neural_networks.py

##########################################################
# pytorch-kaldi v.0.1                                      
# Mirco Ravanelli, Titouan Parcollet
# Mila, University of Montreal
# October 2018
##########################################################


import torch
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
from distutils.util import strtobool
import math
import json

# uncomment below if you want to use SRU
# and you need to install SRU: pip install sru[cuda].
# or you can install it from source code: https://github.com/taolei87/sru.
# import sru

class LayerNorm(nn.Module):

    def __init__(self, features, eps=1e-6):
        super(LayerNorm,self).__init__()
        self.gamma = nn.Parameter(torch.ones(features))
        self.beta = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.gamma * (x - mean) / (std + self.eps) + self.beta


def act_fun(act_type):

 if act_type=="relu":
    return nn.ReLU()
            
 if act_type=="tanh":
    return nn.Tanh()
            
 if act_type=="sigmoid":
    return nn.Sigmoid()
           
 if act_type=="leaky_relu":
    return nn.LeakyReLU(0.2)
            
 if act_type=="elu":
    return nn.ELU()
                     
 if act_type=="softmax":
    return nn.LogSoftmax(dim=1)
        
 if act_type=="linear":
     return nn.LeakyReLU(1) # initializzed like this, but not used in forward!



class MLP(nn.Module):
    def __init__(self, options,inp_dim):
        super(MLP, self).__init__()
        
        self.input_dim=inp_dim
        self.dnn_lay=list(map(int, options['dnn_lay'].split(',')))
        self.dnn_drop=list(map(float, options['dnn_drop'].split(','))) 
        self.dnn_use_batchnorm=list(map(strtobool, options['dnn_use_batchnorm'].split(',')))
        self.dnn_use_laynorm=list(map(strtobool, options['dnn_use_laynorm'].split(','))) 
        self.dnn_use_laynorm_inp=strtobool(options['dnn_use_laynorm_inp'])
        self.dnn_use_batchnorm_inp=strtobool(options['dnn_use_batchnorm_inp'])
        self.dnn_act=options['dnn_act'].split(',')
        
       
        self.wx  = nn.ModuleList([])
        self.bn  = nn.ModuleList([])
        self.ln  = nn.ModuleList([])
        self.act = nn.ModuleList([])
        self.drop = nn.ModuleList([])
       
  
        # input layer normalization
        if self.dnn_use_laynorm_inp:
           self.ln0=LayerNorm(self.input_dim)
          
        # input batch normalization    
        if self.dnn_use_batchnorm_inp:
           self.bn0=nn.BatchNorm1d(self.input_dim,momentum=0.05)
           
           
        self.N_dnn_lay=len(self.dnn_lay)
             
        current_input=self.input_dim
        
        # Initialization of hidden layers
        
        for i in range(self.N_dnn_lay):
            
             # dropout
             self.drop.append(nn.Dropout(p=self.dnn_drop[i]))
             
             # activation
             self.act.append(act_fun(self.dnn_act[i]))
             
             
             add_bias=True
             
             # layer norm initialization
             self.ln.append(LayerNorm(self.dnn_lay[i]))
             self.bn.append(nn.BatchNorm1d(self.dnn_lay[i],momentum=0.05))
             
             if self.dnn_use_laynorm[i] or self.dnn_use_batchnorm[i]:
                 add_bias=False
             
                  
             # Linear operations
             self.wx.append(nn.Linear(current_input, self.dnn_lay[i],bias=add_bias))
             
             # weight initialization
             self.wx[i].weight = torch.nn.Parameter(torch.Tensor(self.dnn_lay[i],current_input).uniform_(-np.sqrt(0.01/(current_input+self.dnn_lay[i])),np.sqrt(0.01/(current_input+self.dnn_lay[i]))))
             self.wx[i].bias = torch.nn.Parameter(torch.zeros(self.dnn_lay[i]))
             
             current_input=self.dnn_lay[i]
             
        self.out_dim=current_input
         
    def forward(self, x):
        
      # Applying Layer/Batch Norm
      if bool(self.dnn_use_laynorm_inp):
        x=self.ln0((x))
        
      if bool(self.dnn_use_batchnorm_inp):

        x=self.bn0((x))
        
      for i in range(self.N_dnn_lay):
           
          if self.dnn_use_laynorm[i] and not(self.dnn_use_batchnorm[i]):
           x = self.drop[i](self.act[i](self.ln[i](self.wx[i](x))))
          
          if self.dnn_use_batchnorm[i] and not(self.dnn_use_laynorm[i]):
           x = self.drop[i](self.act[i](self.bn[i](self.wx[i](x))))
           
          if self.dnn_use_batchnorm[i]==True and self.dnn_use_laynorm[i]==True:
           x = self.drop[i](self.act[i](self.bn[i](self.ln[i](self.wx[i](x)))))
          
          if self.dnn_use_batchnorm[i]==False and self.dnn_use_laynorm[i]==False:
           x = self.drop[i](self.act[i](self.wx[i](x)))
            
          
      return x


class LSTM_cudnn(nn.Module):
    
    def __init__(self, options,inp_dim):
        super(LSTM_cudnn, self).__init__()
        
        self.input_dim=inp_dim
        self.hidden_size=int(options['hidden_size'])
        self.num_layers=int(options['num_layers'])
        self.bias=bool(strtobool(options['bias']))
        self.batch_first=bool(strtobool(options['batch_first']))
        self.dropout=float(options['dropout'])
        self.bidirectional=bool(strtobool(options['bidirectional']))
        
        self.lstm = nn.ModuleList([nn.LSTM(self.input_dim, self.hidden_size, self.num_layers, 
                            bias=self.bias,dropout=self.dropout,bidirectional=self.bidirectional)])
         
        self.out_dim=self.hidden_size+self.bidirectional*self.hidden_size
               
        
    def forward(self, x):
        
        if self.bidirectional:
            h0 = torch.zeros(self.num_layers*2, x.shape[1], self.hidden_size)
            c0 = torch.zeros(self.num_layers*2, x.shape[1], self.hidden_size)
        else:
            h0 = torch.zeros(self.num_layers, x.shape[1], self.hidden_size)
            c0 = torch.zeros(self.num_layers, x.shape[1], self.hidden_size)
            
        if x.is_cuda:
            h0=h0.cuda()
            c0=c0.cuda()
            
        output, (hn, cn) = self.lstm[0](x, (h0, c0))
        
        
        return output
    

class GRU_cudnn(nn.Module):
    
    def __init__(self, options,inp_dim):
        super(GRU_cudnn, self).__init__()
        
        self.input_dim=inp_dim
        self.hidden_size=int(options['hidden_size'])
        self.num_layers=int(options['num_layers'])
        self.bias=bool(strtobool(options['bias']))
        self.batch_first=bool(strtobool(options['batch_first']))
        self.dropout=float(options['dropout'])
        self.bidirectional=bool(strtobool(options['bidirectional']))
        
        self.gru = nn.ModuleList([nn.GRU(self.input_dim, self.hidden_size, self.num_layers, 
                            bias=self.bias,dropout=self.dropout,bidirectional=self.bidirectional)])
         
        self.out_dim=self.hidden_size+self.bidirectional*self.hidden_size
               
        
    def forward(self, x):
        
        if self.bidirectional:
            h0 = torch.zeros(self.num_layers*2, x.shape[1], self.hidden_size)
        else:
            h0 = torch.zeros(self.num_layers, x.shape[1], self.hidden_size)
            
        if x.is_cuda:
            h0=h0.cuda()

        output, hn = self.gru[0](x, h0)
        
        
        return output
 
    
class RNN_cudnn(nn.Module):
    
    def __init__(self, options,inp_dim):
        super(RNN_cudnn, self).__init__()
        
        self.input_dim=inp_dim
        self.hidden_size=int(options['hidden_size'])
        self.num_layers=int(options['num_layers'])
        self.nonlinearity=options['nonlinearity']
        self.bias=bool(strtobool(options['bias']))
        self.batch_first=bool(strtobool(options['batch_first']))
        self.dropout=float(options['dropout'])
        self.bidirectional=bool(strtobool(options['bidirectional']))
        
        self.rnn = nn.ModuleList([nn.RNN(self.input_dim, self.hidden_size, self.num_layers, 
                            nonlinearity=self.nonlinearity,bias=self.bias,dropout=self.dropout,bidirectional=self.bidirectional)])
         
        self.out_dim=self.hidden_size+self.bidirectional*self.hidden_size
               
        
    def forward(self, x):
        
        if self.bidirectional:
            h0 = torch.zeros(self.num_layers*2, x.shape[1], self.hidden_size)
        else:
            h0 = torch.zeros(self.num_layers, x.shape[1], self.hidden_size)
            
        if x.is_cuda:
            h0=h0.cuda()

        output, hn = self.rnn[0](x, h0)
        
        
        return output
    
    
class LSTM(nn.Module):
    
    def __init__(self, options,inp_dim):
        super(LSTM, self).__init__()
        
