xvector.py 30.4 KB
Newer Older
Anthony Larcher's avatar
Anthony Larcher committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
# -*- 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/>.

"""
Anthony Larcher's avatar
Anthony Larcher committed
25
Copyright 2014-2020 Yevhenii Prokopalo, Anthony Larcher
Anthony Larcher's avatar
Anthony Larcher committed
26
"""
Anthony Larcher's avatar
Anthony Larcher committed
27

Anthony Larcher's avatar
Anthony Larcher committed
28
29
import logging
import numpy
Anthony Larcher's avatar
minor    
Anthony Larcher committed
30
import pickle
Anthony Larcher's avatar
Anthony Larcher committed
31
import shutil
Anthony Larcher's avatar
Anthony Larcher committed
32
import torch
Anthony Larcher's avatar
Anthony Larcher committed
33
34
import torch.optim as optim
import torch.multiprocessing as mp
Anthony Larcher's avatar
Anthony Larcher committed
35
36
import yaml

Anthony Larcher's avatar
Anthony Larcher committed
37
from torchvision import transforms
Anthony Larcher's avatar
Anthony Larcher committed
38
from collections import OrderedDict
39
from .xsets import XvectorMultiDataset, StatDataset, VoxDataset
Anthony Larcher's avatar
Anthony Larcher committed
40
41
42
from .xsets import FrequencyMask, CMVN, TemporalMask
from ..bosaris import IdMap
from ..statserver import StatServer
Anthony Larcher's avatar
Anthony Larcher committed
43
from torch.utils.data import DataLoader
Anthony Larcher's avatar
Anthony Larcher committed
44

Anthony Larcher's avatar
Anthony Larcher committed
45
46
__license__ = "LGPL"
__author__ = "Anthony Larcher"
Anthony Larcher's avatar
Anthony Larcher committed
47
__copyright__ = "Copyright 2015-2020 Anthony Larcher"
Anthony Larcher's avatar
Anthony Larcher committed
48
49
50
51
__maintainer__ = "Anthony Larcher"
__email__ = "anthony.larcher@univ-lemans.fr"
__status__ = "Production"
__docformat__ = 'reS'
Anthony Larcher's avatar
Anthony Larcher committed
52
53


Anthony Larcher's avatar
Anthony Larcher committed
54
# logging.basicConfig(stream=sys.stdout, level=logging.INFO)
Anthony Larcher's avatar
Anthony Larcher committed
55
56


57
58
59
60
61
def get_lr(optimizer):
    for param_group in optimizer.param_groups:
        return param_group['lr']


Anthony Larcher's avatar
Anthony Larcher committed
62
63
64
65
66
67
68
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
Anthony Larcher's avatar
Anthony Larcher committed
69
70
71


class Xtractor(torch.nn.Module):
72
73
74
    """
    Class that defines an x-vector extractor based on 5 convolutional layers and a mean standard deviation pooling
    """
Anthony Larcher's avatar
minor    
Anthony Larcher committed
75

Anthony Larcher's avatar
Anthony Larcher committed
76
77
78
79
80
    def __init__(self, speaker_number, config=None):
        """
        If config is None, default architecture is created
        :param config:
        """
Anthony Larcher's avatar
Anthony Larcher committed
81
        super(Xtractor, self).__init__()
Anthony Larcher's avatar
Anthony Larcher committed
82
        self.speaker_number = speaker_number
Anthony Larcher's avatar
Anthony Larcher committed
83
84
        self.feature_size = 24

Anthony Larcher's avatar
Anthony Larcher committed
85
        if config is None:
Anthony Larcher's avatar
Anthony Larcher committed
86
87
            self.activation = torch.nn.ReLU()

Anthony Larcher's avatar
xv    
Anthony Larcher committed
88
            self.sequence_network = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
89
                ("conv1", torch.nn.Conv1d(self.feature_size, 512, 5, dilation=1)),
Anthony Larcher's avatar
Anthony Larcher committed
90
91
92
93
94
95
96
97
                ("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)),
Anthony Larcher's avatar
Anthony Larcher committed
98
                ("conv4", torch.nn.Conv1d(512, 512, 1)),
Anthony Larcher's avatar
Anthony Larcher committed
99
100
                ("activation4", torch.nn.LeakyReLU(0.2)),
                ("norm4", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
101
                ("conv5", torch.nn.Conv1d(512, 1536, 1)),
Anthony Larcher's avatar
Anthony Larcher committed
102
103
104
105
                ("activation5", torch.nn.LeakyReLU(0.2)),
                ("norm5", torch.nn.BatchNorm1d(1536))
            ]))

