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# Copyright 2024 The YourMT3 Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Please see the details in the LICENSE file.
import json
import os
from typing import Dict, Any, Union, Tuple, Optional
import torch
import numpy as np
from einops import rearrange
from torch.utils.data import DataLoader, Dataset
from utils.audio import load_audio_file, slice_padded_array
from utils.tokenizer import EventTokenizerBase, NoteEventTokenizer
from utils.note2event import slice_multiple_note_events_and_ties_to_bundle
from utils.note_event_dataclasses import Note, NoteEvent, NoteEventListsBundle
from utils.task_manager import TaskManager
from config.config import shared_cfg
from config.config import audio_cfg as default_audio_cfg
UNANNOTATED_PROGRAM = 129
class AudioFileDataset(Dataset):
"""
🎧 AudioFileDataset for validation/test:
This dataset class is designed to be used ONLY with `batch_size=None` and
returns sliced audio segments and unsliced notes and sliced note events for
a single song when `__getitem__` is called.
Args:
file_list (Union[str, bytes, os.PathLike], optional):
Path to the file list. e.g. "../../data/yourmt3_indexes/slakh_validation_file_list.json"
task_manager (TaskManager, optional): TaskManager instance. Defaults to TaskManager().
fs (int, optional): Sampling rate. Defaults to 16000.
seg_len_frame (int, optional): Segment length in frames. Defaults to 32767.
seg_hop_frame (int, optional): Segment hop in frames. Defaults to 32767.
sub_batch_size (int, optional): Sub-batch size that will be used in
generation of tokens. Defaults to 32.
max_num_files (int, optional): Maximum number of files to be loaded. Defaults to None.
Variables:
file_list:
'{dataset_name}_{split}_file_list.json' has the following keys:
{
'index':
{
'mtrack_id': mtrack_id,
'n_frames': n of audio frames
'stem_file': Dict of stem audio file info
'mix_audio_file': mtrack.mix_path,
'notes_file': available only for 'validation' and 'test'
'note_events_file': available only for 'train' and 'validation'
'midi_file': mtrack.midi_path
}
}
__getitem__(index) returns:
audio_segment:
torch.FloatTensor: (nearest_N_divisable_by_sub_batch_size, 1, seg_len_frame)
notes_dict:
{
'mtrack_id': int,
'program': List[int],
'is_drum': bool,
'duration_sec': float,
'notes': List[Note],
}
token_array:
torch.LongTensor: (n_segments, seg_len_frame)
"""
def __init__(
self,
file_list: Union[str, bytes, os.PathLike],
task_manager: TaskManager = TaskManager(),
# tokenizer: Optional[EventTokenizerBase] = None,
fs: int = 16000,
seg_len_frame: int = 32767,
seg_hop_frame: int = 32767,
max_num_files: Optional[int] = None) -> None:
# load the file list
with open(file_list, 'r') as f:
fl = json.load(f)
file_list = {int(key): value for key, value in fl.items()}
if max_num_files: # reduce the number of files
self.file_list = dict(list(file_list.items())[:max_num_files])
else:
self.file_list = file_list
self.fs = fs
self.seg_len_frame = seg_len_frame
self.seg_len_sec = seg_len_frame / fs
self.seg_hop_frame = seg_hop_frame
self.task_manager = task_manager
def __getitem__(self, index: int) -> Tuple[np.ndarray, Dict, NoteEventListsBundle]:
# get metadata
metadata = self.file_list[index]
audio_file = metadata['mix_audio_file']
notes_file = metadata['notes_file']
note_events_file = metadata['note_events_file']
# load the audio
audio = load_audio_file(audio_file, dtype=np.int16) # returns bytes
audio = audio / 2**15
audio = audio.astype(np.float32)
audio = audio.reshape(1, -1)
audio_segments = slice_padded_array(
audio,
self.seg_len_frame,
self.seg_hop_frame,
pad=True,
) # (n_segs, seg_len_frame)
audio_segments = rearrange(audio_segments, 'n t -> n 1 t').astype(np.float32)
num_segs = audio_segments.shape[0]
# load all notes and from a file (of a single song)
notes_dict = np.load(notes_file, allow_pickle=True, fix_imports=False).tolist()
# TODO: add midi_file path in preprocessing instead of here
notes_dict['midi_file'] = metadata['midi_file']
# tokenize note_events
note_events_dict = np.load(note_events_file, allow_pickle=True, fix_imports=False).tolist()
if self.task_manager.tokenizer is not None:
# not using seg_len_sec to avoid accumulated rounding errors
start_times = [i * self.seg_hop_frame / self.fs for i in range(num_segs)]
note_event_segments = slice_multiple_note_events_and_ties_to_bundle(
note_events_dict['note_events'],
start_times,
self.seg_len_sec,
)
# Support for multi-channel decoding
if UNANNOTATED_PROGRAM in notes_dict['program']:
has_unannotated_segments = [True] * num_segs
else:
has_unannotated_segments = [False] * num_segs
token_array = self.task_manager.tokenize_note_events_batch(note_event_segments,
start_time_to_zero=False,
sort=True)
# note_token_array = self.task_manager.tokenize_note_events_batch(note_event_segments,
# start_time_to_zero=False,
# sort=True)
# task_token_array = self.task_manager.tokenize_task_events_batch(note_event_segments,
# has_unannotated_segments)
# Shape:
# processed_audio_array: (num_segs, 1, nframe)
# notes_dict: Dict
# note_token_array: (num_segs, decoding_ch, max_note_token_len)
# task_token_array: (num_segs, decoding_ch, max_task_token_len)
# return torch.from_numpy(audio_segments), notes_dict, torch.from_numpy(
# note_token_array).long(), torch.from_numpy(task_token_array).long()
return torch.from_numpy(audio_segments), notes_dict, torch.from_numpy(token_array).long()
# # Tokenize/pad note_event_segments -> array of token and mask
# max_len = self.tokenizer.max_length
# token_array = np.zeros((num_segs, max_len), dtype=np.int32)
# for i, tup in enumerate(list(zip(*note_event_segments.values()))):
# padded_tokens = self.tokenizer.encode_plus(*tup)
# token_array[i, :] = padded_tokens
# return torch.from_numpy(audio_segments), notes_dict, torch.from_numpy(token_array).long()
def __len__(self) -> int:
return len(self.file_list)
def get_eval_dataloader(
dataset_name: str,
split: str = 'validation',
dataloader_config: Dict = {"num_workers": 0},
task_manager: TaskManager = TaskManager(),
# tokenizer: Optional[EventTokenizerBase] = NoteEventTokenizer('mt3'),
max_num_files: Optional[int] = None,
audio_cfg: Optional[Dict] = None,
) -> DataLoader:
"""
🎧 get_audio_file_dataloader:
This function returns a dataloader for AudioFileDataset that returns padded slices
of audio samples with the divisable number of sub-batch size.
"""
data_home = shared_cfg["PATH"]["data_home"]
file_list = f"{data_home}/yourmt3_indexes/{dataset_name}_{split}_file_list.json"
if audio_cfg is None:
audio_cfg = default_audio_cfg
ds = AudioFileDataset(
file_list,
task_manager=task_manager,
# tokenizer=tokenizer,
seg_len_frame=int(audio_cfg["input_frames"]), # Default: 32767
seg_hop_frame=int(audio_cfg["input_frames"]), # Default: 32767
max_num_files=max_num_files)
dl = DataLoader(ds, batch_size=None, collate_fn=lambda k: k, **dataloader_config)
return dl
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