def aggregate(model: Model, output_file: Path, n_tracks: Optional[int] = None):
tracks = tqdm(Track.get_all(limit=n_tracks), desc=str(model))
embeddings = model.get_embeddings_from_file(tracks)
embeddings = np.vstack(embeddings)
embeddings = embeddings.astype(np.float16)
np.save(output_file, embeddings)
def _get_mean_and_std(model: Model, tracks: Iterable[Track]):
embeddings = model.get_embeddings_from_file(tracks)
embeddings_stacked = np.vstack(embeddings)
return embeddings_stacked.mean(axis=0), embeddings_stacked.std(axis=0)
