xvector.py 29.4 KB
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# -*- coding: utf-8 -*-
#
# This file is part of SIDEKIT.
#
# SIDEKIT is a python package for speaker verification.
# Home page: http://www-lium.univ-lemans.fr/sidekit/
#
# SIDEKIT is a python package for speaker verification.
# Home page: http://www-lium.univ-lemans.fr/sidekit/
#
# SIDEKIT is free software: you can redistribute it and/or modify
# it under the terms of the GNU LLesser General Public License as
# published by the Free Software Foundation, either version 3 of the License,
# or (at your option) any later version.
#
# SIDEKIT is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with SIDEKIT.  If not, see <http://www.gnu.org/licenses/>.

"""
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Copyright 2014-2020 Yevhenii Prokopalo, Anthony Larcher
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"""
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import logging
import numpy
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import pickle
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import shutil
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import torch
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import torch.optim as optim
import torch.multiprocessing as mp
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import yaml

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from torchvision import transforms
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from collections import OrderedDict
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from .xsets import XvectorMultiDataset, StatDataset, VoxDataset, SideSet
from .xsets import FrequencyMask, CMVN, TemporalMask, MFCC
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from ..bosaris import IdMap
from ..statserver import StatServer
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from torch.utils.data import DataLoader
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from sklearn.model_selection import train_test_split
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__license__ = "LGPL"
__author__ = "Anthony Larcher"
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__copyright__ = "Copyright 2015-2020 Anthony Larcher"
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__maintainer__ = "Anthony Larcher"
__email__ = "anthony.larcher@univ-lemans.fr"
__status__ = "Production"
__docformat__ = 'reS'
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# logging.basicConfig(stream=sys.stdout, level=logging.INFO)
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def get_lr(optimizer):
    for param_group in optimizer.param_groups:
        return param_group['lr']


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def split_file_list(batch_files, num_processes):
    # Cut the list of files into args.num_processes lists of files
    batch_sub_lists = [[]] * num_processes
    x = [ii for ii in range(len(batch_files))]
    for ii in range(num_processes):
        batch_sub_lists[ii - 1] = [batch_files[z + ii] for z in x[::num_processes] if (z + ii) < len(batch_files)]
    return batch_sub_lists
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class Xtractor(torch.nn.Module):
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    """
    Class that defines an x-vector extractor based on 5 convolutional layers and a mean standard deviation pooling
    """
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    def __init__(self, speaker_number, model_archi=None):
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        """
        If config is None, default architecture is created
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        :param model_archi:
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        """
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        super(Xtractor, self).__init__()
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        self.speaker_number = speaker_number
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        self.feature_size = 24

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        if model_archi is None:
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            self.activation = torch.nn.ReLU()

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            self.sequence_network = torch.nn.Sequential(OrderedDict([
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                ("conv1", torch.nn.Conv1d(self.feature_size, 512, 5, dilation=1)),
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                ("activation1", torch.nn.LeakyReLU(0.2)),
                ("norm1", torch.nn.BatchNorm1d(512)),
                ("conv2", torch.nn.Conv1d(512, 512, 3, dilation=2)),
                ("activation2", torch.nn.LeakyReLU(0.2)),
                ("norm2", torch.nn.BatchNorm1d(512)),
                ("conv3", torch.nn.Conv1d(512, 512, 3, dilation=3)),
                ("activation3", torch.nn.LeakyReLU(0.2)),
                ("norm3", torch.nn.BatchNorm1d(512)),
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                ("conv4", torch.nn.Conv1d(512, 512, 1)),
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                ("activation4", torch.nn.LeakyReLU(0.2)),
                ("norm4", torch.nn.BatchNorm1d(512)),
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                ("conv5", torch.nn.Conv1d(512, 1536, 1)),
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                ("activation5", torch.nn.LeakyReLU(0.2)),
                ("norm5", torch.nn.BatchNorm1d(1536))
            ]))

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            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict([
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                ("linear6", torch.nn.Linear(1536, 512))
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            ]))