        # Reading parameters
        self.input_dim=inp_dim
        self.lstm_lay=list(map(int, options['lstm_lay'].split(',')))
        self.lstm_drop=list(map(float, options['lstm_drop'].split(','))) 
        self.lstm_use_batchnorm=list(map(strtobool, options['lstm_use_batchnorm'].split(',')))
        self.lstm_use_laynorm=list(map(strtobool, options['lstm_use_laynorm'].split(','))) 
        self.lstm_use_laynorm_inp=strtobool(options['lstm_use_laynorm_inp'])
        self.lstm_use_batchnorm_inp=strtobool(options['lstm_use_batchnorm_inp'])
        self.lstm_act=options['lstm_act'].split(',')
        self.lstm_orthinit=strtobool(options['lstm_orthinit'])

        self.bidir=strtobool(options['lstm_bidir'])
        self.use_cuda=strtobool(options['use_cuda'])
        self.to_do=options['to_do']
        
        if self.to_do=='train':
            self.test_flag=False
        else:
            self.test_flag=True
        
        
        # List initialization
        self.wfx  = nn.ModuleList([]) # Forget
        self.ufh  = nn.ModuleList([]) # Forget
        
        self.wix  = nn.ModuleList([]) # Input
        self.uih  = nn.ModuleList([]) # Input  
        
        self.wox  = nn.ModuleList([]) # Output
        self.uoh  = nn.ModuleList([]) # Output  
        
        self.wcx  = nn.ModuleList([]) # Cell state
        self.uch = nn.ModuleList([])  # Cell state
        
        self.ln  = nn.ModuleList([]) # Layer Norm
        self.bn_wfx  = nn.ModuleList([]) # Batch Norm
        self.bn_wix  = nn.ModuleList([]) # Batch Norm
        self.bn_wox  = nn.ModuleList([]) # Batch Norm
        self.bn_wcx = nn.ModuleList([]) # Batch Norm
        
        self.act  = nn.ModuleList([]) # Activations
       
  
        # Input layer normalization
        if self.lstm_use_laynorm_inp:
           self.ln0=LayerNorm(self.input_dim)
          
        # Input batch normalization    
        if self.lstm_use_batchnorm_inp:
           self.bn0=nn.BatchNorm1d(self.input_dim,momentum=0.05)
           
        self.N_lstm_lay=len(self.lstm_lay)
             
        current_input=self.input_dim
        
        # Initialization of hidden layers
        
        for i in range(self.N_lstm_lay):
             
             # Activations
             self.act.append(act_fun(self.lstm_act[i]))
            
             add_bias=True
             
             
             if self.lstm_use_laynorm[i] or self.lstm_use_batchnorm[i]:
                 add_bias=False
             
                  
             # Feed-forward connections
             self.wfx.append(nn.Linear(current_input, self.lstm_lay[i],bias=add_bias))
             self.wix.append(nn.Linear(current_input, self.lstm_lay[i],bias=add_bias))
             self.wox.append(nn.Linear(current_input, self.lstm_lay[i],bias=add_bias))
             self.wcx.append(nn.Linear(current_input, self.lstm_lay[i],bias=add_bias))
            
             # Recurrent connections
             self.ufh.append(nn.Linear(self.lstm_lay[i], self.lstm_lay[i],bias=False))
             self.uih.append(nn.Linear(self.lstm_lay[i], self.lstm_lay[i],bias=False))
             self.uoh.append(nn.Linear(self.lstm_lay[i], self.lstm_lay[i],bias=False))
             self.uch.append(nn.Linear(self.lstm_lay[i], self.lstm_lay[i],bias=False))
             
             if self.lstm_orthinit:
                nn.init.orthogonal_(self.ufh[i].weight)
                nn.init.orthogonal_(self.uih[i].weight)
                nn.init.orthogonal_(self.uoh[i].weight)
                nn.init.orthogonal_(self.uch[i].weight)
            
             
             # batch norm initialization
             self.bn_wfx.append(nn.BatchNorm1d(self.lstm_lay[i],momentum=0.05))
             self.bn_wix.append(nn.BatchNorm1d(self.lstm_lay[i],momentum=0.05))
             self.bn_wox.append(nn.BatchNorm1d(self.lstm_lay[i],momentum=0.05))
             self.bn_wcx.append(nn.BatchNorm1d(self.lstm_lay[i],momentum=0.05))
                
             self.ln.append(LayerNorm(self.lstm_lay[i]))
                
             if self.bidir:
                 current_input=2*self.lstm_lay[i]
             else:
                 current_input=self.lstm_lay[i]
                 
        self.out_dim=self.lstm_lay[i]+self.bidir*self.lstm_lay[i]
            
             
        
    def forward(self, x):

        # Applying Layer/Batch Norm
        if bool(self.lstm_use_laynorm_inp):
            x=self.ln0((x))
        
        if bool(self.lstm_use_batchnorm_inp):
            x_bn=self.bn0(x.view(x.shape[0]*x.shape[1],x.shape[2]))
            x=x_bn.view(x.shape[0],x.shape[1],x.shape[2])

          
        for i in range(self.N_lstm_lay):
            
            # Initial state and concatenation
            if self.bidir:
                h_init = torch.zeros(2*x.shape[1], self.lstm_lay[i])
                x=torch.cat([x,flip(x,0)],1)
            else:
                h_init = torch.zeros(x.shape[1],self.lstm_lay[i])
        
               
            # Drop mask initilization (same mask for all time steps)            
            if self.test_flag==False:
                drop_mask=torch.bernoulli(torch.Tensor(h_init.shape[0],h_init.shape[1]).fill_(1-self.lstm_drop[i]))
            else:
                drop_mask=torch.FloatTensor([1-self.lstm_drop[i]])
                
            if self.use_cuda:
               h_init=h_init.cuda()
               drop_mask=drop_mask.cuda()
               
                 
            # Feed-forward affine transformations (all steps in parallel)
            wfx_out=self.wfx[i](x)
            wix_out=self.wix[i](x)
            wox_out=self.wox[i](x)
            wcx_out=self.wcx[i](x)
            
            # Apply batch norm if needed (all steos in parallel)
            if self.lstm_use_batchnorm[i]:

                wfx_out_bn=self.bn_wfx[i](wfx_out.view(wfx_out.shape[0]*wfx_out.shape[1],wfx_out.shape[2]))
                wfx_out=wfx_out_bn.view(wfx_out.shape[0],wfx_out.shape[1],wfx_out.shape[2])
         
                wix_out_bn=self.bn_wix[i](wix_out.view(wix_out.shape[0]*wix_out.shape[1],wix_out.shape[2]))
                wix_out=wix_out_bn.view(wix_out.shape[0],wix_out.shape[1],wix_out.shape[2])
   
                wox_out_bn=self.bn_wox[i](wox_out.view(wox_out.shape[0]*wox_out.shape[1],wox_out.shape[2]))
                wox_out=wox_out_bn.view(wox_out.shape[0],wox_out.shape[1],wox_out.shape[2])

                wcx_out_bn=self.bn_wcx[i](wcx_out.view(wcx_out.shape[0]*wcx_out.shape[1],wcx_out.shape[2]))
                wcx_out=wcx_out_bn.view(wcx_out.shape[0],wcx_out.shape[1],wcx_out.shape[2]) 
            
            
            # Processing time steps
            hiddens = []
            ct=h_init
            ht=h_init
            
            for k in range(x.shape[0]):
                
                # LSTM equations
                ft=torch.sigmoid(wfx_out[k]+self.ufh[i](ht))
                it=torch.sigmoid(wix_out[k]+self.uih[i](ht))
                ot=torch.sigmoid(wox_out[k]+self.uoh[i](ht))
                ct=it*self.act[i](wcx_out[k]+self.uch[i](ht))*drop_mask+ft*ct
                ht=ot*self.act[i](ct)
                
                if self.lstm_use_laynorm[i]:
                    ht=self.ln[i](ht)
                    
                hiddens.append(ht)
                
