xvector.py 32.3 KB
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# coding: utf-8 -*-
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#
# 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 pdb
import traceback
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import logging
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import matplotlib.pyplot as plt
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import numpy
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import pandas
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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
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from .xsets import IdMapSet
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from .xsets import FrequencyMask, CMVN, TemporalMask, MFCC
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from .res_net import RawPreprocessor, ResBlockWFMS
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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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from .sincnet import SincNet, SincConv1d
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#from torch.utils.tensorboard import SummaryWriter

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import tqdm
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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(format='%(asctime)s %(message)s')

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# Make PyTorch Deterministic
torch.manual_seed(0)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
numpy.random.seed(0)


class GuruMeditation (torch.autograd.detect_anomaly):
    
    def __init__(self):
        super(GuruMeditation, self).__init__()

    def __enter__(self):
        super(GuruMeditation, self).__enter__()
        return self

    def __exit__(self, type, value, trace):
        super(GuruMeditation, self).__exit__()
        if isinstance(value, RuntimeError):
            traceback.print_tb(trace)
            halt(str(value))

    def halt(msg):
        print (msg)
        pdb.set_trace()









def select_n_random(data, labels, n=100):
    '''
    Selects n random datapoints and their corresponding labels from a dataset
    '''
    assert len(data) == len(labels)

    perm = torch.randperm(len(data))
    return data[perm][:n], labels[perm][:n]


def matplotlib_imshow(img, one_channel=False):
    if one_channel:
        img = img.mean(dim=0)
    img = img / 2 + 0.5     # unnormalize
    npimg = img.cpu().numpy()
    if one_channel:
        plt.imshow(npimg, cmap="Greys")
    else:
        plt.imshow(np.transpose(npimg, (1, 2, 0)))

def speech_to_probs(model, speech):
    '''
    Generates predictions and corresponding probabilities from a trained
    network and a list of images
    '''
    output = model(speech)
    # convert output probabilities to predicted class
    _, preds_tensor = torch.max(output, 1)
    preds = numpy.squeeze(preds_tensor.cpu().numpy())
    return preds, [torch.nn.functional.softmax(el, dim=0)[i].item() for i, el in zip(preds, output)]


def plot_classes_preds(model, speech, labels):
    '''
    Generates matplotlib Figure using a trained network, along with images
    and labels from a batch, that shows the network's top prediction along
    with its probability, alongside the actual label, coloring this
    information based on whether the prediction was correct or not.
    Uses the "speech_to_probs" function.
    '''
    preds, probs = speech_to_probs(model, speech)
    # plot the images in the batch, along with predicted and true labels
    fig = plt.figure(figsize=(12, 48))
    for idx in numpy.arange(4):
         ax = fig.add_subplot(1, 4, idx+1, xticks=[], yticks=[])
         #matplotlib_imshow(speech[idx], one_channel=True)
         ax.set_title("{0}, {1:.1f}%\n(label: {2})".format(
                                                           preds[idx],
                                                           probs[idx] * 100.0,
                                                           labels[idx]),
                                                           color=("green" if preds[idx]==labels[idx].item() else "red"))
    return fig



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def get_lr(optimizer):
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    """

    :param optimizer:
    :return:
    """
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    for param_group in optimizer.param_groups:
        return param_group['lr']


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def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', best_filename='model_best.pth.tar'):
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    """

    :param state:
    :param is_best:
    :param filename:
    :param best_filename:
    :return:
    """
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    torch.save(state, filename)
    if is_best:
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        print("BEST MODEL EVER !!!")
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        shutil.copyfile(filename, best_filename)
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class MeanStdPooling(torch.nn.Module):
    """
    Mean and Standard deviation pooling
    """
    def __init__(self):
        """

        """
        super(MeanStdPooling, self).__init__()
        pass

    def forward(self, x):
        """

        :param x:
        :return:
        """
        mean = torch.mean(x, dim=2)
        std = torch.std(x, dim=2)
        return torch.cat([mean, std], dim=1)
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class GruPooling(torch.nn.Module):
    """

    """
    def __init__(self, input_size, gru_node, nb_gru_layer):
        """