Anthony Larcher's avatar
xv    
Anthony Larcher committed
106
            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
107
                ("linear6", torch.nn.Linear(1536, 512))
Anthony Larcher's avatar
Anthony Larcher committed
108
109
            ]))

Anthony Larcher's avatar
xv    
Anthony Larcher committed
110
            self.after_speaker_embedding = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
111
112
                ("activation6", torch.nn.LeakyReLU(0.2)),
                ("norm6", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
113
                ("linear7", torch.nn.Linear(512, 512)),
Anthony Larcher's avatar
Anthony Larcher committed
114
115
                ("activation7", torch.nn.LeakyReLU(0.2)),
                ("norm7", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
116
                ("linear8", torch.nn.Linear(512, self.speaker_number ))
Anthony Larcher's avatar
Anthony Larcher committed
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
            ]))

        else:
            # Load Yaml configuration
            with open(config, 'r') as fh:
                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"):
Anthony Larcher's avatar
Anthony Larcher committed
142
                    segmental_layers.append((k, torch.nn.Conv1d(input_size,
Anthony Larcher's avatar
Anthony Larcher committed
143
144
145
146
147
148
149
150
151
152
153
                                                                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)))

Anthony Larcher's avatar
Anthony Larcher committed
154
            self.sequence_network = torch.nn.Sequential(OrderedDict(segmental_layers))
Anthony Larcher's avatar
Anthony Larcher committed
155
            self.sequence_network_weight_decay = cfg["segmental"]["weight_decay"]
Anthony Larcher's avatar
Anthony Larcher committed
156
157
158

            # Create sequential object for the second part of the network
            input_size = input_size * 2
Anthony Larcher's avatar
xv    
Anthony Larcher committed
159
160
            before_embedding_layers = []
            for k in cfg["before_embedding"].keys():
Anthony Larcher's avatar
Anthony Larcher committed
161
                if k.startswith("lin"):
Anthony Larcher's avatar
Anthony Larcher committed
162
163
                    if cfg["before_embedding"][k]["output"] == "speaker_number":
                        before_embedding_layers.append((k, torch.nn.Linear(input_size, self.speaker_number)))
Anthony Larcher's avatar
Anthony Larcher committed
164
                    else:
Anthony Larcher's avatar
Anthony Larcher committed
165
166
                        before_embedding_layers.append((k, torch.nn.Linear(input_size, cfg["before_embedding"][k]["output"])))
                        input_size = cfg["before_embedding"][k]["output"]
Anthony Larcher's avatar
Anthony Larcher committed
167
168

                elif k.startswith("activation"):
Anthony Larcher's avatar
xv    
Anthony Larcher committed
169
                    before_embedding_layers.append((k, self.activation))
Anthony Larcher's avatar
Anthony Larcher committed
170
171

                elif k.startswith('norm'):
Anthony Larcher's avatar
xv    
Anthony Larcher committed
172
                    before_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))
Anthony Larcher's avatar
Anthony Larcher committed
173
174

                elif k.startswith('dropout'):
Anthony Larcher's avatar
Anthony Larcher committed
175
                    before_embedding_layers.append((k, torch.nn.Dropout(p=cfg["before_embedding"][k])))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
176

Anthony Larcher's avatar
Anthony Larcher committed
177
            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict(before_embedding_layers))
Anthony Larcher's avatar
Anthony Larcher committed
178
            self.before_speaker_embedding_weight_decay = cfg["before_embedding"]["weight_decay"]
Anthony Larcher's avatar
xv    
Anthony Larcher committed
179
180
181
182
183

            # Create sequential object for the second part of the network
            after_embedding_layers = []
            for k in cfg["after_embedding"].keys():
                if k.startswith("lin"):
Anthony Larcher's avatar
Anthony Larcher committed
184
185
                    if cfg["after_embedding"][k]["output"] == "speaker_number":
                        after_embedding_layers.append((k, torch.nn.Linear(input_size, self.speaker_number)))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
186
                    else:
Anthony Larcher's avatar
Anthony Larcher committed
187
188
                        after_embedding_layers.append((k, torch.nn.Linear(input_size, cfg["after_embedding"][k]["output"])))
                        input_size = cfg["after_embedding"][k]["output"]
Anthony Larcher's avatar
xv    
Anthony Larcher committed
189
190
191
192
193
194
195
196

