155 lines
5.9 KiB
Python
155 lines
5.9 KiB
Python
import random
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from math import pi
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import torch
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import torchaudio
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from torch.utils.data import Dataset
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from tqdm import tqdm
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from data.audio.unsupervised_audio_dataset import UnsupervisedAudioDataset, load_audio
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from data.util import load_paths_from_cache
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# Just all ones.
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from utils.util import opt_get
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def _integration_fn_fully_enabled(n):
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return torch.ones((n,))
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# Randomly assigns up to 5 blocks of the output tensor the value '1'. Rest is zero
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def _integration_fn_spiky(n):
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fn = torch.zeros((n,))
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spikes = random.randint(1,5)
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for _ in range(spikes):
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sz = random.randint(n//8, n//2)
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pos = random.randint(0, n)
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extent = min(n, sz+pos)
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fn[pos:extent] = 1
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return fn
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# Uses a sinusoidal ramp up and down (of random length) to a peak which is held for a random duration.
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def _integration_fn_smooth(n):
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center = random.randint(1, n-2)
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max_duration=n-center-1
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duration = random.randint(max_duration//4, max_duration)
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end = center+duration
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ramp_up_sz = random.randint(n//16,n//4)
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ramp_up = torch.sin(pi*torch.arange(0,ramp_up_sz)/(2*ramp_up_sz))
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if ramp_up_sz > center:
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ramp_up = ramp_up[(ramp_up_sz-center):]
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ramp_up_sz = center
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ramp_down_sz = random.randint(n//16,n//4)
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ramp_down = torch.flip(torch.sin(pi*torch.arange(0,ramp_down_sz)/(2*ramp_down_sz)), dims=[0])
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if ramp_down_sz > (n-end):
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ramp_down = ramp_down[:(n-end)]
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ramp_down_sz = n-end
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fn = torch.zeros((n,))
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fn[(center-ramp_up_sz):center] = ramp_up
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fn[center:end] = 1
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fn[end:(end+ramp_down_sz)] = ramp_down
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return fn
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'''
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Wraps a unsupervised_audio_dataset and applies noise to the output clips, then provides labels depending on what
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noise was added.
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'''
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class AudioWithNoiseDataset(Dataset):
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def __init__(self, opt):
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self.underlying_dataset = UnsupervisedAudioDataset(opt)
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self.env_noise_paths = load_paths_from_cache(opt['env_noise_paths'], opt['env_noise_cache'])
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self.music_paths = load_paths_from_cache(opt['music_paths'], opt['music_cache'])
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self.min_volume = opt_get(opt, ['min_noise_volume'], .2)
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self.max_volume = opt_get(opt, ['max_noise_volume'], .5)
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self.sampling_rate = self.underlying_dataset.sampling_rate
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def __getitem__(self, item):
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out = self.underlying_dataset[item]
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clip = out['clip']
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augpath = ''
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augvol = 0
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try:
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# Randomly adjust clip volume, regardless of the selection, between
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clipvol = (random.random() * (.8-.5) + .5)
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clip = clip * clipvol
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label = random.randint(0,3)
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aug = torch.zeros_like(clip)
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if label != 0: # 0 is basically "leave it alone"
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augvol = (random.random() * (self.max_volume-self.min_volume) + self.min_volume)
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if label == 1:
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augpath = random.choice(self.env_noise_paths)
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intg_fns = [_integration_fn_fully_enabled]
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elif label == 2:
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augpath = random.choice(self.music_paths)
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intg_fns = [_integration_fn_fully_enabled]
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augvol *= .5 # Music is often severely in the background.
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elif label == 3:
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augpath = random.choice(self.underlying_dataset.audiopaths)
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intg_fns = [_integration_fn_smooth, _integration_fn_fully_enabled]
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aug = load_audio(augpath, self.underlying_dataset.sampling_rate)
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if aug.shape[1] > clip.shape[1]:
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n, cn = aug.shape[1], clip.shape[1]
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gap = n-cn
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placement = random.randint(0, gap)
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aug = aug[:, placement:placement+cn]
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aug = random.choice(intg_fns)(aug.shape[1]) * aug
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aug = aug * augvol
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if aug.shape[1] < clip.shape[1]:
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gap = clip.shape[1] - aug.shape[1]
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placement = random.randint(0, gap-1)
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aug = torch.nn.functional.pad(aug, (placement, gap-placement))
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clip = clip + aug
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clip.clip_(-1, 1)
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except:
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print(f"Exception encountered processing {item}, re-trying because this is often just a failed aug.")
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return self[item]
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out['clip'] = clip
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out['label'] = label
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out['aug'] = aug
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out['augpath'] = augpath
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out['augvol'] = augvol
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out['clipvol'] = clipvol
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return out
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def __len__(self):
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return len(self.underlying_dataset)
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if __name__ == '__main__':
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params = {
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'mode': 'unsupervised_audio_with_noise',
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'path': ['\\\\192.168.5.3\\rtx3080_audio_y\\split\\books2', '\\\\192.168.5.3\\rtx3080_audio\\split\\books1', '\\\\192.168.5.3\\rtx3080_audio\\split\\cleaned-2'],
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'cache_path': 'E:\\audio\\remote-cache2.pth',
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'sampling_rate': 22050,
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'pad_to_samples': 80960,
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'phase': 'train',
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'n_workers': 0,
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'batch_size': 16,
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'extra_samples': 4,
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'env_noise_paths': ['E:\\audio\\UrbanSound\\filtered', 'E:\\audio\\UrbanSound\\MSSND'],
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'env_noise_cache': 'E:\\audio\\UrbanSound\\cache.pth',
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'music_paths': ['E:\\audio\\music\\FMA\\fma_large', 'E:\\audio\\music\\maestro\\maestro-v3.0.0'],
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'music_cache': 'E:\\audio\\music\\cache.pth',
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}
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from data import create_dataset, create_dataloader, util
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ds = create_dataset(params)
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dl = create_dataloader(ds, params)
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i = 0
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for b in tqdm(dl):
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for b_ in range(b['clip'].shape[0]):
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#pass
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torchaudio.save(f'{i}_clip_{b_}_{b["label"][b_].item()}.wav', b['clip'][b_], ds.sampling_rate)
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torchaudio.save(f'{i}_clip_{b_}_aug.wav', b['aug'][b_], ds.sampling_rate)
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print(f'{i} aug path: {b["augpath"][b_]} aug volume: {b["augvol"][b_]} clip volume: {b["clipvol"][b_]}')
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i += 1
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