midi_to_csv.py

midi_to_csv.py

import mido
import pandas as pd
import argparse
import os
from collections import defaultdict

def midi_to_csv(midi_path, bpm):
    mid = mido.MidiFile(midi_path)

    active_notes = defaultdict(int)
    rows = []
    current_time = 0
    for i, track in enumerate(mid.tracks):
        for msg in track:
            if msg.type in ['note_on', 'note_off']:
                # Convert ticks to seconds
                current_time += mido.tick2second(msg.time, mid.ticks_per_beat, mido.bpm2tempo(bpm))
                # Convert seconds to frames (24 frames per second)
                frame = round(current_time * 24)

                if msg.type == 'note_on':
                    active_notes[msg.note] = msg.velocity
                elif msg.type == 'note_off':
                    active_notes[msg.note] = 0

                # Create a new row for each frame
                row = {'frame': frame}
                row.update(active_notes)
                rows.append(row)

    df = pd.DataFrame(rows).fillna(0)

    # Derive output file name from input file name
    dir_name = os.path.dirname(midi_path)
    base_name = os.path.basename(midi_path)
    name_without_extension = os.path.splitext(base_name)[0]
    csv_path = os.path.join(dir_name, name_without_extension + '.csv')

    df.to_csv(csv_path, index=False)

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Convert MIDI file to CSV.')
    parser.add_argument('midi_file', type=str, help='The path to the MIDI file to convert.')
    parser.add_argument('bpm', type=float, help='The beats per minute to use for conversion.')
    
    args = parser.parse_args()
    midi_to_csv(args.midi_file, args.bpm)