xvector.py 29.9 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
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


Anthony Larcher's avatar
Anthony Larcher committed
60
61
logging.basicConfig(format='%(asctime)s %(message)s')

62
def get_lr(optimizer):
Anthony Larcher's avatar
Anthony Larcher committed
63
64
65
66
67
    """

    :param optimizer:
    :return:
    """
68
69
70
71
    for param_group in optimizer.param_groups:
        return param_group['lr']


Anthony Larcher's avatar
Anthony Larcher committed
72
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', best_filename='model_best.pth.tar'):
Anthony Larcher's avatar
Anthony Larcher committed
73
74
75
76
77
78
79
80
    """

    :param state:
    :param is_best:
    :param filename:
    :param best_filename:
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
81
82
83
    torch.save(state, filename)
    if is_best:
        shutil.copyfile(filename, best_filename)
Anthony Larcher's avatar
Anthony Larcher committed
84

Anthony Larcher's avatar
Anthony Larcher committed
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
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
105

Anthony Larcher's avatar
Anthony Larcher committed
106

Anthony Larcher's avatar
Anthony Larcher committed
107
108
109
110
111
112
113
114
115
116
117
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
118
        super(GruPooling, self).__init__()
Anthony Larcher's avatar
Anthony Larcher committed
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
        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
141

Anthony Larcher's avatar
Anthony Larcher committed
142
class Xtractor(torch.nn.Module):
143
144
145
    """
    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
146

Anthony Larcher's avatar
Anthony Larcher committed
147
    def __init__(self, speaker_number, model_archi="xvector", norm_embedding=False):
Anthony Larcher's avatar
Anthony Larcher committed
148
149
        """
        If config is None, default architecture is created
Anthony Larcher's avatar
Anthony Larcher committed
150
        :param model_archi:
Anthony Larcher's avatar
Anthony Larcher committed
151
        """
Anthony Larcher's avatar
Anthony Larcher committed
152
        super(Xtractor, self).__init__()
Anthony Larcher's avatar
Anthony Larcher committed
153
        self.speaker_number = speaker_number
Anthony Larcher's avatar
Anthony Larcher committed
154
        self.feature_size = None
Anthony Larcher's avatar
Anthony Larcher committed
155
        self.norm_embedding = norm_embedding
Anthony Larcher's avatar
Anthony Larcher committed
156

Anthony Larcher's avatar
Anthony Larcher committed
157
        if model_archi == "xvector":
Anthony Larcher's avatar
Anthony Larcher committed
158
            self.feature_size = 30
Anthony Larcher's avatar
Anthony Larcher committed
159
160
161
            self.activation = torch.nn.LeakyReLU(0.2)

            self.preprocessor = None
Anthony Larcher's avatar
Anthony Larcher committed
162

Anthony Larcher's avatar
xv    
Anthony Larcher committed
163
            self.sequence_network = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
164
                ("conv1", torch.nn.Conv1d(self.feature_size, 512, 5, dilation=1)),
Anthony Larcher's avatar
Anthony Larcher committed
165
166
167
168
169
170
171
172
                ("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
173
                ("conv4", torch.nn.Conv1d(512, 512, 1)),
Anthony Larcher's avatar
Anthony Larcher committed
174
175
                ("activation4", torch.nn.LeakyReLU(0.2)),
                ("norm4", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
176
                ("conv5", torch.nn.Conv1d(512, 1536, 1)),
Anthony Larcher's avatar
Anthony Larcher committed
177
178
179
180
                ("activation5", torch.nn.LeakyReLU(0.2)),
                ("norm5", torch.nn.BatchNorm1d(1536))
            ]))

Anthony Larcher's avatar
pooling    
Anthony Larcher committed
181
182
            self.stat_pooling = MeanStdPooling()

Anthony Larcher's avatar
xv    
Anthony Larcher committed
183
            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
184
                ("linear6", torch.nn.Linear(3072, 512))
Anthony Larcher's avatar
Anthony Larcher committed
185
186
            ]))

