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c22b544 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | import torch
import torch.nn.functional as F
import copy
def conv_rir(dry_sources, rirs, ch_num, device):
batch_size = len(dry_sources)
assert len(rirs) == batch_size
# それぞれ最大長を取得
max_dry_len = max(w.size(0) for w in dry_sources)
max_rir_len = max(rir.shape[-1] for rir in rirs)
# --- dry_sourcesパディング＆ch展開 ---
padded_dry_sources = []
for w in dry_sources:
pad = max_dry_len - w.size(0)
w = torch.nn.functional.pad(w, (0, pad)) # (time,)
w = w.unsqueeze(0).repeat(ch_num, 1) # (ch_num, time)
padded_dry_sources.append(w)
dry_sources = torch.cat(padded_dry_sources, dim=0).unsqueeze(0) # (1, batch*ch_num, time)
# --- rirパディング＆展開 ---
padded_rirs = []
for rir in rirs:
pad = max_rir_len - rir.size(-1)
rir = torch.nn.functional.pad(rir, (0, pad)) # (ch_num, rir_time)
rir = rir.flip(-1)
rir = rir.unsqueeze(1) # (ch_num, 1, rir_time)
padded_rirs.append(rir)
rirs = torch.cat(padded_rirs, dim=0) # (batch*ch_num, 1, rir_time)
# --- torch tensor ---
dry_sources = dry_sources.to(device)
rirs = rirs.to(device)
assert dry_sources.shape == (1, batch_size * ch_num, max_dry_len)
assert rirs.shape == (batch_size * ch_num, 1, max_rir_len)
# --- convolution ---
mix = F.conv1d(dry_sources, rirs, padding=max_rir_len-1, groups=rirs.size(0))
# --- 整形 ---
mix = mix.squeeze(1) # (batch*ch_num, time)
mix = mix.view(batch_size, ch_num, -1) # (batch, ch_num, time)
return mix
class CollateFN:
def __init__(self, rir_dataset, device, ch_num=2):
self.rir_dataset = rir_dataset
self.device = device
self.ch_num = ch_num
def make_batch(self, mixed, metas):
assert (len(mixed) % 4) == 0
n = len(mixed)
out_wavs = []
out_metas = []
# 1. 前半: 単一音源としてそのまま追加
for i in range(n // 2):
out_wavs.append(mixed[i]) # (2, time)
out_metas.append([metas[i]]) # メタデータはリストでラップ
# 2. 後半: 2つの音源をミックスして追加（n//4組）
for i in range(n // 2, n, 2):
m1 = mixed[i]
m2 = mixed[i + 1]
mix = m1 + m2 # ステレオのまま加算
mix = mix / 2 # 正規化（音量の過大を防ぐ）
out_wavs.append(mix)
out_metas.append([metas[i], metas[i + 1]])
# --- tensor化 ---
out_wavs = torch.stack(out_wavs, dim=0) # (3n//4, 2, time)
assert len(out_wavs) == len(out_metas)
assert out_wavs.shape == (3*n//4, 2, mixed.shape[2])
return (out_wavs, out_metas)
def __call__(self, batch):
# --- input collection ---
dry_sources = []
rirs = []
metas = []
for waveform, meta_ in batch:
rir, doa = self.rir_dataset.get()
assert len(rir) == self.ch_num
meta = copy.deepcopy(meta_)
meta["doa"] = doa
dry_sources.append(waveform)
rirs.append(rir)
metas.append(meta)
# --- convolution ---
mixed = conv_rir(dry_sources, rirs, self.ch_num, self.device)
# --- output prep ---
return self.make_batch(mixed, metas)
if __name__ == '__main__':
import os
import torchaudio
from audio_dataset import AudioCapsDataset
from collate import CollateFN
from rir_dataset import RIRDataset
from util import set_seed
set_seed()
device = "cuda" if torch.cuda.is_available() else "cpu"
batch_size = 8
config = {
"wav_dir": "../data/wav/",
"csv_path": "../data/fixed_audiocaps2.0/train.csv",
}
# Train Dataset & Dataloader
rir_dataset = RIRDataset("train")
audio_dataset = AudioCapsDataset(config)
collate_fn = CollateFN(rir_dataset, device)
batch = [audio_dataset[i] for i in range(batch_size)]
wavs, metas = collate_fn(batch)
out_dir = "output/debug/collate"
os.makedirs(out_dir, exist_ok=True)
for i, (wav, meta) in enumerate(batch):
torchaudio.save(os.path.join(out_dir, f"input_{i}.wav"), wav.unsqueeze(0), audio_dataset.sample_rate)
for i, wav in enumerate(wavs):
torchaudio.save(os.path.join(out_dir, f"output_{i}.wav"), wav.cpu(), audio_dataset.sample_rate)
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