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            self.after_speaker_embedding = torch.nn.Sequential(OrderedDict([
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                ("activation6", torch.nn.LeakyReLU(0.2)),
                ("norm6", torch.nn.BatchNorm1d(512)),
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                ("linear7", torch.nn.Linear(512, 512)),
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                ("activation7", torch.nn.LeakyReLU(0.2)),
                ("norm7", torch.nn.BatchNorm1d(512)),
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                ("linear8", torch.nn.Linear(512, int(self.speaker_number)))
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            ]))

        else:
            # Load Yaml configuration
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            with open(model_archi, 'r') as fh:
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                cfg = yaml.load(fh, Loader=yaml.FullLoader)

            # Get Feature size
            self.feature_size = cfg["feature_size"]
            input_size = self.feature_size

            # Get activation function
            if cfg["activation"] == 'LeakyReLU':
                self.activation = torch.nn.LeakyReLU(0.2)
            elif cfg["activation"] == 'PReLU':
                self.activation = torch.nn.PReLU()
            elif cfg["activation"] == 'ReLU6':
                self.activation = torch.nn.ReLU6()
            else:
                self.activation = torch.nn.ReLU()

            # Create sequential object for the first part of the network
            segmental_layers = []
            for k in cfg["segmental"].keys():
                if k.startswith("conv"):
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                    segmental_layers.append((k, torch.nn.Conv1d(input_size,
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                                                                cfg["segmental"][k]["output_channels"],
                                                                cfg["segmental"][k]["kernel_size"],
                                                                cfg["segmental"][k]["dilation"])))
                    input_size = cfg["segmental"][k]["output_channels"]

                elif k.startswith("activation"):
                    segmental_layers.append((k, self.activation))

                elif k.startswith('norm'):
                    segmental_layers.append((k, torch.nn.BatchNorm1d(input_size)))

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            self.sequence_network = torch.nn.Sequential(OrderedDict(segmental_layers))
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            self.sequence_network_weight_decay = cfg["segmental"]["weight_decay"]
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            # Create sequential object for the second part of the network
            input_size = input_size * 2
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            before_embedding_layers = []
            for k in cfg["before_embedding"].keys():
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                if k.startswith("lin"):
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                    if cfg["before_embedding"][k]["output"] == "speaker_number":
                        before_embedding_layers.append((k, torch.nn.Linear(input_size, self.speaker_number)))
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                    else:
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                        before_embedding_layers.append((k, torch.nn.Linear(input_size, cfg["before_embedding"][k]["output"])))
                        input_size = cfg["before_embedding"][k]["output"]
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                elif k.startswith("activation"):
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                    before_embedding_layers.append((k, self.activation))
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                elif k.startswith('norm'):
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                    before_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))
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                elif k.startswith('dropout'):
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                    before_embedding_layers.append((k, torch.nn.Dropout(p=cfg["before_embedding"][k])))
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            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict(before_embedding_layers))
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            self.before_speaker_embedding_weight_decay = cfg["before_embedding"]["weight_decay"]
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            # Create sequential object for the second part of the network
            after_embedding_layers = []
            for k in cfg["after_embedding"].keys():
                if k.startswith("lin"):
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                    if cfg["after_embedding"][k]["output"] == "speaker_number":
                        after_embedding_layers.append((k, torch.nn.Linear(input_size, self.speaker_number)))
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                    else:
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                        after_embedding_layers.append((k, torch.nn.Linear(input_size, cfg["after_embedding"][k]["output"])))
                        input_size = cfg["after_embedding"][k]["output"]
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                elif k.startswith("activation"):
                    after_embedding_layers.append((k, self.activation))

                elif k.startswith('norm'):
                    after_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))

                elif k.startswith('dropout'):
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                    after_embedding_layers.append((k, torch.nn.Dropout(p=cfg["after_embedding"][k])))
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            self.after_speaker_embedding = torch.nn.Sequential(OrderedDict(after_embedding_layers))
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            self.after_speaker_embedding_weight_decay = cfg["after_embedding"]["weight_decay"]
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    def forward(self, x, is_eval=False):
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        """

        :param x:
        :return:
        """
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        x = self.sequence_network(x)
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        # Mean and Standard deviation pooling
        mean = torch.mean(x, dim=2)
        std = torch.std(x, dim=2)
        x = torch.cat([mean, std], dim=1)

        x = self.before_speaker_embedding(x)
        if is_eval:
            return x
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        x = self.after_speaker_embedding(x)
        return x
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def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', best_filename='model_best.pth.tar'):
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, best_filename)

