Файл: Направление подготовки 09. 03. 04 Программная инженерия.docx

//doi.org/10.1145/3197517.3201357.

38. 
    Eff-UNet: A Novel Architecture for Semantic Segmentation in Unstructured Environment / B. Baheti [и др.] // 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). — 2020. — С. 1473—1481. — DOI: 10.1109/CVPRW50498.2020.00187.
    
39. 
    Exploring convolutional, recurrent, and hybrid deep neural networks for speech and music detection in a large audio dataset / D. de Benito [и др.] // EURASIP Journal on Audio, Speech, and Music Processing. — 2019. — Июнь. — Т. 2019. — DOI: 10.1186/s13636-019-0152-1.
    
40. 
    Gallardo-Antolín A., Montero J. M. On combining acoustic and modulation spectrograms in an attention LSTM-based system for speech intelligibility level classification // Neurocomputing. — 2021. — Т. 456. — С. 49—60. — ISSN 0925-2312. — DOI: https : / / doi . org / 10 . 1016 / j .
    

neucom.2021.05.065.—URL:https://www.sciencedirect.com/science/article/pii/S0925231221008183.

41. 
    KingmaD.P., BaJ.Adam: A Method for Stochastic Optimization // 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings / под ред. Y. Bengio, Y. LeCun. — 2015. — URL: http://arxiv.org/abs/1412. 6980.
    

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А. VideoEncoder

importtorchimporttorch.nnasnnimporttorch.nn.functionalasFfromparamsimportVideoHyperParamsdefinit_weights(m):if isinstance(m,nn.Conv2d): torch.nn.init.xavier_uniform_(m.weight) m.bias.data.fill_(0.01)classVideoEncoder(nn.Module):x=self.conv1(x)x=F.leaky_relu(self.conv2(x))x=torch.squeeze(x)out,(hn,cn)=self.BiLSTM(x,(hn,cn))res=self.BiLSTM_proj(out) returnres

В. AudioEncoder

importtorch.nnasnnfromparamsimportAudioHyperParamsclassAudioEncoder(nn.Module):

Г. Генератор

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importtorch.nnasnn importtorchfromparamsimportAudioHyperParamsdefinit_weights(m):if isinstance(m,nn.Conv1d): torch.nn.init.xavier_uniform(m.weight)m.bias.data.fill_(0.01)classGenerator(nn.Module):out=out.transpose(1,2) returnout

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Д. Дискриминатор

importtorchimporttorch.nnasnnfromparamsimportAudioHyperParams,VideoHyperParamsdefinit_weights(m):if isinstance(m,nn.Conv1d): torch.nn.init.xavier_uniform(m.weight)m.bias.data.fill_(0.01)if isinstance(m,nn.Conv2d): torch.nn.init.xavier_uniform(m.weight)m.bias.data.fill_(0.01)classDiscriminator(nn.Module):nn.ReLU(True), nn.Conv1d(120,160,kernel_size=6,stride=3), nn.Conv1d(160,200,kernel_size=6,stride=3), nn.Conv1d(200,1,kernel_size=6,stride=3),)self.down_sampling.apply(init_weights) self.sigmoid=nn.Sigmoid()defforward(self,frames,mel_spec):mel=self.mel_conv(mel_spec)frames=torch.squeeze(frames) bs,_,_,_=frames.shapeframes=self.frames_post_conv(frames) frames=torch.reshape(frames,(bs,80,−1))concat=torch.cat([frames,mel],dim=2)out=self.down_sampling(concat)out=torch.squeeze(out) out=self.sigmoid(out)returnout

Е. Тренировка нейросетевого компонента

importtorchimporttorch.optimasoptim importtorch.nnasnnimporttorchvision.transformsastransformsfromdataimportVideoDatasetfromtorch.utils.dataimportDataLoader importosfromtqdmimport

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tqdm import warnings

import matplotlib.pyplot as plt import librosa.display

warnings.simplefilter(action=’ignore’, category=UserWarning) from discriminator import Discriminator

from main_generator import MainGenerator

from params import TrainParams

device = torch.device(’cuda’ if torch.cuda.is_available() else ’cpu’) G = MainGenerator().to(device)

D = Discriminator().to(device)

G_optimizer = optim.Adam(G.parameters(), lr=TrainParams.LEARNING_RATE, betas=(TrainParams.BETA1, 0.999))

D_optimizer = optim.Adam(D.parameters(),

lr=TrainParams.LEARNING_RATE, betas=(TrainParams.BETA1, 0.999)) loss_bce_gen = nn.BCELoss()

loss_bce_discr = nn.BCELoss() loss_mce = nn.MSELoss()

test_transformer = transforms.Compose([ transforms.ToTensor(),

])

test_ds = VideoDataset(root_dir=’C:/Projects/soundGAN−main/features_data/hammer’, transform=test_transformer)

loader = DataLoader(test_ds, batch_size=int(TrainParams.BATCH_SIZE), shuffle=True)

print(’Loader initialized’)

d_scores = open(’d_scores.txt’, ’w’) g_scores = open(’g_scores.txt’, ’w’) os.makedirs(’checkpoints’, exist_ok=True)

def set_requires_grad(nets, requires_grad=False): if not isinstance(nets, list):

nets = [nets] for net in nets:

if net is not None:

for param in net.parameters(): param.requires_grad = requires_grad
os.makedirs(’figures’, exist_ok=True)
if name == ’main’:

print(f’Start training on {device}’)

for epoch in range(TrainParams.EPOCHS):

progress_bar = tqdm(enumerate(loader), total=len(loader)) for idx, batch in progress_bar:

idx += 1

real_mel_spec = batch[0].to(device) video_frames = batch[1].to(device)
real_labels = torch.ones(TrainParams.BATCH_SIZE).to(device) fake_labels = torch.zeros(TrainParams.BATCH_SIZE).to(device)
# Train Discriminator set_requires_grad(G, False) set_requires_grad(D, True)

with torch.no_grad():

fake_mel_spec = G(video_frames, real_mel_spec)

fake_outputs = D(video_frames, fake_mel_spec.detach()) real_outputs = D(video_frames, real_mel_spec)

real_loss = loss_bce_discr(real_outputs, real_labels) fake_loss = loss_bce_discr(fake_outputs, fake_labels)

D_loss = (real_loss + fake_loss) * 0.5 D_optimizer.zero_grad() D_loss.backward()

D_optimizer.step() # Train Generator

set_requires_grad(G, True) set_requires_grad(D,

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