                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'):
Anthony Larcher's avatar
Anthony Larcher committed
197
                    after_embedding_layers.append((k, torch.nn.Dropout(p=cfg["after_embedding"][k])))
Anthony Larcher's avatar
Anthony Larcher committed
198

Anthony Larcher's avatar
Anthony Larcher committed
199
            self.after_speaker_embedding = torch.nn.Sequential(OrderedDict(after_embedding_layers))
Anthony Larcher's avatar
Anthony Larcher committed
200
            self.after_speaker_embedding_weight_decay = cfg["after_embedding"]["weight_decay"]
Anthony Larcher's avatar
Anthony Larcher committed
201

Anthony Larcher's avatar
Anthony Larcher committed
202
    def forward(self, x, is_eval=False):
203
204
205
206
207
        """

        :param x:
        :return:
        """
Anthony Larcher's avatar
Anthony Larcher committed
208
        x = self.sequence_network(x)
209

Anthony Larcher's avatar
Anthony Larcher committed
210
211
212
213
214
215
216
217
        # 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
218

Anthony Larcher's avatar
Anthony Larcher committed
219
220
        x = self.after_speaker_embedding(x)
        return x
Anthony Larcher's avatar
Anthony Larcher committed
221

Anthony Larcher's avatar
Anthony Larcher committed
222

Anthony Larcher's avatar
Anthony Larcher committed
223
224
225
226
227
228
229
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):
def xtrain(speaker_number, config=None, model_name=None)
230
231
232
233
234
235
236
    """
    Initialize and train an x-vector on a single GPU

    :param args:
    :return:
    """
    # If we start from an existing model
Anthony Larcher's avatar
Anthony Larcher committed
237
    if model_name is not None:
238
        # Load the model
Anthony Larcher's avatar
Anthony Larcher committed
239
240
241
242
        logging.critical(f"*** Load model from = {model_name}")
        checkpoint = torch.load(model_name)
        model = Xtractor(speaker_number, config)
        model.load_state_dict(checkpoint["model_state_dict"])
243
244
    else:
        # Initialize a first model and save to disk
Anthony Larcher's avatar
Anthony Larcher committed
245
246
        if config is None:
            model = Xtractor(speaker_number)
Anthony Larcher's avatar
Anthony Larcher committed
247
        else:
Anthony Larcher's avatar
Anthony Larcher committed
248
249
250
            model = Xtractor(speaker_number, config)

    model.train()
Anthony Larcher's avatar
Anthony Larcher committed
251
252
253
254
255

    if torch.cuda.device_count() > 1:
        print("Let's use", torch.cuda.device_count(), "GPUs!")
        model = torch.nn.DataParallel(model)

256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
    model.cuda()

    # Split the training data in train and cv
    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
    pickle.dump(speaker_dict, open("spk_dictionary.pkl", "wb"))

    cv_portion = 0.007
    idx = numpy.arange(len(total_seg_df))
    numpy.random.shuffle(idx)
    train_seg_df = total_seg_df.iloc[idx[:int((1 - cv_portion) * len(idx))]].reset_index()
    cv_seg_df = total_seg_df.iloc[idx[int((1 - cv_portion) * len(idx)):]].reset_index()

    current_model_file_name = "initial_model"
    torch.save(model.state_dict(), current_model_file_name)

Anthony Larcher's avatar
Anthony Larcher committed
277
278
279
280
281
282
    optimizer = torch.optim.SGD([
        {'params': model.sequence_network.parameters(), 'weight_decay': self.sequence_network_weight_decay},
        {'params': model.before_speaker_embedding.parameters(), 'weight_decay': self.before_speaker_embedding_weight_decay},
        {'params': model.after_speaker_embedding.parameters(), 'weight_decay': self.after_speaker_embedding_weight_decay}],
        lr=args.lr, momentum=0.9
    )
283
284
285
286
287
288
289
290
291
292
293
294
295
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')

    for epoch in range(1, args.epochs + 1):
        # Process one epoch and return the current model
        model = train_epoch(model, epoch, train_seg_df, speaker_dict, optimizer, args)