Anthony Larcher's avatar
xv    
Anthony Larcher committed
187
            self.after_speaker_embedding = torch.nn.Sequential(OrderedDict([
Anthony Larcher's avatar
Anthony Larcher committed
188
189
                ("activation6", torch.nn.LeakyReLU(0.2)),
                ("norm6", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
190
                ("dropout6", torch.nn.Dropout(p=0.05)),
Anthony Larcher's avatar
Anthony Larcher committed
191
                ("linear7", torch.nn.Linear(512, 512)),
Anthony Larcher's avatar
Anthony Larcher committed
192
193
                ("activation7", torch.nn.LeakyReLU(0.2)),
                ("norm7", torch.nn.BatchNorm1d(512)),
Anthony Larcher's avatar
Anthony Larcher committed
194
                ("linear8", torch.nn.Linear(512, int(self.speaker_number)))
Anthony Larcher's avatar
Anthony Larcher committed
195
196
            ]))

Anthony Larcher's avatar
Anthony Larcher committed
197
198
199
200
            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
201
202
203
204
        elif model_archi == "rawnet2":
            filts = [128, [128, 128], [128, 256], [256, 256]]
            self.norm_embedding = True

Anthony Larcher's avatar
Anthony Larcher committed
205
            self.preprocessor = RawPreprocessor(nb_samp=32000,
Anthony Larcher's avatar
Anthony Larcher committed
206
                                                in_channels=1,
Anthony Larcher's avatar
Anthony Larcher committed
207
208
                                                out_channels=filts[0],
                                                kernel_size=3)
Anthony Larcher's avatar
Anthony Larcher committed
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229

            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
rawnet2    
Anthony Larcher committed
230
231
232
233
234
235
            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

Anthony Larcher's avatar
Anthony Larcher committed
236
237
        else:
            # Load Yaml configuration
Anthony Larcher's avatar
Anthony Larcher committed
238
            with open(model_archi, 'r') as fh:
Anthony Larcher's avatar
Anthony Larcher committed
239
240
                cfg = yaml.load(fh, Loader=yaml.FullLoader)

Anthony Larcher's avatar
Anthony Larcher committed
241
242
243
            """
            Prepare Preprocessor
            """
Anthony Larcher's avatar
Anthony Larcher committed
244
            self.preprocessor = None
Anthony Larcher's avatar
Anthony Larcher committed
245
246
            if "preprocessor" in cfg:
                if cfg['preprocessor']["type"] == "sincnet":
Anthony Larcher's avatar
Anthony Larcher committed
247
                    self.preprocessor = SincNet(
Anthony Larcher's avatar
Anthony Larcher committed
248
249
250
251
252
253
254
255
256
257
258
259
                        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
260
                    self.feature_size = self.preprocessor.dimension
Anthony Larcher's avatar
Anthony Larcher committed
261
                elif cfg['preprocessor']["type"] == "rawnet2":
Anthony Larcher's avatar
Anthony Larcher committed
262
                    self.preprocessor = RawPreprocessor(nb_samp=int(cfg['preprocessor']["sampling_frequency"] * cfg['preprocessor']["duration"]),
Anthony Larcher's avatar
Anthony Larcher committed
263
                                                        in_channels=1,
Anthony Larcher's avatar
Anthony Larcher committed
264
265
266
267
268
269
                                                        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"]
Anthony Larcher's avatar
Anthony Larcher committed
270
271

            """
Anthony Larcher's avatar
minor    
Anthony Larcher committed
272
            Prepare sequence network
Anthony Larcher's avatar
Anthony Larcher committed
273
            """
Anthony Larcher's avatar
Anthony Larcher committed
274
            # Get Feature size
Anthony Larcher's avatar
Anthony Larcher committed
275
276
277
            if self.feature_size is None:
                self.feature_size = cfg["feature_size"]

