Add text encoder based on adaptive Weight

TTS/tts/layers/vits/networks.py

@@ -4,7 +4,6 @@ import torch
 from torch import nn
 
 from TTS.tts.layers.glow_tts.glow import WN
-from TTS.tts.layers.glow_tts.transformer import RelativePositionTransformer
 from TTS.tts.utils.helpers import sequence_mask
 
 LRELU_SLOPE = 0.1
@@ -25,81 +24,6 @@ def init_weights(m, mean=0.0, std=0.01):
 def get_padding(kernel_size, dilation=1):
     return int((kernel_size * dilation - dilation) / 2)
 
-
-class TextEncoder(nn.Module):
-    def __init__(
-        self,
-        n_vocab: int,
-        out_channels: int,
-        hidden_channels: int,
-        hidden_channels_ffn: int,
-        num_heads: int,
-        num_layers: int,
-        kernel_size: int,
-        dropout_p: float,
-        language_emb_dim: int = None,
-    ):
-        """Text Encoder for VITS model.
-
-        Args:
-            n_vocab (int): Number of characters for the embedding layer.
-            out_channels (int): Number of channels for the output.
-            hidden_channels (int): Number of channels for the hidden layers.
-            hidden_channels_ffn (int): Number of channels for the convolutional layers.
-            num_heads (int): Number of attention heads for the Transformer layers.
-            num_layers (int): Number of Transformer layers.
-            kernel_size (int): Kernel size for the FFN layers in Transformer network.
-            dropout_p (float): Dropout rate for the Transformer layers.
-        """
-        super().__init__()
-        self.out_channels = out_channels
-        self.hidden_channels = hidden_channels
-
-        self.emb = nn.Embedding(n_vocab, hidden_channels)
-
-        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
-
-        if language_emb_dim:
-            hidden_channels += language_emb_dim
-
-        self.encoder = RelativePositionTransformer(
-            in_channels=hidden_channels,
-            out_channels=hidden_channels,
-            hidden_channels=hidden_channels,
-            hidden_channels_ffn=hidden_channels_ffn,
-            num_heads=num_heads,
-            num_layers=num_layers,
-            kernel_size=kernel_size,
-            dropout_p=dropout_p,
-            layer_norm_type="2",
-            rel_attn_window_size=4,
-        )
-
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def forward(self, x, x_lengths, lang_emb=None):
-        """
-        Shapes:
-            - x: :math:`[B, T]`
-            - x_length: :math:`[B]`
-        """
-        assert x.shape[0] == x_lengths.shape[0]
-        x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
-
-        # concat the lang emb in embedding chars
-        if lang_emb is not None:
-            x = torch.cat((x, lang_emb.transpose(2, 1).expand(x.size(0), x.size(1), -1)), dim=-1)
-
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)  # [b, 1, t]
-
-        x = self.encoder(x * x_mask, x_mask)
-        stats = self.proj(x) * x_mask
-
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return x, m, logs, x_mask
-
-
 class ResidualCouplingBlock(nn.Module):
     def __init__(
         self,

TTS/tts/layers/vits/text_encoder.py

@@ -0,0 +1,653 @@
+import math
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from TTS.tts.utils.helpers import sequence_mask
+from TTS.tts.layers.generic.normalization import LayerNorm, LayerNorm2
+# import sys
+# sys.setrecursionlimit(9999999)
+class AdaptiveWeightConv(nn.Module):
+    def __init__(self, conv_module, in_channels, out_channels, kernel_size, r=0, alpha=1, dropout=0., num_classes=None, **kwargs):
+        super(AdaptiveWeightConv, self).__init__()
+        self.conv = conv_module(in_channels, out_channels, kernel_size, **kwargs)
+        self.r = r
+