#def xtrain(args):
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def xtrain(speaker_number,
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           dataset_yaml,
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           epochs=10,
           lr=0.01,
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           model_yaml=None,
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           model_name=None,
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           num_thread=1):
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    """
    Initialize and train an x-vector on a single GPU

    :param args:
    :return:
    """
    # If we start from an existing model
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    if model_name is not None:
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        # Load the model
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        logging.critical(f"*** Load model from = {model_name}")
        checkpoint = torch.load(model_name)
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        model = Xtractor(speaker_number, model_yaml)
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        model.load_state_dict(checkpoint["model_state_dict"])
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    else:
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        # Initialize a first model
        if model_yaml is None:
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            model = Xtractor(speaker_number)
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        else:
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            model = Xtractor(speaker_number, model_yaml)
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    model.train()
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    if torch.cuda.device_count() > 1:
        print("Let's use", torch.cuda.device_count(), "GPUs!")
        model = torch.nn.DataParallel(model)

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    model.to(device)
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    """
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    Set the dataloaders according to the dataset_yaml
    
    First we load the dataframe from CSV file in order to split it for training and validation purpose
    Then we provide those two 
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    """
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    with open(dataset_yaml, "r") as fh:
        dataset_params = yaml.load(fh, Loader=yaml.FullLoader)
        df = pandas.read_csv(dataset_params["dataset_description"])
    training_df, validation_df = train_test_split(df, test_size=dataset_params["validation_ratio"])
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    torch.manual_seed(seed)
    training_set = SideSet(dataset_yaml, set_type="train", dataset_df=training_df)
    training_loader = DataLoader(training_set,
                                 batch_size=dataset_params["batch_size"],
                                 shuffle=True,
                                 num_workers=num_thread)
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    validation_set = SideSet(dataset_yaml, set_type="validation", dataset_df=validation_df)
    validation_loader = DataLoader(validation_set,
                                   batch_size=dataset_params["batch_size"],
                                   num_workers=num_thread)
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    """
    Set the training options
    """
    if type(model) is Xtractor:
        optimizer = torch.optim.SGD([
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            {'params': model.sequence_network.parameters(),
             'weight_decay': model.sequence_network_weight_decay},
            {'params': model.before_speaker_embedding.parameters(),
             'weight_decay': model.before_speaker_embedding_weight_decay},
            {'params': model.after_speaker_embedding.parameters(),
             'weight_decay': model.after_speaker_embedding_weight_decay}],
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            lr=lr, momentum=0.9
        )
    else:
        optimizer = torch.optim.SGD([
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            {'params': model.module.sequence_network.parameters(),
             'weight_decay': model.module.sequence_network_weight_decay},
            {'params': model.module.before_speaker_embedding.parameters(),
             'weight_decay': model.module.before_speaker_embedding_weight_decay},
            {'params': model.module.after_speaker_embedding.parameters(),
             'weight_decay': model.module.after_speaker_embedding_weight_decay}],
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            lr=lr, momentum=0.9
        )
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    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')

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    for epoch in range(1, epochs + 1):
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        # Process one epoch and return the current model
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        model = train_epoch(model, epoch, training_loader, optimizer, dataset_params["log_interval"])
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        # Add the cross validation here
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        accuracy, val_loss = cross_validation(model, validation_loader)
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        logging.critical("*** Cross validation accuracy = {} %".format(accuracy))

        # Decrease learning rate according to the scheduler policy
        scheduler.step(val_loss)

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        # remember best accuracy and save checkpoint
        is_best = accuracy > best_accuracy
        best_accuracy = max(accuracy, best_accuracy)

        save_checkpoint({
            'epoch': epoch,
            'model_state_dict': model.state_dict(),
            'optimizer_state_dict': optimizer.state_dict(),
            'accuracy': best_accuracy,
            'scheduler': scheduler
        }, is_best, filename = tmp_model_name+".pt", best_filename=output_model_name+'.pt')

        if is_best:
            best_accuracy_epoch = epoch
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    logging.critical(f"Best accuracy {best_accuracy * 100.} obtained at epoch {best_accuracy_epoch}")
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def train_epoch(model, epoch, training_loader, optimizer, log_interval):
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    """