        :param input_size:
        :param gru_node:
        :param nb_gru_layer:
        """
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        super(GruPooling, self).__init__()
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        self.lrelu_keras = torch.nn.LeakyReLU(negative_slope = 0.3)
        self.bn_before_gru = torch.nn.BatchNorm1d(num_features = input_size)
        self.gru = torch.nn.GRU(input_size = input_size,
                          hidden_size = gru_node,
                          num_layers = nb_gru_layer,
                          batch_first = True)

    def forward(self, x):
        """

        :param x:
        :return:
        """
        x = self.bn_before_gru(x)
        x = self.lrelu_keras(x)
        x = x.permute(0, 2, 1)  #(batch, filt, time) >> (batch, time, filt)
        self.gru.flatten_parameters()
        x, _ = self.gru(x)
        x = x[:,-1,:]

        return x

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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="xvector", norm_embedding=False):
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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 = None
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        self.norm_embedding = norm_embedding
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        if model_archi == "xvector":
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            self.feature_size = 30
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            self.activation = torch.nn.LeakyReLU(0.2)

            self.preprocessor = None
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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.stat_pooling = MeanStdPooling()

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            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict([
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                ("linear6", torch.nn.Linear(3072, 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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                ("dropout6", torch.nn.Dropout(p=0.05)),
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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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            ]))

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            self.sequence_network_weight_decay = 0.0002
            self.before_speaker_embedding_weight_decay = 0.002
            self.after_speaker_embedding_weight_decay = 0.002

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        elif model_archi == "rawnet2":
            filts = [128, [128, 128], [128, 256], [256, 256]]
            self.norm_embedding = True

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            self.preprocessor = RawPreprocessor(nb_samp=32000,
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                                                in_channels=1,
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                                                out_channels=filts[0],
                                                kernel_size=3)
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            self.sequence_network = torch.nn.Sequential(OrderedDict([
                ("block0", ResBlockWFMS(nb_filts=filts[1], first=True)),
                ("block1", ResBlockWFMS(nb_filts=filts[1])),
                ("block2", ResBlockWFMS(nb_filts=filts[2])),
                ("block3", ResBlockWFMS(nb_filts=[filts[2][1], filts[2][1]])),
                ("block4", ResBlockWFMS(nb_filts=[filts[2][1], filts[2][1]])),
                ("block5", ResBlockWFMS(nb_filts=[filts[2][1], filts[2][1]]))
            ]))

            self.stat_pooling = GruPooling(input_size=filts[2][-1],
                                           gru_node=1024,
                                           nb_gru_layer=1)

            self.before_speaker_embedding = torch.nn.Linear(in_features = 1024,
                                                            out_features = 1024)

            self.after_speaker_embedding = torch.nn.Linear(in_features = 1024,
                                                           out_features = int(self.speaker_number),
                                                           bias = True)

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            self.preprocessor_weight_decay = 0.000
            self.sequence_network_weight_decay = 0.000
            self.stat_pooling_weight_decay = 0.000
            self.before_speaker_embedding_weight_decay = 0.00
            self.after_speaker_embedding_weight_decay = 0.00

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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)

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            """
            Prepare Preprocessor
            """
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            self.preprocessor = None
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            if "preprocessor" in cfg:
                if cfg['preprocessor']["type"] == "sincnet":
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                    self.preprocessor = SincNet(
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                        waveform_normalize=cfg['preprocessor']["waveform_normalize"],
                        sample_rate=cfg['preprocessor']["sample_rate"],
                        min_low_hz=cfg['preprocessor']["min_low_hz"],
                        min_band_hz=cfg['preprocessor']["min_band_hz"],
                        out_channels=cfg['preprocessor']["out_channels"],
                        kernel_size=cfg['preprocessor']["kernel_size"],
                        stride=cfg['preprocessor']["stride"],
                        max_pool=cfg['preprocessor']["max_pool"],
                        instance_normalize=cfg['preprocessor']["instance_normalize"],
                        activation=cfg['preprocessor']["activation"],
                        dropout=cfg['preprocessor']["dropout"]
                    )
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                    self.feature_size = self.preprocessor.dimension
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                elif cfg['preprocessor']["type"] == "rawnet2":
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                    self.preprocessor = RawPreprocessor(nb_samp=int(cfg['preprocessor']["sampling_frequency"] * cfg['preprocessor']["duration"]),
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                                                        in_channels=1,
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                                                        out_channels=cfg["feature_size"],
                                                        kernel_size=cfg['preprocessor']["kernel_size"],
                                                        stride=cfg['preprocessor']["stride"],
                                                        padding=cfg['preprocessor']["padding"],
                                                        dilation=cfg['preprocessor']["dilation"])
                    self.feature_size = cfg["feature_size"]
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            """
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            Prepare sequence network
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            """
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            # Get Feature size
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            if self.feature_size is None:
                self.feature_size = cfg["feature_size"]