+        # it is only support when kernel_size is equal to 1
+        if kernel_size != 1:
+            self.r = 0
+
+        self.alpha = alpha
+        self.num_classes = num_classes
+
+        self.weight = self.conv.weight
+        # Optional dropout
+        if dropout > 0.:
+            self.dropout = nn.Dropout(p=dropout)
+        else:
+            self.dropout = lambda x: x
+
+        assert isinstance(kernel_size, int)
+        # Actual trainable parameters
+        if self.r > 0:
+            # create layers for each class
+
+            # self.W_Ms = nn.Parameter(
+            #     self.conv.weight.new_zeros((r * kernel_size, in_channels * kernel_size))
+            # )
+
+            # self.W_Bs = nn.Parameter(
+            #   self.conv.weight.new_zeros((out_channels//self.conv.groups*kernel_size, r*kernel_size))
+            # )
+            W_Ms_layers = [nn.Parameter(self.conv.weight.new_zeros((r * kernel_size, in_channels * kernel_size))) for _ in range(0, num_classes)]
+            self.W_Ms = nn.ParameterList(W_Ms_layers)
+            W_Bs_layers = [nn.Parameter(self.conv.weight.new_zeros((out_channels//self.conv.groups*kernel_size, r*kernel_size))) for _ in range(0, num_classes)]
+            self.W_Bs = nn.ParameterList(W_Bs_layers)
+
+            # self.m_shape = (r * kernel_size, in_channels * kernel_size)
+            # self.W_Ms = nn.Embedding(num_classes, self.m_shape[0] * self.m_shape[1])
+            # self.b_shape = (out_channels//self.conv.groups*kernel_size, r*kernel_size)
+            # self.W_Bs = nn.Embedding(num_classes, self.b_shape[0] * self.b_shape[1])
+            self.scaling = self.alpha / self.r
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        self.conv.reset_parameters()
+        if hasattr(self, 'W_Ms'):
+            # initialize A the same way as the default for nn.Linear and B to zero
+            
+            # nn.init.kaiming_uniform_(self.W_Ms, a=math.sqrt(5))
+            # nn.init.zeros_(self.W_Bs)
+
+            for i in range(0, self.num_classes):
+                nn.init.kaiming_uniform_(self.W_Ms[i], a=math.sqrt(5))
+                nn.init.zeros_(self.W_Bs[i])
+            
+            # nn.init.kaiming_uniform_(self.W_Ms.weight, a=math.sqrt(5))
+            # nn.init.zeros_(self.W_Bs.weight)
+
+    def train(self, mode=True):
+        super(AdaptiveWeightConv, self).train(mode)
+
+    def forward(self, x, class_id=None):
+        if self.r > 0:
+            # W_M_layer = self.W_Ms(class_ids).view(class_ids.size(0), *self.m_shape)
+            # W_B_layer = self.W_Bs(class_ids).view(class_ids.size(0), *self.b_shape)
+            W_M_layer = self.W_Ms[class_id]
+            W_B_layer = self.W_Bs[class_id]
+            # W_M_layer = self.W_Ms
+            # W_B_layer = self.W_Bs
+
+            # print(self.conv.weight.shape, (W_B_layer @ W_M_layer).shape, W_M_layer.shape, W_B_layer.shape)
+            return self.conv._conv_forward(
+                x, 
+                self.conv.weight + (W_B_layer @ W_M_layer).view(self.conv.weight.shape) * self.scaling,
+                self.conv.bias
+            )
+        return self.conv(x)
+
+class Conv2d(AdaptiveWeightConv):
+    def __init__(self, *args, **kwargs):
+        super(Conv2d, self).__init__(nn.Conv2d, *args, **kwargs)
+
+class Conv1d(AdaptiveWeightConv):
+    def __init__(self, *args, **kwargs):
+        super(Conv1d, self).__init__(nn.Conv1d, *args, **kwargs)
+
+# Can Extend to other ones like this
+
+class Conv3d(AdaptiveWeightConv):
+    def __init__(self, *args, **kwargs):
+        super(Conv3d, self).__init__(nn.Conv3d, *args, **kwargs)
+
+
+class RelativePositionMultiHeadAttention(nn.Module):
+    """Multi-head attention with Relative Positional embedding.