    :param model:
    :param epoch:
    :param train_seg_df:
    :param speaker_dict:
    :param optimizer:
    :param args:
    :return:
    """
    criterion = torch.nn.CrossEntropyLoss()

    accuracy = 0.0
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    for batch_idx, (data, target, _, __) in enumerate(training_loader):
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        target = target.squeeze()
        optimizer.zero_grad()
        output = model(data.to(device))
        loss = criterion(output, target.to(device))
        loss.backward()
        optimizer.step()
        accuracy += (torch.argmax(output.data, 1) == target.to(device)).sum()

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        if batch_idx % log_interval == 0:
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            logging.critical('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tAccuracy: {:.3f}'.format(
                epoch, batch_idx + 1, train_loader.__len__(),
                       100. * batch_idx / train_loader.__len__(), loss.item(),
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                       100.0 * accuracy.item() / ((batch_idx + 1) * batch_size)))
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    return model


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def cross_validation(model, validation_loader):
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    """

    :param args:
    :param model:
    :param cv_seg_df:
    :return:
    """
    model.eval()
    model.to(device)

    accuracy = 0.0
    criterion = torch.nn.CrossEntropyLoss()

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    for batch_idx, (data, target, _, __) in enumerate(validation_loader):
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        target = target.squeeze()
        output = model(data.to(device))
        accuracy += (torch.argmax(output.data, 1) == target.to(device)).sum()

    loss = criterion(output, target.to(device))

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    return 100. * accuracy.cpu().numpy() / ((batch_idx + 1) * batch_size), loss
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def xtrain_asynchronous(args):
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    """
    Initialize and train an x-vector in asynchronous manner

    :param args:
    :return:
    """
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    # Initialize a first model and save to disk
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    model = Xtractor(args.class_number, args.dropout)
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    current_model_file_name = "initial_model"
    torch.save(model.state_dict(), current_model_file_name)

    for epoch in range(1, args.epochs + 1):
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        current_model_file_name = train_asynchronous_epoch(epoch, args, current_model_file_name)
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        # Add the cross validation here
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        accuracy = cross_asynchronous_validation(args, current_model_file_name)
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        print("*** Cross validation accuracy = {} %".format(accuracy))
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        # Decrease learning rate after every epoch
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        args.lr = args.lr * 0.9
        print("        Decrease learning rate: {}".format(args.lr))
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def train_asynchronous_epoch(epoch, args, initial_model_file_name):
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    """
    Process one training epoch using an asynchronous implementation of the training

    :param epoch:
    :param args:
    :param initial_model_file_name:
    :return:
    """
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    # Compute the megabatch number
    with open(args.batch_training_list, 'r') as fh:
        batch_file_list = [l.rstrip() for l in fh]

    # Shorten the batch_file_list to be a multiple of

    megabatch_number = len(batch_file_list) // (args.averaging_step * args.num_processes)
    megabatch_size = args.averaging_step * args.num_processes
    print("Epoch {}, number of megabatches = {}".format(epoch, megabatch_number))

    current_model = initial_model_file_name

    # For each sublist: run an asynchronous training and averaging of the model
    for ii in range(megabatch_number):
        print('Process megabatch [{} / {}]'.format(ii + 1, megabatch_number))
        current_model = train_asynchronous(epoch,
                                           args,
                                           current_model,
                                           batch_file_list[megabatch_size * ii: megabatch_size * (ii + 1)],
                                           ii,
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                                           megabatch_number)  # function that split train, fuse and write the new model
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    return current_model


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def train_asynchronous(epoch, args, initial_model_file_name, batch_file_list, megabatch_idx, megabatch_number):
    """
    Process one mega-batch of data asynchronously, average the model parameters across
    subrocesses and return the updated version of the model

    :param epoch:
    :param args:
    :param initial_model_file_name:
    :param batch_file_list:
    :param megabatch_idx:
    :param megabatch_number:
    :return:
    """
    # Split the list of files for each process
    sub_lists = split_file_list(batch_file_list, args.num_processes)