Anthony Larcher's avatar
Anthony Larcher committed
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
            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
294
                    segmental_layers.append((k, torch.nn.Conv1d(input_size,
Anthony Larcher's avatar
Anthony Larcher committed
295
                                                                cfg["segmental"][k]["output_channels"],
Anthony Larcher's avatar
Anthony Larcher committed
296
297
                                                                kernel_size=cfg["segmental"][k]["kernel_size"],
                                                                dilation=cfg["segmental"][k]["dilation"])))
Anthony Larcher's avatar
Anthony Larcher committed
298
299
300
301
302
303
304
305
                    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
306
            self.sequence_network = torch.nn.Sequential(OrderedDict(segmental_layers))
Anthony Larcher's avatar
Anthony Larcher committed
307
            self.sequence_network_weight_decay = cfg["segmental"]["weight_decay"]
Anthony Larcher's avatar
Anthony Larcher committed
308

Anthony Larcher's avatar
pooling    
Anthony Larcher committed
309
310
311
312
313
314
315
316
317
            """
            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
318
319
320
            """
            Prepapre last part of the network (after pooling)
            """
Anthony Larcher's avatar
Anthony Larcher committed
321
322
            # Create sequential object for the second part of the network
            input_size = input_size * 2
Anthony Larcher's avatar
xv    
Anthony Larcher committed
323
324
            before_embedding_layers = []
            for k in cfg["before_embedding"].keys():
Anthony Larcher's avatar
Anthony Larcher committed
325
                if k.startswith("lin"):
Anthony Larcher's avatar
Anthony Larcher committed
326
327
                    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
328
                    else:
Anthony Larcher's avatar
Anthony Larcher committed
329
330
                        before_embedding_layers.append((k, torch.nn.Linear(input_size,
                                                                           cfg["before_embedding"][k]["output"])))
Anthony Larcher's avatar
Anthony Larcher committed
331
                        input_size = cfg["before_embedding"][k]["output"]
Anthony Larcher's avatar
Anthony Larcher committed
332
333

                elif k.startswith("activation"):
Anthony Larcher's avatar
xv    
Anthony Larcher committed
334
                    before_embedding_layers.append((k, self.activation))
Anthony Larcher's avatar
Anthony Larcher committed
335
336

                elif k.startswith('norm'):
Anthony Larcher's avatar
xv    
Anthony Larcher committed
337
                    before_embedding_layers.append((k, torch.nn.BatchNorm1d(input_size)))
Anthony Larcher's avatar
Anthony Larcher committed
338
339

                elif k.startswith('dropout'):
Anthony Larcher's avatar
Anthony Larcher committed
340
                    before_embedding_layers.append((k, torch.nn.Dropout(p=cfg["before_embedding"][k])))
Anthony Larcher's avatar
xv    
Anthony Larcher committed
341

Anthony Larcher's avatar
Anthony Larcher committed
342
            self.before_speaker_embedding = torch.nn.Sequential(OrderedDict(before_embedding_layers))
Anthony Larcher's avatar
Anthony Larcher committed
343
            self.before_speaker_embedding_weight_decay = cfg["before_embedding"]["weight_decay"]
Anthony Larcher's avatar
xv    
Anthony Larcher committed
344
345
346
347
348

            # 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
349
350
                    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
351
                    else:
Anthony Larcher's avatar
Anthony Larcher committed
352
353
                        after_embedding_layers.append((k, torch.nn.Linear(input_size,
                                                                          cfg["after_embedding"][k]["output"])))
Anthony Larcher's avatar
Anthony Larcher committed
354
                        input_size = cfg["after_embedding"][k]["output"]
Anthony Larcher's avatar
xv    
Anthony Larcher committed
355
356
357
358
359
360
361
362

