aac_datasets.datasets.macs module¶

class MACS( root: str | Path | None = None, subset: typing_extensions.Literal[full] = 'full', download: bool = False, transform: Callable[[MACSItem], Any] | None = None, verbose: int = 0, force_download: bool = False, verify_files: bool = False, *, clean_archives: bool = True, flat_captions: bool = False, )[source]¶

Bases: AACDataset[MACSItem]

Unofficial MACS PyTorch dataset.

Dataset folder tree¶

{root}
└── MACS
    ├── audio
    │    └── (3930 wav files, ~13GB)
    ├── LICENCE.txt
    ├── MACS.yaml
    ├── MACS_competence.csv
    └── tau_meta
        ├── fold1_evaluate.csv
        ├── fold1_test.csv
        ├── fold1_train.csv
        └── meta.csv

CARD: ClassVar[MACSCard] = <aac_datasets.datasets.functional.macs.MACSCard object>¶

property download: bool¶

get_annotator_id_to_competence_dict() → Dict[int, float][source]¶: Get annotator to competence dictionary.

get_competence( annotator_id: int, ) → float[source]¶: Get competence value for a specific annotator id.

property root: str¶

property sr: int¶

property subset: typing_extensions.Literal[full]¶

class MACSItem[source]¶

Bases: TypedDict

Dataclass representing a single MACS item.

annotators_ids: List[str]¶

audio: Tensor¶

captions: List[str]¶

competences: List[float]¶

dataset: str¶

duration: float¶

fname: str¶

identifier: str¶

index: int¶

scene_label: str¶

sr: int¶

subset: typing_extensions.Literal[full]¶

tags: List[List[str]]¶