xvector.py 27.1 KB
Newer Older
Anthony Larcher's avatar
Anthony Larcher committed
1
# coding: utf-8 -*-
Anthony Larcher's avatar
Anthony Larcher committed
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
#
# 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
Anthony Larcher committed
30
import pandas
Anthony Larcher's avatar
minor    
Anthony Larcher committed
31
import pickle
Anthony Larcher's avatar
Anthony Larcher committed
32
import shutil
Anthony Larcher's avatar
Anthony Larcher committed
33
import torch
Anthony Larcher's avatar
Anthony Larcher committed
34
35
import torch.optim as optim
import torch.multiprocessing as mp
Anthony Larcher's avatar
Anthony Larcher committed
36
37
import yaml

Anthony Larcher's avatar
Anthony Larcher committed
38
from torchvision import transforms
Anthony Larcher's avatar
Anthony Larcher committed
39
from collections import OrderedDict
Anthony Larcher's avatar
Anthony Larcher committed
40
from .xsets import XvectorMultiDataset, StatDataset, VoxDataset, SideSet
Anthony Larcher's avatar
Anthony Larcher committed
41
from .xsets import IdMapSet
Anthony Larcher's avatar
Anthony Larcher committed
42
from .xsets import FrequencyMask, CMVN, TemporalMask, MFCC
Anthony Larcher's avatar
Anthony Larcher committed
43
from .res_net import RawPreprocessor, ResBlockWFMS
Anthony Larcher's avatar
Anthony Larcher committed
44
45
from ..bosaris import IdMap
from ..statserver import StatServer
Anthony Larcher's avatar
Anthony Larcher committed
46
from torch.utils.data import DataLoader
Anthony Larcher's avatar
Anthony Larcher committed
47
from sklearn.model_selection import train_test_split
Anthony Larcher's avatar
Anthony Larcher committed
48
from .sincnet import SincNet, SincConv1d
Anthony Larcher's avatar
Anthony Larcher committed
49
from tqdm import tqdm
Anthony Larcher's avatar
Anthony Larcher committed
50

Anthony Larcher's avatar
Anthony Larcher committed
51
52
__license__ = "LGPL"
__author__ = "Anthony Larcher"
Anthony Larcher's avatar
Anthony Larcher committed
53
__copyright__ = "Copyright 2015-2020 Anthony Larcher"
Anthony Larcher's avatar
Anthony Larcher committed
54
55
56
57
__maintainer__ = "Anthony Larcher"
__email__ = "anthony.larcher@univ-lemans.fr"
__status__ = "Production"
__docformat__ = 'reS'
Anthony Larcher's avatar
Anthony Larcher committed
58
59


60
def get_lr(optimizer):
Anthony Larcher's avatar
Anthony Larcher committed
61
62
63
64
65
    """

    :param optimizer:
    :return:
    """
66
67
68
69
    for param_group in optimizer.param_groups:
        return param_group['lr']


Anthony Larcher's avatar
Anthony Larcher committed
70
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', best_filename='model_best.pth.tar'):
Anthony Larcher's avatar
Anthony Larcher committed
71
72
73
74
75
76
77
78
    """

    :param state:
    :param is_best:
    :param filename:
    :param best_filename:
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
79
80
81
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, best_filename)
Anthony Larcher's avatar
Anthony Larcher committed
82

Anthony Larcher's avatar
Anthony Larcher committed
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
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)
Anthony Larcher's avatar
Anthony Larcher committed
103

Anthony Larcher's avatar
Anthony Larcher committed
104
105
106
107
108
109
110
111
112
113
114
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:
        """
Anthony Larcher's avatar
Anthony Larcher committed
115
        super(GruPooling, self).__init__()
Anthony Larcher's avatar
Anthony Larcher committed
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
        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

Anthony Larcher's avatar
Anthony Larcher committed
138
class Xtractor(torch.nn.Module):
139
140
141
    """
    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
142