                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
363
                    after_embedding_layers.append((k, torch.nn.Dropout(p=cfg["after_embedding"][k])))
Anthony Larcher's avatar
Anthony Larcher committed
364

Anthony Larcher's avatar
Anthony Larcher committed
365
            self.after_speaker_embedding = torch.nn.Sequential(OrderedDict(after_embedding_layers))
Anthony Larcher's avatar
Anthony Larcher committed
366
            self.after_speaker_embedding_weight_decay = cfg["after_embedding"]["weight_decay"]
Anthony Larcher's avatar
Anthony Larcher committed
367

Anthony Larcher's avatar
Anthony Larcher committed
368

Anthony Larcher's avatar
Anthony Larcher committed
369
    def forward(self, x, is_eval=False):
370
371
372
        """

        :param x:
Anthony Larcher's avatar
Anthony Larcher committed
373
        :param is_eval:
374
375
        :return:
        """
Anthony Larcher's avatar
Anthony Larcher committed
376
377
378
        if self.preprocessor is not None:
            x = self.preprocessor(x)

Anthony Larcher's avatar
Anthony Larcher committed
379
        x = self.sequence_network(x)
380

Anthony Larcher's avatar
Anthony Larcher committed
381
        # Mean and Standard deviation pooling
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
382
383
384
385
        #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
386
387
388
389

        x = self.before_speaker_embedding(x)
        if is_eval:
            return x
390

Anthony Larcher's avatar
Anthony Larcher committed
391
392
393
394
        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
395
396
        x = self.after_speaker_embedding(x)
        return x
Anthony Larcher's avatar
Anthony Larcher committed
397

Anthony Larcher's avatar
Anthony Larcher committed
398

Anthony Larcher's avatar
Anthony Larcher committed
399
def xtrain(speaker_number,
Anthony Larcher's avatar
Anthony Larcher committed
400
           dataset_yaml,
Anthony Larcher's avatar
minor    
Anthony Larcher committed
401
           epochs=100,
Anthony Larcher's avatar
Anthony Larcher committed
402
           lr=0.01,
Anthony Larcher's avatar
Anthony Larcher committed
403
           model_yaml=None,
Anthony Larcher's avatar
Anthony Larcher committed
404
           model_name=None,
Anthony Larcher's avatar
Anthony Larcher committed
405
           tmp_model_name=None,
Anthony Larcher's avatar
minor    
Anthony Larcher committed
406
           best_model_name=None,
Anthony Larcher's avatar
Anthony Larcher committed
407
           multi_gpu=True,
408
           clipping=False,
Anthony Larcher's avatar
Anthony Larcher committed
409
           opt='sgd',
Anthony Larcher's avatar
Anthony Larcher committed
410
411
           reset_parts=[],
           freeze_parts=[],
Anthony Larcher's avatar
Anthony Larcher committed
412
           num_thread=1):
413
414
    """

Anthony Larcher's avatar
Anthony Larcher committed
415
416
417
418
419
420
421
422
423
424
425
    :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:
426
427
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
428
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
Anthony Larcher's avatar
Anthony Larcher committed
429

Anthony Larcher's avatar
Anthony Larcher committed
430
431
    # Start from scratch
    if model_name is None:
Anthony Larcher's avatar
Anthony Larcher committed
432
        # Initialize a first model
Anthony Larcher's avatar
rawnet2    
Anthony Larcher committed
433
        if model_yaml == "xvector":
Anthony Larcher's avatar
Anthony Larcher committed
434
            model = Xtractor(speaker_number, "xvector")
Anthony Larcher's avatar
rawnet2    
Anthony Larcher committed
435
        elif model_yaml == "rawnet2":
Anthony Larcher's avatar
pooling    
Anthony Larcher committed
436
            model = Xtractor(speaker_number, "rawnet2")
Anthony Larcher's avatar
Anthony Larcher committed
437
        else:
Anthony Larcher's avatar
Anthony Larcher committed
438
            model = Xtractor(speaker_number, model_yaml)
Anthony Larcher's avatar
Anthony Larcher committed
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
    # 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)}

