Slakh2100 has a very challenging file structure. Each track consists of different number of sources. i.e. different number of midi and flac tracks for each mix.flac:
Track00001
└─── all_src.mid
└─── metadata.yaml
└─── MIDI
│ └─── S01.mid
│ │ ...
│ └─── SXX.mid
└─── mix.flac
└─── stems
└─── S01.flac
│ ...
└─── SXX.flac
This dataset is suitable for the following tasks:
- Music Instrument Recognition
- Automatic Music Transcription (AMT)
- Music Source Separation (MSS)
By default, each SXX.flac and SXX.mid corresponds to one Komplete 12 plugin/patch defined in this json file. We map these plugins/patches into MIDI instruments based on our custom MIDI map. Based on this custom map, all SXX.mid for each track become one TrackXXXXX.pkl file under the instruments_classification_notes_MIDI_class folder; all SXX.flac for each track are remapped to:
waveforms
└─── train
│ └─── Track00001
│ └─── Bass.flac
│ └─── Drums.flac
│ │ ...
│ └─── Voiceflac
│ └─── waveform.flac
│
└─── validation
└─── test
The remapping of SXX.flac also includes audio downsampling to 16kHz. So we also have waveform.flac as the downsampled version of mix.flac
Each idx under the __getitem__ function corresponds to each track TrackXXXXX in the dataset. Then, the audio mix waveform.flac is loaded as the waveform key.
The dataset can be loaded using:
from slakh_loader.slakh2100 import Slakh2100
from slakh_loader.MIDI_program_map import (
MIDI_Class_NUM,
MIDIClassName2class_idx,
class_idx2MIDIClass,
)
dataset = Slakh2100('train',
'./waveforms/',
'./instruments_classification_notes_MIDI_class/',
segment_seconds=11,
frames_per_second = 100,
transcription = True,
random_crop = False,
source = True ,
name_to_ix=MIDIClassName2class_idx,
ix_to_name=class_idx2MIDIClass,
plugin_labels_num=MIDI_Class_NUM,
sample_rate=16000,
)
dataset[0] returns a dictionary consisting of the following keys: target_dict, sources, source_masks, waveform, start_sample, valid_length, flac_name, instruments, plugin_id.
target_dict: contains the frame rolls, onset rolls, and masks for different sources, and have the following .
target_dict
└─── Electric Guitar
│ └─── onset_roll
│ └─── reg_onset_roll
│ └─── frame_roll
│ └─── mask_roll
└─── Organ
│ └─── ...
│ ...
└─── Voice