Anthony Larcher's avatar
Anthony Larcher committed
143
    def __init__(self, speaker_number, model_archi="xvector", norm_embedding=False):
Anthony Larcher's avatar
Anthony Larcher committed
144
145
        """
        If config is None, default architecture is created
Anthony Larcher's avatar
Anthony Larcher committed
146
        :param model_archi:
Anthony Larcher's avatar
Anthony Larcher committed
147
        """
Anthony Larcher's avatar
Anthony Larcher committed
148
        super(Xtractor, self).__init__()
Anthony Larcher's avatar
Anthony Larcher committed
149
        self.speaker_number = speaker_number
Anthony Larcher's avatar
Anthony Larcher committed
150
        self.feature_size = None
Anthony Larcher's avatar
Anthony Larcher committed
151
        self.norm_embedding = norm_embedding
Anthony Larcher's avatar
Anthony Larcher committed
152

Anthony Larcher's avatar
Anthony Larcher committed
153
        if model_archi == "xvector":
Anthony Larcher's avatar
Anthony Larcher committed
154
            self.feature_size = 30
Anthony Larcher's avatar
Anthony Larcher committed
155
156
157
            self.activation = torch.nn.LeakyReLU(0.2)

            self.preprocessor = None
Anthony Larcher's avatar
Anthony Larcher committed
158

Anthony Larcher's avatar
xv    
Anthony Larcher committed
159
            self.sequence_network = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
160
                ("conv1", torch.nn.Conv1d(self.feature_size, 512, 5, dilation=1)),
Anthony Larcher's avatar
Anthony Larcher committed
161
162
163
164
165
166
167
168
                ("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
169
                ("conv4", torch.nn.Conv1d(512, 512, 1)),
Anthony Larcher's avatar
Anthony Larcher committed
170
171
                ("activation4", torch.nn.LeakyReLU(0.2)),
                ("norm4", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
172
                ("conv5", torch.nn.Conv1d(512, 1536, 1)),
Anthony Larcher's avatar
Anthony Larcher committed
173
174
175
176
                ("activation5", torch.nn.LeakyReLU(0.2)),
                ("norm5", torch.nn.BatchNorm1d(1536))
            ]))

Anthony Larcher's avatar
pooling    
Anthony Larcher committed
177
178
            self.stat_pooling = MeanStdPooling()

Anthony Larcher's avatar
xv    
Anthony Larcher committed
179
            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
180
                ("linear6", torch.nn.Linear(3072, 512))
Anthony Larcher's avatar
Anthony Larcher committed
181
182
            ]))

Anthony Larcher's avatar
xv    
Anthony Larcher committed
183
            self.after_speaker_embedding = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
184
185
                ("activation6", torch.nn.LeakyReLU(0.2)),
                ("norm6", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
186
                ("dropout6", torch.nn.Dropout(p=0.05)),
Anthony Larcher's avatar
Anthony Larcher committed
187
                ("linear7", torch.nn.Linear(512, 512)),
Anthony Larcher's avatar
Anthony Larcher committed
188
189
                ("activation7", torch.nn.LeakyReLU(0.2)),
                ("norm7", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
190
                ("linear8", torch.nn.Linear(512, int(self.speaker_number)))
Anthony Larcher's avatar
Anthony Larcher committed
191
192
            ]))

Anthony Larcher's avatar
Anthony Larcher committed
193
194
195
196
            self.sequence_network_weight_decay = 0.0002
            self.before_speaker_embedding_weight_decay = 0.002
            self.after_speaker_embedding_weight_decay = 0.002

Anthony Larcher's avatar
Anthony Larcher committed
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
        elif model_archi == "rawnet2":
            filts = [128, [128, 128], [128, 256], [256, 256]]
            self.norm_embedding = True

            self.preprocessor = RawPreprocessor(nb_samp=48000,
                                                in_channels=1,
                                                filts=filts[0],
                                                first_conv=3)

            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)

Anthony Larcher's avatar
Anthony Larcher committed
226
227
        else:
            # Load Yaml configuration
Anthony Larcher's avatar
Anthony Larcher committed
228
            with open(model_archi, 'r') as fh:
Anthony Larcher's avatar
Anthony Larcher committed
229
230
                cfg = yaml.load(fh, Loader=yaml.FullLoader)

