diff --git a/wav2lip/models384/__init__.py b/wav2lip/models384/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/wav2lip/models384/conv.py b/wav2lip/models384/conv.py
new file mode 100644
index 0000000..fec03c5
--- /dev/null
+++ b/wav2lip/models384/conv.py
@@ -0,0 +1,44 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+class Conv2d(nn.Module):
+    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.conv_block = nn.Sequential(
+                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
+                            nn.BatchNorm2d(cout)
+                            )
+        self.act = nn.ReLU()
+        self.residual = residual
+
+    def forward(self, x):
+        out = self.conv_block(x)
+        if self.residual:
+            out += x
+        return self.act(out)
+
+class nonorm_Conv2d(nn.Module):
+    def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.conv_block = nn.Sequential(
+                            nn.Conv2d(cin, cout, kernel_size, stride, padding),
+                            )
+        self.act = nn.LeakyReLU(negative_slope=0.01)
+
+    def forward(self, x):
+        out = self.conv_block(x)
+        return self.act(out)
+
+class Conv2dTranspose(nn.Module):
+    def __init__(self, cin, cout, kernel_size, stride, padding, output_padding=0, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.conv_block = nn.Sequential(
+                            nn.ConvTranspose2d(cin, cout, kernel_size, stride, padding, output_padding),
+                            nn.BatchNorm2d(cout)
+                            )
+        self.act = nn.ReLU()
+
+    def forward(self, x):
+        out = self.conv_block(x)
+        return self.act(out)
diff --git a/wav2lip/models384/syncnet.py b/wav2lip/models384/syncnet.py
new file mode 100644
index 0000000..211bfd0
--- /dev/null
+++ b/wav2lip/models384/syncnet.py
@@ -0,0 +1,92 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .conv import Conv2d
+
+class SyncNet_color(nn.Module):
+    def __init__(self):
+        super(SyncNet_color, self).__init__()
+
+        self.face_encoder = nn.Sequential(
+            Conv2d(15, 16, kernel_size=(7, 7), stride=1, padding=3, act="leaky"), # 192, 384
+
+            Conv2d(16, 32, kernel_size=5, stride=(1, 2), padding=1, act="leaky"), # 192, 192
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),  
+
+            Conv2d(32, 64, kernel_size=3, stride=2, padding=1, act="leaky"), # 96, 96
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+            
+            Conv2d(64, 128, kernel_size=3, stride=2, padding=1, act="leaky"), # 48, 48
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+    
+            Conv2d(128, 256, kernel_size=3, stride=2, padding=1, act="leaky"), # 24, 24
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),  
+
+            ###################
+            # Modified blocks
+            ##################
+            Conv2d(256, 512, kernel_size=3, stride=2, padding=1, act="leaky"),
+            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),  # 12, 12
+
+            Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, act="leaky"),
+            Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+            Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),  # 6, 6
+
+            Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1, act="leaky"), # 3, 3
+            Conv2d(1024, 1024, kernel_size=3, stride=1, padding=0, act="leaky"),
+            Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0, act="relu")) # 1, 1
+            ##################
+
+        # print(summary(self.face_encoder, (15, 96, 192)), act="relu")
+
+        self.audio_encoder = nn.Sequential(
+            Conv2d(1, 32, kernel_size=3, stride=1, padding=1, act="leaky"),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+            Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+
+            Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1, act="leaky"),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+            Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+
+            Conv2d(64, 128, kernel_size=3, stride=3, padding=1, act="leaky"),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+            Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+
+            Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1, act="leaky"),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+            Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+
+            ###################
+            # Modified blocks
+            ##################
+            Conv2d(256, 512, kernel_size=3, stride=1, padding=1, act="leaky"),
+            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+            Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True, act="leaky"),
+
+            Conv2d(512, 1024, kernel_size=3, stride=1, padding=0, act="relu"),
+            Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0, act="relu"))
+            ##################
+
+        # print(summary(self.audio_encoder, (1, 80, 16)))
+
+    def forward(self, audio_sequences, face_sequences): # audio_sequences := (B, dim, T)
+        face_embedding = self.face_encoder(face_sequences)
+        audio_embedding = self.audio_encoder(audio_sequences)
+
+        audio_embedding = audio_embedding.view(audio_embedding.size(0), -1)
+        face_embedding = face_embedding.view(face_embedding.size(0), -1)
+
+        audio_embedding = F.normalize(audio_embedding, p=2, dim=1)
+        face_embedding = F.normalize(face_embedding, p=2, dim=1)
+
+
+        return audio_embedding, face_embedding
+
+    def audio_forward(self, audio_sequences):
+        return self.audio_encoder(audio_sequences)
diff --git a/wav2lip/models384/wav2lip.py b/wav2lip/models384/wav2lip.py
new file mode 100644
index 0000000..7606748
--- /dev/null
+++ b/wav2lip/models384/wav2lip.py
@@ -0,0 +1,198 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .conv import Conv2dTranspose, Conv2d, nonorm_Conv2d
+
+
+class SpatialAttention(nn.Module):
+    def __init__(self, kernel_size=7):
+        super(SpatialAttention, self).__init__()
+
+        self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=kernel_size // 2, bias=False)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        avg_out = torch.mean(x, dim=1, keepdim=True)
+        max_out, _ = torch.max(x, dim=1, keepdim=True)
+        x = torch.cat([avg_out, max_out], dim=1)
+        x = self.conv1(x)
+        return self.sigmoid(x)
+
+
+class SAM(nn.Module):
+    def __init__(self):
+        super(SAM, self).__init__()
+        self.sa = SpatialAttention()
+
+    def forward(self, sp, se):
+        sp_att = self.sa(sp)
+        out = se * sp_att + se
+        return out