        # Add the cross validation here
        accuracy, val_loss = cross_validation(args, model, cv_seg_df, speaker_dict)
        logging.critical("*** Cross validation accuracy = {} %".format(accuracy))

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

Anthony Larcher's avatar
Anthony Larcher committed
296
297
298
299
300
301
302
303
304
305
306
307
308
309
        # 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
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339


def train_epoch(model, epoch, train_seg_df, speaker_dict, optimizer, args):
    """

    :param model:
    :param epoch:
    :param train_seg_df:
    :param speaker_dict:
    :param optimizer:
    :param args:
    :return:
    """
    device = torch.device("cuda:0")

    torch.manual_seed(args.seed)

    train_transform = []
    if not args.train_transformation == '':
        trans = args.train_transformation.split(',')
        for t in trans:
            if "CMVN" in t:
                train_transform.append(CMVN())
            if "FrequencyMask" in t:
                a = int(t.split("-")[0].split("(")[1])
                b = int(t.split("-")[1].split(")")[0])
                train_transform.append(FrequencyMask(a, b))
            if "TemporalMask" in t:
                a = int(t.split("(")[1].split(")")[0])
                train_transform.append(TemporalMask(a))
Anthony Larcher's avatar
Anthony Larcher committed
340
    train_set = VoxDataset(train_seg_df, speaker_dict, args.duration, transform=transforms.Compose(train_transform),
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
                           spec_aug_ratio=args.spec_aug, temp_aug_ratio=args.temp_aug)
    train_loader = DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=15)

    criterion = torch.nn.CrossEntropyLoss()

    accuracy = 0.0
    for batch_idx, (data, target, _, __) in enumerate(train_loader):
        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()

        if batch_idx % args.log_interval == 0:
            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(),
                       100.0 * accuracy.item() / ((batch_idx + 1) * args.batch_size)))
    return model


# def cross_validation(args, model):
#
#     with open(args.cross_validation_list, 'r') as fh:
#         cross_validation_list = [l.rstrip() for l in fh]
#     cv_loader = XvectorMultiDataset(cross_validation_list, args.batch_path)
#
#     model.eval()
#     device = torch.device("cuda:0")
#     model.to(device)
#
#     accuracy = 0.0
#     bi = 0
#     for batch_idx, (data, target) in enumerate(cv_loader):
#         output = model(data.to(device))
#         accuracy += (torch.argmax(output.data, 1) == target.to(device)).sum()
#         bi = batch_idx
#     return 100. * accuracy.cpu().numpy() / ((bi + 1) * args.batch_size)


def cross_validation(args, model, cv_seg_df, speaker_dict):
    """

    :param args:
    :param model:
    :param cv_seg_df:
    :return:
    """
    cv_transform = []
    if not args.cv_transformation == '':
        trans = args.cv_transformation.split(',')
        for t in trans:
            if "CMVN" in t:
                cv_transform.append(CMVN())
            if "FrequencyMask" in t:
                a = t.split(",")[0].split("(")[1]
                b = t.split(",")[1].split("(")[0]
                cv_transform.append(FrequencyMask(a, b))
            if "TemporalMask" in t:
                a = t.split(",")[0].split("(")[1]
                cv_transform.append(TemporalMask(a, b))
    cv_set = VoxDataset(cv_seg_df, speaker_dict, 500, transform=transforms.Compose(cv_transform),
                        spec_aug_ratio=args.spec_aug, temp_aug_ratio=args.temp_aug)
    cv_loader = DataLoader(cv_set, batch_size=args.batch_size, shuffle=False, num_workers=15)
    model.eval()
    device = torch.device("cuda:0")
    model.to(device)

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

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

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

    return 100. * accuracy.cpu().numpy() / ((batch_idx + 1) * args.batch_size), loss


def xtrain_asynchronous(args):
425
426
427
428
429
430
    """
    Initialize and train an x-vector in asynchronous manner