Anthony Larcher's avatar
Anthony Larcher committed
231
232
233
            """
            Prepare Preprocessor
            """
Anthony Larcher's avatar
Anthony Larcher committed
234
            self.preprocessor = None
Anthony Larcher's avatar
Anthony Larcher committed
235
236
            if "preprocessor" in cfg:
                if cfg['preprocessor']["type"] == "sincnet":
Anthony Larcher's avatar
Anthony Larcher committed
237
                    self.preprocessor = SincNet(
Anthony Larcher's avatar
Anthony Larcher committed
238
239
240
241
242
243
244
245
246
247
248
249
                        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"]
                    )
Anthony Larcher's avatar
Anthony Larcher committed
250
                    self.feature_size = self.preprocessor.dimension
Anthony Larcher's avatar
Anthony Larcher committed
251
                elif cfg['preprocessor']["type"] == "rawnet2":
Anthony Larcher's avatar
Anthony Larcher committed
252
253
254
255
                    self.preprocessor = RawPreprocessor(nb_samp=48000,
                                                        in_channels=1,
                                                        filts=128,
                                                        first_conv=3)
Anthony Larcher's avatar
Anthony Larcher committed
256
257

            """
Anthony Larcher's avatar
minor    
Anthony Larcher committed
258
            Prepare sequence network
Anthony Larcher's avatar
Anthony Larcher committed
259
            """
Anthony Larcher's avatar
Anthony Larcher committed
260
            # Get Feature size
Anthony Larcher's avatar
Anthony Larcher committed
261
262
263
            if self.feature_size is None:
                self.feature_size = cfg["feature_size"]

Anthony Larcher's avatar
Anthony Larcher committed
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
            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
280
                    segmental_layers.append((k, torch.nn.Conv1d(input_size,
Anthony Larcher's avatar
Anthony Larcher committed
281
                                                                cfg["segmental"][k]["output_channels"],
Anthony Larcher's avatar
Anthony Larcher committed
282
283
                                                                kernel_size=cfg["segmental"][k]["kernel_size"],
                                                                dilation=cfg["segmental"][k]["dilation"])))
Anthony Larcher's avatar
Anthony Larcher committed
284
285
286
287
288
289
290
291
                    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
292
            self.sequence_network = torch.nn.Sequential(OrderedDict(segmental_layers))
Anthony Larcher's avatar
Anthony Larcher committed
293
            self.sequence_network_weight_decay = cfg["segmental"]["weight_decay"]
Anthony Larcher's avatar
Anthony Larcher committed
294

Anthony Larcher's avatar
pooling    
Anthony Larcher committed
295
296
297
298
299
300
301
302
303
            """
            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"])

Anthony Larcher's avatar
Anthony Larcher committed
304
305
306
            """
            Prepapre last part of the network (after pooling)
            """
Anthony Larcher's avatar
Anthony Larcher committed
307
308
            # Create sequential object for the second part of the network
            input_size = input_size * 2
Anthony Larcher's avatar
xv    
Anthony Larcher committed
309
310
            before_embedding_layers = []
            for k in cfg["before_embedding"].keys():
Anthony Larcher's avatar
Anthony Larcher committed
311
                if k.startswith("lin"):
Anthony Larcher's avatar
Anthony Larcher committed
312
313
                    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
314
                    else:
Anthony Larcher's avatar
Anthony Larcher committed
315
316
                        before_embedding_layers.append((k, torch.nn.Linear(input_size,
                                                                           cfg["before_embedding"][k]["output"])))
Anthony Larcher's avatar
Anthony Larcher committed
317
                        input_size = cfg["before_embedding"][k]["output"]
Anthony Larcher's avatar
Anthony Larcher committed
318
319

                elif k.startswith("activation"):
Anthony Larcher's avatar
xv    
Anthony Larcher committed
320
                    before_embedding_layers.append((k, self.activation))
Anthony Larcher's avatar
Anthony Larcher committed
321
322

                elif k.startswith('norm'):
Anthony Larcher's avatar
xv    
Anthony Larcher committed
323
                    before_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))
Anthony Larcher's avatar
Anthony Larcher committed
324
325

                elif k.startswith('dropout'):
Anthony Larcher's avatar
Anthony Larcher committed
326
                    before_embedding_layers.append((k, torch.nn.Dropout(p=cfg["before_embedding"][k])))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
327

Anthony Larcher's avatar
Anthony Larcher committed
328
            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict(before_embedding_layers))
Anthony Larcher's avatar
Anthony Larcher committed
329
            self.before_speaker_embedding_weight_decay = cfg["before_embedding"]["weight_decay"]
Anthony Larcher's avatar
xv    
Anthony Larcher committed
330
331
332
333
334