+
+
+class Wav2Lip(nn.Module):
+    def __init__(self, audio_encoder=None):
+        super(Wav2Lip, self).__init__()
+        self.sam = SAM()
+        self.face_encoder_blocks = nn.ModuleList([
+            nn.Sequential(Conv2d(6, 16, kernel_size=7, stride=1, padding=3),
+                          Conv2d(16, 16, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(16, 16, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(16, 16, kernel_size=3, stride=1, padding=1, residual=True)),  # 192, 192
+
+            nn.Sequential(Conv2d(16, 32, kernel_size=3, stride=2, padding=1),  # 96, 96
+                          Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(32, 64, kernel_size=3, stride=2, padding=1),  # 48, 48
+                          Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(64, 128, kernel_size=3, stride=2, padding=1),  # 24, 24
+                          Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(128, 256, kernel_size=3, stride=2, padding=1),  # 12, 12
+                          Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(256, 512, kernel_size=3, stride=2, padding=1),  # 6, 6
+                          Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(512, 1024, kernel_size=3, stride=2, padding=1),  # 3, 3
+                          Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True)),
+
+            nn.Sequential(Conv2d(1024, 1024, kernel_size=3, stride=1, padding=0),  # 1, 1
+                          Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0),
+                          Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0)), ])
+  
+
+        if audio_encoder is None:
+            self.audio_encoder = nn.Sequential(
+                Conv2d(1, 32, kernel_size=3, stride=1, padding=1),
+                Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+                Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True),
+
+                Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1),
+                Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+                Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+
+                Conv2d(64, 128, kernel_size=3, stride=3, padding=1),
+                Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+                Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+
+                Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1),
+                Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+                Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+
+
+                Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
+                Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+                Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+
+                Conv2d(512, 1024, kernel_size=3, stride=1, padding=0),
+                Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0))
+
+        else:
+            self.audio_encoder = audio_encoder
+
+            for p in self.audio_encoder.parameters():
+                p.requires_grad = False
+
+        self.audio_refine = nn.Sequential(
+            Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0),
+            Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0))
+
+        self.face_decoder_blocks = nn.ModuleList([
+            nn.Sequential(Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0), ),  # + 1024
+
+
+            nn.Sequential(Conv2dTranspose(2048, 1024, kernel_size=3, stride=1, padding=0),  # 3,3
+                          Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True), ),  # + 1024
+
+            nn.Sequential(Conv2dTranspose(2048, 1024, kernel_size=3, stride=2, padding=1, output_padding=1),
+                          Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True), ),  # 6, 6  + 512
+
+            nn.Sequential(Conv2dTranspose(1536, 768, kernel_size=3, stride=2, padding=1, output_padding=1),
+                          Conv2d(768, 768, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(768, 768, kernel_size=3, stride=1, padding=1, residual=True), ),  # 12, 12  + 256
+
+            nn.Sequential(Conv2dTranspose(1024, 512, kernel_size=3, stride=2, padding=1, output_padding=1),
+                          Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True), ),  # 24, 24  + 128
+
+
+            nn.Sequential(Conv2dTranspose(640, 256, kernel_size=3, stride=2, padding=1, output_padding=1),
+                          Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True), ),  # 48, 48  + 64
+
+            nn.Sequential(Conv2dTranspose(320, 128, kernel_size=3, stride=2, padding=1, output_padding=1),
+                          Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True), ),  # 96, 96  + 32
+
+            nn.Sequential(Conv2dTranspose(160, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
+                          Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True),
+                          Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True), ), ])  # 192, 192  + 16
+
+        self.output_block = nn.Sequential(Conv2d(80, 32, kernel_size=3, stride=1, padding=1),
+                                          nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0),
+                                          nn.Sigmoid())
+
+    def freeze_audio_encoder(self):
+        for p in self.audio_encoder.parameters():
+            p.requires_grad = False
+
+    def forward(self, audio_sequences, face_sequences):
+
+        B = audio_sequences.size(0)
+
+        input_dim_size = len(face_sequences.size())
+        if input_dim_size > 4:
+            audio_sequences = torch.cat([audio_sequences[:, i] for i in range(audio_sequences.size(1))], dim=0)
+            face_sequences = torch.cat([face_sequences[:, :, i] for i in range(face_sequences.size(2))], dim=0)
+
+        audio_embedding = self.audio_encoder(audio_sequences)  # B, 512, 1, 1
+
+
+
+        feats = []
+        x = face_sequences
+
+        for f in self.face_encoder_blocks:
+            x = f(x)
+            feats.append(x)
+
+        x = audio_embedding
+        for f in self.face_decoder_blocks:
+            x = f(x)
+            try:
+                x = self.sam(feats[-1], x)
+                x = torch.cat((x, feats[-1]), dim=1)
+            except Exception as e:
+                print(x.size())
+                print(feats[-1].size())
+                raise e
+
+            feats.pop()
+
+        x = self.output_block(x)
+
+        if input_dim_size > 4:
+            x = torch.split(x, B, dim=0)  # [(B, C, H, W)]
+            outputs = torch.stack(x, dim=2)  # (B, C, T, H, W)
+
+        else:
+            outputs = x
+
+        return outputs
+