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            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"],
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                                                                kernel_size=cfg["segmental"][k]["kernel_size"],
                                                                dilation=cfg["segmental"][k]["dilation"])))
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                    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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            """
            Pooling
            """
            self.stat_pooling = MeanStdPooling()
            if cfg["stat_pooling"]["type"] == "GRU":
                self.stat_pooling = GruPooling(input_size=cfg["stat_pooling"]["input_size"],
                                               gru_node=cfg["stat_pooling"]["gru_node"],
                                               nb_gru_layer=cfg["stat_pooling"]["nb_gru_layer"])

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            """
            Prepapre last part of the network (after pooling)
            """
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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"])))
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                        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"])))
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                        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:
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        :param is_eval:
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        :return:
        """
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        if self.preprocessor is not None:
            x = self.preprocessor(x)
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            print("go through preprocessor")
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        x = self.sequence_network(x)
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        # Mean and Standard deviation pooling
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        #mean = torch.mean(x, dim=2)
        #std = torch.std(x, dim=2)
        #x = torch.cat([mean, std], dim=1)
        x = self.stat_pooling(x)
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        x = self.before_speaker_embedding(x)
        if is_eval:
            return x
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        if self.norm_embedding:
            x_norm = x.norm(p=2,dim=1, keepdim=True) / 10.
            x = torch.div(x, x_norm)

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        x = self.after_speaker_embedding(x)
        return x
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def xtrain(speaker_number,
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           dataset_yaml,
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           epochs=100,
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           lr=0.01,
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           model_yaml=None,
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           model_name=None,
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           tmp_model_name=None,
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           best_model_name=None,
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           multi_gpu=True,
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           clipping=False,
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           opt='sgd',
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           reset_parts=[],
           freeze_parts=[],
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           num_thread=1):
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    """

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    :param speaker_number:
    :param dataset_yaml:
    :param epochs:
    :param lr:
    :param model_yaml:
    :param model_name:
    :param tmp_model_name:
    :param best_model_name:
    :param multi_gpu:
    :param clipping:
    :param num_thread:
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    :return:
    """
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    # Add for tensorboard
    # Writer will output to ./runs/ directory by default
    #writer = SummaryWriter("runs/xvectors_experiments_2")
    writer = None
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    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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    # Start from scratch
    if model_name is None:
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        # Initialize a first model
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        if model_yaml == "xvector":
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            model = Xtractor(speaker_number, "xvector")
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        elif model_yaml == "rawnet2":
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            model = Xtractor(speaker_number, "rawnet2")
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        else:
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            model = Xtractor(speaker_number, model_yaml)
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    # If we start from an existing model
    else:
        # Load the model
        logging.critical(f"*** Load model from = {model_name}")
        checkpoint = torch.load(model_name)
        model = Xtractor(speaker_number, model_yaml)

        """
        Here we remove all layers that we don't want to reload
        
        """
        pretrained_dict = checkpoint["model_state_dict"]
        for part in reset_parts:
            pretrained_dict = {k: v for k, v in pretrained_dict.items() if not k.startswith(part)}

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        new_model_dict = model.state_dict()
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        new_model_dict.update(pretrained_dict)
        model.load_state_dict(new_model_dict)

    # Freeze required layers
    for name, param in model.named_parameters():
        if name.split(".")[0] in freeze_parts:
            param.requires_grad = False
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    if torch.cuda.device_count() > 1 and multi_gpu:
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        print("Let's use", torch.cuda.device_count(), "GPUs!")
        model = torch.nn.DataParallel(model)
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    else:
        print("Train on a single GPU")
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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(dataset_params['seed'])
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    training_set = SideSet(dataset_yaml, 
                           set_type="train", 
                           dataset_df=training_df, 
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                           chunk_per_segment=dataset_params['train']['chunk_per_segment'], 
                           overlap=dataset_params['train']['overlap'])
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    training_loader = DataLoader(training_set,
                                 batch_size=dataset_params["batch_size"],
                                 shuffle=True,
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                                 drop_last=True,
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                                 pin_memory=True,
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                                 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"],
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                                   drop_last=True,
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                                   pin_memory=True,
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                                   num_workers=num_thread)
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    # Add for TensorBoard
    #dataiter = iter(training_loader)
    #data, labels = dataiter.next()
    #writer.add_graph(model, data)