            # 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
335
336
                    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
337
                    else:
Anthony Larcher's avatar
Anthony Larcher committed
338
339
                        after_embedding_layers.append((k, torch.nn.Linear(input_size,
                                                                          cfg["after_embedding"][k]["output"])))
Anthony Larcher's avatar
Anthony Larcher committed
340
                        input_size = cfg["after_embedding"][k]["output"]
Anthony Larcher's avatar
xv    
Anthony Larcher committed
341
342
343
344
345
346
347
348

                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
349
                    after_embedding_layers.append((k, torch.nn.Dropout(p=cfg["after_embedding"][k])))
Anthony Larcher's avatar
Anthony Larcher committed
350

Anthony Larcher's avatar
Anthony Larcher committed
351
            self.after_speaker_embedding = torch.nn.Sequential(OrderedDict(after_embedding_layers))
Anthony Larcher's avatar
Anthony Larcher committed
352
            self.after_speaker_embedding_weight_decay = cfg["after_embedding"]["weight_decay"]
Anthony Larcher's avatar
Anthony Larcher committed
353

Anthony Larcher's avatar
Anthony Larcher committed
354
    def forward(self, x, is_eval=False):
355
356
357
        """

        :param x:
Anthony Larcher's avatar
Anthony Larcher committed
358
        :param is_eval:
359
360
        :return:
        """
Anthony Larcher's avatar
Anthony Larcher committed
361
362
363
        if self.preprocessor is not None:
            x = self.preprocessor(x)

Anthony Larcher's avatar
Anthony Larcher committed
364
        x = self.sequence_network(x)
365

Anthony Larcher's avatar
Anthony Larcher committed
366
        # Mean and Standard deviation pooling
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
367
368
369
370
        #mean = torch.mean(x, dim=2)
        #std = torch.std(x, dim=2)
        #x = torch.cat([mean, std], dim=1)
        x = self.stat_pooling(x)
Anthony Larcher's avatar
Anthony Larcher committed
371
372
373
374

        x = self.before_speaker_embedding(x)
        if is_eval:
            return x
375

Anthony Larcher's avatar
Anthony Larcher committed
376
377
378
379
        if self.norm_embedding:
            x_norm = x.norm(p=2,dim=1, keepdim=True) / 10.
            x = torch.div(x, x_norm)

Anthony Larcher's avatar
Anthony Larcher committed
380
381
        x = self.after_speaker_embedding(x)
        return x
Anthony Larcher's avatar
Anthony Larcher committed
382

Anthony Larcher's avatar
Anthony Larcher committed
383

Anthony Larcher's avatar
Anthony Larcher committed
384
def xtrain(speaker_number,
Anthony Larcher's avatar
Anthony Larcher committed
385
           dataset_yaml,
Anthony Larcher's avatar
minor    
Anthony Larcher committed
386
           epochs=100,
Anthony Larcher's avatar
Anthony Larcher committed
387
           lr=0.01,
Anthony Larcher's avatar
Anthony Larcher committed
388
           model_yaml=None,
Anthony Larcher's avatar
Anthony Larcher committed
389
           model_name=None,
Anthony Larcher's avatar
Anthony Larcher committed
390
           tmp_model_name=None,
Anthony Larcher's avatar
minor    
Anthony Larcher committed
391
           best_model_name=None,
Anthony Larcher's avatar
Anthony Larcher committed
392
           multi_gpu=True,
393
           clipping=False,
Anthony Larcher's avatar
Anthony Larcher committed
394
           opt='sgd',
Anthony Larcher's avatar
Anthony Larcher committed
395
           num_thread=1):
396
397
    """

Anthony Larcher's avatar
Anthony Larcher committed
398
399
400
401
402
403
404
405
406
407
408
    :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:
409
410
    :return:
    """
Anthony Larcher's avatar
minor    
Anthony Larcher committed
411
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
Anthony Larcher's avatar
Anthony Larcher committed
412

