Move UnsupervisedAudioDataset to use my new mp3 loader
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@ -1,14 +1,15 @@
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import random
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import sys
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from math import pi
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import librosa
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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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from data.util import load_paths_from_cache, find_files_of_type, is_audio_file
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# Just all ones.
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from utils.util import opt_get
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@ -57,6 +58,14 @@ def _integration_fn_smooth(n):
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return fn
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def load_rir(path, sr, max_sz):
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rir = load_audio(path, sr).abs()
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if rir.shape[-1] > max_sz:
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rir = rir[:, :max_sz]
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rir = (rir / torch.norm(rir, p=2)).flip([1])
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return rir
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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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@ -66,11 +75,24 @@ class AudioWithNoiseDataset(Dataset):
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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.openair_paths = find_files_of_type('img', opt['openair_path'], qualifier=is_audio_file)[0]
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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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self.use_gpu_for_reverb_compute = opt_get(opt, ['use_gpu_for_reverb_compute'], True)
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self.openair_kernels = None
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def load_openair_kernels(self):
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if self.use_gpu_for_reverb_compute and self.openair_kernels is None:
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# Load the openair reverbs as CUDA tensors.
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self.openair_kernels = []
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for oa in self.openair_paths:
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self.openair_kernels.append(load_rir(oa, self.underlying_dataset.sampling_rate, self.underlying_dataset.sampling_rate*2).cuda())
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def __getitem__(self, item):
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# Load on the fly to prevent GPU memory sharing across process errors.
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self.load_openair_kernels()
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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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@ -80,18 +102,22 @@ class AudioWithNoiseDataset(Dataset):
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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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label = random.randint(0, 4) # Current excludes GSM corruption.
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#label = 2
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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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if label > 0 and label < 4: # 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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# Add environmental noise.
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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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# Add music
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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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# Add another voice.
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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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@ -107,11 +133,28 @@ class AudioWithNoiseDataset(Dataset):
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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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elif label == 4:
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# Perform reverb (to simulate being in a large room with an omni-mic). This is performed by convolving
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# impulse recordings from openair over the input clip.
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if self.use_gpu_for_reverb_compute:
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rir = random.choice(self.openair_kernels)
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else:
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augpath = random.choice(self.openair_paths)
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rir = load_rir(augpath, self.underlying_dataset.sampling_rate, clip.shape[-1])
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clip = torch.nn.functional.pad(clip, (rir.shape[1]-1, 0))
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if self.use_gpu_for_reverb_compute:
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clip = clip.cuda()
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clip = torch.nn.functional.conv1d(clip.unsqueeze(0), rir.unsqueeze(0)).squeeze(0).cpu()
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elif label == 5:
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# Apply the GSM codec to simulate cellular phone audio.
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clip = torchaudio.functional.apply_codec(clip, self.underlying_dataset.sampling_rate, format="gsm")
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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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print(sys.exc_info())
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#raise # Uncomment to surface exceptions.
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return self[item]
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clip.clip_(-1, 1)
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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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@ -127,18 +170,19 @@ class AudioWithNoiseDataset(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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'path': ['\\\\192.168.5.3\\rtx3080_audio\\split\\cleaned\\books0'],
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'cache_path': 'E:\\audio\\remote-cache3.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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'batch_size': 4,
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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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'openair_path': 'D:\\data\\audio\\openair\\resampled'
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}
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from data import create_dataset, create_dataloader, util
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@ -147,8 +191,7 @@ if __name__ == '__main__':
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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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#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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@ -20,6 +20,11 @@ from utils.util import opt_get
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def load_audio(audiopath, sampling_rate):
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if audiopath[-4:] == '.wav':
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audio, lsr = load_wav_to_torch(audiopath)
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elif audiopath[-4:] == '.mp3':
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# https://github.com/neonbjb/pyfastmp3decoder - Definitely worth it.
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from pyfastmp3decoder.mp3decoder import load_mp3
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audio, lsr = load_mp3(audiopath, sampling_rate)
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audio = torch.FloatTensor(audio)
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else:
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audio, lsr = open_audio(audiopath)
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audio = torch.FloatTensor(audio)
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@ -149,8 +154,8 @@ class UnsupervisedAudioDataset(torch.utils.data.Dataset):
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if __name__ == '__main__':
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params = {
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'mode': 'unsupervised_audio',
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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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'path': ['\\\\192.168.5.3\\rtx3080_audio\\split\\cleaned\\books0'],
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'cache_path': 'E:\\audio\\remote-cache3.pth',
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'sampling_rate': 22050,
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'pad_to_samples': 40960,
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'phase': 'train',
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@ -81,7 +81,7 @@ if __name__ == "__main__":
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torch.backends.cudnn.benchmark = True
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want_metrics = False
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_diffusion_vocoder_10-25.yml')
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parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/test_diffusion_vocoder_10-28.yml')
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opt = option.parse(parser.parse_args().opt, is_train=False)
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opt = option.dict_to_nonedict(opt)
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utils.util.loaded_options = opt
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