+    https://arxiv.org/pdf/1809.04281.pdf
+
+    It learns positional embeddings for a window of neighbours. For keys and values,
+    it learns different set of embeddings. Key embeddings are agregated with the attention
+    scores and value embeddings are aggregated with the output.
+
+    Note:
+        Example with relative attention window size 2
+
+        - input = [a, b, c, d, e]
+        - rel_attn_embeddings = [e(t-2), e(t-1), e(t+1), e(t+2)]
+
+        So it learns 4 embedding vectors (in total 8) separately for key and value vectors.
+
+        Considering the input c
+
+        - e(t-2) corresponds to c -> a
+        - e(t-2) corresponds to c -> b
+        - e(t-2) corresponds to c -> d
+        - e(t-2) corresponds to c -> e
+
+        These embeddings are shared among different time steps. So input a, b, d and e also uses
+        the same embeddings.
+
+        Embeddings are ignored when the relative window is out of limit for the first and the last
+        n items.
+
+    Args:
+        channels (int): input and inner layer channels.
+        out_channels (int): output channels.
+        num_heads (int): number of attention heads.
+        rel_attn_window_size (int, optional): relation attention window size.
+            If 4, for each time step next and previous 4 time steps are attended.
+            If default, relative encoding is disabled and it is a regular transformer.
+            Defaults to None.
+        heads_share (bool, optional): [description]. Defaults to True.
+        dropout_p (float, optional): dropout rate. Defaults to 0..
+        input_length (int, optional): intput length for positional encoding. Defaults to None.
+        proximal_bias (bool, optional): enable/disable proximal bias as in the paper. Defaults to False.
+        proximal_init (bool, optional): enable/disable poximal init as in the paper.
+            Init key and query layer weights the same. Defaults to False.
+    """
+
+    def __init__(
+        self,
+        channels,
+        out_channels,
+        num_heads,
+        rel_attn_window_size=None,
+        heads_share=True,
+        dropout_p=0.0,
+        input_length=None,
+        proximal_bias=False,
+        proximal_init=False,
+        num_adaptive_weight_classes=None,
+    ):
+        super().__init__()
+        assert channels % num_heads == 0, " [!] channels should be divisible by num_heads."
+        # class attributes
+        self.channels = channels
+        self.out_channels = out_channels
+        self.num_heads = num_heads
+        self.rel_attn_window_size = rel_attn_window_size
+        self.heads_share = heads_share
+        self.input_length = input_length
+        self.proximal_bias = proximal_bias
+        self.dropout_p = dropout_p
+        self.attn = None
+        # query, key, value layers
+        self.k_channels = channels // num_heads
+        self.conv_q = Conv1d(channels, channels, 1, r=1 if num_adaptive_weight_classes else 0, num_classes=num_adaptive_weight_classes)
+        self.conv_k = Conv1d(channels, channels, 1, r=1 if num_adaptive_weight_classes else 0, num_classes=num_adaptive_weight_classes)
+        self.conv_v = Conv1d(channels, channels, 1, r=1 if num_adaptive_weight_classes else 0, num_classes=num_adaptive_weight_classes)
+        # output layers
+        self.conv_o = Conv1d(channels, out_channels, 1, r=1 if num_adaptive_weight_classes else 0, num_classes=num_adaptive_weight_classes)
+        self.dropout = nn.Dropout(dropout_p)
+        # relative positional encoding layers
+        if rel_attn_window_size is not None:
+            n_heads_rel = 1 if heads_share else num_heads
+            rel_stddev = self.k_channels**-0.5
+            emb_rel_k = nn.Parameter(
+                torch.randn(n_heads_rel, rel_attn_window_size * 2 + 1, self.k_channels) * rel_stddev
+            )
+            emb_rel_v = nn.Parameter(
+                torch.randn(n_heads_rel, rel_attn_window_size * 2 + 1, self.k_channels) * rel_stddev
+            )
+            self.register_parameter("emb_rel_k", emb_rel_k)
+            self.register_parameter("emb_rel_v", emb_rel_v)
+
+        # init layers
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        # proximal bias
+        if proximal_init:
+            self.conv_k.weight.data.copy_(self.conv_q.weight.data)
+            self.conv_k.bias.data.copy_(self.conv_q.bias.data)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+
+    def forward(self, x, c, attn_mask=None, class_id=None):
+        """
+        Shapes:
+            - x: :math:`[B, C, T]`
+            - c: :math:`[B, C, T]`
+            - attn_mask: :math:`[B, 1, T, T]`
+        """
+        q = self.conv_q(x, class_id=class_id)
+        k = self.conv_k(c, class_id=class_id)
+        v = self.conv_v(c, class_id=class_id)
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+        x = self.conv_o(x, class_id=class_id)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.num_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.num_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.num_heads, self.k_channels, t_s).transpose(2, 3)
+        # compute raw attention scores
+        scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
+        # relative positional encoding for scores
+        if self.rel_attn_window_size is not None:
+            assert t_s == t_t, "Relative attention is only available for self-attention."
