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"""preprocess_mir1k.py"""
import os
import shutil
import glob
import re
import json
from typing import Dict, List, Tuple
import numpy as np
from utils.audio import get_audio_file_info, load_audio_file
from utils.midi import midi2note, note_event2midi
from utils.note2event import note2note_event, mix_notes, sort_notes, validate_notes, trim_overlapping_notes
from utils.event2note import event2note_event
from utils.note_event_dataclasses import Note, NoteEvent
from utils.utils import note_event2token2note_event_sanity_check, freq_to_midi
MT3_TEST_IDS = [1, 2, 12, 13, 24, 25, 31, 38, 39]
PROGRAM_STR2NUM = {
'vn': 40,
'va': 41,
'vc': 42,
'db': 43,
'fl': 73,
'ob': 68,
'cl': 71,
'sax': 65, # The type of sax used in the dataset is not clear. We guess it would be alto sax.
'bn': 70,
'tpt': 56,
'hn': 60, # Just annotated as horn. We guess it would be french horn, due to the pitch range.
'tbn': 57,
'tba': 58,
}
def delete_hidden_files(base_dir):
for hidden_file in glob.glob(os.path.join(base_dir, '**/.*'), recursive=True):
os.remove(hidden_file)
print(f"Deleted: {hidden_file}")
def convert_annotation_to_notes(id, program, ann_files):
notes = []
for ann_file, prog in zip(ann_files, program):
data = np.loadtxt(ann_file)
onset = data[:, 0]
freq = data[:, 1]
duration = data[:, 2]
notes_by_instr = []
for o, f, d in zip(onset, freq, duration):
notes_by_instr.append(
Note(
is_drum=False,
program=prog,
onset=o,
offset=o + d,
pitch=freq_to_midi(f),
velocity=1))
notes = mix_notes([notes, notes_by_instr], sort=True, trim_overlap=True, fix_offset=True)
notes = sort_notes(notes)
note_events = note2note_event(notes, sort=True)
duration_sec = note_events[-1].time + 0.01
return { # notes
'urmp_id': id,
'program': program,
'is_drum': [0] * len(program),
'duration_sec': duration_sec,
'notes': notes,
}, { # note_events
'guitarset_id': id,
'program': program,
'is_drum': [0] * len(program),
'duration_sec': duration_sec,
'note_events': note_events,
}
def create_audio_stem(audio_tracks, id, program, n_frames):
max_length = max([len(tr) for tr in audio_tracks])
max_length = max(max_length, n_frames)
n_tracks = len(audio_tracks)
audio_array = np.zeros((n_tracks, max_length), dtype=np.float16)
for j, audio in enumerate(audio_tracks):
audio_array[j, :len(audio)] = audio
return {
'urmp_id': id,
'program': np.array(program),
'is_drum': np.array([0] * len(program), dtype=np.int64),
'n_frames': n_frames, # int
'audio_array': audio_array # (n_tracks, n_frames)
}
def data_bug_fix(base_dir):
files = glob.glob(os.path.join(base_dir, '15_Surprise_tpt_tpt_tbn', '*3_tpt*.*'))
for file in files:
new_file = file.replace('3_tpt', '3_tbn')
shutil.move(file, new_file)
print(f"Renamed: {file} -> {new_file}")
def preprocess_urmp16k(data_home=os.PathLike,
dataset_name='urmp',
delete_source_files: bool = False,
sanity_check=True) -> None:
"""
URMP dataset does not have official split information. We follow the split used in MT3 paper.
About:
- 44 pieces of classical music
- Duet, Trio, Quartet, Quintet of strings or winds or mixed
- Multi-stem audio
- MIDI file is unaligned, it is for score
- Annotation (10ms hop) is provided.
