Singing Pitch Tracker

pitch-mcp

MCP server for real-time pitch detection and score alignment. Listens to a singer via microphone and reports their current position in a score and whether they are singing in tune.

What it does

Covers three goals:

• Goal 4 — Show the current measure/beat position while singing
• Goal 5 — Report pitch accuracy (too high / too low / on pitch)
• Goal 6 — Identify where in the score a singer is based on a hummed or sung phrase

Supports both offline analysis of pre-recorded audio and real-time microphone input.

Tools

Scores are given as musicxml_path (a .musicxml, .xml or .mxl file, preferred) or as inline musicxml text, never both. Paths must lie inside the output root (SHEET_MUSIC_OUTPUT_ROOT, default the working directory), the client's MCP roots or a folder in SHEET_MUSIC_INPUT_ROOTS.

analyze_recording, load_score and start_practice take an optional measures: [start, end] (printed bar numbers, inclusive; a pickup is 0) to practise or analyse only those bars. Reported measures keep their printed numbers.

Installation

cd pitch-mcp

uv sync

For real-time monitoring (start_monitoring), PortAudio is required:

# Ubuntu / Debian

sudo apt install libportaudio2

Quick install: on Ubuntu/Debian/Linux Mint, bash install.sh handles uv and the optional libportaudio2 install for you — see SETUP.md for the full non-technical walkthrough. Ready-made client configs (Claude Desktop, Cursor, Windsurf, Continue, Zed) are in examples/. If something goes wrong, check TROUBLESHOOTING.md.

Running

uv run pitch-mcp

Configuration

crepe requires a manual TensorFlow install (~500 MB) and downloads ~50 MB of model weights on first use. The default librosa backend (pYIN algorithm) works well for singing voice with no extra setup.

Usage examples

// Offline analysis of a recording

{ "tool": "analyze_recording", "arguments": { "audio_path": "/path/to/recording.wav", "musicxml_path": "/path/to/score.mxl", "part": "Soprano" } }

// Real-time session {"tool": "describe_score", "arguments": {"musicxml_path": "/path/to/score.mxl"}} // → returns parts with IDs ("P1" …), names and ranges

{"tool": "start_practice", "arguments": {"musicxml_path": "/path/to/score.mxl", "part": "Alto", "measures": [17, 24]}} // → returns {"session_id": "abc123", "measure_count": 8, ..., "status": "started"} // (the same as load_score followed by start_monitoring)

{"tool": "get_current_position", "arguments": {"session_id": "abc123"}} // → returns measure, beat, expected pitch, detected pitch, accuracy

{"tool": "stop_monitoring", "arguments": {"session_id": "abc123"}} // → summary with accuracy_histogram and highlights, e.g. "flat in m.19–21 (-32 cents avg over 5 notes)"

Audio must be 16-bit PCM WAV. MP3 and FLAC are not supported.

Testing

# Unit tests (no microphone or audio required)

VIRTUAL_ENV= .venv/bin/pytest tests/ -v

Integration tests (offline analysis against a committed fixture pair)

VIRTUAL_ENV= .venv/bin/pytest tests/ -v -m integration

Manual tests (real microphone required — skip in CI)

VIRTUAL_ENV= .venv/bin/pytest tests/ -v -m manual

294 of the 300 total tests are unit tests (292 run, 2 skipped), run against mocks and synthetic (in-memory) WAV data — none require real audio hardware or pre-recorded fixtures. The other 6 are marked integration (4 — offline analysis against the committed tests/fixtures/ pair) or manual (2 — full real-microphone session lifecycle) and are excluded from a plain pytest tests/ run by tests/conftest.py; select them explicitly with -m integration / -m manual.

Dependencies

• librosa — pYIN pitch detection (primary backend, pure Python)
• music21 — score parsing and pitch calculations
• sounddevice — real-time microphone input
• numpy, scipy — numerics
• mcp — MCP protocol

System requirements

• Python 3.12+
• libportaudio2 — required for real-time microphone input (start_monitoring)
• No system libraries required for offline analysis

Phase status