            # Stacking hidden states
            h=torch.stack(hiddens)
            
            # Bidirectional concatenations
            if self.bidir:
                h_f=h[:,0:int(x.shape[1]/2)]
                h_b=flip(h[:,int(x.shape[1]/2):x.shape[1]].contiguous(),0)
                h=torch.cat([h_f,h_b],2)
                
            # Setup x for the next hidden layer
            x=h

              
        return x
    
class GRU(nn.Module):
    
    def __init__(self, options,inp_dim):
        super(GRU, self).__init__()
        
        # Reading parameters
        self.input_dim=inp_dim
        self.gru_lay=list(map(int, options['gru_lay'].split(',')))
        self.gru_drop=list(map(float, options['gru_drop'].split(','))) 
        self.gru_use_batchnorm=list(map(strtobool, options['gru_use_batchnorm'].split(',')))
        self.gru_use_laynorm=list(map(strtobool, options['gru_use_laynorm'].split(','))) 
        self.gru_use_laynorm_inp=strtobool(options['gru_use_laynorm_inp'])
        self.gru_use_batchnorm_inp=strtobool(options['gru_use_batchnorm_inp'])
        self.gru_orthinit=strtobool(options['gru_orthinit'])
        self.gru_act=options['gru_act'].split(',')
        self.bidir=strtobool(options['gru_bidir'])
        self.use_cuda=strtobool(options['use_cuda'])
        self.to_do=options['to_do']
        
        if self.to_do=='train':
            self.test_flag=False
        else:
            self.test_flag=True
        
        
        # List initialization
        self.wh  = nn.ModuleList([])
        self.uh  = nn.ModuleList([])
        
        self.wz  = nn.ModuleList([]) # Update Gate
        self.uz  = nn.ModuleList([]) # Update Gate
        
        self.wr  = nn.ModuleList([]) # Reset Gate
        self.ur  = nn.ModuleList([]) # Reset Gate  
        
        
        self.ln  = nn.ModuleList([]) # Layer Norm
        self.bn_wh  = nn.ModuleList([]) # Batch Norm
        self.bn_wz  = nn.ModuleList([]) # Batch Norm
        self.bn_wr  = nn.ModuleList([]) # Batch Norm

        
        self.act  = nn.ModuleList([]) # Activations
       
  
        # Input layer normalization
        if self.gru_use_laynorm_inp:
           self.ln0=LayerNorm(self.input_dim)
          
        # Input batch normalization    
        if self.gru_use_batchnorm_inp:
           self.bn0=nn.BatchNorm1d(self.input_dim,momentum=0.05)
           
        self.N_gru_lay=len(self.gru_lay)
             
        current_input=self.input_dim
        
        # Initialization of hidden layers
        
        for i in range(self.N_gru_lay):
             
             # Activations
             self.act.append(act_fun(self.gru_act[i]))
            
             add_bias=True
             
             
             if self.gru_use_laynorm[i] or self.gru_use_batchnorm[i]:
                 add_bias=False
             
                  
             # Feed-forward connections
             self.wh.append(nn.Linear(current_input, self.gru_lay[i],bias=add_bias))
             self.wz.append(nn.Linear(current_input, self.gru_lay[i],bias=add_bias))
             self.wr.append(nn.Linear(current_input, self.gru_lay[i],bias=add_bias))
             
            
             # Recurrent connections
             self.uh.append(nn.Linear(self.gru_lay[i], self.gru_lay[i],bias=False))
             self.uz.append(nn.Linear(self.gru_lay[i], self.gru_lay[i],bias=False))
             self.ur.append(nn.Linear(self.gru_lay[i], self.gru_lay[i],bias=False))

             if self.gru_orthinit:
                nn.init.orthogonal_(self.uh[i].weight)
                nn.init.orthogonal_(self.uz[i].weight)
                nn.init.orthogonal_(self.ur[i].weight)

             
             # batch norm initialization
             self.bn_wh.append(nn.BatchNorm1d(self.gru_lay[i],momentum=0.05))
             self.bn_wz.append(nn.BatchNorm1d(self.gru_lay[i],momentum=0.05))
             self.bn_wr.append(nn.BatchNorm1d(self.gru_lay[i],momentum=0.05))

                
             self.ln.append(LayerNorm(self.gru_lay[i]))
                
             if self.bidir:
                 current_input=2*self.gru_lay[i]
             else:
                 current_input=self.gru_lay[i]
                 
        self.out_dim=self.gru_lay[i]+self.bidir*self.gru_lay[i]
            
             
        
    def forward(self, x):
        
        # Applying Layer/Batch Norm
        if bool(self.gru_use_laynorm_inp):
            x=self.ln0((x))
        
        if bool(self.gru_use_batchnorm_inp):
            x_bn=self.bn0(x.view(x.shape[0]*x.shape[1],x.shape[2]))
            x=x_bn.view(x.shape[0],x.shape[1],x.shape[2])

          
        for i in range(self.N_gru_lay):
            
            # Initial state and concatenation
            if self.bidir:
                h_init = torch.zeros(2*x.shape[1], self.gru_lay[i])
                x=torch.cat([x,flip(x,0)],1)
            else:
                h_init = torch.zeros(x.shape[1],self.gru_lay[i])
        
               
            # Drop mask initilization (same mask for all time steps)            
            if self.test_flag==False:
                drop_mask=torch.bernoulli(torch.Tensor(h_init.shape[0],h_init.shape[1]).fill_(1-self.gru_drop[i]))
            else:
                drop_mask=torch.FloatTensor([1-self.gru_drop[i]])
                
            if self.use_cuda:
               h_init=h_init.cuda()
               drop_mask=drop_mask.cuda()
               
                 
            # Feed-forward affine transformations (all steps in parallel)
            wh_out=self.wh[i](x)
            wz_out=self.wz[i](x)
            wr_out=self.wr[i](x)

            
            # Apply batch norm if needed (all steos in parallel)
            if self.gru_use_batchnorm[i]:

                wh_out_bn=self.bn_wh[i](wh_out.view(wh_out.shape[0]*wh_out.shape[1],wh_out.shape[2]))
                wh_out=wh_out_bn.view(wh_out.shape[0],wh_out.shape[1],wh_out.shape[2])
         
                wz_out_bn=self.bn_wz[i](wz_out.view(wz_out.shape[0]*wz_out.shape[1],wz_out.shape[2]))
                wz_out=wz_out_bn.view(wz_out.shape[0],wz_out.shape[1],wz_out.shape[2])
   
                wr_out_bn=self.bn_wr[i](wr_out.view(wr_out.shape[0]*wr_out.shape[1],wr_out.shape[2]))
                wr_out=wr_out_bn.view(wr_out.shape[0],wr_out.shape[1],wr_out.shape[2])

            
            # Processing time steps
            hiddens = []
            ht=h_init
            
            for k in range(x.shape[0]):
                
                # gru equation
                zt=torch.sigmoid(wz_out[k]+self.uz[i](ht))
                rt=torch.sigmoid(wr_out[k]+self.ur[i](ht))
                at=wh_out[k]+self.uh[i](rt*ht)
                hcand=self.act[i](at)*drop_mask
                ht=(zt*ht+(1-zt)*hcand)
                
                
                if self.gru_use_laynorm[i]:
                    ht=self.ln[i](ht)
                    
                hiddens.append(ht)
                
            # Stacking hidden states
            h=torch.stack(hiddens)
            
            # Bidirectional concatenations
            if self.bidir:
                h_f=h[:,0:int(x.shape[1]/2)]
                h_b=flip(h[:,int(x.shape[1]/2):x.shape[1]].contiguous(),0)
                h=torch.cat([h_f,h_b],2)
                