Anthony Larcher's avatar
Anthony Larcher committed
454
        new_model_dict = model.state_dict()
Anthony Larcher's avatar
Anthony Larcher committed
455
456
457
458
459
460
461
        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
Anthony Larcher's avatar
Anthony Larcher committed
462

Anthony Larcher's avatar
Anthony Larcher committed
463
    if torch.cuda.device_count() > 1 and multi_gpu:
Anthony Larcher's avatar
Anthony Larcher committed
464
465
        print("Let's use", torch.cuda.device_count(), "GPUs!")
        model = torch.nn.DataParallel(model)
Anthony Larcher's avatar
Anthony Larcher committed
466
467
    else:
        print("Train on a single GPU")
Anthony Larcher's avatar
Anthony Larcher committed
468
    model.to(device)
Anthony Larcher's avatar
Anthony Larcher committed
469

Anthony Larcher's avatar
Anthony Larcher committed
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
    if device.type == 'cuda':
        print(torch.cuda.get_device_name())
        print('Memory Usage:')
        print('Allocated:', round(torch.cuda.memory_allocated()/1024**3,1), 'GB')
        print('Cached:   ', round(torch.cuda.memory_cached()/1024**3,1), 'GB')

    test = torch.tensor(numpy.ones((128, 16000 * 5), dtype=numpy.float32))
    test.to(device)


    if device.type == 'cuda':
        print(torch.cuda.get_device_name())
        print('Memory Usage:')
        print('Allocated:', round(torch.cuda.memory_allocated()/1024**3,1), 'GB')
        print('Cached:   ', round(torch.cuda.memory_cached()/1024**3,1), 'GB')

Anthony Larcher's avatar
Anthony Larcher committed
486
    """
Anthony Larcher's avatar
Anthony Larcher committed
487
488
489
490
    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
491
    """
Anthony Larcher's avatar
Anthony Larcher committed
492
493
494
495
    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"])
496

Anthony Larcher's avatar
Anthony Larcher committed
497
    torch.manual_seed(dataset_params['seed'])
498
499
500
    training_set = SideSet(dataset_yaml, 
                           set_type="train", 
                           dataset_df=training_df, 
Anthony Larcher's avatar
Anthony Larcher committed
501
502
                           chunk_per_segment=dataset_params['train']['chunk_per_segment'], 
                           overlap=dataset_params['train']['overlap'])
Anthony Larcher's avatar
Anthony Larcher committed
503
504
505
    training_loader = DataLoader(training_set,
                                 batch_size=dataset_params["batch_size"],
                                 shuffle=True,
Anthony Larcher's avatar
sincxv    
Anthony Larcher committed
506
                                 drop_last=True,
Anthony Larcher's avatar
Anthony Larcher committed
507
                                 num_workers=num_thread)
508

Anthony Larcher's avatar
Anthony Larcher committed
509
510
511
    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
512
                                   drop_last=True,
Anthony Larcher's avatar
Anthony Larcher committed
513
                                   num_workers=num_thread)
Anthony Larcher's avatar
Anthony Larcher committed
514
515
516
517

    """
    Set the training options
    """
Anthony Larcher's avatar
Anthony Larcher committed
518
519
    if opt == 'sgd':
        _optimizer = torch.optim.SGD
Anthony Larcher's avatar
Anthony Larcher committed
520
        _options = {'lr': lr, 'momentum': 0.9}
Anthony Larcher's avatar
Anthony Larcher committed
521
522
    elif opt == 'adam':
        _optimizer = torch.optim.Adam
Anthony Larcher's avatar
Anthony Larcher committed
523
        _options = {'lr': lr}
Anthony Larcher's avatar
Anthony Larcher committed
524
525
    elif opt == 'rmsprop':
        _optimizer = torch.optim.RMSprop
Anthony Larcher's avatar
Anthony Larcher committed
526
        _options = {'lr': lr}
Anthony Larcher's avatar
Anthony Larcher committed
527