+            # get relative key embeddings
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings)
+            rel_logits = self._relative_position_to_absolute_position(rel_logits)
+            scores_local = rel_logits / math.sqrt(self.k_channels)
+            scores = scores + scores_local
+        # proximan bias
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attn_proximity_bias(t_s).to(device=scores.device, dtype=scores.dtype)
+        # attention score masking
+        if mask is not None:
+            # add small value to prevent oor error.
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.input_length is not None:
+                block_mask = torch.ones_like(scores).triu(-1 * self.input_length).tril(self.input_length)
+                scores = scores * block_mask + -1e4 * (1 - block_mask)
+        # attention score normalization
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        # apply dropout to attention weights
+        p_attn = self.dropout(p_attn)
+        # compute output
+        output = torch.matmul(p_attn, value)
+        # relative positional encoding for values
+        if self.rel_attn_window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
+            output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
+        output = output.transpose(2, 3).contiguous().view(b, d, t_t)  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    @staticmethod
+    def _matmul_with_relative_values(p_attn, re):
+        """
+        Args:
+            p_attn (Tensor): attention weights.
+            re (Tensor): relative value embedding vector. (a_(i,j)^V)
+
+        Shapes:
+            -p_attn: :math:`[B, H, T, V]`
+            -re: :math:`[H or 1, V, D]`
+            -logits: :math:`[B, H, T, D]`
+        """
+        logits = torch.matmul(p_attn, re.unsqueeze(0))
+        return logits
+
+    @staticmethod
+    def _matmul_with_relative_keys(query, re):
+        """
+        Args:
+            query (Tensor): batch of query vectors. (x*W^Q)
+            re (Tensor): relative key embedding vector. (a_(i,j)^K)
+
+        Shapes:
+            - query: :math:`[B, H, T, D]`
+            - re: :math:`[H or 1, V, D]`
+            - logits: :math:`[B, H, T, V]`
+        """
+        # logits = torch.einsum('bhld, kmd -> bhlm', [query, re.to(query.dtype)])
+        logits = torch.matmul(query, re.unsqueeze(0).transpose(-2, -1))
+        return logits
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        """Convert embedding vestors to a tensor of embeddings"""
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.rel_attn_window_size + 1), 0)
+        slice_start_position = max((self.rel_attn_window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(relative_embeddings, [0, 0, pad_length, pad_length, 0, 0])
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position]
+        return used_relative_embeddings
+
+    @staticmethod
+    def _relative_position_to_absolute_position(x):
+        """Converts tensor from relative to absolute indexing for local attention.
+        Shapes:
+            x: :math:`[B, C, T, 2 * T - 1]`
+        Returns:
+            A Tensor of shape :math:`[B, C, T, T]`
+        """
+        batch, heads, length, _ = x.size()
+        # Pad to shift from relative to absolute indexing.
