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"""preprocess_mir1k.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, load_audio_file
from utils.midi import midi2note, note_event2midi
from utils.note2event import note2note_event, 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
# def create_spleeter_audio_stem(vocal_audio_file, accomp_audio_file, mir_st500_id) -> Dict:
# program = MIR_ST500_PROGRAM
# is_drum = [0, 0]
# audio_tracks = [] # multi-channel audio array (C, T)
# vocal_audio = load_audio_file(vocal_audio_file, dtype=np.int16) / 2**15 # returns bytes
# audio_tracks.append(vocal_audio.astype(np.float16))
# accomp_audio = load_audio_file(accomp_audio_file, dtype=np.int16) / 2**15 # returns bytes
# audio_tracks.append(accomp_audio.astype(np.float16))
# max_length = max(len(vocal_audio), len(accomp_audio))
# # collate all the audio tracks into a single array
# n_tracks = 2
# audio_array = np.zeros((n_tracks, max_length), dtype=np.float16)
# for j, audio in enumerate(audio_tracks):
# audio_array[j, :len(audio)] = audio
# stem_content = {
# 'mir_st500_id': mir_st500_id,
# 'program': np.array(program, dtype=np.int64),
# 'is_drum': np.array(is_drum, dtype=np.int64),
# 'n_frames': max_length, # int
# 'audio_array': audio_array # (n_tracks, n_frames)
# }
# return stem_content
# def create_note_note_event_midi_from_mir1k_annotation(ann, midi_file, mir_st500_id):
# """
# Args:
# ann: List[List[float, float, float]] # [onset, offset, pitch]
# mir_st500_id: str
# Returns:
# notes: List[Note]
# note_events: List[NoteEvent]
# midi: List[List[int]]
# """
# notes = []
# for onset, offset, pitch in ann:
# notes.append(
# Note(
# is_drum=False,
# program=100,
# onset=float(onset),
# offset=float(offset),
# pitch=int(pitch),
# velocity=1))
# notes = sort_notes(notes)
# notes = validate_notes(notes)
# notes = trim_overlapping_notes(notes)
# note_events = note2note_event(notes)
# # Write midi file
# note_event2midi(note_events, midi_file)
# print(f"Created {midi_file}")
# return { # notes
# 'mir_st500_id': mir_st500_id,
# 'program': MIR_ST500_PROGRAM,
# 'is_drum': [0, 0],
# 'duration_sec': note_events[-1].time,
# 'notes': notes,
# }, { # note_events
# 'mir_st500_id': mir_st500_id,
# 'program': MIR_ST500_PROGRAM,
# 'is_drum': [0, 0],
# 'duration_sec': note_events[-1].time,
# 'note_events': note_events,
# }
def preprocess_mir1k_16k(data_home=os.PathLike, dataset_name='mir1k', sanity_check=False) -> None:
"""
Splits:
- train: index 1 to 400, 346 files (54 files missing)
- test: index 401 to 500, 94 files (6 files missing)
- all: 440 files (60 files missing)
Writes:
- {dataset_name}_{split}_file_list.json: a dictionary with the following keys:
{
index:
{
'mir_st500_id': mir_st500_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], # [100, 129], 100 for singing voice, and 129 for unannotated
'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) |