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    """
    Set the training options
    """
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    if opt == 'sgd':
        _optimizer = torch.optim.SGD
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        _options = {'lr': lr, 'momentum': 0.9}
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    elif opt == 'adam':
        _optimizer = torch.optim.Adam
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        _options = {'lr': lr}
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    elif opt == 'rmsprop':
        _optimizer = torch.optim.RMSprop
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        _options = {'lr': lr}
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    params = [
        {
            'params': [
                param for name, param in model.named_parameters() if 'bn' not in name
            ]
        },
        {
            'params': [
                param for name, param in model.named_parameters() if 'bn' in name
            ],
            'weight_decay': 0
        },
    ]

    optimizer = torch.optim.Adam(params,
                                 lr=0.001,
                                 weight_decay=0.0001,
                                 amsgrad=1)

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    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', verbose=True)
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    best_accuracy = 0.0
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    best_accuracy_epoch = 1
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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"],
                            device=device,
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                            clipping=clipping,
                            tb_writer=writer)
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        # Add the cross validation here
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        accuracy, val_loss = cross_validation(model, validation_loader, device=device)
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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)

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        if type(model) is Xtractor:
            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=best_model_name+'.pt')
        else:
            save_checkpoint({
                'epoch': epoch,
                'model_state_dict': model.module.state_dict(),
                'optimizer_state_dict': optimizer.state_dict(),
                'accuracy': best_accuracy,
                'scheduler': scheduler
            }, is_best, filename=tmp_model_name+".pt", best_filename=best_model_name+'.pt')
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        if is_best:
            best_accuracy_epoch = epoch
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    #writer.close()
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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, device, clipping=False, tb_writer=None):
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    """

    :param model:
    :param epoch:
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    :param training_loader:
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    :param optimizer:
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    :param log_interval:
    :param device:
    :param clipping:
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    :return:
    """
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    model.train()
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    criterion = torch.nn.CrossEntropyLoss(reduction='mean')
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    accuracy = 0.0
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    running_loss = 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))
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        #with GuruMeditation():
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        loss = criterion(output, target.to(device))
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        if not torch.isnan(loss):
            loss.backward()
            if clipping:
                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.)
            running_loss += loss.item()
            optimizer.step()

            running_loss += loss.item()
            accuracy += (torch.argmax(output.data, 1) == target.to(device)).sum()

            if batch_idx % log_interval == 0:
                batch_size = target.shape[0]
                logging.critical('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tAccuracy: {:.3f}'.format(
                    epoch, batch_idx + 1, training_loader.__len__(),
                    100. * batch_idx / training_loader.__len__(), loss.item(),
                    100.0 * accuracy.item() / ((batch_idx + 1) * batch_size)))
                #tb_writer.add_scalar('training loss',
                #                     running_loss / log_interval,
                #                     epoch * len(training_loader) + batch_idx)
                #tb_writer.add_scalar('training_accuracy',
                #                      100.0 * accuracy.item() / ((batch_idx + 1) * batch_size),
                #                      epoch * len(training_loader) + batch_idx)

                # ...log a Matplotlib Figure showing the model's predictions on a
                # random mini-batch
                #tb_writer.add_figure('predictions vs. actuals',
                #                     plot_classes_preds(model, data.to(device), target.to(device)),
                #                     global_step=epoch * len(training_loader) + batch_idx)

            running_loss = 0.0
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    return model


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def cross_validation(model, validation_loader, device):
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    """

    :param model:
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    :param validation_loader:
    :param device:
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    :return:
    """
    model.eval()

    accuracy = 0.0
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    loss = 0.0
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    criterion = torch.nn.CrossEntropyLoss()
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    with torch.no_grad():
        for batch_idx, (data, target) in enumerate(validation_loader):
            batch_size = target.shape[0]
            target = target.squeeze()
            output = model(data.to(device))
            accuracy += (torch.argmax(output.data, 1) == target.to(device)).sum()
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            loss += criterion(output, target.to(device))
    