    #
    output_queue = mp.Queue()
    # output_queue = multiprocessing.Queue()

    processes = []
    for rank in range(args.num_processes):
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        p = mp.Process(target=train_asynchronous_worker,
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                       args=(rank, epoch, args, initial_model_file_name, sub_lists[rank], output_queue)
                       )
        # We first train the model across `num_processes` processes
        p.start()
        processes.append(p)

    # Average the models and write the new one to disk
    asynchronous_model = []
    for ii in range(args.num_processes):
        asynchronous_model.append(dict(output_queue.get()))

    for p in processes:
        p.join()

    av_model = Xtractor(args.class_number, args.dropout)
    tmp = av_model.state_dict()

    average_param = dict()
    for k in list(asynchronous_model[0].keys()):
        average_param[k] = asynchronous_model[0][k]

        for mod in asynchronous_model[1:]:
            average_param[k] += mod[k]

        if 'num_batches_tracked' not in k:
            tmp[k] = torch.FloatTensor(average_param[k] / len(asynchronous_model))

    # return the file name of the new model
    current_model_file_name = "{}/model_{}_epoch_{}_batch_{}".format(args.model_path, args.expe_id, epoch,
                                                                     megabatch_idx)
    torch.save(tmp, current_model_file_name)
    if megabatch_idx == megabatch_number:
        torch.save(tmp, "{}/model_{}_epoch_{}".format(args.model_path, args.expe_id, epoch))

    return current_model_file_name


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def train_asynchronous_worker(rank, epoch, args, initial_model_file_name, batch_list, output_queue):
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    """


    :param rank:
    :param epoch:
    :param args:
    :param initial_model_file_name:
    :param batch_list:
    :param output_queue:
    :return:
    """
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    model = Xtractor(args.class_number, args.dropout)
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    model.load_state_dict(torch.load(initial_model_file_name))
    model.train()

    torch.manual_seed(args.seed + rank)
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    train_loader = XvectorMultiDataset(batch_list, args.batch_path)
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    device = torch.device("cuda:{}".format(rank))
    model.to(device)

    optimizer = optim.Adam([{'params': model.frame_conv0.parameters(), 'weight_decay': args.l2_frame},
                            {'params': model.frame_conv1.parameters(), 'weight_decay': args.l2_frame},
                            {'params': model.frame_conv2.parameters(), 'weight_decay': args.l2_frame},
                            {'params': model.frame_conv3.parameters(), 'weight_decay': args.l2_frame},
                            {'params': model.frame_conv4.parameters(), 'weight_decay': args.l2_frame},
                            {'params': model.seg_lin0.parameters(), 'weight_decay': args.l2_seg},
                            {'params': model.seg_lin1.parameters(), 'weight_decay': args.l2_seg},
                            {'params': model.seg_lin2.parameters(), 'weight_decay': args.l2_seg}
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                            ], lr=args.lr)
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    criterion = torch.nn.CrossEntropyLoss()
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    accuracy = 0.0
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        output = model(data.to(device))
        loss = criterion(output, target.to(device))
        loss.backward()
        optimizer.step()
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        accuracy += (torch.argmax(output.data, 1) == target.to(device)).sum()
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        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tAccuracy: {:.3f}'.format(
                epoch, batch_idx + 1, train_loader.__len__(),
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                100. * batch_idx / train_loader.__len__(), loss.item(),
                100.0 * accuracy.item() / ((batch_idx + 1) * args.batch_size)))
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    model_param = OrderedDict()
    params = model.state_dict()
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    for k in list(params.keys()):
        model_param[k] = params[k].cpu().detach().numpy()
    output_queue.put(model_param)
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def cross_asynchronous_validation(args, current_model_file_name):
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    """

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    :param args:
    :param current_model_file_name:
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    :return:
    """
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    with open(args.cross_validation_list, 'r') as fh:
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        cross_validation_list = [l.rstrip() for l in fh]
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        sub_lists = split_file_list(cross_validation_list, args.num_processes)
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    #
    output_queue = mp.Queue()
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    processes = []
    for rank in range(args.num_processes):
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        p = mp.Process(target=cv_asynchronous_worker,
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                       args=(rank, args, current_model_file_name, sub_lists[rank], output_queue)
                       )
        # We first evaluate the model across `num_processes` processes
        p.start()
        processes.append(p)
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    # Average the models and write the new one to disk
    result = []
    for ii in range(args.num_processes):
        result.append(output_queue.get())
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    for p in processes:
        p.join()
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    # Compute the global accuracy
    accuracy = 0.0
    total_batch_number = 0
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    for bn, acc in result:
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        accuracy += acc
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        total_batch_number += bn
    
