diff --git a/UDS2W2LGLU8.py b/UDS2W2LGLU8.py
deleted file mode 100644
index 9acfd66..0000000
--- a/UDS2W2LGLU8.py
+++ /dev/null
@@ -1,135 +0,0 @@
-import math
-
-import torch
-import torch.nn as nn
-from torch.nn.functional import log_softmax
-import torch.nn.functional as F
-from torch.nn.parameter import Parameter
-from data import melfuture
-from uyghur import uyghur_latin
-from BaseModel import BaseModel
-
-class Swish(nn.Module):
-    def forward(self, x):
-        return x * x.sigmoid()
-
-class Mish(nn.Module):
-    def forward(self, x):
-        #inlining this saves 1 second per epoch (V100 GPU) vs having a temp x and then returning x(!)
-        return x *( torch.tanh(F.softplus(x)))
-
-class UDS2W2LGLU8(BaseModel):
-    def __init__(self,num_features_input,load_best=False):
-        super(UDS2W2LGLU8, self).__init__('UDS2W2LGLU8')
-        self.smoothing = 0.01
-        self.conv = nn.Sequential(
-            nn.Conv2d(1, 32, kernel_size=(41, 11), stride=(2, 2), padding=(20, 5), bias=False),
-            nn.BatchNorm2d(32),
-            nn.Hardtanh(0, 20, inplace=True),
-            nn.Conv2d(32, 32, kernel_size=(21, 11), stride=(2, 1), padding=(10, 5),bias=False),
-            nn.BatchNorm2d(32),
-            nn.Hardtanh(0, 20, inplace=True),
-        )
-        self.lstm1 = nn.GRU(1024, 256, num_layers=1 , batch_first=True, bidirectional=True)
-        self.cnn1  = nn.Sequential(
-            ResBGLU(256, 256, 11, 0.2, 2),
-            ResBGLU(256, 256, 11, 0.2),
-            ResBGLU(256, 256, 11, 0.2),
-            ResBGLU(256, 256, 11, 0.2),
-            ResBGLU(256, 256, 11, 0.2),
-        )
-        self.lstm2 = nn.GRU(256, 384, num_layers=1 , batch_first=True, bidirectional=True)
-        self.cnn2 = nn.Sequential(
-            ResBGLU(384, 384, 13, 0.2),
-            ResBGLU(384, 384, 13, 0.2),
-            ResBGLU(384, 384, 13, 0.2),
-
-            ResBGLU(384, 512, 17, 0.2),
-            ResBGLU(512, 512, 17, 0.3),
-            ResBGLU(512, 512, 1, 0.3),
-        )
-        self.outlayer = nn.Conv1d(512, uyghur_latin.vocab_size, 1, 1)
-        self.softMax = nn.LogSoftmax(dim=1)
-
-        self.checkpoint = 'results/' + self.ModelName
-        self._load(load_best)
-        print(f'The model has {self.parameters_count(self):,} trainable parameters')
-
-
-    def smooth_labels(self, x):
-        sl = x.size(1)
-        return (1.0 - self.smoothing) * x + self.smoothing / sl
-
-    def forward(self, x, lengths):
-        out_lens = lengths//2
-
-        x.unsqueeze_(1)
-        out = self.conv(x)
-
-        b, c, h, w = out.size()
-        out = out.view(b, c*h, w).contiguous() #.permute(0,2,1)
-
-        out = out.permute(0,2,1)
-        out = nn.utils.rnn.pack_padded_sequence(out, out_lens, batch_first=True)
-        out, _ = self.lstm1(out)        
-        out, _ = nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
-
-        out = (out[:, :, :self.lstm1.hidden_size] + out[:, :, self.lstm1.hidden_size:]).contiguous()
-        out = self.cnn1(out.permute(0,2,1))
-
-        out_lens = out_lens//2
-        out = out.permute(0,2,1)
-        out = nn.utils.rnn.pack_padded_sequence(out, out_lens, batch_first=True)
-        out,_ = self.lstm2(out)
-        out, _ = nn.utils.rnn.pad_packed_sequence(out, batch_first=True)
-
-        out = (out[:, :, :self.lstm2.hidden_size] + out[:, :, self.lstm2.hidden_size:]).contiguous()
-        out = self.cnn2(out.permute(0,2,1))
-        out = self.outlayer(out)
-        #out = self.smooth_labels(out)
-        out = self.softMax(out)
-        return out, out_lens
-
-
-class ResBGLU(nn.Module):
-    def __init__(self, in_channel, out_channel, kernel, d = 0.4, stride = 1):
-        super().__init__()
-
-        self.isRes = (in_channel == out_channel and stride == 1)
-        pad = (kernel-1)//2
-        self.conv = nn.Sequential(
-            nn.Conv1d(in_channel, out_channel*2, kernel_size = kernel, stride = stride , padding=pad, bias=False),
-            nn.BatchNorm1d(out_channel*2),
-            nn.GLU(dim=1)
-            )
-
-        self.fc = nn.Sequential(
-            nn.BatchNorm1d(out_channel),
-            Mish(),
-        )
-        self.drop = nn.Dropout(d)
-
-    def forward(self, x):
-        out  = self.conv(x)
-        if self.isRes:
-            out  = self.fc(out+x)
-            
-        out  = self.drop(out)
-        return out
-
-
-
-if __name__ == "__main__":
-    from data import featurelen, melfuture
-    device ="cpu"
-
-    net = UDS2W2LGLU8(featurelen).to(device)
-    text = net.predict("test1.wav",device)
-    print(text)
-    text = net.predict("test2.wav",device)
-    print(text)
-
-
-    #net.best_cer = 1.0
-    #net.save(78)
-