Anthony Larcher's avatar
Anthony Larcher committed
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
    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)

    #if type(model) is Xtractor:
    #    optimizer = _optimizer([
    #        {'params': model.preprocessor.parameters(),
    #         'weight_decay': model.preprocessor_weight_decay},
    #        {'params': model.sequence_network.parameters(),
    #         'weight_decay': model.sequence_network_weight_decay},
    #        {'params': model.stat_pooling.parameters(),
    #         'weight_decay': model.stat_pooling_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}],
    #        **_options
    #    )
    #else:
    #    optimizer = _optimizer([
    #        {'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}],
    #        **_options
    #    )

Anthony Larcher's avatar
Anthony Larcher committed
572
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', verbose=True)
573

Anthony Larcher's avatar
sincxv    
Anthony Larcher committed
574
    best_accuracy = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
575
    best_accuracy_epoch = 1
Anthony Larcher's avatar
Anthony Larcher committed
576
    for epoch in range(1, epochs + 1):
577
        # Process one epoch and return the current model
Anthony Larcher's avatar
Anthony Larcher committed
578
579
580
581
582
583
584
        model = train_epoch(model,
                            epoch,
                            training_loader,
                            optimizer,
                            dataset_params["log_interval"],
                            device=device,
                            clipping=clipping)
585
586

        # Add the cross validation here
Anthony Larcher's avatar
Anthony Larcher committed
587
        accuracy, val_loss = cross_validation(model, validation_loader, device=device)
588
589
590
591
        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
592
        print(f"Learning rate is {optimizer.param_groups[0]['lr']}")
593

Anthony Larcher's avatar
Anthony Larcher committed
594
595
596
597
        # remember best accuracy and save checkpoint
        is_best = accuracy > best_accuracy
        best_accuracy = max(accuracy, best_accuracy)

Anthony Larcher's avatar
Anthony Larcher committed
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
        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
614
615
616

        if is_best:
            best_accuracy_epoch = epoch
617

Anthony Larcher's avatar
Anthony Larcher committed
618
    logging.critical(f"Best accuracy {best_accuracy * 100.} obtained at epoch {best_accuracy_epoch}")
619

Anthony Larcher's avatar
Anthony Larcher committed
620
def train_epoch(model, epoch, training_loader, optimizer, log_interval, device, clipping=False):
621
622
623
624
    """

    :param model:
    :param epoch:
Anthony Larcher's avatar
Anthony Larcher committed
625
    :param training_loader:
626
    :param optimizer:
Anthony Larcher's avatar
Anthony Larcher committed
627
628
629
    :param log_interval:
    :param device:
    :param clipping:
630
631
    :return:
    """
Anthony Larcher's avatar
Anthony Larcher committed
632
    model.train()
Anthony Larcher's avatar
Anthony Larcher committed
633
    criterion = torch.nn.CrossEntropyLoss(reduction='mean')
634
635

    accuracy = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
636
    for batch_idx, (data, target) in enumerate(training_loader):
637
638
639
640
641
        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
642
643
        if clipping:
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.)
644
645
646
        optimizer.step()
        accuracy += (torch.argmax(output.data, 1) == target.to(device)).sum()

Anthony Larcher's avatar
Anthony Larcher committed
647
        if batch_idx % log_interval == 0:
Anthony Larcher's avatar
Anthony Larcher committed
648
            batch_size = target.shape[0]
649
            logging.critical('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}\tAccuracy: {:.3f}'.format(
Anthony Larcher's avatar
Anthony Larcher committed
650
                epoch, batch_idx + 1, training_loader.__len__(),
Anthony Larcher's avatar
Anthony Larcher committed
651
652
                100. * batch_idx / training_loader.__len__(), loss.item(),
                100.0 * accuracy.item() / ((batch_idx + 1) * batch_size)))
653
654
655
    return model