413
    # If we start from an existing model
Anthony Larcher's avatar
Anthony Larcher committed
414
415
416
417
418
419
420
    if model_name is not None:
        # Load the model
        logging.critical(f"*** Load model from = {model_name}")
        checkpoint = torch.load(model_name)
        model = Xtractor(speaker_number, model_yaml)
        model.load_state_dict(checkpoint["model_state_dict"])
    else:
Anthony Larcher's avatar
Anthony Larcher committed
421
        # Initialize a first model
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
422
423
424
425
        if model_yaml is "xvector":
            model = Xtractor(speaker_number, "xverctor")
        elif model_yaml is "rawnet2":
            model = Xtractor(speaker_number, "rawnet2")
Anthony Larcher's avatar
Anthony Larcher committed
426
        else:
Anthony Larcher's avatar
Anthony Larcher committed
427
            model = Xtractor(speaker_number, model_yaml)
Anthony Larcher's avatar
Anthony Larcher committed
428

Anthony Larcher's avatar
Anthony Larcher committed
429
    if torch.cuda.device_count() > 1 and multi_gpu:
Anthony Larcher's avatar
Anthony Larcher committed
430
431
        print("Let's use", torch.cuda.device_count(), "GPUs!")
        model = torch.nn.DataParallel(model)
Anthony Larcher's avatar
Anthony Larcher committed
432
433
    else:
        print("Train on a single GPU")
Anthony Larcher's avatar
Anthony Larcher committed
434
    model.to(device)
Anthony Larcher's avatar
Anthony Larcher committed
435
436

    """
Anthony Larcher's avatar
Anthony Larcher committed
437
438
439
440
    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 
Anthony Larcher's avatar
Anthony Larcher committed
441
    """
Anthony Larcher's avatar
Anthony Larcher committed
442
443
444
445
    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"])
446

Anthony Larcher's avatar
Anthony Larcher committed
447
    torch.manual_seed(dataset_params['seed'])
448
449
450
    training_set = SideSet(dataset_yaml, 
                           set_type="train", 
                           dataset_df=training_df, 
Anthony Larcher's avatar
Anthony Larcher committed
451
452
                           chunk_per_segment=dataset_params['train']['chunk_per_segment'], 
                           overlap=dataset_params['train']['overlap'])
Anthony Larcher's avatar
Anthony Larcher committed
453
454
455
    training_loader = DataLoader(training_set,
                                 batch_size=dataset_params["batch_size"],
                                 shuffle=True,
Anthony Larcher's avatar
sincxv    
Anthony Larcher committed
456
                                 drop_last=True,
Anthony Larcher's avatar
Anthony Larcher committed
457
                                 num_workers=num_thread)
458

Anthony Larcher's avatar
Anthony Larcher committed
459
460
461
    validation_set = SideSet(dataset_yaml, set_type="validation", dataset_df=validation_df)
    validation_loader = DataLoader(validation_set,
                                   batch_size=dataset_params["batch_size"],
Anthony Larcher's avatar
sincxv    
Anthony Larcher committed
462
                                   drop_last=True,
Anthony Larcher's avatar
Anthony Larcher committed
463
                                   num_workers=num_thread)
Anthony Larcher's avatar
Anthony Larcher committed
464
465
466
467

    """
    Set the training options
    """
Anthony Larcher's avatar
Anthony Larcher committed
468
469
    if opt == 'sgd':
        _optimizer = torch.optim.SGD
Anthony Larcher's avatar
Anthony Larcher committed
470
        _options = {'lr': lr, 'momentum': 0.9}
Anthony Larcher's avatar
Anthony Larcher committed
471
472
    elif opt == 'adam':
        _optimizer = torch.optim.Adam
Anthony Larcher's avatar
Anthony Larcher committed
473
        _options = {'lr': lr}
Anthony Larcher's avatar
Anthony Larcher committed
474
475
    elif opt == 'rmsprop':
        _optimizer = torch.optim.RMSprop
Anthony Larcher's avatar
Anthony Larcher committed
476
        _options = {'lr': lr}
Anthony Larcher's avatar
Anthony Larcher committed
477

Anthony Larcher's avatar
Anthony Larcher committed
478
    if type(model) is Xtractor:
Anthony Larcher's avatar
Anthony Larcher committed
479
        optimizer = _optimizer([
Anthony Larcher's avatar
Anthony Larcher committed
480
481
482
483
484
485
            {'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}],
Anthony Larcher's avatar
Anthony Larcher committed
486
            **_options
Anthony Larcher's avatar
Anthony Larcher committed
487
488
        )
    else:
Anthony Larcher's avatar
Anthony Larcher committed
489
        optimizer = _optimizer([
Anthony Larcher's avatar
Anthony Larcher committed
490
491
492
493
494
495
            {'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}],
Anthony Larcher's avatar
Anthony Larcher committed
496
            **_options
Anthony Larcher's avatar
Anthony Larcher committed
497
        )
Anthony Larcher's avatar
Anthony Larcher committed
498
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', verbose=True)
499

