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Move UnsupervisedAudioDataset to use my new mp3 loader

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This commit is contained in:
James Betker 2021-10-28 22:33:12 -06:00
parent 2afea126d7
commit 579f0a70ee
3 changed files with 62 additions and 14 deletions
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65
codes/data/audio/audio_with_noise_dataset.py
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@ -1,14 +1,15 @@
import random
import sys
from math import pi
import librosa
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
from data.util import load_paths_from_cache, find_files_of_type, is_audio_file
# Just all ones.
from utils.util import opt_get
@ -57,6 +58,14 @@ def _integration_fn_smooth(n):
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.
@ -66,11 +75,24 @@ class AudioWithNoiseDataset(Dataset):
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
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):
# Load on the fly to prevent GPU memory sharing across process errors.
self.load_openair_kernels()
out = self.underlying_dataset[item]
clip = out['clip']
augpath = ''
@ -80,18 +102,22 @@ class AudioWithNoiseDataset(Dataset):
clipvol = (random.random() * (.8-.5) + .5)
clip = clip * clipvol
label = random.randint(0,3)
label = random.randint(0, 4) # Current excludes GSM corruption.
#label = 2
aug = torch.zeros_like(clip)
if label != 0: # 0 is basically "leave it alone"
if label > 0 and label < 4: # 0 is basically "leave it alone"
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:
# Add another voice.
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)
@ -107,11 +133,28 @@ class AudioWithNoiseDataset(Dataset):
placement = random.randint(0, gap-1)
aug = torch.nn.functional.pad(aug, (placement, gap-placement))
clip = clip + aug
clip.clip_(-1, 1)
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:
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.
return self[item]
clip.clip_(-1, 1)
out['clip'] = clip
out['label'] = label
out['aug'] = aug
@ -127,18 +170,19 @@ class AudioWithNoiseDataset(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',
'path': ['\\\\192.168.5.3\\rtx3080_audio\\split\\cleaned\\books0'],
'cache_path': 'E:\\audio\\remote-cache3.pth',
'sampling_rate': 22050,
'pad_to_samples': 80960,
'phase': 'train',
'n_workers': 0,
'batch_size': 16,
'batch_size': 4,
'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',
'openair_path': 'D:\\data\\audio\\openair\\resampled'
}
from data import create_dataset, create_dataloader, util
@ -147,8 +191,7 @@ if __name__ == '__main__':
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)
#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

9
codes/data/audio/unsupervised_audio_dataset.py
View File

@ -20,6 +20,11 @@ from utils.util import opt_get
def load_audio(audiopath, sampling_rate):
if audiopath[-4:] == '.wav':
audio, lsr = load_wav_to_torch(audiopath)
elif audiopath[-4:] == '.mp3':
# https://github.com/neonbjb/pyfastmp3decoder - Definitely worth it.
from pyfastmp3decoder.mp3decoder import load_mp3
audio, lsr = load_mp3(audiopath, sampling_rate)
audio = torch.FloatTensor(audio)
else:
audio, lsr = open_audio(audiopath)
audio = torch.FloatTensor(audio)
@ -149,8 +154,8 @@ class UnsupervisedAudioDataset(torch.utils.data.Dataset):
if __name__ == '__main__':
params = {
'mode': 'unsupervised_audio',
'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',
'path': ['\\\\192.168.5.3\\rtx3080_audio\\split\\cleaned\\books0'],
'cache_path': 'E:\\audio\\remote-cache3.pth',
'sampling_rate': 22050,
'pad_to_samples': 40960,
'phase': 'train',

2
codes/scripts/diffusion/diffusion_noise_surfer.py
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@ -81,7 +81,7 @@ if __name__ == "__main__":
torch.backends.cudnn.benchmark = True
want_metrics = False
parser = argparse.ArgumentParser()
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_diffusion_vocoder_10-25.yml')
parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_diffusion_vocoder_10-28.yml')
opt = option.parse(parser.parse_args().opt, is_train=False)
opt = option.dict_to_nonedict(opt)
utils.util.loaded_options = opt