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    return 100. * accuracy.cpu().numpy() / ((batch_idx + 1) * batch_size), \
           loss.cpu().numpy() / ((batch_idx + 1) * batch_size)


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def extract_embeddings(idmap_name,
                       speaker_number,
                       model_filename,
                       model_yaml,
                       data_root_name ,
                       device,
                       file_extension="wav",
                       transform_pipeline=None):
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    if isinstance(idmap_name, IdMap):
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        idmap = idmap_name
    else:
        idmap = IdMap(idmap_name)

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    # Create dataset to load the data
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    dataset = IdMapSet(idmap_name=idmap_name,
                       data_root_path=data_root_name,
                       file_extension=file_extension,
                       transform_pipeline=transform_pipeline)
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    # Load the model
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    if isinstance(model_filename, str):
        checkpoint = torch.load(model_filename)
        model = Xtractor(speaker_number, model_archi=model_yaml)
        model.load_state_dict(checkpoint["model_state_dict"])
    else:
        model = model_filename

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    model.eval()
    model.to(device)
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    # Get the size of embeddings to extract
    name = list(model.before_speaker_embedding.state_dict().keys())[-1].split('.')[0] + '.weight'
    emb_size = model.before_speaker_embedding.state_dict()[name].shape[0]
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    # Create the StatServer
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    embeddings = StatServer()
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    embeddings.modelset = idmap.leftids
    embeddings.segset = idmap.rightids
    embeddings.start = idmap.start
    embeddings.stop = idmap.stop
    embeddings.stat0 = numpy.ones((embeddings.modelset.shape[0], 1))
    embeddings.stat1 = numpy.ones((embeddings.modelset.shape[0], emb_size))
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    # Process the data
    with torch.no_grad():
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        for idx in tqdm.tqdm(range(len(dataset))):
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            data, mod, seg, start, stop = dataset[idx]
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            vec = model(data[None, :, :].to(device), is_eval=True)
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            #current_idx = numpy.argwhere(numpy.logical_and(idmap.leftids == mod, idmap.rightids == seg))[0][0]
            embeddings.start[idx] = start
            embeddings.stop[idx] = stop
            embeddings.modelset[idx] = mod
            embeddings.segset[idx] = seg
            embeddings.stat1[idx, :] = vec.detach().cpu()
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    return embeddings


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def extract_sliding_embedding(idmap_name,
                              window_length,
                              sample_rate,
                              overlap,
                              speaker_number,
                              model_filename,
                              model_yaml,
                              data_root_name ,
                              device,
                              file_extension="wav",
                              transform_pipeline=None):

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    # From the original IdMap, create the new one to extract x-vectors
    input_idmap = IdMap(idmap_name)

    # Create temporary lists
    nb_chunks = 0
    model_names = []
    segment_names = []
    starts = []
    stops = []
    for mod, seg, start, stop in zip(input_idmap.leftids, input_idmap.rightids, input_idmap.start, input_idmap.stop):
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        # Compute the number of chunks to process
        chunk_starts = numpy.arange(start,
                                    stop - int(sample_rate * window_length),
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                                    int(sample_rate * (window_length - overlap)))
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        # Create a numpy array to store the current x-vectors
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        model_names.append(numpy.array([mod + f"_{ii}" for ii in range(len(chunk_starts))]).astype("U"))
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        segment_names.append(numpy.array([seg, ] * chunk_starts.shape[0]))
        starts.append(chunk_starts)
        stops.append(chunk_starts + sample_rate * window_length)

        nb_chunks += len(chunk_starts)

    sliding_idmap = IdMap()
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    sliding_idmap.leftids = numpy.hstack(model_names)
    sliding_idmap.rightids = numpy.hstack(segment_names)
    sliding_idmap.start = numpy.hstack(starts)
    sliding_idmap.stop = numpy.hstack(stops)
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    assert sliding_idmap.validate()
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    embeddings = extract_embeddings(sliding_idmap,
                                 speaker_number,
                                 model_filename,
                                 model_yaml,
                                 data_root_name,
                                 device,
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                                 file_extension=file_extension,
                                 transform_pipeline=transform_pipeline)
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    return embeddings
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