Anthony Larcher's avatar
sincxv    
Anthony Larcher committed
500
    best_accuracy = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
501
    best_accuracy_epoch = 1
Anthony Larcher's avatar
Anthony Larcher committed
502
    for epoch in range(1, epochs + 1):
503
        # Process one epoch and return the current model
Anthony Larcher's avatar
Anthony Larcher committed
504
505
506
507
508
509
510
        model = train_epoch(model,
                            epoch,
                            training_loader,
                            optimizer,
                            dataset_params["log_interval"],
                            device=device,
                            clipping=clipping)
511
512

        # Add the cross validation here
Anthony Larcher's avatar
Anthony Larcher committed
513
        accuracy, val_loss = cross_validation(model, validation_loader, device=device)
514
515
516
517
        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
518
        print(f"Learning rate is {optimizer.param_groups[0]['lr']}")
519

Anthony Larcher's avatar
Anthony Larcher committed
520
521
522
523
        # remember best accuracy and save checkpoint
        is_best = accuracy > best_accuracy
        best_accuracy = max(accuracy, best_accuracy)

Anthony Larcher's avatar
Anthony Larcher committed
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
        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')
Anthony Larcher's avatar
Anthony Larcher committed
540
541
542

        if is_best:
            best_accuracy_epoch = epoch
543

Anthony Larcher's avatar
Anthony Larcher committed
544
    logging.critical(f"Best accuracy {best_accuracy * 100.} obtained at epoch {best_accuracy_epoch}")
545

Anthony Larcher's avatar
Anthony Larcher committed
546

Anthony Larcher's avatar
Anthony Larcher committed
547
def train_epoch(model, epoch, training_loader, optimizer, log_interval, device, clipping=False):
548
549
550
551
    """

    :param model:
    :param epoch:
Anthony Larcher's avatar
Anthony Larcher committed
552
    :param training_loader:
553
    :param optimizer:
Anthony Larcher's avatar
Anthony Larcher committed
554
555
556
    :param log_interval:
    :param device:
    :param clipping:
557
558
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
559
    model.train()
Anthony Larcher's avatar
Anthony Larcher committed
560
    criterion = torch.nn.CrossEntropyLoss(reduction='mean')
561
562

    accuracy = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
563
    for batch_idx, (data, target) in enumerate(training_loader):
564
565
566
567
568
        target = target.squeeze()
        optimizer.zero_grad()
        output = model(data.to(device))
        loss = criterion(output, target.to(device))
        loss.backward()
Anthony Larcher's avatar
Anthony Larcher committed
569
570
        if clipping:
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.)
571
572
573
        optimizer.step()
        accuracy += (torch.argmax(output.data, 1) == target.to(device)).sum()

Anthony Larcher's avatar
Anthony Larcher committed
574
        if batch_idx % log_interval == 0:
Anthony Larcher's avatar
Anthony Larcher committed
575
            batch_size = target.shape[0]
576
            logging.critical('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tAccuracy: {:.3f}'.format(
Anthony Larcher's avatar
Anthony Larcher committed
577
                epoch, batch_idx + 1, training_loader.__len__(),
Anthony Larcher's avatar
Anthony Larcher committed
578
579
                100. * batch_idx / training_loader.__len__(), loss.item(),
                100.0 * accuracy.item() / ((batch_idx + 1) * batch_size)))
580
581
582
    return model


Anthony Larcher's avatar
Anthony Larcher committed
583
def cross_validation(model, validation_loader, device):
584
585
586
    """

    :param model:
Anthony Larcher's avatar
Anthony Larcher committed
587
588
    :param validation_loader:
    :param device:
589
590
591
592
593
    :return:
    """
    model.eval()

    accuracy = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
594
    loss = 0.0
595
    criterion = torch.nn.CrossEntropyLoss()
Anthony Larcher's avatar
Anthony Larcher committed
596
597
598
599
600
601
    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()
602