            # Setup x for the next hidden layer
            x=h

              
        return x

class logMelFb(nn.Module):
    def __init__(self, options,inp_dim):
        super(logMelFb, self).__init__()
        import torchaudio
        self._sample_rate = int(options['logmelfb_nr_sample_rate'])
        self._nr_of_filters = int(options['logmelfb_nr_filt'])
        self._stft_window_size = int(options['logmelfb_stft_window_size'])
        self._stft_window_shift = int(options['logmelfb_stft_window_shift'])
        self._use_cuda = strtobool(options['use_cuda'])
        self.out_dim = self._nr_of_filters
        self._mspec = torchaudio.transforms.MelSpectrogram(
            sr=self._sample_rate,
            n_fft=self._stft_window_size,
            ws=self._stft_window_size,
            hop=self._stft_window_shift,
            n_mels=self._nr_of_filters,
        )
    
    def forward(self, x):
        def _safe_log(inp, epsilon=1e-20):
            eps = torch.FloatTensor([epsilon])
            if self._use_cuda:
                eps = eps.cuda()
            log_inp = torch.log10(torch.max(inp, eps.expand_as(inp)))
            return log_inp
        assert x.shape[-1] == 1, 'Multi channel time signal processing not suppored yet'
        x_reshape_for_stft = torch.squeeze(x, -1).transpose(0, 1)
        if self._use_cuda:
            window = self._mspec.window(self._stft_window_size).cuda()
        else:
            window = self._mspec.window(self._stft_window_size)
        x_stft = torch.stft(
            x_reshape_for_stft, 
            self._stft_window_size, 
            hop_length = self._stft_window_shift, 
            center = False, 
            window = window,
        )
        x_power_stft = x_stft.pow(2).sum(-1)
        x_power_stft_reshape_for_filterbank_mult = x_power_stft.transpose(1, 2)
        mel_spec = self._mspec.fm(x_power_stft_reshape_for_filterbank_mult).transpose(0, 1)
        log_mel_spec = _safe_log(mel_spec)
        out = log_mel_spec 
        return out

class channel_averaging(nn.Module):
    def __init__(self, options,inp_dim):
        super(channel_averaging, self).__init__()
        self._use_cuda = strtobool(options['use_cuda'])
        channel_weights = [float(e) for e in options['chAvg_channelWeights'].split(',')]
        self._nr_of_channels = len(channel_weights)
        numpy_weights = np.asarray(channel_weights, dtype=np.float32) * 1.0 / np.sum(channel_weights)
        self._weights = torch.from_numpy(numpy_weights) 
        if self._use_cuda:
            self._weights = self._weights.cuda()
        self.out_dim = 1
    
    def forward(self, x):
        assert self._nr_of_channels == x.shape[-1]
        out = torch.einsum('tbc,c->tb', x, self._weights).unsqueeze(-1)
        return out

class liGRU(nn.Module):
    
    def __init__(self, options,inp_dim):
        super(liGRU, self).__init__()
        
        # Reading parameters
        self.input_dim=inp_dim
        self.ligru_lay=list(map(int, options['ligru_lay'].split(',')))
        self.ligru_drop=list(map(float, options['ligru_drop'].split(','))) 
        self.ligru_use_batchnorm=list(map(strtobool, options['ligru_use_batchnorm'].split(',')))
        self.ligru_use_laynorm=list(map(strtobool, options['ligru_use_laynorm'].split(','))) 
        self.ligru_use_laynorm_inp=strtobool(options['ligru_use_laynorm_inp'])
        self.ligru_use_batchnorm_inp=strtobool(options['ligru_use_batchnorm_inp'])
        self.ligru_orthinit=strtobool(options['ligru_orthinit'])
        self.ligru_act=options['ligru_act'].split(',')
        self.bidir=strtobool(options['ligru_bidir'])
        self.use_cuda=strtobool(options['use_cuda'])
        self.to_do=options['to_do']
        
        if self.to_do=='train':
            self.test_flag=False
        else:
            self.test_flag=True
        
        
        # List initialization
        self.wh  = nn.ModuleList([])
        self.uh  = nn.ModuleList([])
        
        self.wz  = nn.ModuleList([]) # Update Gate
        self.uz  = nn.ModuleList([]) # Update Gate
              
        
        self.ln  = nn.ModuleList([]) # Layer Norm
        self.bn_wh  = nn.ModuleList([]) # Batch Norm
        self.bn_wz  = nn.ModuleList([]) # Batch Norm


        
        self.act  = nn.ModuleList([]) # Activations
       
  
        # Input layer normalization
        if self.ligru_use_laynorm_inp:
           self.ln0=LayerNorm(self.input_dim)
          
        # Input batch normalization    
        if self.ligru_use_batchnorm_inp:
           self.bn0=nn.BatchNorm1d(self.input_dim,momentum=0.05)
           
        self.N_ligru_lay=len(self.ligru_lay)
             
        current_input=self.input_dim
        
        # Initialization of hidden layers
        
        for i in range(self.N_ligru_lay):
             
             # Activations
             self.act.append(act_fun(self.ligru_act[i]))
            
             add_bias=True
             
             
             if self.ligru_use_laynorm[i] or self.ligru_use_batchnorm[i]:
                 add_bias=False
             
                  
             # Feed-forward connections
             self.wh.append(nn.Linear(current_input, self.ligru_lay[i],bias=add_bias))
             self.wz.append(nn.Linear(current_input, self.ligru_lay[i],bias=add_bias))

             
            
             # Recurrent connections
             self.uh.append(nn.Linear(self.ligru_lay[i], self.ligru_lay[i],bias=False))
             self.uz.append(nn.Linear(self.ligru_lay[i], self.ligru_lay[i],bias=False))

             if self.ligru_orthinit:
             	nn.init.orthogonal_(self.uh[i].weight)
             	nn.init.orthogonal_(self.uz[i].weight)


             
             # batch norm initialization
             self.bn_wh.append(nn.BatchNorm1d(self.ligru_lay[i],momentum=0.05))
             self.bn_wz.append(nn.BatchNorm1d(self.ligru_lay[i],momentum=0.05))


                
             self.ln.append(LayerNorm(self.ligru_lay[i]))
                
             if self.bidir:
                 current_input=2*self.ligru_lay[i]
             else:
                 current_input=self.ligru_lay[i]
                 
        self.out_dim=self.ligru_lay[i]+self.bidir*self.ligru_lay[i]
            
             
        
    def forward(self, x):
        
        # Applying Layer/Batch Norm
        if bool(self.ligru_use_laynorm_inp):
            x=self.ln0((x))
        
        if bool(self.ligru_use_batchnorm_inp):
            x_bn=self.bn0(x.view(x.shape[0]*x.shape[1],x.shape[2]))
            x=x_bn.view(x.shape[0],x.shape[1],x.shape[2])

          
        for i in range(self.N_ligru_lay):
            
            # Initial state and concatenation
            if self.bidir:
                h_init = torch.zeros(2*x.shape[1], self.ligru_lay[i])
                x=torch.cat([x,flip(x,0)],1)
            else:
                h_init = torch.zeros(x.shape[1],self.ligru_lay[i])
        
               
            # Drop mask initilization (same mask for all time steps)            
            if self.test_flag==False:
                drop_mask=torch.bernoulli(torch.Tensor(h_init.shape[0],h_init.shape[1]).fill_(1-self.ligru_drop[i]))
            else:
                drop_mask=torch.FloatTensor([1-self.ligru_drop[i]])
                
            if self.use_cuda:
               h_init=h_init.cuda()
               drop_mask=drop_mask.cuda()
               
                 
            # Feed-forward affine transformations (all steps in parallel)
            wh_out=self.wh[i](x)
            wz_out=self.wz[i](x)


            
            # Apply batch norm if needed (all steos in parallel)
            if self.ligru_use_batchnorm[i]:

                wh_out_bn=self.bn_wh[i](wh_out.view(wh_out.shape[0]*wh_out.shape[1],wh_out.shape[2]))
                wh_out=wh_out_bn.view(wh_out.shape[0],wh_out.shape[1],wh_out.shape[2])
         
                wz_out_bn=self.bn_wz[i](wz_out.view(wz_out.shape[0]*wz_out.shape[1],wz_out.shape[2]))
                wz_out=wz_out_bn.view(wz_out.shape[0],wz_out.shape[1],wz_out.shape[2])


            
            # Processing time steps
            hiddens = []
            ht=h_init
            
            for k in range(x.shape[0]):
                
                # ligru equation
                zt=torch.sigmoid(wz_out[k]+self.uz[i](ht))
                at=wh_out[k]+self.uh[i](ht)
                hcand=self.act[i](at)*drop_mask
                ht=(zt*ht+(1-zt)*hcand)
                
                
                if self.ligru_use_laynorm[i]:
                    ht=self.ln[i](ht)
                    
                hiddens.append(ht)
                
            # Stacking hidden states
            h=torch.stack(hiddens)
            
            # Bidirectional concatenations
            if self.bidir:
                h_f=h[:,0:int(x.shape[1]/2)]
                h_b=flip(h[:,int(x.shape[1]/2):x.shape[1]].contiguous(),0)
                h=torch.cat([h_f,h_b],2)
                