+        x = F.pad(x, [0, 1, 0, 0, 0, 0, 0, 0])
+        # Pad extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(x_flat, [0, length - 1, 0, 0, 0, 0])
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1 :]
+        return x_final
+
+    @staticmethod
+    def _absolute_position_to_relative_position(x):
+        """
+        Shapes:
+            - x: :math:`[B, C, T, T]`
+            - ret: :math:`[B, C, T, 2*T-1]`
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(x, [0, length - 1, 0, 0, 0, 0, 0, 0])
+        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, [length, 0, 0, 0, 0, 0])
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    @staticmethod
+    def _attn_proximity_bias(length):
+        """Produce an attention mask that discourages distant
+        attention values.
+        Args:
+            length (int): an integer scalar.
+        Returns:
+            a Tensor with shape :math:`[1, 1, T, T]`
+        """
+        # L
+        r = torch.arange(length, dtype=torch.float32)
+        # L x L
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        # scale mask values
+        diff = -torch.log1p(torch.abs(diff))
+        # 1 x 1 x L x L
+        return diff.unsqueeze(0).unsqueeze(0)
+
+
+class FeedForwardNetwork(nn.Module):
+    """Feed Forward Inner layers for Transformer.
+
+    Args:
+        in_channels (int): input tensor channels.
+        out_channels (int): output tensor channels.
+        hidden_channels (int): inner layers hidden channels.
+        kernel_size (int): conv1d filter kernel size.
+        dropout_p (float, optional): dropout rate. Defaults to 0.
+    """
+    def __init__(self, in_channels, out_channels, hidden_channels, kernel_size, dropout_p=0.0, causal=False, num_adaptive_weight_classes=None):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.kernel_size = kernel_size
+        self.dropout_p = dropout_p
+
+        if causal:
+            self.padding = self._causal_padding
+        else:
+            self.padding = self._same_padding
+
+        self.conv_1 = Conv1d(in_channels, hidden_channels, kernel_size, r=1 if num_adaptive_weight_classes else 0, num_classes=num_adaptive_weight_classes)
+        self.conv_2 = Conv1d(hidden_channels, out_channels, kernel_size, r=1 if num_adaptive_weight_classes else 0, num_classes=num_adaptive_weight_classes)
+        self.dropout = nn.Dropout(dropout_p)
+
+    def forward(self, x, x_mask, class_id=None):
+        x = self.conv_1(self.padding(x * x_mask), class_id=class_id)
+        x = torch.relu(x)
+        x = self.dropout(x)
+        x = self.conv_2(self.padding(x * x_mask), class_id=class_id)
+        return x * x_mask
+
+    def _causal_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = self.kernel_size - 1
+        pad_r = 0
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, self._pad_shape(padding))
+        return x
+
+    def _same_padding(self, x):
+        if self.kernel_size == 1:
+            return x
+        pad_l = (self.kernel_size - 1) // 2
+        pad_r = self.kernel_size // 2
+        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
+        x = F.pad(x, self._pad_shape(padding))
+        return x
+
+    @staticmethod
+    def _pad_shape(padding):
+        l = padding[::-1]
+        pad_shape = [item for sublist in l for item in sublist]
+        return pad_shape
+
+
+class RelativePositionTransformer(nn.Module):
+    """Transformer with Relative Potional Encoding.
+    https://arxiv.org/abs/1803.02155
+
+    Args:
+        in_channels (int): number of channels of the input tensor.
+        out_chanels (int): number of channels of the output tensor.
+        hidden_channels (int): model hidden channels.
+        hidden_channels_ffn (int): hidden channels of FeedForwardNetwork.
+        num_heads (int): number of attention heads.
+        num_layers (int): number of transformer layers.
+        kernel_size (int, optional): kernel size of feed-forward inner layers. Defaults to 1.
+        dropout_p (float, optional): dropout rate for self-attention and feed-forward inner layers_per_stack. Defaults to 0.
+        rel_attn_window_size (int, optional): relation attention window size.
+            If 4, for each time step next and previous 4 time steps are attended.
+            If default, relative encoding is disabled and it is a regular transformer.
+            Defaults to None.
+        input_length (int, optional): input lenght to limit position encoding. Defaults to None.