Anthony Larcher's avatar
Anthony Larcher committed
603
604
            loss += criterion(output, target.to(device))
    
Anthony Larcher's avatar
Anthony Larcher committed
605
606
607
608
    return 100. * accuracy.cpu().numpy() / ((batch_idx + 1) * batch_size), \
           loss.cpu().numpy() / ((batch_idx + 1) * batch_size)


Anthony Larcher's avatar
Anthony Larcher committed
609
610
611
612
613
614
615
616
def extract_embeddings(idmap_name,
                       speaker_number,
                       model_filename,
                       model_yaml,
                       data_root_name ,
                       device,
                       file_extension="wav",
                       transform_pipeline=None):
Anthony Larcher's avatar
Anthony Larcher committed
617

Anthony Larcher's avatar
Anthony Larcher committed
618
    if isinstance(idmap_name, IdMap):
619
620
621
622
        idmap = idmap_name
    else:
        idmap = IdMap(idmap_name)

Anthony Larcher's avatar
Anthony Larcher committed
623
    # Create dataset to load the data
Anthony Larcher's avatar
Anthony Larcher committed
624
625
626
627
    dataset = IdMapSet(idmap_name=idmap_name,
                       data_root_path=data_root_name,
                       file_extension=file_extension,
                       transform_pipeline=transform_pipeline)
Anthony Larcher's avatar
Anthony Larcher committed
628
629

    # Load the model
630
631
632
633
634
635
636
    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

Anthony Larcher's avatar
Anthony Larcher committed
637
638
    model.eval()
    model.to(device)
639

Anthony Larcher's avatar
Anthony Larcher committed
640
641
642
    # 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]
Anthony Larcher's avatar
Anthony Larcher committed
643
    
Anthony Larcher's avatar
Anthony Larcher committed
644
    # Create the StatServer
Anthony Larcher's avatar
Anthony Larcher committed
645
    embeddings = StatServer()
Anthony Larcher's avatar
Anthony Larcher committed
646
647
648
649
650
651
    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))
Anthony Larcher's avatar
Anthony Larcher committed
652

Anthony Larcher's avatar
Anthony Larcher committed
653
654
    # Process the data
    with torch.no_grad():
Anthony Larcher's avatar
Anthony Larcher committed
655
        for idx in tqdm(range(len(dataset))):
Anthony Larcher's avatar
Anthony Larcher committed
656
            data, mod, seg, start, stop = dataset[idx]
Anthony Larcher's avatar
Anthony Larcher committed
657
            vec = model(data[None, :, :].to(device), is_eval=True)
Anthony Larcher's avatar
Anthony Larcher committed
658
659
660
661
662
663
            #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()
Anthony Larcher's avatar
Anthony Larcher committed
664
665
666
667

    return embeddings


Anthony Larcher's avatar
Anthony Larcher committed
668
669
670
671
672
673
674
675
676
677
678
679
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):

680
681
682
683
684
685
686
687
688
689
690
691
692
693

    # 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):
        # Compute the number of chunks to process
        chunk_starts = numpy.arange(start,
                                    stop - int(sample_rate * window_length),
Anthony Larcher's avatar
Anthony Larcher committed
694
                                    int(sample_rate * (window_length - overlap)))
695
696

        # Create a numpy array to store the current x-vectors
Anthony Larcher's avatar
Anthony Larcher committed
697
        model_names.append(numpy.array([mod + f"_{ii}" for ii in range(len(chunk_starts))]).astype("U"))
698
699
700
701
702
703
704
        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()
Anthony Larcher's avatar
Anthony Larcher committed
705
706
707
708
    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)
709
    assert sliding_idmap.validate()
Anthony Larcher's avatar
Anthony Larcher committed
710

Anthony Larcher's avatar
Anthony Larcher committed
711
712
713
714
715
716
    embeddings = extract_embeddings(sliding_idmap,
                                 speaker_number,
                                 model_filename,
                                 model_yaml,
                                 data_root_name,
                                 device,
Anthony Larcher's avatar
Anthony Larcher committed
717
718
                                 file_extension=file_extension,
                                 transform_pipeline=transform_pipeline)
Anthony Larcher's avatar
Anthony Larcher committed
719
720

    return embeddings
Anthony Larcher's avatar
Anthony Larcher committed
721
722