            # Setup x for the next hidden layer
            x=h

              
        return x
    
class minimalGRU(nn.Module):
    
    def __init__(self, options,inp_dim):
        super(minimalGRU, self).__init__()
        
        # Reading parameters
        self.input_dim=inp_dim
        self.minimalgru_lay=list(map(int, options['minimalgru_lay'].split(',')))
        self.minimalgru_drop=list(map(float, options['minimalgru_drop'].split(','))) 
        self.minimalgru_use_batchnorm=list(map(strtobool, options['minimalgru_use_batchnorm'].split(',')))
        self.minimalgru_use_laynorm=list(map(strtobool, options['minimalgru_use_laynorm'].split(','))) 
        self.minimalgru_use_laynorm_inp=strtobool(options['minimalgru_use_laynorm_inp'])
        self.minimalgru_use_batchnorm_inp=strtobool(options['minimalgru_use_batchnorm_inp'])
        self.minimalgru_orthinit=strtobool(options['minimalgru_orthinit'])
        self.minimalgru_act=options['minimalgru_act'].split(',')
        self.bidir=strtobool(options['minimalgru_bidir'])
        self.use_cuda=strtobool(options['use_cuda'])
        self.to_do=options['to_do']
        
        if self.to_do=='train':
            self.test_flag=False
        else:
            self.test_flag=True
        
        
        # List initialization
        self.wh  = nn.ModuleList([])
        self.uh  = nn.ModuleList([])
        
        self.wz  = nn.ModuleList([]) # Update Gate
        self.uz  = nn.ModuleList([]) # Update Gate
              
        
        self.ln  = nn.ModuleList([]) # Layer Norm
        self.bn_wh  = nn.ModuleList([]) # Batch Norm
        self.bn_wz  = nn.ModuleList([]) # Batch Norm


        
        self.act  = nn.ModuleList([]) # Activations
       
  
        # Input layer normalization
        if self.minimalgru_use_laynorm_inp:
           self.ln0=LayerNorm(self.input_dim)
          
        # Input batch normalization    
        if self.minimalgru_use_batchnorm_inp:
           self.bn0=nn.BatchNorm1d(self.input_dim,momentum=0.05)
           
        self.N_minimalgru_lay=len(self.minimalgru_lay)
             
        current_input=self.input_dim
        
        # Initialization of hidden layers
        
        for i in range(self.N_minimalgru_lay):
             
             # Activations
             self.act.append(act_fun(self.minimalgru_act[i]))
            
             add_bias=True
             
             
             if self.minimalgru_use_laynorm[i] or self.minimalgru_use_batchnorm[i]:
                 add_bias=False
             
                  
             # Feed-forward connections
             self.wh.append(nn.Linear(current_input, self.minimalgru_lay[i],bias=add_bias))
             self.wz.append(nn.Linear(current_input, self.minimalgru_lay[i],bias=add_bias))

             
            
             # Recurrent connections
             self.uh.append(nn.Linear(self.minimalgru_lay[i], self.minimalgru_lay[i],bias=False))
             self.uz.append(nn.Linear(self.minimalgru_lay[i], self.minimalgru_lay[i],bias=False))

             if self.minimalgru_orthinit:
             	nn.init.orthogonal_(self.uh[i].weight)
             	nn.init.orthogonal_(self.uz[i].weight)


             
             # batch norm initialization
             self.bn_wh.append(nn.BatchNorm1d(self.minimalgru_lay[i],momentum=0.05))
             self.bn_wz.append(nn.BatchNorm1d(self.minimalgru_lay[i],momentum=0.05))


                
             self.ln.append(LayerNorm(self.minimalgru_lay[i]))
                
             if self.bidir:
                 current_input=2*self.minimalgru_lay[i]
             else:
                 current_input=self.minimalgru_lay[i]
                 
        self.out_dim=self.minimalgru_lay[i]+self.bidir*self.minimalgru_lay[i]
            
             
        
    def forward(self, x):
        
        # Applying Layer/Batch Norm
        if bool(self.minimalgru_use_laynorm_inp):
            x=self.ln0((x))
        
        if bool(self.minimalgru_use_batchnorm_inp):
            x_bn=self.bn0(x.view(x.shape[0]*x.shape[1],x.shape[2]))
            x=x_bn.view(x.shape[0],x.shape[1],x.shape[2])

          
        for i in range(self.N_minimalgru_lay):
            
            # Initial state and concatenation
            if self.bidir:
                h_init = torch.zeros(2*x.shape[1], self.minimalgru_lay[i])
                x=torch.cat([x,flip(x,0)],1)
            else:
                h_init = torch.zeros(x.shape[1],self.minimalgru_lay[i])
        
               
            # Drop mask initilization (same mask for all time steps)            
            if self.test_flag==False:
                drop_mask=torch.bernoulli(torch.Tensor(h_init.shape[0],h_init.shape[1]).fill_(1-self.minimalgru_drop[i]))
            else:
                drop_mask=torch.FloatTensor([1-self.minimalgru_drop[i]])
                
            if self.use_cuda:
               h_init=h_init.cuda()
               drop_mask=drop_mask.cuda()
               
                 
            # Feed-forward affine transformations (all steps in parallel)
            wh_out=self.wh[i](x)
            wz_out=self.wz[i](x)


            
            # Apply batch norm if needed (all steos in parallel)
            if self.minimalgru_use_batchnorm[i]:

                wh_out_bn=self.bn_wh[i](wh_out.view(wh_out.shape[0]*wh_out.shape[1],wh_out.shape[2]))
                wh_out=wh_out_bn.view(wh_out.shape[0],wh_out.shape[1],wh_out.shape[2])
         
                wz_out_bn=self.bn_wz[i](wz_out.view(wz_out.shape[0]*wz_out.shape[1],wz_out.shape[2]))
                wz_out=wz_out_bn.view(wz_out.shape[0],wz_out.shape[1],wz_out.shape[2])


            
            # Processing time steps
            hiddens = []
            ht=h_init
            
            for k in range(x.shape[0]):
                
                # minimalgru equation
                zt=torch.sigmoid(wz_out[k]+self.uz[i](ht))
                at=wh_out[k]+self.uh[i](zt*ht)
                hcand=self.act[i](at)*drop_mask
                ht=(zt*ht+(1-zt)*hcand)
                
                
                if self.minimalgru_use_laynorm[i]:
                    ht=self.ln[i](ht)
                    
                hiddens.append(ht)
                
            # Stacking hidden states
            h=torch.stack(hiddens)
            
            # Bidirectional concatenations
            if self.bidir:
                h_f=h[:,0:int(x.shape[1]/2)]
                h_b=flip(h[:,int(x.shape[1]/2):x.shape[1]].contiguous(),0)
                h=torch.cat([h_f,h_b],2)
                
            # Setup x for the next hidden layer
            x=h

              
        return x

class RNN(nn.Module):
    
    def __init__(self, options,inp_dim):
        super(RNN, self).__init__()
        
        # Reading parameters
        self.input_dim=inp_dim
        self.rnn_lay=list(map(int, options['rnn_lay'].split(',')))
        self.rnn_drop=list(map(float, options['rnn_drop'].split(','))) 
        self.rnn_use_batchnorm=list(map(strtobool, options['rnn_use_batchnorm'].split(',')))
        self.rnn_use_laynorm=list(map(strtobool, options['rnn_use_laynorm'].split(','))) 
        self.rnn_use_laynorm_inp=strtobool(options['rnn_use_laynorm_inp'])
        self.rnn_use_batchnorm_inp=strtobool(options['rnn_use_batchnorm_inp'])
        self.rnn_orthinit=strtobool(options['rnn_orthinit'])
        self.rnn_act=options['rnn_act'].split(',')
        self.bidir=strtobool(options['rnn_bidir'])
        self.use_cuda=strtobool(options['use_cuda'])
        self.to_do=options['to_do']
        
        if self.to_do=='train':
            self.test_flag=False
        else:
            self.test_flag=True
        