    :param args:
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
431
    # Initialize a first model and save to disk
Anthony Larcher's avatar
Anthony Larcher committed
432
    model = Xtractor(args.class_number, args.dropout)
Anthony Larcher's avatar
Anthony Larcher committed
433
434
435
436
    current_model_file_name = "initial_model"
    torch.save(model.state_dict(), current_model_file_name)

    for epoch in range(1, args.epochs + 1):
437
        current_model_file_name = train_asynchronous_epoch(epoch, args, current_model_file_name)
Anthony Larcher's avatar
Anthony Larcher committed
438
439

        # Add the cross validation here
440
        accuracy = cross_asynchronous_validation(args, current_model_file_name)
Anthony Larcher's avatar
Anthony Larcher committed
441
        print("*** Cross validation accuracy = {} %".format(accuracy))
Anthony Larcher's avatar
Anthony Larcher committed
442

Anthony Larcher's avatar
Anthony Larcher committed
443
        # Decrease learning rate after every epoch
Anthony Larcher's avatar
sad    
Anthony Larcher committed
444
445
        args.lr = args.lr * 0.9
        print("        Decrease learning rate: {}".format(args.lr))
Anthony Larcher's avatar
Anthony Larcher committed
446

Anthony Larcher's avatar
Anthony Larcher committed
447

448
def train_asynchronous_epoch(epoch, args, initial_model_file_name):
449
450
451
452
453
454
455
456
    """
    Process one training epoch using an asynchronous implementation of the training

    :param epoch:
    :param args:
    :param initial_model_file_name:
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
    # 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,
477
                                           megabatch_number)  # function that split train, fuse and write the new model
Anthony Larcher's avatar
Anthony Larcher committed
478
479
480
    return current_model


481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
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):
503
        p = mp.Process(target=train_asynchronous_worker,
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
                       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


541
def train_asynchronous_worker(rank, epoch, args, initial_model_file_name, batch_list, output_queue):
542
543
544
545
546
547
548
549
550
551
552
    """


    :param rank:
    :param epoch:
    :param args:
    :param initial_model_file_name:
    :param batch_list:
    :param output_queue:
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
553
    model = Xtractor(args.class_number, args.dropout)
Anthony Larcher's avatar
Anthony Larcher committed
554
555
556
557
    model.load_state_dict(torch.load(initial_model_file_name))
    model.train()

    torch.manual_seed(args.seed + rank)
Anthony Larcher's avatar
Anthony Larcher committed
558
    train_loader = XvectorMultiDataset(batch_list, args.batch_path)
Anthony Larcher's avatar
Anthony Larcher committed
559
560
561
562
563
564
565
566
567
568
569
570

    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}
Anthony Larcher's avatar
Anthony Larcher committed
571
                            ], lr=args.lr)
Anthony Larcher's avatar
Anthony Larcher committed
572

Anthony Larcher's avatar
Anthony Larcher committed
573
    criterion = torch.nn.CrossEntropyLoss()
Anthony Larcher's avatar
Anthony Larcher committed
574
575
576
577
578
579
580
581

    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()
Anthony Larcher's avatar
Anthony Larcher committed
582

Anthony Larcher's avatar
Anthony Larcher committed
583
        accuracy += (torch.argmax(output.data, 1) == target.to(device)).sum()
Anthony Larcher's avatar
Anthony Larcher committed
584

Anthony Larcher's avatar
Anthony Larcher committed
585
586
587
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tAccuracy: {:.3f}'.format(
                epoch, batch_idx + 1, train_loader.__len__(),
588
589
                100. * batch_idx / train_loader.__len__(), loss.item(),
                100.0 * accuracy.item() / ((batch_idx + 1) * args.batch_size)))
Anthony Larcher's avatar
Anthony Larcher committed
590

Anthony Larcher's avatar
Anthony Larcher committed
591
592
    model_param = OrderedDict()
    params = model.state_dict()
Anthony Larcher's avatar
Anthony Larcher committed
593

Anthony Larcher's avatar
Anthony Larcher committed
594
595
596
    for k in list(params.keys()):
        model_param[k] = params[k].cpu().detach().numpy()
    output_queue.put(model_param)
Anthony Larcher's avatar
Anthony Larcher committed
597
598


599
def cross_asynchronous_validation(args, current_model_file_name):
Anthony Larcher's avatar
Anthony Larcher committed
600
601
    """