+        layer_norm_type (str, optional): type "1" uses torch tensor operations and type "2" uses torch layer_norm
+            primitive. Use type "2", type "1: is for backward compat. Defaults to "1".
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        hidden_channels: int,
+        hidden_channels_ffn: int,
+        num_heads: int,
+        num_layers: int,
+        kernel_size=1,
+        dropout_p=0.0,
+        rel_attn_window_size: int = None,
+        input_length: int = None,
+        layer_norm_type: str = "1",
+        num_adaptive_weight_classes: int = None,
+    ):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.hidden_channels_ffn = hidden_channels_ffn
+        self.num_heads = num_heads
+        self.num_layers = num_layers
+        self.kernel_size = kernel_size
+        self.dropout_p = dropout_p
+        self.rel_attn_window_size = rel_attn_window_size
+
+        self.dropout = nn.Dropout(dropout_p)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+
+        for idx in range(self.num_layers):
+            self.attn_layers.append(
+                RelativePositionMultiHeadAttention(
+                    hidden_channels if idx != 0 else in_channels,
+                    hidden_channels,
+                    num_heads,
+                    rel_attn_window_size=rel_attn_window_size,
+                    dropout_p=dropout_p,
+                    input_length=input_length,
+                    num_adaptive_weight_classes=num_adaptive_weight_classes,
+                )
+            )
+            if layer_norm_type == "1":
+                self.norm_layers_1.append(LayerNorm(hidden_channels))
+            elif layer_norm_type == "2":
+                self.norm_layers_1.append(LayerNorm2(hidden_channels))
+            else:
+                raise ValueError(" [!] Unknown layer norm type")
+
+            if hidden_channels != out_channels and (idx + 1) == self.num_layers:
+                self.proj = Conv1d(hidden_channels, out_channels, 1, r=1 if num_adaptive_weight_classes else 0, num_classes=num_adaptive_weight_classes)
+
+            self.ffn_layers.append(
+                FeedForwardNetwork(
+                    hidden_channels,
+                    hidden_channels if (idx + 1) != self.num_layers else out_channels,
+                    hidden_channels_ffn,
+                    kernel_size,
+                    dropout_p=dropout_p,
+                    num_adaptive_weight_classes=num_adaptive_weight_classes,
+                )
+            )
+
+            if layer_norm_type == "1":
+                self.norm_layers_2.append(LayerNorm(hidden_channels if (idx + 1) != self.num_layers else out_channels))
+            elif layer_norm_type == "2":
+                self.norm_layers_2.append(LayerNorm2(hidden_channels if (idx + 1) != self.num_layers else out_channels))
+            else:
+                raise ValueError(" [!] Unknown layer norm type")
+
+    def forward(self, x, x_mask, class_id=None):
+        """
+        Shapes:
+            - x: :math:`[B, C, T]`
+            - x_mask: :math:`[B, 1, T]`
+        """
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        for i in range(self.num_layers):
+            x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask, class_id=class_id)
+            y = self.dropout(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask, class_id=class_id)
+            y = self.dropout(y)
+
+            if (i + 1) == self.num_layers and hasattr(self, "proj"):
+                x = self.proj(x, class_id=class_id)
+
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x
+
+class TextEncoder(nn.Module):
+    def __init__(
+        self,
+        n_vocab: int,
+        out_channels: int,
+        hidden_channels: int,
+        hidden_channels_ffn: int,
+        num_heads: int,
+        num_layers: int,
+        kernel_size: int,
+        dropout_p: float,
+        language_emb_dim: int = None,
+        num_adaptive_weight_classes: int = None,
+    ):
+        """Text Encoder for VITS model.
+
+        Args:
+            n_vocab (int): Number of characters for the embedding layer.
+            out_channels (int): Number of channels for the output.
+            hidden_channels (int): Number of channels for the hidden layers.
+            hidden_channels_ffn (int): Number of channels for the convolutional layers.
+            num_heads (int): Number of attention heads for the Transformer layers.
+            num_layers (int): Number of Transformer layers.
+            kernel_size (int): Kernel size for the FFN layers in Transformer network.