        
        # List initialization
        self.wh  = nn.ModuleList([])
        self.uh  = nn.ModuleList([])
                   
        
        self.ln  = nn.ModuleList([]) # Layer Norm
        self.bn_wh  = nn.ModuleList([]) # Batch Norm

        
        self.act  = nn.ModuleList([]) # Activations
       
  
        # Input layer normalization
        if self.rnn_use_laynorm_inp:
           self.ln0=LayerNorm(self.input_dim)
          
        # Input batch normalization    
        if self.rnn_use_batchnorm_inp:
           self.bn0=nn.BatchNorm1d(self.input_dim,momentum=0.05)
           
        self.N_rnn_lay=len(self.rnn_lay)
             
        current_input=self.input_dim
        
        # Initialization of hidden layers
        
        for i in range(self.N_rnn_lay):
             
             # Activations
             self.act.append(act_fun(self.rnn_act[i]))
            
             add_bias=True
             
             
             if self.rnn_use_laynorm[i] or self.rnn_use_batchnorm[i]:
                 add_bias=False
             
                  
             # Feed-forward connections
             self.wh.append(nn.Linear(current_input, self.rnn_lay[i],bias=add_bias))
            
             # Recurrent connections
             self.uh.append(nn.Linear(self.rnn_lay[i], self.rnn_lay[i],bias=False))

             if self.rnn_orthinit:
             	nn.init.orthogonal_(self.uh[i].weight)
          
             # batch norm initialization
             self.bn_wh.append(nn.BatchNorm1d(self.rnn_lay[i],momentum=0.05))

             self.ln.append(LayerNorm(self.rnn_lay[i]))
                
             if self.bidir:
                 current_input=2*self.rnn_lay[i]
             else:
                 current_input=self.rnn_lay[i]
                 
        self.out_dim=self.rnn_lay[i]+self.bidir*self.rnn_lay[i]
            
             
        
    def forward(self, x):
        
        # Applying Layer/Batch Norm
        if bool(self.rnn_use_laynorm_inp):
            x=self.ln0((x))
        
        if bool(self.rnn_use_batchnorm_inp):
            x_bn=self.bn0(x.view(x.shape[0]*x.shape[1],x.shape[2]))
            x=x_bn.view(x.shape[0],x.shape[1],x.shape[2])

          
        for i in range(self.N_rnn_lay):
            
            # Initial state and concatenation
            if self.bidir:
                h_init = torch.zeros(2*x.shape[1], self.rnn_lay[i])
                x=torch.cat([x,flip(x,0)],1)
            else:
                h_init = torch.zeros(x.shape[1],self.rnn_lay[i])
        
               
            # Drop mask initilization (same mask for all time steps)            
            if self.test_flag==False:
                drop_mask=torch.bernoulli(torch.Tensor(h_init.shape[0],h_init.shape[1]).fill_(1-self.rnn_drop[i]))
            else:
                drop_mask=torch.FloatTensor([1-self.rnn_drop[i]])
                
            if self.use_cuda:
               h_init=h_init.cuda()
               drop_mask=drop_mask.cuda()
               
                 
            # Feed-forward affine transformations (all steps in parallel)
            wh_out=self.wh[i](x)
            
            # Apply batch norm if needed (all steos in parallel)
            if self.rnn_use_batchnorm[i]:

                wh_out_bn=self.bn_wh[i](wh_out.view(wh_out.shape[0]*wh_out.shape[1],wh_out.shape[2]))
                wh_out=wh_out_bn.view(wh_out.shape[0],wh_out.shape[1],wh_out.shape[2])
         


            
            # Processing time steps
            hiddens = []
            ht=h_init
            
            for k in range(x.shape[0]):
                
                # rnn equation
                at=wh_out[k]+self.uh[i](ht)
                ht=self.act[i](at)*drop_mask
                
                
                if self.rnn_use_laynorm[i]:
                    ht=self.ln[i](ht)
                    
                hiddens.append(ht)
                
            # Stacking hidden states
            h=torch.stack(hiddens)
            
            # Bidirectional concatenations
            if self.bidir:
                h_f=h[:,0:int(x.shape[1]/2)]
                h_b=flip(h[:,int(x.shape[1]/2):x.shape[1]].contiguous(),0)
                h=torch.cat([h_f,h_b],2)
                
            # Setup x for the next hidden layer
            x=h

              
        return x

class CNN(nn.Module):
    
    def __init__(self,options,inp_dim):
       super(CNN,self).__init__()
    
       # Reading parameters
       self.input_dim=inp_dim
       self.cnn_N_filt=list(map(int, options['cnn_N_filt'].split(',')))

       self.cnn_len_filt=list(map(int, options['cnn_len_filt'].split(',')))
       self.cnn_max_pool_len=list(map(int, options['cnn_max_pool_len'].split(',')))
       
       self.cnn_act=options['cnn_act'].split(',')
       self.cnn_drop=list(map(float, options['cnn_drop'].split(',')))
       
       self.cnn_use_laynorm=list(map(strtobool, options['cnn_use_laynorm'].split(',')))
       self.cnn_use_batchnorm=list(map(strtobool, options['cnn_use_batchnorm'].split(',')))
       self.cnn_use_laynorm_inp=strtobool(options['cnn_use_laynorm_inp'])
       self.cnn_use_batchnorm_inp=strtobool(options['cnn_use_batchnorm_inp'])
       
       self.N_cnn_lay=len(self.cnn_N_filt)
       self.conv  = nn.ModuleList([])
       self.bn  = nn.ModuleList([])
       self.ln  = nn.ModuleList([])
       self.act = nn.ModuleList([])
       self.drop = nn.ModuleList([])
       
       
       if self.cnn_use_laynorm_inp:
           self.ln0=LayerNorm(self.input_dim)
           
       if self.cnn_use_batchnorm_inp:
           self.bn0=nn.BatchNorm1d([self.input_dim],momentum=0.05)
           
       current_input=self.input_dim 
       
       for i in range(self.N_cnn_lay):

         N_filt=int(self.cnn_N_filt[i])
         len_filt=int(self.cnn_len_filt[i])
         
         # dropout
         self.drop.append(nn.Dropout(p=self.cnn_drop[i]))
         
         # activation
         self.act.append(act_fun(self.cnn_act[i]))
            
         # layer norm initialization         
         self.ln.append(LayerNorm([N_filt,int((current_input-self.cnn_len_filt[i]+1)/self.cnn_max_pool_len[i])]))

         self.bn.append(nn.BatchNorm1d(N_filt,int((current_input-self.cnn_len_filt[i]+1)/self.cnn_max_pool_len[i]),momentum=0.05))

            

         if i==0:
          self.conv.append(nn.Conv1d(1, N_filt, len_filt))
              
         else:
          self.conv.append(nn.Conv1d(self.cnn_N_filt[i-1], self.cnn_N_filt[i], self.cnn_len_filt[i]))
          
         current_input=int((current_input-self.cnn_len_filt[i]+1)/self.cnn_max_pool_len[i])

         
       self.out_dim=current_input*N_filt



    def forward(self, x):
        
       batch=x.shape[0]
       seq_len=x.shape[1]
       
       if bool(self.cnn_use_laynorm_inp):
        x=self.ln0((x))
        
       if bool(self.cnn_use_batchnorm_inp):
        x=self.bn0((x))
        
       x=x.view(batch,1,seq_len)
       
       for i in range(self.N_cnn_lay):
           
         if self.cnn_use_laynorm[i]:
          x = self.drop[i](self.act[i](self.ln[i](F.max_pool1d(self.conv[i](x), self.cnn_max_pool_len[i]))))
          
         if self.cnn_use_batchnorm[i]:
          x = self.drop[i](self.act[i](self.bn[i](F.max_pool1d(self.conv[i](x), self.cnn_max_pool_len[i]))))

         if self.cnn_use_batchnorm[i]==False and self.cnn_use_laynorm[i]==False:
          x = self.drop[i](self.act[i](F.max_pool1d(self.conv[i](x), self.cnn_max_pool_len[i])))

       
       x = x.view(batch,-1)

       return x


class SincNet(nn.Module):
    
    def __init__(self,options,inp_dim):
       super(SincNet,self).__init__()
    