Anthony Larcher's avatar
Anthony Larcher committed
602
603
    :param args:
    :param current_model_file_name:
Anthony Larcher's avatar
Anthony Larcher committed
604
605
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
606
    with open(args.cross_validation_list, 'r') as fh:
Anthony Larcher's avatar
Anthony Larcher committed
607
        cross_validation_list = [l.rstrip() for l in fh]
Anthony Larcher's avatar
Anthony Larcher committed
608
        sub_lists = split_file_list(cross_validation_list, args.num_processes)
Anthony Larcher's avatar
Anthony Larcher committed
609

Anthony Larcher's avatar
Anthony Larcher committed
610
611
    #
    output_queue = mp.Queue()
Anthony Larcher's avatar
Anthony Larcher committed
612

Anthony Larcher's avatar
Anthony Larcher committed
613
614
    processes = []
    for rank in range(args.num_processes):
615
        p = mp.Process(target=cv_asynchronous_worker,
Anthony Larcher's avatar
Anthony Larcher committed
616
617
618
619
620
                       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)
Anthony Larcher's avatar
Anthony Larcher committed
621

Anthony Larcher's avatar
Anthony Larcher committed
622
623
624
625
    # Average the models and write the new one to disk
    result = []
    for ii in range(args.num_processes):
        result.append(output_queue.get())
Anthony Larcher's avatar
Anthony Larcher committed
626

Anthony Larcher's avatar
Anthony Larcher committed
627
628
    for p in processes:
        p.join()
Anthony Larcher's avatar
Anthony Larcher committed
629

Anthony Larcher's avatar
Anthony Larcher committed
630
631
632
    # Compute the global accuracy
    accuracy = 0.0
    total_batch_number = 0
Anthony Larcher's avatar
Anthony Larcher committed
633
    for bn, acc in result:
Anthony Larcher's avatar
Anthony Larcher committed
634
        accuracy += acc
Anthony Larcher's avatar
Anthony Larcher committed
635
636
        total_batch_number += bn
    
Anthony Larcher's avatar
Anthony Larcher committed
637
    return 100. * accuracy / (total_batch_number * args.batch_size)
Anthony Larcher's avatar
Anthony Larcher committed
638
639


640
def cv_asynchronous_worker(rank, args, current_model_file_name, batch_list, output_queue):
Anthony Larcher's avatar
Anthony Larcher committed
641
    model = Xtractor(args.class_number, args.dropout)
Anthony Larcher's avatar
Anthony Larcher committed
642
643
    model.load_state_dict(torch.load(current_model_file_name))
    model.eval()
Anthony Larcher's avatar
Anthony Larcher committed
644

Anthony Larcher's avatar
Anthony Larcher committed
645
    cv_loader = XvectorMultiDataset(batch_list, args.batch_path)
Anthony Larcher's avatar
Anthony Larcher committed
646

Anthony Larcher's avatar
Anthony Larcher committed
647
648
    device = torch.device("cuda:{}".format(rank))
    model.to(device)
Anthony Larcher's avatar
Anthony Larcher committed
649

Anthony Larcher's avatar
Anthony Larcher committed
650
    accuracy = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
651
    for batch_idx, (data, target) in enumerate(cv_loader):
Anthony Larcher's avatar
Anthony Larcher committed
652
653
        output = model(data.to(device))
        accuracy += (torch.argmax(output.data, 1) == target.to(device)).sum()
Anthony Larcher's avatar
Anthony Larcher committed
654
    output_queue.put((cv_loader.__len__(), accuracy.cpu().numpy()))
Anthony Larcher's avatar
Anthony Larcher committed
655

Anthony Larcher's avatar
hot    
Anthony Larcher committed
656

657
def extract_idmap(args, device_id, segment_indices, fs_params, idmap_name, output_queue):
Anthony Larcher's avatar
Anthony Larcher committed
658
    """
Anthony Larcher's avatar
Anthony Larcher committed
659
660
    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
Anthony Larcher's avatar
Anthony Larcher committed
661
    """
662
    # device = torch.device("cuda:{}".format(device_ID))
Anthony Larcher's avatar
Anthony Larcher committed
663
    device = torch.device('cpu')
Anthony Larcher's avatar
Anthony Larcher committed
664
665
666
667
668
669
670
671
672
673
674
675
676

