| import numpy as np | |
| import random | |
| import torch | |
| import os | |
| class RIRDataset: | |
| def _load(self, fname): | |
| return torch.from_numpy(np.load(fname)).float() | |
| def __init__(self, subset): | |
| assert subset in ["train", "val", "test"] | |
| start_room_id, end_room_id = { | |
| "train": (0, 336), | |
| "val": (336, 378), | |
| "test": (378, 420), | |
| }[subset] | |
| self.data = [ | |
| self._load(os.path.join( | |
| os.path.dirname(__file__), | |
| "../data/rir_generator/", | |
| f"output/rir/{str(n).zfill(4)}.npy" | |
| )) | |
| for n in range(start_room_id, end_room_id) | |
| ] | |
| self.room_num = end_room_id - start_room_id | |
| def get(self): | |
| """ランダムにRIRを1つ返す""" | |
| rid = random.randint(0, self.room_num-1) | |
| doaid = random.randint(0, 359) | |
| doa = doaid/180 | |
| if doa > 1: | |
| doa = 2 - doa | |
| doa = 2 * doa - 1 | |
| return (self.data[rid][doaid], doa) | |