       # Reading parameters
       self.input_dim=inp_dim
       self.sinc_N_filt=list(map(int, options['sinc_N_filt'].split(',')))

       self.sinc_len_filt=list(map(int, options['sinc_len_filt'].split(',')))
       self.sinc_max_pool_len=list(map(int, options['sinc_max_pool_len'].split(',')))
       
       self.sinc_act=options['sinc_act'].split(',')
       self.sinc_drop=list(map(float, options['sinc_drop'].split(',')))
       
       self.sinc_use_laynorm=list(map(strtobool, options['sinc_use_laynorm'].split(',')))
       self.sinc_use_batchnorm=list(map(strtobool, options['sinc_use_batchnorm'].split(',')))
       self.sinc_use_laynorm_inp=strtobool(options['sinc_use_laynorm_inp'])
       self.sinc_use_batchnorm_inp=strtobool(options['sinc_use_batchnorm_inp'])
       
       self.N_sinc_lay=len(self.sinc_N_filt)
       
       self.sinc_sample_rate=int(options['sinc_sample_rate'])
       self.sinc_min_low_hz=int(options['sinc_min_low_hz'])
       self.sinc_min_band_hz=int(options['sinc_min_band_hz'])

       
       self.conv  = nn.ModuleList([])
       self.bn  = nn.ModuleList([])
       self.ln  = nn.ModuleList([])
       self.act = nn.ModuleList([])
       self.drop = nn.ModuleList([])
       
       
       if self.sinc_use_laynorm_inp:
           self.ln0=LayerNorm(self.input_dim)
           
       if self.sinc_use_batchnorm_inp:
           self.bn0=nn.BatchNorm1d([self.input_dim],momentum=0.05)
           
       current_input=self.input_dim 
       
       for i in range(self.N_sinc_lay):

         N_filt=int(self.sinc_N_filt[i])
         len_filt=int(self.sinc_len_filt[i])
         
         # dropout
         self.drop.append(nn.Dropout(p=self.sinc_drop[i]))
         
         # activation
         self.act.append(act_fun(self.sinc_act[i]))
            
         # layer norm initialization         
         self.ln.append(LayerNorm([N_filt,int((current_input-self.sinc_len_filt[i]+1)/self.sinc_max_pool_len[i])]))

         self.bn.append(nn.BatchNorm1d(N_filt,int((current_input-self.sinc_len_filt[i]+1)/self.sinc_max_pool_len[i]),momentum=0.05))

            

         if i==0:
          self.conv.append(SincConv(1, N_filt, len_filt,sample_rate=self.sinc_sample_rate, min_low_hz=self.sinc_min_low_hz, min_band_hz=self.sinc_min_band_hz))
              
         else:
          self.conv.append(nn.Conv1d(self.sinc_N_filt[i-1], self.sinc_N_filt[i], self.sinc_len_filt[i]))
          
         current_input=int((current_input-self.sinc_len_filt[i]+1)/self.sinc_max_pool_len[i])

         
       self.out_dim=current_input*N_filt



    def forward(self, x):
        
       batch=x.shape[0]
       seq_len=x.shape[1]
       
       if bool(self.sinc_use_laynorm_inp):
        x=self.ln0(x)
        
       if bool(self.sinc_use_batchnorm_inp):
        x=self.bn0(x)
        
       x=x.view(batch,1,seq_len)
       
       for i in range(self.N_sinc_lay):
           
         if self.sinc_use_laynorm[i]:
          x = self.drop[i](self.act[i](self.ln[i](F.max_pool1d(self.conv[i](x), self.sinc_max_pool_len[i]))))
          
         if self.sinc_use_batchnorm[i]:
          x = self.drop[i](self.act[i](self.bn[i](F.max_pool1d(self.conv[i](x), self.sinc_max_pool_len[i]))))

         if self.sinc_use_batchnorm[i]==False and self.sinc_use_laynorm[i]==False:
          x = self.drop[i](self.act[i](F.max_pool1d(self.conv[i](x), self.sinc_max_pool_len[i])))

       
       x = x.view(batch,-1)

       return x



class SincConv(nn.Module):
    """Sinc-based convolution
    Parameters
    ----------
    in_channels : `int`
        Number of input channels. Must be 1.
    out_channels : `int`
        Number of filters.
    kernel_size : `int`
        Filter length.
    sample_rate : `int`, optional
        Sample rate. Defaults to 16000.
    Usage
    -----
    See `torch.nn.Conv1d`
    Reference
    ---------
    Mirco Ravanelli, Yoshua Bengio,
    "Speaker Recognition from raw waveform with SincNet".
    https://arxiv.org/abs/1808.00158
    """

    @staticmethod
    def to_mel(hz):
        return 2595 * np.log10(1 + hz / 700)

    @staticmethod
    def to_hz(mel):
        return 700 * (10 ** (mel / 2595) - 1)

    def __init__(self, in_channels, out_channels, kernel_size,
                 stride=1, padding=0, dilation=1, bias=False, groups=1,
                 sample_rate=16000, min_low_hz=50, min_band_hz=50):

        super(SincConv,self).__init__()

        if in_channels != 1:
            #msg = (f'SincConv only support one input channel '
            #       f'(here, in_channels = {in_channels:d}).')
            msg = "SincConv only support one input channel (here, in_channels = {%i})" % (in_channels)
            raise ValueError(msg)

        self.out_channels = out_channels
        self.kernel_size = kernel_size
        
        # Forcing the filters to be odd (i.e, perfectly symmetrics)
        if kernel_size%2==0:
            self.kernel_size=self.kernel_size+1
            
        self.stride = stride
        self.padding = padding
        self.dilation = dilation

        if bias:
            raise ValueError('SincConv does not support bias.')
        if groups > 1:
            raise ValueError('SincConv does not support groups.')

        self.sample_rate = sample_rate
        self.min_low_hz = min_low_hz
        self.min_band_hz = min_band_hz

        # initialize filterbanks such that they are equally spaced in Mel scale
        low_hz = 30
        high_hz = self.sample_rate / 2 - (self.min_low_hz + self.min_band_hz)

        mel = np.linspace(self.to_mel(low_hz),
                          self.to_mel(high_hz),
                          self.out_channels + 1)
        hz = self.to_hz(mel) / self.sample_rate
        

        # filter lower frequency (out_channels, 1)
        self.low_hz_ = nn.Parameter(torch.Tensor(hz[:-1]).view(-1, 1))

        # filter frequency band (out_channels, 1)
        self.band_hz_ = nn.Parameter(torch.Tensor(np.diff(hz)).view(-1, 1))

        # Hamming window
        #self.window_ = torch.hamming_window(self.kernel_size)
        n_lin=torch.linspace(0, self.kernel_size, steps=self.kernel_size)
        self.window_=0.54-0.46*torch.cos(2*math.pi*n_lin/self.kernel_size);

        # (kernel_size, 1)
        n = (self.kernel_size - 1) / 2
        self.n_ = torch.arange(-n, n+1).view(1, -1) / self.sample_rate


    def sinc(self, x):
        # Numerically stable definition
        x_left=x[:,0:int((x.shape[1]-1)/2)]
        y_left=torch.sin(x_left) / x_left
        y_right= torch.flip(y_left,dims=[1])
        
        sinc=torch.cat([y_left,torch.ones([x.shape[0],1]).to(x.device),y_right],dim=1)
        

        return sinc

    def forward(self, waveforms):
        """
        Parameters
        ----------
        waveforms : `torch.Tensor` (batch_size, 1, n_samples)
            Batch of waveforms.
        Returns
        -------
        features : `torch.Tensor` (batch_size, out_channels, n_samples_out)
            Batch of sinc filters activations.
        """

        self.n_ = self.n_.to(waveforms.device)

        self.window_ = self.window_.to(waveforms.device)

        low = self.min_low_hz / self.sample_rate + torch.abs(self.low_hz_)
        high = low + self.min_band_hz /self.sample_rate + torch.abs(self.band_hz_)

        f_times_t = torch.matmul(low, self.n_)

        low_pass1 = 2 * low * self.sinc(
            2 * math.pi * f_times_t * self.sample_rate)
        


        f_times_t = torch.matmul(high, self.n_)
        low_pass2 = 2 * high * self.sinc(
            2 * math.pi * f_times_t * self.sample_rate)
        

        band_pass = low_pass2 - low_pass1
        max_, _ = torch.max(band_pass, dim=1, keepdim=True)
        band_pass = band_pass / max_

        self.filters = (band_pass * self.window_).view(
            self.out_channels, 1, self.kernel_size)

        return F.conv1d(waveforms, self.filters, stride=self.stride,
                        padding=self.padding, dilation=self.dilation,
                         bias=None, groups=1) 
        
        
class SincConv_fast(nn.Module):
    """Sinc-based convolution
    Parameters
    ----------
    in_channels : `int`
        Number of input channels. Must be 1.
    out_channels : `int`
        Number of filters.
    kernel_size : `int`
        Filter length.
    sample_rate : `int`, optional
        Sample rate. Defaults to 16000.
    Usage
    -----
    See `torch.nn.Conv1d`
    Reference
    ---------
    Mirco Ravanelli, Yoshua Bengio,
    "Speaker Recognition from raw waveform with SincNet".
    https://arxiv.org/abs/1808.00158
    """

