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