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    return 100. * accuracy / (total_batch_number * args.batch_size)
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def cv_asynchronous_worker(rank, args, current_model_file_name, batch_list, output_queue):
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    model = Xtractor(args.class_number, args.dropout)
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    model.load_state_dict(torch.load(current_model_file_name))
    model.eval()
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    cv_loader = XvectorMultiDataset(batch_list, args.batch_path)
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    device = torch.device("cuda:{}".format(rank))
    model.to(device)
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    accuracy = 0.0
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    for batch_idx, (data, target) in enumerate(cv_loader):
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        output = model(data.to(device))
        accuracy += (torch.argmax(output.data, 1) == target.to(device)).sum()
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    output_queue.put((cv_loader.__len__(), accuracy.cpu().numpy()))
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def extract_idmap(args, device_id, segment_indices, fs_params, idmap_name, output_queue):
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    """
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    Function that takes a model and an idmap and extract all x-vectors based on this model
    and return a StatServer containing the x-vectors
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    """
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    # device = torch.device("cuda:{}".format(device_ID))
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    device = torch.device('cpu')
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    # Create the dataset
    tmp_idmap = IdMap(idmap_name)
    idmap = IdMap()
    idmap.leftids = tmp_idmap.leftids[segment_indices]
    idmap.rightids = tmp_idmap.rightids[segment_indices]
    idmap.start = tmp_idmap.start[segment_indices]
    idmap.stop = tmp_idmap.stop[segment_indices]

    segment_loader = StatDataset(idmap, fs_params)

    # Load the model
    model_file_name = '/'.join([args.model_path, args.model_name])
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    model = Xtractor(args.class_number, args.dropout)
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    model.load_state_dict(torch.load(model_file_name))
    model.eval()

    # Get the size of embeddings
    emb_a_size = model.seg_lin0.weight.data.shape[0]
    emb_b_size = model.seg_lin1.weight.data.shape[0]

    # Create a Tensor to store all x-vectors on the GPU
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    emb_1 = numpy.zeros((idmap.leftids.shape[0], emb_a_size)).astype(numpy.float32)
    emb_2 = numpy.zeros((idmap.leftids.shape[0], emb_b_size)).astype(numpy.float32)
    emb_3 = numpy.zeros((idmap.leftids.shape[0], emb_b_size)).astype(numpy.float32)
    emb_4 = numpy.zeros((idmap.leftids.shape[0], emb_b_size)).astype(numpy.float32)
    emb_5 = numpy.zeros((idmap.leftids.shape[0], emb_b_size)).astype(numpy.float32)
    emb_6 = numpy.zeros((idmap.leftids.shape[0], emb_b_size)).astype(numpy.float32)
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    # Send on selected device
    model.to(device)

    # Loop to extract all x-vectors
    for idx, (model_id, segment_id, data) in enumerate(segment_loader):
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        logging.critical('Process file {}, [{} / {}]'.format(segment_id, idx, segment_loader.__len__()))
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        if list(data.shape)[2] < 20:
            pass
        else:
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            seg_1, seg_2, seg_3, seg_4, seg_5, seg_6 = model.extract(data.to(device))
            emb_1[idx, :] = seg_1.detach().cpu()
            emb_2[idx, :] = seg_2.detach().cpu()
            emb_3[idx, :] = seg_3.detach().cpu()
            emb_4[idx, :] = seg_4.detach().cpu()
            emb_5[idx, :] = seg_5.detach().cpu()
            emb_6[idx, :] = seg_6.detach().cpu()
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    output_queue.put((segment_indices, emb_1, emb_2, emb_3, emb_4, emb_5, emb_6))
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def extract_parallel(args, fs_params):
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    """

    :param args:
    :param fs_params:
    :return:
    """
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    emb_a_size = 512
    emb_b_size = 512