    @staticmethod
    def to_mel(hz):
        return 2595 * np.log10(1 + hz / 700)

    @staticmethod
    def to_hz(mel):
        return 700 * (10 ** (mel / 2595) - 1)

    def __init__(self, in_channels, out_channels, kernel_size,
                 stride=1, padding=0, dilation=1, bias=False, groups=1,
                 sample_rate=16000, min_low_hz=50, min_band_hz=50):

        super(SincConv_fast,self).__init__()

        if in_channels != 1:
            #msg = (f'SincConv only support one input channel '
            #       f'(here, in_channels = {in_channels:d}).')
            msg = "SincConv only support one input channel (here, in_channels = {%i})" % (in_channels)
            raise ValueError(msg)

        self.out_channels = out_channels
        self.kernel_size = kernel_size
        
        # Forcing the filters to be odd (i.e, perfectly symmetrics)
        if kernel_size%2==0:
            self.kernel_size=self.kernel_size+1
            
        self.stride = stride
        self.padding = padding
        self.dilation = dilation

        if bias:
            raise ValueError('SincConv does not support bias.')
        if groups > 1:
            raise ValueError('SincConv does not support groups.')

        self.sample_rate = sample_rate
        self.min_low_hz = min_low_hz
        self.min_band_hz = min_band_hz

        # initialize filterbanks such that they are equally spaced in Mel scale
        low_hz = 30
        high_hz = self.sample_rate / 2 - (self.min_low_hz + self.min_band_hz)

        mel = np.linspace(self.to_mel(low_hz),
                          self.to_mel(high_hz),
                          self.out_channels + 1)
        hz = self.to_hz(mel)
        

        # filter lower frequency (out_channels, 1)
        self.low_hz_ = nn.Parameter(torch.Tensor(hz[:-1]).view(-1, 1))

        # filter frequency band (out_channels, 1)
        self.band_hz_ = nn.Parameter(torch.Tensor(np.diff(hz)).view(-1, 1))

        # Hamming window
        #self.window_ = torch.hamming_window(self.kernel_size)
        n_lin=torch.linspace(0, (self.kernel_size/2)-1, steps=int((self.kernel_size/2))) # computing only half of the window
        self.window_=0.54-0.46*torch.cos(2*math.pi*n_lin/self.kernel_size);


        # (kernel_size, 1)
        n = (self.kernel_size - 1) / 2.0
        self.n_ = 2*math.pi*torch.arange(-n, 0).view(1, -1) / self.sample_rate # Due to symmetry, I only need half of the time axes

 


    def forward(self, waveforms):
        """
        Parameters
        ----------
        waveforms : `torch.Tensor` (batch_size, 1, n_samples)
            Batch of waveforms.
        Returns
        -------
        features : `torch.Tensor` (batch_size, out_channels, n_samples_out)
            Batch of sinc filters activations.
        """

        self.n_ = self.n_.to(waveforms.device)

        self.window_ = self.window_.to(waveforms.device)

        low = self.min_low_hz  + torch.abs(self.low_hz_)
        
        high = torch.clamp(low + self.min_band_hz + torch.abs(self.band_hz_),self.min_low_hz,self.sample_rate/2)
        band=(high-low)[:,0]
        
        f_times_t_low = torch.matmul(low, self.n_)
        f_times_t_high = torch.matmul(high, self.n_)

        band_pass_left=((torch.sin(f_times_t_high)-torch.sin(f_times_t_low))/(self.n_/2))*self.window_ # Equivalent of Eq.4 of the reference paper (SPEAKER RECOGNITION FROM RAW WAVEFORM WITH SINCNET). I just have expanded the sinc and simplified the terms. This way I avoid several useless computations. 
        band_pass_center = 2*band.view(-1,1)
        band_pass_right= torch.flip(band_pass_left,dims=[1])
        
        
        band_pass=torch.cat([band_pass_left,band_pass_center,band_pass_right],dim=1)

        
        band_pass = band_pass / (2*band[:,None])
        

        self.filters = (band_pass).view(
            self.out_channels, 1, self.kernel_size)

        return F.conv1d(waveforms, self.filters, stride=self.stride,
                        padding=self.padding, dilation=self.dilation,
                         bias=None, groups=1) 

        
def flip(x, dim):
    xsize = x.size()
    dim = x.dim() + dim if dim < 0 else dim
    x = x.contiguous()
    x = x.view(-1, *xsize[dim:])
    x = x.view(x.size(0), x.size(1), -1)[:, getattr(torch.arange(x.size(1)-1, -1, -1), ('cpu','cuda')[x.is_cuda])().long(), :]
    return x.view(xsize)

class SRU(nn.Module):
    def __init__(self, options, inp_dim):
        super(SRU, self).__init__()
        self.input_dim = inp_dim
        self.hidden_size = int(options['sru_hidden_size'])
        self.num_layers = int(options['sru_num_layers'])
        self.dropout = float(options['sru_dropout'])
        self.rnn_dropout = float(options['sru_rnn_dropout'])
        self.use_tanh = bool(strtobool(options['sru_use_tanh']))
        self.use_relu = bool(strtobool(options['sru_use_relu']))
        self.use_selu = bool(strtobool(options['sru_use_selu']))
        self.weight_norm = bool(strtobool(options['sru_weight_norm']))
        self.layer_norm = bool(strtobool(options['sru_layer_norm']))
        self.bidirectional = bool(strtobool(options['sru_bidirectional']))
        self.is_input_normalized = bool(strtobool(options['sru_is_input_normalized']))
        self.has_skip_term = bool(strtobool(options['sru_has_skip_term']))
        self.rescale = bool(strtobool(options['sru_rescale']))
        self.highway_bias = float(options['sru_highway_bias'])
        self.n_proj = int(options['sru_n_proj'])
        self.sru = sru.SRU(self.input_dim, self.hidden_size,
                            num_layers=self.num_layers,
                            dropout=self.dropout,
                            rnn_dropout=self.rnn_dropout,
                            bidirectional=self.bidirectional,
                            n_proj=self.n_proj,
                            use_tanh=self.use_tanh,
                            use_selu=self.use_selu,
                            use_relu=self.use_relu,
                            weight_norm=self.weight_norm,
                            layer_norm=self.layer_norm,
                            has_skip_term=self.has_skip_term,
                            is_input_normalized=self.is_input_normalized,
                            highway_bias=self.highway_bias,
                            rescale=self.rescale)
        self.out_dim = self.hidden_size+self.bidirectional*self.hidden_size

    def forward(self, x):
        if self.bidirectional:
            h0 = torch.zeros(self.num_layers, x.shape[1], self.hidden_size*2)
        else:
            h0 = torch.zeros(self.num_layers, x.shape[1], self.hidden_size)
        if x.is_cuda:
            h0 = h0.cuda()
        output, hn = self.sru(x, c0=h0)
        return output


class PASE(nn.Module):
    def __init__(self, options, inp_dim):
        super(PASE, self).__init__()
        
        # To use PASE within PyTorch-Kaldi, please clone the current PASE repository: https://github.com/santi-pdp/pase 
        # Note that you have to clone the dev branch.
        # Take a look into the requirements (requirements.txt) and install in your environment what is missing. An important requirement is QRNN (https://github.com/salesforce/pytorch-qrnn). 
        # Before starting working with PASE, it could make sense to a quick test  with QRNN independently (see “usage” section in the QRNN repository).
        # Remember to install pase. This way it can be used outside the pase folder directory.  To do it, go into the pase folder and type:
        # "python setup.py install"
        
        from pase.models.frontend import wf_builder

        self.input_dim = inp_dim
        self.pase_cfg= options['pase_cfg']
        self.pase_model= options['pase_model']
        
        self.pase = wf_builder(self.pase_cfg)
        
        self.pase.load_pretrained(self.pase_model, load_last=True, verbose=True)
        
        # Reading the out_dim from the config file:
        with open(self.pase_cfg) as json_file:
            config = json.load(json_file)
        
        
        self.out_dim = int(config['emb_dim'])
        

    def forward(self, x):
        
        x=x.unsqueeze(0).unsqueeze(0)
        output=self.pase(x) 
        
        return output