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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, enable_scl=True):
self.rir_dataset = rir_dataset
self.device = device
self.ch_num = ch_num
self.enable_scl = enable_scl
def make_batch(self, mixed, metas):
assert (len(mixed) % 4) == 0
n = len(mixed) // 2
out_wavs = []
out_metas = []
# 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
lis = list(range(n // 2, n, 2)) + \
list(range(n + n//2, 2*n, 2))
assert len(lis) == (n//2)
for i in lis:
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)
assert len(out_wavs) == len(out_metas)
if self.enable_scl:
assert out_wavs.shape == (n, 2, mixed.shape[2])
else:
assert out_wavs.shape == ((3*n)//4, 2, mixed.shape[2])
return (out_wavs, out_metas)
def __call__(self, batch):
n = len(batch)
# --- input collection ---
dry_sources = []
rirs = []
metas = []
rir_candidates = [self.rir_dataset.get() for _ in range(n)]
# --- main ---
for i, (waveform, meta_) in enumerate(batch):
rir, doa = rir_candidates[i]
assert len(rir) == self.ch_num
meta = copy.deepcopy(meta_)
meta["doa"] = doa
dry_sources.append(waveform)
rirs.append(rir)
metas.append(meta)
# --- sub ---
if self.enable_scl:
for i, (waveform, meta_) in enumerate(batch):
if i%2 == 0:
j = i + 1
else:
j = i - 1
rir, doa = rir_candidates[j]
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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