import random
import sys
from math import pi
import librosa
import torch
import torchaudio
from torch.utils.data import Dataset
from tqdm import tqdm
import torch.nn.functional as F
from data.audio.unsupervised_audio_dataset import UnsupervisedAudioDataset, load_audio
from data.util import load_paths_from_cache, find_files_of_type, is_audio_file
# 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
def load_rir(path, sr, max_sz):
rir = load_audio(path, sr).abs()
if rir.shape[-1] > max_sz:
rir = rir[:, :max_sz]
rir = (rir / torch.norm(rir, p=2)).flip([1])
return rir
'''
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.openair_paths = find_files_of_type('img', opt['openair_path'], qualifier=is_audio_file)[0]
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
self.use_gpu_for_reverb_compute = opt_get(opt, ['use_gpu_for_reverb_compute'], True)
self.openair_kernels = None
self.current_item_fetch = 0
self.fetch_error_count = 0
def load_openair_kernels(self):
if self.use_gpu_for_reverb_compute and self.openair_kernels is None:
# Load the openair reverbs as CUDA tensors.
self.openair_kernels = []
for oa in self.openair_paths:
self.openair_kernels.append(load_rir(oa, self.underlying_dataset.sampling_rate, self.underlying_dataset.sampling_rate*2).cuda())
def __getitem__(self, item):
if self.current_item_fetch != item:
self.current_item_fetch = item
self.fetch_error_count = 0
# Load on the fly to prevent GPU memory sharing across process errors.
self.load_openair_kernels()
out = self.underlying_dataset[item]
clip = out['clip']
dlen = clip.shape[-1]
clip = clip[:, :out['clip_lengths']]
padding_room = dlen - clip.shape[-1]
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, 4) # Current excludes GSM corruption.
#label = 3
if label > 0 and label < 4: # 0 is basically "leave it alone"
aug_needed = True
augvol = (random.random() * (self.max_volume-self.min_volume) + self.min_volume)
if label == 1:
# Add environmental noise.
augpath = random.choice(self.env_noise_paths)
intg_fns = [_integration_fn_fully_enabled]
elif label == 2:
# Add music
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)
# This can take two forms:
if padding_room < 22000 or random.random() < .5:
# (1) The voices talk over one another. If there is no padding room, we always take this choice.
intg_fns = [_integration_fn_smooth, _integration_fn_fully_enabled]
else:
# (2) There are simply two voices in the clip, separated from one another.
# This is a special case that does not use the same logic as the rest of the augmentations.
aug = load_audio(augpath, self.underlying_dataset.sampling_rate)
# Pad with some random silence
aug = F.pad(aug, (random.randint(20,4000), 0))
# Fit what we can given the padding room we have.
aug = aug[:, :padding_room]
clip = torch.cat([clip, aug], dim=1)
# Restore some meta-parameters.
padding_room = dlen - clip.shape[-1]
out['clip_lengths'] = torch.tensor(clip.shape[-1])
aug_needed = False
if aug_needed:
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
elif label == 4:
# Perform reverb (to simulate being in a large room with an omni-mic). This is performed by convolving
# impulse recordings from openair over the input clip.
if self.use_gpu_for_reverb_compute:
rir = random.choice(self.openair_kernels)
else:
augpath = random.choice(self.openair_paths)
rir = load_rir(augpath, self.underlying_dataset.sampling_rate, clip.shape[-1])
clip = torch.nn.functional.pad(clip, (rir.shape[1]-1, 0))
if self.use_gpu_for_reverb_compute:
clip = clip.cuda()
clip = torch.nn.functional.conv1d(clip.unsqueeze(0), rir.unsqueeze(0)).squeeze(0).cpu()
elif label == 5:
# Apply the GSM codec to simulate cellular phone audio.
clip = torchaudio.functional.apply_codec(clip, self.underlying_dataset.sampling_rate, format="gsm")
except:
if self.fetch_error_count > 10:
print(f"Exception encountered processing {item}, re-trying because this is often just a failed aug.")
print(sys.exc_info())
#raise # Uncomment to surface exceptions.
self.fetch_error_count += 1
return self[item]
clip.clip_(-1, 1)
# Restore padding.
clip = F.pad(clip, (0, padding_room))
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': ['y:/clips/books1'],
'cache_path': 'D:\\data\\clips_for_noise_classifier.pth',
'sampling_rate': 22050,
'pad_to_samples': 400000,
'do_augmentation': False,
'phase': 'train',
'n_workers': 4,
'batch_size': 256,
'extra_samples': 0,
'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',
'openair_path': 'D:\\data\\audio\\openair\\resampled',
'use_gpu_for_reverb_compute': False,
}
from data import create_dataset, create_dataloader, util
ds = create_dataset(params)
dl = create_dataloader(ds, params, pin_memory=False)
i = 0
for b in tqdm(dl):
for b_ in range(b['clip'].shape[0]):
#torchaudio.save(f'{i}_clip_{b_}_{b["label"][b_].item()}.wav', b['clip'][b_][:, :b['clip_lengths'][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