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    idmap = IdMap(args.idmap)
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    x_server_1 = StatServer(idmap, 1, emb_a_size)
    x_server_2 = StatServer(idmap, 1, emb_b_size)
    x_server_3 = StatServer(idmap, 1, emb_b_size)
    x_server_4 = StatServer(idmap, 1, emb_b_size)
    x_server_5 = StatServer(idmap, 1, emb_b_size)
    x_server_6 = StatServer(idmap, 1, emb_b_size)

    x_server_1.stat0 = numpy.ones(x_server_1.stat0.shape)
    x_server_2.stat0 = numpy.ones(x_server_2.stat0.shape)
    x_server_3.stat0 = numpy.ones(x_server_3.stat0.shape)
    x_server_4.stat0 = numpy.ones(x_server_4.stat0.shape)
    x_server_5.stat0 = numpy.ones(x_server_5.stat0.shape)
    x_server_6.stat0 = numpy.ones(x_server_6.stat0.shape)
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    # Split the indices
    mega_batch_size = idmap.leftids.shape[0] // args.num_processes
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    logging.critical("Number of sessions to process: {}".format(idmap.leftids.shape[0]))

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    segment_idx = []
    for ii in range(args.num_processes):
        segment_idx.append(
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            numpy.arange(ii * mega_batch_size, numpy.min([(ii + 1) * mega_batch_size, idmap.leftids.shape[0]])))

    for idx, si in enumerate(segment_idx):
        logging.critical("Number of session on process {}: {}".format(idx, len(si)))
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    # Extract x-vectors in parallel
    output_queue = mp.Queue()

    processes = []
    for rank in range(args.num_processes):
        p = mp.Process(target=extract_idmap,
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                       args=(args, rank, segment_idx[rank], fs_params, args.idmap, output_queue)
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                       )
        # We first train the model across `num_processes` processes
        p.start()
        processes.append(p)

    # Get the x-vectors and fill the StatServer
    for ii in range(args.num_processes):
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        indices, seg_1, seg_2, seg_3, seg_4, seg_5, seg_6 = output_queue.get()
        x_server_1.stat1[indices, :] = seg_1
        x_server_2.stat1[indices, :] = seg_2
        x_server_3.stat1[indices, :] = seg_3
        x_server_4.stat1[indices, :] = seg_4
        x_server_5.stat1[indices, :] = seg_5
        x_server_6.stat1[indices, :] = seg_6
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    for p in processes:
        p.join()

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    return x_server_1, x_server_2, x_server_3, x_server_4, x_server_5, x_server_6
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def extract_embeddings(args):
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    """

    :param args:
    :param device_id:
    :param fs_params:
    :return:
    """
    device = torch.device("cuda:0")

    # Load the model
    logging.critical("*** Load model from = {}/{}".format(args.model_path, args.init_model_name))
    model_file_name = '/'.join([args.model_path, args.init_model_name])
    model = torch.load(model_file_name)
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    model = torch.nn.DataParallel(model)
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    model.eval()
    model.to(device)

    # Get the list of files
    total_seg_df = pickle.load(open(args.batch_training_list, "rb"))

    speaker_dict = {}
    tmp = total_seg_df.speaker_id.unique()
    tmp.sort()
    for idx, spk in enumerate(tmp):
        speaker_dict[spk] = idx

    extract_transform = [CMVN(), ]
    extract_set = VoxDataset(total_seg_df, speaker_dict, None, transform=transforms.Compose(extract_transform),
                             spec_aug_ratio=args.spec_aug, temp_aug_ratio=args.temp_aug)
    extract_loader = DataLoader(extract_set, batch_size=1, shuffle=False, num_workers=5)
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    #CREER UN TENSEUR DE LA BONNE TAILLE POUR STOCKER LES X-VECTEURS
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    for batch_idx, (data, target, _, __) in enumerate(extract_loader):
        print("extrait x-vecteur numero {}".format(batch_idx))
        embedding = model.produce_embeddings(data.to(device))
        #REMPLIR LE TENSEUR AVEC LE NOUVEAU X-VECTEUR
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    #FAIRE CORRESPONDRE LES SPK_ID AVEC LES X-VECTEURS
    #RENVOYER LE TENSEUR DE X-VECTEURS SUR LE CPU OU L ECRTIRE SUR LE DISQUE