Add logic for performing inference using gpt_tts with dual-encoder modes

codes/models/gpt_voice/gpt_tts.py

@@ -90,11 +90,26 @@ class GptTts(nn.Module):
         if len(mel_seq) >= self.max_mel_frames:
             print("Warning! Encountered frame limit before a stop token. Output is likely wrong.")
 
-        # Prevent sending invalid tokens to the VAE. Also pad to a length of 3, which is required by the DVAE.
+        # Format mel_seq so that the DVAE can actually use it (it is a two-tiered DVAE)
-        mel_seq = mel_seq * (mel_seq < 512)
+        cleaned = []
-        padding_needed = 3-(mel_seq.shape[1]%3)
+        for j in range(mel_seq.shape[0]):
-        mel_seq = F.pad(mel_seq, (0,padding_needed))
+            s = mel_seq[j][1:-1]  # Strip out BOS and EOS tokens.
-        return mel_seq
+            gt = s >= 512
+            l = (len(s)) // 3
+            for i in reversed(range(l)):
+                if gt[i]:
+                    l = i+1
+                    break
+            top = s[:l]
+            top = top + (top < 512) * 512
+            bottom = s[l:l*3]
+            bottom = bottom * (bottom < 512)
+            combined = torch.cat([top,bottom], dim=0)
+            assert not torch.any(combined < 0)
+            combined = combined * (combined < 1024)
+            cleaned.append(combined)
+
+        return torch.stack(cleaned)
 
 
 @register_model