine-learning]

?? Voice Note to MIDI

Transform your voice memos, humming, and melodic recordings into clean, quantized MIDI files ready for your DAW.

What It Does

This skill provides a complete audio-to-MIDI conversion pipeline that:

1. Stem Separation - Uses HPSS (Harmonic-Percussive Source Separation) to isolate melodic content from drums, noise, and background sounds
2. ML-Powered Pitch Detection - Leverages Spotify's Basic Pitch model for accurate fundamental frequency extraction
3. Key Detection - Automatically detects the musical key of your recording using Krumhansl-Kessler key profiles
4. Intelligent Quantization - Snaps notes to a configurable timing grid with optional key-aware pitch correction
5. Post-Processing - Applies octave pruning, overlap-based harmonic removal, and legato note merging for clean output

Pipeline Architecture

Audio Input (WAV/M4A/MP3)
    ↓
┌─────────────────────────────────────┐
│ Step 1: Stem Separation (HPSS)     │
│ - Isolate harmonic content          │
│ - Remove drums/percussion           │
│ - Noise gating                      │
└─────────────────────────────────────┘
    ↓
┌─────────────────────────────────────┐
│ Step 2: Pitch Detection             │
│ - Basic Pitch ML model (Spotify)    │
│ - Polyphonic note detection         │
│ - Onset/offset estimation           │
└─────────────────────────────────────┘
    ↓
┌─────────────────────────────────────┐
│ Step 3: Analysis                    │
│ - Pitch class distribution          │
│ - Key detection                     │
│ - Dominant note identification      │
└─────────────────────────────────────┘
    ↓
┌─────────────────────────────────────┐
│ Step 4: Quantization & Cleanup      │
│ - Timing grid snap                  │
│ - Key-aware pitch correction        │
│ - Octave pruning (harmonic removal) │
│ - Overlap-based pruning             │
│ - Note merging (legato)             │
│ - Velocity normalization            │
└─────────────────────────────────────┘
    ↓
MIDI Output (Standard MIDI File)

Setup

Prerequisites

• Python 3.11+ (Python 3.14+ recommended)
• FFmpeg (for audio format support)
• pip

Installation

Quick Install (Recommended):

cd /path/to/voice-note-to-midi
./setup.sh

This automated script will:

• Check Python 3.11+ is installed
• Create the ~/melody-pipeline directory
• Set up the virtual environment
• Install all dependencies (basic-pitch, librosa, music21, etc.)
• Download and configure the hum2midi script
• Add melody-pipeline to your PATH

Manual Install:

If you prefer manual setup:

mkdir -p ~/melody-pipeline
cd ~/melody-pipeline
python3 -m venv venv-bp
source venv-bp/bin/activate
pip install basic-pitch librosa soundfile mido music21
chmod +x ~/melody-pipeline/hum2midi

5. Add to your PATH (optional):

echo 'export PATH="$HOME/melody-pipeline:$PATH"' >> ~/.bashrc
source ~/.bashrc

Verify Installation

cd ~/melody-pipeline
./hum2midi --help

Usage

Basic Usage

Convert a voice memo to MIDI:

./hum2midi my_humming.wav

This creates my_humming.mid with 16th-note quantization.

Specify Output File

./hum2midi input.wav output.mid

Command-Line Options

Usage Examples

Quantize to eighth notes

./hum2midi melody.wav --grid 1/8

Key-aware quantization (recommended for tonal music)

./hum2midi song.wav --key-aware

Require longer minimum notes

./hum2midi humming.wav --min-note 100

Skip analysis for faster processing

./hum2midi quick.wav --no-analysis

Combine options

./hum2midi recording.wav output.mid --grid 1/8 --key-aware --min-note 80

Processing MIDI Input

You can also process existing MIDI files through the quantization pipeline:

./hum2midi input.mid output.mid --grid 1/16 --key-aware

This skips the audio processing steps and goes directly to analysis and quantization.