+            dropout_p (float): Dropout rate for the Transformer layers.
+        """
+        super().__init__()
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+
+        self.emb = nn.Embedding(n_vocab, hidden_channels)
+
+        nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5)
+
+        if language_emb_dim:
+            hidden_channels += language_emb_dim
+
+        self.encoder = RelativePositionTransformer(
+            in_channels=hidden_channels,
+            out_channels=hidden_channels,
+            hidden_channels=hidden_channels,
+            hidden_channels_ffn=hidden_channels_ffn,
+            num_heads=num_heads,
+            num_layers=num_layers,
+            kernel_size=kernel_size,
+            dropout_p=dropout_p,
+            layer_norm_type="2",
+            rel_attn_window_size=4,
+            num_adaptive_weight_classes=num_adaptive_weight_classes,
+        )
+
+        self.proj = Conv1d(hidden_channels, out_channels * 2, 1, r=1 if num_adaptive_weight_classes else 0, num_classes=num_adaptive_weight_classes)
+
+    def forward(self, x, x_lengths, lang_emb=None, class_id=None):
+        """
+        Shapes:
+            - x: :math:`[B, T]`
+            - x_length: :math:`[B]`
+        """
+        assert x.shape[0] == x_lengths.shape[0]
+        x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
+
+        # concat the lang emb in embedding chars
+        if lang_emb is not None:
+            x = torch.cat((x, lang_emb.transpose(2, 1).expand(x.size(0), x.size(1), -1)), dim=-1)
+
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)  # [b, 1, t]
+
+        x = self.encoder(x * x_mask, x_mask, class_id=class_id)
+        stats = self.proj(x, class_id=class_id) * x_mask
+
+        m, logs = torch.split(stats, self.out_channels, dim=1)
+        return x, m, logs, x_mask
+
+if __name__ == '__main__':
+    txt_enc = TextEncoder(
+        n_vocab=100,
+        out_channels=192,
+        hidden_channels=192,
+        hidden_channels_ffn=80,
+        num_heads=2,
+        num_layers=1,
+        kernel_size=3,
+        dropout_p=0.0,
+        language_emb_dim=None,
+        num_adaptive_weight_classes=5,
+    )
+
+    B = 3
+    T = 50
+    x = torch.ones(B, 100).long()
+    x_lengths = torch.ones(B).long()
+    x_lengths[0]=50
+    class_id = 1
+
+    out = txt_enc(
+        x,
+        x_lengths,
+        class_id=class_id
+    )
+    print("With adaptive Weights:",out[0].shape)
+
+    txt_enc = TextEncoder(
+        n_vocab=100,
+        out_channels=192,
+        hidden_channels=192,
+        hidden_channels_ffn=80,
+        num_heads=2,
+        num_layers=1,
+        kernel_size=3,
+        dropout_p=0.0,
+        language_emb_dim=None,
+        num_adaptive_weight_classes=5,
+    )
+
+    out = txt_enc(
+        x,
+        x_lengths,
+        class_id=class_id
+    )
+    print("Without adaptive Weights:",out[0].shape)

TTS/tts/models/vits.py

@@ -22,7 +22,8 @@ from TTS.tts.configs.shared_configs import CharactersConfig
 from TTS.tts.datasets.dataset import TTSDataset, _parse_sample
 from TTS.tts.layers.glow_tts.duration_predictor import DurationPredictor
 from TTS.tts.layers.vits.discriminator import VitsDiscriminator
-from TTS.tts.layers.vits.networks import PosteriorEncoder, ResidualCouplingBlocks, TextEncoder
+from TTS.tts.layers.vits.networks import PosteriorEncoder, ResidualCouplingBlocks
+from TTS.tts.layers.vits.text_encoder import TextEncoder
 from TTS.tts.layers.vits.stochastic_duration_predictor import StochasticDurationPredictor
 from TTS.tts.models.base_tts import BaseTTS
 from TTS.tts.utils.fairseq import rehash_fairseq_vits_checkpoint