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Score.to_lilypond() gains chord_names/fretboards/tab flags (plus chord_part and fretboard) that render a chord part as a lead sheet from a single \chordmode block. Fret diagrams use PyTheory's own Fretboard voicings via \storePredefinedDiagram, so they match what it would actually play rather than LilyPond's computed defaults. New helper _chord_to_chordmode(chord) derives the chordmode modifier from Chord.quality (faithful even when the symbol re-spells, e.g. C6 -> Am7). Default output is unchanged. Verified by compiling the output with LilyPond 2.26 (lead sheet + a 17-quality battery, incl. m7+ and aug7). Adds 7 tests (string assertions + a lilypond-compile test marked slow). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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4.1 KiB
name, description, license, allowed-tools
| name | description | license | allowed-tools |
|---|---|---|---|
| transcription-and-notation-with-pytheory | Transcribe audio and convert between music formats with PyTheory. Use when the user wants to turn a recording (WAV/hum/melody) into notes or MIDI, identify the chord in an audio clip, import a MIDI file, or export a score/melody to MIDI, sheet music (MusicXML, LilyPond, ABC), or guitar tab. | MIT | Write, Read, Bash(python3:*), Bash(uv run:*) |
Transcription & Notation
Getting music into PyTheory from audio/MIDI, and out to MIDI, sheet music, and tab.
Transcribe a recording → notes / MIDI
from pytheory import Score
score = Score.from_wav("hum.wav", bpm=80) # estimates tempo if bpm omitted
for name, part in score.parts.items():
print(name, len(part.notes), "notes")
score.save_midi("hum.mid")
Score.from_wav(path, *, bpm=None, quantize=None, split=False, fmin=50, fmax=1500).quantize=0.25snaps to sixteenths;split=Trueseparates a full mix into bass + melody (and drums) instead of one monophonicmelodypart..m4a/.mp3work ifafconvert/ffmpegis available; WAV always works.- CLI equivalent:
pytheory transcribe hum.m4a out.mid(add--split,--quantize 0.25,--bpm 90).
Identify the chord in an audio buffer
from pytheory.audio import identify_chord
import scipy.io.wavfile
sr, data = scipy.io.wavfile.read("clip.wav")
identify_chord(data, sr)
# {'symbol': 'D7', 'confidence': 0.76, 'notes': ['D', 'F#', 'A', 'C']} (or None)
Returns a best-guess symbol with a confidence (0..1) and the detected
notes, or None if it can't tell. Works best on clean, sustained chords; it's
a real-time recognizer, not a perfect oracle. (The live version is
pytheory tune --chords, in the guitar skill.)
Import MIDI
from pytheory import Score
score = Score.from_midi("song.mid")
Export to every format
score.save_midi("song.mid") # MIDI (drums ch 10)
open("song.abc", "w").write(score.to_abc(title="Song", key="C"))
open("song.xml", "w").write(score.to_musicxml(title="Song")) # MusicXML for notation apps
open("song.ly", "w").write(score.to_lilypond(title="Song", key="C"))
print(score.to_tab("part_name")) # ASCII guitar tab for a part
to_tab(part_name, tuning="guitar", frets=24)turns a single part into tab.to_musicxmlopens in MuseScore/Finale/Sibelius;to_lilypondengraves to PDF via LilyPond;to_abcis compact plain-text notation.
Lead sheets (chord symbols + fret diagrams)
to_lilypond can render a chord part as a lead sheet — chord names, fret
diagrams, and/or tab above the melody staff:
ly = score.to_lilypond(chord_names=True, fretboards=True, tab=True)
# chord_names -> a ChordNames row (C G Am F)
# fretboards -> a FretBoards row using PyTheory's OWN voicings (not LilyPond's)
# tab -> a TabStaff of the progression
# chord_part="comp" picks which part supplies the harmony (else the first
# chord-bearing part); fretboard=Fretboard.guitar(...) sets the diagram source
The fret diagrams come straight from PyTheory's Fretboard, so they match
score.to_tab() / what it would actually play. Compile with
lilypond leadsheet.ly → PDF.
A complete round-trip
from pytheory import Score, Key
score = Score.from_wav("melody.wav", quantize=0.25) # hum -> notes
key = Key.detect(*[n.tone.name for n in score.parts["melody"].notes if n.tone])
score.save_midi("melody.mid") # -> DAW
open("melody.xml", "w").write(score.to_musicxml(title="My Melody")) # -> sheet music
print("Detected key:", key)
Tips
- Transcription is monophonic by default — one note at a time. Use
split=Truefor full mixes. - Pass
bpm=if you know the tempo; otherwise it's estimated and timing/quantize is interpreted against that estimate. identify_chordreturns a dict (orNone) — checkconfidencebefore trusting thesymbol.- NumPy/SciPy ship as PyTheory dependencies, so
scipy.io.wavfile(for reading the audio buffer) needs no extra install.