| | """preprocess_maps.py""" |
| | import os |
| | import glob |
| | import re |
| | import json |
| | from typing import Dict, List, Tuple |
| | import numpy as np |
| | from utils.audio import get_audio_file_info |
| | from utils.midi import midi2note, note_event2midi |
| | from utils.note2event import note2note_event, note_event2event |
| | from utils.event2note import event2note_event |
| | from utils.note_event_dataclasses import Note, NoteEvent |
| | from utils.utils import note_event2token2note_event_sanity_check |
| | |
| |
|
| |
|
| | def create_note_event_and_note_from_midi(mid_file: str, |
| | id: str, |
| | ignore_pedal: bool = False) -> Tuple[Dict, Dict]: |
| | """Extracts note or note_event and metadata from midi: |
| | |
| | Returns: |
| | notes (dict): note events and metadata. |
| | note_events (dict): note events and metadata. |
| | """ |
| | notes, dur_sec = midi2note( |
| | mid_file, |
| | binary_velocity=True, |
| | ch_9_as_drum=False, |
| | force_all_drum=False, |
| | force_all_program_to=0, |
| | trim_overlap=True, |
| | fix_offset=True, |
| | quantize=True, |
| | verbose=0, |
| | minimum_offset_sec=0.01, |
| | drum_offset_sec=0.01, |
| | ignore_pedal=ignore_pedal) |
| | return { |
| | 'maps_id': id, |
| | 'program': [0], |
| | 'is_drum': [0], |
| | 'duration_sec': dur_sec, |
| | 'notes': notes, |
| | }, { |
| | 'maps_id': id, |
| | 'program': [0], |
| | 'is_drum': [0], |
| | 'duration_sec': dur_sec, |
| | 'note_events': note2note_event(notes), |
| | } |
| |
|
| |
|
| | def rewrite_midi_120bpm(file: os.PathLike, note_events: List[NoteEvent]): |
| | """Rewrite midi file with 120 bpm.""" |
| | note_event2midi(note_events, file) |
| | return |
| |
|
| |
|
| | |
| | |
| | |
| | |
| | |
| |
|
| |
|
| | def preprocess_maps16k(data_home=os.PathLike, |
| | dataset_name='maps', |
| | ignore_pedal=False, |
| | sanity_check=False) -> None: |
| | """ |
| | Splits: |
| | - train: following the convention described in Cheuk et al. (2021), |
| | we filter out the songs overlapping with the MAPS test set. |
| | 139 pieces from MUS folder are left for training. |
| | - test: 60 files (MUS) |
| | - all: 270 files including (unfiltered) train and test. This is used |
| | for the evaluation on the MusicNet test set. |
| | |
| | |
| | Writes: |
| | - {dataset_name}_{split}_file_list.json: a dictionary with the following keys: |
| | { |
| | index: |
| | { |
| | 'maps_id': maps_id, |
| | 'n_frames': (int), |
| | 'mix_audio_file': 'path/to/mix.wav', |
| | 'notes_file': 'path/to/notes.npy', |
| | 'note_events_file': 'path/to/note_events.npy', |
| | 'midi_file': 'path/to/midi.mid', |
| | 'program': List[int], |
| | 'is_drum': List[int], # 0 or 1 |
| | } |
| | } |
| | """ |
| |
|
| | |
| | base_dir = os.path.join(data_home, dataset_name + '_yourmt3_16k') |
| | output_index_dir = os.path.join(data_home, 'yourmt3_indexes') |
| | os.makedirs(output_index_dir, exist_ok=True) |
| |
|
| | |
| | train_mid_pattern = os.path.join(base_dir, 'train/**/MUS/*.mid') |
| | test_mid_pattern = os.path.join(base_dir, 'test/**/MUS/*.mid') |
| | all_mid_pattern = os.path.join(base_dir, '**/MUS/*.mid') |
| |
|
| | train_mid_files = glob.glob(train_mid_pattern, recursive=True) |
| | test_mid_files = glob.glob(test_mid_pattern, recursive=True) |
| | all_mid_files = glob.glob(all_mid_pattern, recursive=True) |
| |
|
| | |
| | songnames_in_test_files = [] |
| | for file in test_mid_files: |
| | filename = os.path.basename(file) |
| | match = re.search(r"MAPS_MUS-([\w-]+)_", filename) |
| | if match: |
| | songnames_in_test_files.append(match.group(1)) |
| |
|
| | filtered_train_mid_files = [] |
| | filtered_train_wav_files = [] |
| | for train_file in train_mid_files: |
| | if not any( |
| | songname in os.path.basename(train_file) for songname in songnames_in_test_files): |
| | filtered_train_mid_files.append(train_file) |
| | filtered_train_wav_files.append(train_file.replace('.mid', '.wav')) |
| | assert len(filtered_train_mid_files) == len(filtered_train_wav_files) == 139 |
| |
|
| | |
| | for i, mid_file in enumerate(all_mid_files): |
| | maps_id = os.path.basename(mid_file)[:-4] |
| | notes, note_events = create_note_event_and_note_from_midi( |
| | mid_file=mid_file, id=maps_id, ignore_pedal=ignore_pedal) |
| |
|
| | if sanity_check: |
| | |
| | print(f'Sanity check for {i}: {maps_id}...') |
| | note_event2token2note_event_sanity_check(note_events['note_events'], notes['notes']) |
| |
|
| | notes_file = mid_file.replace('.mid', '_notes.npy') |
| | np.save(notes_file, notes, allow_pickle=True, fix_imports=False) |
| | print(f'Created {notes_file}') |
| |
|
| | note_events_file = mid_file.replace('.mid', '_note_events.npy') |
| | np.save(note_events_file, note_events, allow_pickle=True, fix_imports=False) |
| | print(f'Created {note_events_file}') |
| |
|
| | |
| | rewrite_midi_120bpm(mid_file, note_events['note_events']) |
| | print(f'Overwrote {mid_file} with 120 bpm') |
| |
|
| | |
| | pass |
| |
|
| | |
| | mid_files_by_split = { |
| | 'train': filtered_train_mid_files, |
| | 'test': test_mid_files, |
| | 'all': all_mid_files, |
| | } |
| |
|
| | for split in ['train', 'test', 'all']: |
| | file_list = {} |
| | for i, mid_file in enumerate(mid_files_by_split[split]): |
| | |
| | wav_file = mid_file.replace('.mid', '.wav') |
| | if not os.path.exists(wav_file): |
| | raise FileNotFoundError(f'Wav file not found: {wav_file}') |
| |
|
| | file_list[i] = { |
| | 'maps_id': os.path.basename(mid_file)[:-4], |
| | 'n_frames': get_audio_file_info(wav_file)[1], |
| | 'mix_audio_file': wav_file, |
| | 'notes_file': mid_file.replace('.mid', '_notes.npy'), |
| | 'note_events_file': mid_file.replace('.mid', '_note_events.npy'), |
| | 'midi_file': mid_file, |
| | 'program': [0], |
| | 'is_drum': [0], |
| | } |
| | output_file = os.path.join(output_index_dir, f'{dataset_name}_{split}_file_list.json') |
| | with open(output_file, 'w') as f: |
| | json.dump(file_list, f, indent=4) |
| | print(f'Created {output_file}') |
| |
|