    # 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])
Anthony Larcher's avatar
Anthony Larcher committed
677
    model = Xtractor(args.class_number, args.dropout)
Anthony Larcher's avatar
Anthony Larcher committed
678
679
680
681
682
683
684
685
    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
Anthony Larcher's avatar
Anthony Larcher committed
686
687
688
689
690
691
    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)
Anthony Larcher's avatar
Anthony Larcher committed
692
693
694
695
696
697

    # Send on selected device
    model.to(device)

    # Loop to extract all x-vectors
    for idx, (model_id, segment_id, data) in enumerate(segment_loader):
Anthony Larcher's avatar
Anthony Larcher committed
698
        logging.critical('Process file {}, [{} / {}]'.format(segment_id, idx, segment_loader.__len__()))
Anthony Larcher's avatar
Anthony Larcher committed
699

Anthony Larcher's avatar
Anthony Larcher committed
700
701
702
        if list(data.shape)[2] < 20:
            pass
        else:
Anthony Larcher's avatar
Anthony Larcher committed
703
704
705
706
707
708
709
            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()
Anthony Larcher's avatar
Anthony Larcher committed
710

Anthony Larcher's avatar
Anthony Larcher committed
711
    output_queue.put((segment_indices, emb_1, emb_2, emb_3, emb_4, emb_5, emb_6))
Anthony Larcher's avatar
Anthony Larcher committed
712
713


Anthony Larcher's avatar
Anthony Larcher committed
714
def extract_parallel(args, fs_params):
715
716
717
718
719
720
    """

    :param args:
    :param fs_params:
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
721
722
723
    emb_a_size = 512
    emb_b_size = 512

Anthony Larcher's avatar
Anthony Larcher committed
724
    idmap = IdMap(args.idmap)
Anthony Larcher's avatar
Anthony Larcher committed
725

Anthony Larcher's avatar
Anthony Larcher committed
726
727
728
729
730
731
732
733
734
735
736
737
738
    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)
Anthony Larcher's avatar
Anthony Larcher committed
739
740
741

    # Split the indices
    mega_batch_size = idmap.leftids.shape[0] // args.num_processes
Anthony Larcher's avatar
Anthony Larcher committed
742
743
744

    logging.critical("Number of sessions to process: {}".format(idmap.leftids.shape[0]))

Anthony Larcher's avatar
Anthony Larcher committed
745
746
747
    segment_idx = []
    for ii in range(args.num_processes):
        segment_idx.append(
Anthony Larcher's avatar
Anthony Larcher committed
748
749
750
751
            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)))
Anthony Larcher's avatar
Anthony Larcher committed
752
753
754
755
756
757
758

    # Extract x-vectors in parallel
    output_queue = mp.Queue()

    processes = []
    for rank in range(args.num_processes):
        p = mp.Process(target=extract_idmap,
Anthony Larcher's avatar
Anthony Larcher committed
759
                       args=(args, rank, segment_idx[rank], fs_params, args.idmap, output_queue)
Anthony Larcher's avatar
Anthony Larcher committed
760
761
762
763
764
765
766
                       )
        # 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):
Anthony Larcher's avatar
Anthony Larcher committed
767
768
769
770
771
772
773
        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
Anthony Larcher's avatar
Anthony Larcher committed
774
775
776
777

    for p in processes:
        p.join()

Anthony Larcher's avatar
Anthony Larcher committed
778
    return x_server_1, x_server_2, x_server_3, x_server_4, x_server_5, x_server_6
Anthony Larcher's avatar
Anthony Larcher committed
779
780


Anthony Larcher's avatar
Anthony Larcher committed
781
def extract_embeddings(args):
782
783
784
785
786
787
788
789
790
791
792
793
794
    """

    :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)
Anthony Larcher's avatar
Anthony Larcher committed
795
    model = torch.nn.DataParallel(model)
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
    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)
Anthony Larcher's avatar
Anthony Larcher committed
812

813
    #CREER UN TENSEUR DE LA BONNE TAILLE POUR STOCKER LES X-VECTEURS
Anthony Larcher's avatar
Anthony Larcher committed
814

815
816
817
818
    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
Anthony Larcher's avatar
Anthony Larcher committed
819

820
821
    #FAIRE CORRESPONDRE LES SPK_ID AVEC LES X-VECTEURS
    #RENVOYER LE TENSEUR DE X-VECTEURS SUR LE CPU OU L ECRTIRE SUR LE DISQUE