File size: 5,430 Bytes
568494b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
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
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoModel
from transformers.modeling_utils import PreTrainedModel ,PretrainedConfig


class Pooling(nn.Module):
    def __init__(self):
        super().__init__()
    def compute_length_from_mask(self, mask):
        """
        mask: (batch_size, T)
        Assuming that the sampling rate is 16kHz, the frame shift is 20ms
        """
        wav_lens = torch.sum(mask, dim=1) # (batch_size, )
        feat_lens = torch.div(wav_lens-1, 16000*0.02, rounding_mode="floor") + 1
        feat_lens = feat_lens.int().tolist()
        return feat_lens
        
    def forward(self, x, mask):
        raise NotImplementedError
    
class MeanPooling(Pooling):
    def __init__(self):
        super().__init__()
    def forward(self, xs, mask):
        """
        xs: (batch_size, T, feat_dim)
        mask: (batch_size, T)

        => output: (batch_size, feat_dim)
        """
        feat_lens = self.compute_length_from_mask(mask)
        pooled_list = []
        for x, feat_len in zip(xs, feat_lens):
            pooled = torch.mean(x[:feat_len], dim=0) # (feat_dim, )
            pooled_list.append(pooled)
        pooled = torch.stack(pooled_list, dim=0) # (batch_size, feat_dim)
        return pooled
    

class AttentiveStatisticsPooling(Pooling):
    """
    AttentiveStatisticsPooling
    Paper: Attentive Statistics Pooling for Deep Speaker Embedding
    Link: https://arxiv.org/pdf/1803.10963.pdf
    """
    def __init__(self, input_size):
        super().__init__()
        self._indim = input_size
        self.sap_linear = nn.Linear(input_size, input_size)
        self.attention = nn.Parameter(torch.FloatTensor(input_size, 1))
        torch.nn.init.normal_(self.attention, mean=0, std=1)

    def forward(self, xs, mask):
        """
        xs: (batch_size, T, feat_dim)
        mask: (batch_size, T)

        => output: (batch_size, feat_dim*2)
        """
        feat_lens = self.compute_length_from_mask(mask)
        pooled_list = []
        for x, feat_len in zip(xs, feat_lens):
            x = x[:feat_len].unsqueeze(0)
            h = torch.tanh(self.sap_linear(x))
            w = torch.matmul(h, self.attention).squeeze(dim=2)
            w = F.softmax(w, dim=1).view(x.size(0), x.size(1), 1)
            mu = torch.sum(x * w, dim=1)
            rh = torch.sqrt((torch.sum((x**2) * w, dim=1) - mu**2).clamp(min=1e-5))
            x = torch.cat((mu, rh), 1).squeeze(0)
            pooled_list.append(x)
        return torch.stack(pooled_list)



    
class EmotionRegression(nn.Module):
    def __init__(self, *args, **kwargs):
        super(EmotionRegression, self).__init__()
        input_dim = args[0]
        hidden_dim = args[1]
        num_layers = args[2]
        output_dim = args[3]
        p = kwargs.get("dropout", 0.5)

        self.fc=nn.ModuleList([
            nn.Sequential(
                nn.Linear(input_dim, hidden_dim), nn.LayerNorm(hidden_dim), nn.ReLU(), nn.Dropout(p)
            )
        ])
        for lidx in range(num_layers-1):
            self.fc.append(
                nn.Sequential(
                    nn.Linear(hidden_dim, hidden_dim), nn.LayerNorm(hidden_dim), nn.ReLU(), nn.Dropout(p)
                )
            )
        self.out = nn.Sequential(
            nn.Linear(hidden_dim, output_dim)
        )
        
        self.inp_drop = nn.Dropout(p)
    def get_repr(self, x):
        h = self.inp_drop(x)
        for lidx, fc in enumerate(self.fc):
            h=fc(h)
        return h
    
    def forward(self, x):
        h=self.get_repr(x)
        result = self.out(h)
        return result
    
class SERConfig(PretrainedConfig):
    model_type = "ser"

    def __init__(
        self,
        num_classes: int = 1,
        num_attention_heads = 16,
        num_hidden_layers = 24,
        hidden_size = 1024,
        classifier_hidden_layers = 1,
        classifier_dropout_prob  = 0.5,
        ssl_type= "microsoft/wavlm-large",
        torch_dtype= "float32",
        **kwargs,
    ):
        self.num_classes = num_classes
        self.num_attention_heads = num_attention_heads
        self.num_hidden_layers = num_hidden_layers
        self.hidden_size = hidden_size
        self.classifier_hidden_layers = classifier_hidden_layers
        self.classifier_dropout_prob = classifier_dropout_prob
        self.ssl_type = ssl_type
        self.torch_dtype = torch_dtype
        super().__init__(**kwargs)
        
class SERModel(PreTrainedModel):
    config_class = SERConfig
    
    def __init__(self, config):
        super().__init__(config)
        self.ssl_model = AutoModel.from_pretrained(config.ssl_type)
        self.ssl_model.freeze_feature_encoder()
        
        self.pool_model = AttentiveStatisticsPooling(config.hidden_size)
    
        self.ser_model = EmotionRegression(config.hidden_size*2, 
                                               config.hidden_size, 
                                               config.classifier_hidden_layers, 
                                               config.num_classes, 
                                               dropout=config.classifier_dropout_prob)
        
        
    def forward(self, x, mask):
        ssl = self.ssl_model(x, attention_mask=mask).last_hidden_state

        ssl = self.pool_model(ssl, mask)
        
        pred = self.ser_model(ssl)
        
        return pred