diff --git a/e2_tts_pytorch/e2_tts.py b/e2_tts_pytorch/e2_tts.py
index e5351d2..4cb3554 100644
--- a/e2_tts_pytorch/e2_tts.py
+++ b/e2_tts_pytorch/e2_tts.py
@@ -189,6 +189,15 @@ def pad_to_length(
 
     return t[..., :length]
 
+def interpolate_1d(
+    x: Tensor,
+    length: int,
+    mode = 'bilinear'
+):
+    x = rearrange(x, 'n d -> 1 d n 1')
+    x = F.interpolate(x, (length, 1), mode = mode)
+    return rearrange(x, '1 d n 1 -> n d')
+
 # to mel spec
 
 class MelSpec(Module):
@@ -287,6 +296,7 @@ def forward(
         self,
         text: Int['b nt'],
         max_seq_len: int,
+        **kwargs
     ) -> Float['b n d']:
 
         text = text + 1 # shift all other token ids up by 1 and use 0 as filler token
@@ -296,6 +306,77 @@ def forward(
 
         return self.embed(text)
 
+class InterpolatedCharacterEmbed(Module):
+    def __init__(
+        self,
+        dim,
+        num_embeds = 256,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.embed = nn.Embedding(num_embeds, dim)
+
+        self.abs_pos_mlp = Sequential(
+            Rearrange('... -> ... 1'),
+            Linear(1, dim),
+            nn.SiLU(),
+            Linear(dim, dim)
+        )
+
+    def forward(
+        self,
+        text: Int['b nt'],
+        max_seq_len: int,
+        mask: Bool['b n'] | None = None
+    ) -> Float['b n d']:
+
+        device = text.device
+        seq = torch.arange(max_seq_len, device = device)
+
+        mask = default(mask, (None,))
+
+        interp_embeds = []
+        interp_abs_positions = []
+
+        for one_text, one_mask in zip_longest(text, mask):
+
+            valid_text = one_text >= 0
+            one_text = one_text[valid_text]
+            one_text_embed = self.embed(one_text)
+
+            # save the absolute positions
+
+            text_seq_len = one_text.shape[0]
+
+            # determine audio sequence length from mask
+
+            audio_seq_len = max_seq_len
+            if exists(one_mask):
+                audio_seq_len = one_mask.sum().long().item()
+
+            # interpolate text embedding to audio embedding length
+
+            interp_text_embed = interpolate_1d(one_text_embed, audio_seq_len)
+            interp_abs_pos = torch.linspace(0, text_seq_len, audio_seq_len, device = device)
+
+            interp_embeds.append(interp_text_embed)
+            interp_abs_positions.append(interp_abs_pos)
+
+        interp_embeds = pad_sequence(interp_embeds)
+        interp_abs_positions = pad_sequence(interp_abs_positions)
+
+        interp_embeds = F.pad(interp_embeds, (0, 0, 0, max_seq_len - interp_embeds.shape[-2]))
+        interp_abs_positions = pad_to_length(interp_abs_positions, max_seq_len)
+
+        # pass interp absolute positions through mlp for implicit positions
+
+        interp_embeds = interp_embeds + self.abs_pos_mlp(interp_abs_positions)
+
+        if exists(mask):
+            interp_embeds = einx.where('b n, b n d, -> b n d', mask, interp_embeds, 0.)
+
+        return interp_embeds
+
 # text audio cross conditioning in multistream setup
 
 class TextAudioCrossCondition(Module):
@@ -303,7 +384,7 @@ def __init__(
         self,
         dim,
         dim_text,
-        cond_audio_to_text = True
+        cond_audio_to_text = True,
     ):
         super().__init__()
         self.text_to_audio = nn.Linear(dim_text + dim, dim, bias = False)
@@ -612,7 +693,10 @@ def __init__(
         self.num_channels = default(num_channels, self.mel_spec.n_mel_channels)
 
         self.transformer = transformer
+
         dim = transformer.dim
+        dim_text = transformer.dim_text
+
         self.dim = dim
 
         self.proj_in = Linear(self.num_channels, self.dim)
@@ -630,7 +714,7 @@ def __init__(
         else:
             raise ValueError(f'unknown tokenizer string {tokenizer}')
 
-        self.embed_text = CharacterEmbed(transformer.dim_text, num_embeds = text_num_embeds, **char_embed_kwargs)
+        self.embed_text = CharacterEmbed(dim_text, num_embeds = text_num_embeds, **char_embed_kwargs)
 
         # to prediction
 
@@ -726,6 +810,7 @@ def __init__(
         mel_spec_kwargs: dict = dict(),
         frac_lengths_mask: tuple[float, float] = (0.7, 1.),
         concat_cond = False,
+        interpolated_text = False,
         text_num_embeds: int | None = None,
         tokenizer: (
             Literal['char_utf8', 'phoneme_en'] |
@@ -801,7 +886,9 @@ def __init__(
 
         # text embedding
 
-        self.embed_text = CharacterEmbed(dim_text, num_embeds = text_num_embeds, **char_embed_kwargs)
+        text_embed_klass = CharacterEmbed if not interpolated_text else InterpolatedCharacterEmbed
+
+        self.embed_text = text_embed_klass(dim_text, num_embeds = text_num_embeds, **char_embed_kwargs)
 
         # default vocos for mel -> audio
 
@@ -840,7 +927,7 @@ def transformer_with_pred_head(
 
         text_embed = None
         if exists(text) and not drop_text_cond:
-            text_embed = self.embed_text(text, seq_len)
+            text_embed = self.embed_text(text, seq_len, mask = mask)
 
         # attend
 
diff --git a/pyproject.toml b/pyproject.toml
index 5d7d752..d2db022 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "e2-tts-pytorch"
-version = "1.0.4"
+version = "1.0.5"
 description = "E2-TTS in Pytorch"
 authors = [
     { name = "Phil Wang", email = "lucidrains@gmail.com" }