diff --git a/codes/models/audio/tts/unified_voice2.py b/codes/models/audio/tts/unified_voice2.py
index 80e7a394..3c72332c 100644
--- a/codes/models/audio/tts/unified_voice2.py
+++ b/codes/models/audio/tts/unified_voice2.py
@@ -243,7 +243,8 @@ class UnifiedVoice(nn.Module):
                  mel_length_compression=1024, number_text_tokens=256,
                  start_text_token=255, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192,
                  stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True,
-                 checkpointing=True, average_conditioning_embeddings=False, freeze_everything_but_position_embeddings=False):
+                 checkpointing=True, average_conditioning_embeddings=False, freeze_everything_but_position_embeddings=False,
+                 tortoise_compat=True):
         """
         Args:
             layers: Number of layers in transformer stack.
@@ -281,6 +282,7 @@ class UnifiedVoice(nn.Module):
         self.mel_length_compression = mel_length_compression
         self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
         self.average_conditioning_embeddings = average_conditioning_embeddings
+        self.tortoise_compat = tortoise_compat # credit to https://github.com/152334H/DL-Art-School/commit/ae80992817059acf6eef38a680efa5124cee570b
         # nn.Embedding
         self.text_embedding = ml.Embedding(self.number_text_tokens, model_dim)
         if use_mel_codes_as_input:
@@ -301,6 +303,7 @@ class UnifiedVoice(nn.Module):
         self.text_head = ml.Linear(model_dim, self.number_text_tokens)
         self.mel_head = ml.Linear(model_dim, self.number_mel_codes)
 
+
         # Initialize the embeddings per the GPT-2 scheme
         embeddings = [self.text_embedding]
         if use_mel_codes_as_input:
@@ -386,6 +389,8 @@ class UnifiedVoice(nn.Module):
         If return_attentions is specified, only logits are returned.
         If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
         """
+        if self.tortoise_compat:
+            wav_lengths *= self.mel_length_compression
         # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
         # chopping the inputs by the maximum actual length.
         max_text_len = text_lengths.max()
@@ -414,14 +419,15 @@ class UnifiedVoice(nn.Module):
         mel_emb = self.mel_embedding(mel_inp)
         mel_emb = mel_emb + self.mel_pos_embedding(mel_codes)
 
+        sub = -2 if self.tortoise_compat else -1
         if text_first:
             text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions, return_latent=return_latent)
             if return_latent:
-                return mel_logits[:, :-1]  # Despite the name, these are not logits.
+                return mel_logits[:, :-sub]  # Despite the name, these are not logits.
         else:
             mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions, return_latent=return_latent)
             if return_latent:
-                return text_logits[:, :-1]  # Despite the name, these are not logits
+                return text_logits[:, :-sub]  # Despite the name, these are not logits
 
         if return_attentions:
             return mel_logits