Anthony Larcher's avatar
Anthony Larcher committed
656
def cross_validation(model, validation_loader, device):
657
658
659
    """

    :param model:
Anthony Larcher's avatar
Anthony Larcher committed
660
661
    :param validation_loader:
    :param device:
662
663
664
665
666
    :return:
    """
    model.eval()

    accuracy = 0.0
Anthony Larcher's avatar
Anthony Larcher committed
667
    loss = 0.0
668
    criterion = torch.nn.CrossEntropyLoss()
Anthony Larcher's avatar
Anthony Larcher committed
669
670
671
672
673
674
    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()
675

Anthony Larcher's avatar
Anthony Larcher committed
676
677
            loss += criterion(output, target.to(device))
    
Anthony Larcher's avatar
Anthony Larcher committed
678
679
680
681
    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
682
683
684
685
686
687
688
689
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
690

Anthony Larcher's avatar
Anthony Larcher committed
691
    if isinstance(idmap_name, IdMap):
692
693
694
695
        idmap = idmap_name
    else:
        idmap = IdMap(idmap_name)

Anthony Larcher's avatar
Anthony Larcher committed
696
    # Create dataset to load the data
Anthony Larcher's avatar
Anthony Larcher committed
697
698
699
700
    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
701
702

    # Load the model
703
704
705
706
707
708
709
    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
710
711
    model.eval()
    model.to(device)
712

Anthony Larcher's avatar
Anthony Larcher committed
713
714
715
    # 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
716
    
Anthony Larcher's avatar
Anthony Larcher committed
717
    # Create the StatServer
Anthony Larcher's avatar
Anthony Larcher committed
718
    embeddings = StatServer()
Anthony Larcher's avatar
Anthony Larcher committed
719
720
721
722
723
724
    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
725

Anthony Larcher's avatar
Anthony Larcher committed
726
727
    # Process the data
    with torch.no_grad():
Anthony Larcher's avatar
Anthony Larcher committed
728
        for idx in tqdm.tqdm(range(len(dataset))):
Anthony Larcher's avatar
Anthony Larcher committed
729
            data, mod, seg, start, stop = dataset[idx]
Anthony Larcher's avatar
Anthony Larcher committed
730
            vec = model(data[None, :, :].to(device), is_eval=True)
Anthony Larcher's avatar
Anthony Larcher committed
731
732
733
734
735
736
            #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
737
738
739
740

    return embeddings


Anthony Larcher's avatar
Anthony Larcher committed
741
742
743
744
745
746
747
748
749
750
751
752
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):

753
754
755
756
757
758
759
760
761
762
763

    # 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):
Anthony Larcher's avatar
Anthony Larcher committed
764

765
766
767
        # 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
768
                                    int(sample_rate * (window_length - overlap)))
769
770

        # Create a numpy array to store the current x-vectors
Anthony Larcher's avatar
Anthony Larcher committed
771
        model_names.append(numpy.array([mod + f"_{ii}" for ii in range(len(chunk_starts))]).astype("U"))
772
773
774
775
776
777
778
        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
779
780
781
782
    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)
783
    assert sliding_idmap.validate()
Anthony Larcher's avatar
Anthony Larcher committed
784

Anthony Larcher's avatar
Anthony Larcher committed
785
786
787
788
789
790
    embeddings = extract_embeddings(sliding_idmap,
                                 speaker_number,
                                 model_filename,
                                 model_yaml,
                                 data_root_name,
                                 device,
Anthony Larcher's avatar
Anthony Larcher committed
791
792
                                 file_extension=file_extension,
                                 transform_pipeline=transform_pipeline)
Anthony Larcher's avatar
Anthony Larcher committed
793
794

    return embeddings
Anthony Larcher's avatar
Anthony Larcher committed
795