Sample Output

═══════════════════════════════════════════════════════════════
  hum2midi - Melody-to-MIDI Pipeline (Basic Pitch Edition)
  [Key-Aware Mode Enabled]
═══════════════════════════════════════════════════════════════

Input:  my_humming.wav
Output: my_humming.mid

→ Step 1: Stem Separation (HPSS)
  Isolating melodic content...
  Loaded: 5.23s @ 44100Hz
  ? Melody stem extracted → 5.23s

→ Step 2: Audio-to-MIDI Conversion (Basic Pitch)
  Running Spotify's Basic Pitch ML model on melody stem...
  ? Raw MIDI generated (Basic Pitch)

→ Step 3: Pitch Analysis & Key Detection
  Notes detected: 42 total, 7 unique
  Note range: C3 - G4
  Pitch classes: C3, E3, G3, A3, C4, D4, G4
  Dominant note: G3 (23.8% of notes)
  Detected key: G major

→ Step 4: Quantization & Cleanup
  Octave pruning: removed 3 harmonic notes above 67 (median+12)
  Overlap pruning: removed 2 harmonic notes at overlapping positions
  Note merging: merged 5 staccato chunks into legato notes (gap<=60 ticks)
  Grid:   240 ticks (1/16)
  Notes:  38 notes
  Key:    G major
  Key-aware: 2 notes corrected to scale
  Tempo:  120 BPM
  ? Quantized MIDI saved

═══════════════════════════════════════════════════════════════
  ? Done! Output: my_humming.mid
═══════════════════════════════════════════════════════════════

?? ANALYSIS SUMMARY
─────────────────────────────────────────────────────────────
  Detected Notes: C3, E3, G3, A3, C4, D4, G4
  Detected Key:   G major
  Quantization:   Key-aware mode (notes snapped to scale)

MIDI Info: 38 notes, 7 unique pitches, 120 BPM
Pitches: C3, E3, G3, A3, C4, D4, G4

Notes & Limitations

Audio Quality Matters

• Clear, loud melody produces the best results
• Background noise can cause false note detection
• Reverb and effects may confuse pitch detection
• Close-mic'd vocals work significantly better than room recordings

Musical Considerations

• Monophonic sources work best (single melody line)
• Polyphonic audio (chords, multiple instruments) will produce messy results
• Vibrato and pitch bends may be quantized to stepped pitches
• Rapid note passages may be missed or merged

Technical Limitations

• Tempo is fixed at 120 BPM in output (time positions are preserved, but tempo may need adjustment in your DAW)
• Note velocities are normalized but may need manual adjustment
• Very short notes (<50ms) may be filtered out by default
• Extreme pitch ranges may cause octave detection issues

Post-Processing Recommendations

After generating MIDI, you may want to:

1. Import into your DAW and adjust tempo to match your original recording
2. Quantize further if stricter timing is needed
3. Adjust note velocities for dynamics
4. Apply swing/groove templates if the rigid grid sounds too mechanical
5. Edit individual notes that were misdetected (common with fast runs)

Supported Audio Formats

Input formats supported via FFmpeg:

• WAV, AIFF, FLAC (uncompressed, best quality)
• MP3, M4A, AAC (compressed, acceptable)
• OGG, OPUS (open source formats)
• Most other formats FFmpeg supports

Troubleshooting

No notes detected

• Check that input file isn't silent or corrupted
• Try increasing --min-note threshold
• Verify audio has clear melodic content (not just noise)

Too many notes / messy output

• Enable octave pruning and overlap pruning (on by default)
• Use --key-aware to constrain to musical scale
• Check for background noise in source audio

Wrong key detected

• Key detection works best with at least 8-10 measures of music
• Chromatic passages may confuse the detector
• Manually review and adjust in your DAW if needed

Notes in wrong octave

• Basic Pitch sometimes detects harmonics instead of fundamentals
• The pipeline includes pruning, but some may slip through
• Use your DAW's transpose function for simple octave shifts

References

• Basic Pitch - Spotify's polyphonic pitch detection model
• librosa HPSS - Harmonic-Percussive Source Separation
• Krumhansl-Kessler Key Profiles - Key detection algorithm

License

This skill integrates Basic Pitch by Spotify, which is licensed under Apache 2.0. The pipeline script and documentation are provided under MIT license.