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import torch
from models import FBankCrossEntropyNet
import tqdm
import multiprocessing
import time
import numpy as np
from models import DynamicLinearClassifier
MODEL_PATH = './weights/triplet_loss_trained_model.pth'
model_instance = FBankCrossEntropyNet()
model_instance.load_state_dict(torch.load(MODEL_PATH, map_location=lambda storage, loc: storage))
use_cuda = False
kwargs = {'num_workers': multiprocessing.cpu_count(),
'pin_memory': True} if use_cuda else {}
def train_classification(model, device, train_loader, optimizer, epoch, log_interval):
model.train()
losses = []
accuracy = 0
for batch_idx, (x, y) in enumerate(tqdm.tqdm(train_loader)):
x, y = x.to(device), y.to(device)
x = model_instance(x)
optimizer.zero_grad()
out = model(x)
loss = model.loss(out, y)
with torch.no_grad():
pred = torch.argmax(out, dim=1)
accuracy += torch.sum((pred == y))
losses.append(loss.item())
loss.backward()
optimizer.step()
if batch_idx % log_interval == 0:
print('{} Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
time.ctime(time.time()),
epoch, batch_idx * len(x), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
accuracy_mean = (100. * accuracy) / len(train_loader.dataset)
return np.mean(losses), accuracy_mean.item()
def test_classification(model, device, test_loader, log_interval=None):
model.eval()
losses = []
accuracy = 0
with torch.no_grad():
for batch_idx, (x, y) in enumerate(tqdm.tqdm(test_loader)):
x, y = x.to(device), y.to(device)
x = model_instance(x)
out = model(x)
test_loss_on = model.loss(out, y).item()
losses.append(test_loss_on)
pred = torch.argmax(out, dim=1)
accuracy += torch.sum((pred == y))
if log_interval is not None and batch_idx % log_interval == 0:
print('{} Test: [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
time.ctime(time.time()),
batch_idx * len(x), len(test_loader.dataset),
100. * batch_idx / len(test_loader), test_loss_on))
test_loss = np.mean(losses)
accuracy_mean = (100. * accuracy) / len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} , ({:.4f})%\n'.format(
test_loss, accuracy, len(test_loader.dataset), accuracy_mean))
return test_loss, accuracy_mean.item()
def speaker_probability(tensor):
counts = {}
total = 0
for value in tensor:
value = int(value)
counts[value] = counts.get(value, 0) + 1
total += 1
probabilities = {}
for key, value in counts.items():
probabilities['speaker '+str(key)] = value / total
return probabilities
def inference_speaker_classification(
file_speaker,
num_class=3,
num_layers= 2,
model_instance=model_instance,
model_path='saved_models_cross_entropy_classification/0.pth'
):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
from utils.preprocessing import extract_fbanks
fbanks = extract_fbanks(file_speaker)
model = DynamicLinearClassifier(num_layers =num_layers ,output_size=num_class)
cpkt = torch.load(model_path)
model.load_state_dict(cpkt)
model = model.double()
model.to(device)
model_instance = model_instance.double()
model_instance.eval()
model_instance.to(device)
with torch.no_grad():
x = torch.from_numpy(fbanks)
embedings = model_instance(x.to(device))
# print(embedings.shape)
# embedings=embedings.unsqueeze(0)
output = model(embedings)
output = torch.argmax(output,dim=-1)
speaker_pro = speaker_probability(output)
print(speaker_pro)
return speaker_pro
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