- There is timing issue for annotation
- We do not use video
Splits:
- train: 35 files, following MT3
- test: 9 files, follwing MT3
- all: 44 files
Writes:
- {dataset_name}_{split}_file_list.json: a dictionary with the following keys:
{
index:
{
'urmp_id': urmp_id,
'n_frames': (int),
'stem_file': 'path/to/stem.npy',
'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', # this is 120bpm converted midi file from note_events
'program': List[int], #
'is_drum': List[int], # [0] or [1]
}
}
"""
# Directory and file paths
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)
# Databug fix
data_bug_fix(base_dir)
# Delete hidden files
delete_hidden_files(base_dir)
# Create file list for split==all
file_list = dict()
for dir_name in sorted(os.listdir(base_dir)):
if dir_name.startswith('.'):
continue
if 'Supplementary' in dir_name:
continue
# urmp_id
id = dir_name.split('_')[0]
title = dir_name.split('_')[1]
# program
program_strings = dir_name.split('_')[2:]
program = [PROGRAM_STR2NUM[p] for p in program_strings]
# is_drum
is_drum = [0] * len(program)
# file paths
stem_file = os.path.join(base_dir, dir_name, 'stem.npy')
mix_audio_file = glob.glob(os.path.join(base_dir, dir_name, 'AuMix*.wav'))[0]
notes_file = os.path.join(base_dir, dir_name, 'notes.npy')
note_events_file = os.path.join(base_dir, dir_name, 'note_events.npy')
midi_file = os.path.join(base_dir, dir_name, f'{str(id)}_120bpm_converted.mid')
# n_frames
fs, n_frames, n_channels = get_audio_file_info(mix_audio_file)
assert fs == 16000 and n_channels == 1
# Fill out a file list
file_list[id] = {
'urmp_id': id,
'n_frames': n_frames,
'stem_file': stem_file,
'mix_audio_file': mix_audio_file,
'notes_file': notes_file,
'note_events_file': note_events_file,
'midi_file': midi_file,
'program': program,
'is_drum': is_drum,
}
# Process Annotations
ann_files = [
os.path.join(base_dir, dir_name, f'Notes_{i+1}_{p}_{str(id)}_{title}.txt')
for i, p in enumerate(program_strings)
]
# Check if all files exist
for ann_file in ann_files:
assert os.path.exists(ann_file), f"{ann_file} does not exist."
assert len(program) == len(ann_files)
# Create and save notes and note_events from annotation
notes, note_events = convert_annotation_to_notes(id, program, ann_files)
np.save(notes_file, notes, allow_pickle=True, fix_imports=False)
print(f'Created {notes_file}')
np.save(note_events_file, note_events, allow_pickle=True, fix_imports=False)
print(f'Created {note_events_file}')
# Create 120bpm MIDI file from note_events
note_event2midi(note_events['note_events'], midi_file)
print(f'Created {midi_file}')
# Process Audio
audio_tracks = []
for i, p in enumerate(program_strings):
audio_sep_file = os.path.join(base_dir, dir_name, f'AuSep_{i+1}_{p}_{id}_{title}.wav')
audio_track = load_audio_file(audio_sep_file, dtype=np.int16) / 2**15 # returns bytes
audio_tracks.append(audio_track.astype(np.float16))
if delete_source_files:
os.remove(audio_sep_file)
stem_content = create_audio_stem(audio_tracks, id, program, n_frames)
np.save(stem_file, stem_content, allow_pickle=True, fix_imports=False)
print(f'Created {stem_file}')
# Sanity check
if sanity_check:
recon_notes, _ = midi2note(midi_file)
recon_note_events = note2note_event(recon_notes)
note_event2token2note_event_sanity_check(recon_note_events, notes['notes'])
# File existence check
assert os.path.exists(mix_audio_file)
# Create index for splits
file_list_all = {}
for i, key in enumerate(file_list.keys()):
file_list_all[i] = file_list[key]
file_list_train = {}
i = 0
for key in file_list.keys():
if int(key) not in MT3_TEST_IDS:
file_list_train[i] = file_list[key]
i += 1
file_list_test = {}
i = 0
for key in file_list.keys():
if int(key) in MT3_TEST_IDS:
file_list_test[i] = file_list[key]
i += 1
all_fl = {'all': file_list_all, 'train': file_list_train, 'test': file_list_test}
# Save index
for split in ['all', 'train', 'test']:
output_index_file = os.path.join(output_index_dir, f'{dataset_name}_{split}_file_list.json')
with open(output_index_file, 'w') as f:
json.dump(all_fl[split], f, indent=4)
print(f'Created {output_index_file}') |