diff --git a/codes/models/gpt_voice/gpt_tts.py b/codes/models/gpt_voice/gpt_tts.py
index 96ad2ce4..b9884a3d 100644
--- a/codes/models/gpt_voice/gpt_tts.py
+++ b/codes/models/gpt_voice/gpt_tts.py
@@ -71,9 +71,9 @@ class GptTts(nn.Module):
         return loss_text.mean(), loss_mel.mean(), mel_codes, mel_targets
 
     def inference(self, text_inputs):
-        b, _ = text_inputs.shape
+        b, s = text_inputs.shape
         text_emb = self.text_embedding(text_inputs)
-        text_emb = text_emb + self.text_pos_embedding(torch.arange(text_inputs.shape[1], device=text_inputs.device))
+        text_emb = text_emb + self.text_pos_embedding(torch.arange(s, device=text_inputs.device))
 
         mel_seq = torch.full((b,1), fill_value=self.MEL_START_TOKEN, device=text_emb.device)
         stop_encountered = torch.zeros((b,), device=text_emb.device)
@@ -98,7 +98,46 @@ class GptTts(nn.Module):
         return mel_seq
 
     def inference_beam(self, text_inputs):
-        pass
+        beam_width = 16
+
+        b, s = text_inputs.shape
+        assert b == 1  # Beam search only works on batches of one.
+        text_emb = self.text_embedding(text_inputs)
+        text_emb = text_emb + self.text_pos_embedding(torch.arange(s, device=text_inputs.device))
+        mel_seq = torch.full((b,1), fill_value=self.MEL_START_TOKEN, device=text_emb.device)
+        probabilities = torch.ones((b,), device=text_emb.device)
+        while len(mel_seq) < self.max_mel_frames:
+            mel_emb = self.mel_embedding(mel_seq)
+            mel_emb = mel_emb + self.mel_pos_embedding(torch.arange(mel_emb.shape[1], device=mel_emb.device))
+            if text_emb.shape[0] != mel_emb.shape[0]:
+                text_emb = text_emb.repeat(mel_emb.shape[0], 1, 1)
+            emb = torch.cat([text_emb, mel_emb], dim=1)
+            enc = self.gpt(emb)
+            mel_logits = self.final_norm(enc[:, text_emb.shape[1]:])
+            mel_logits = self.mel_head(mel_logits)
+            topk = torch.topk(F.softmax(mel_logits[:, -1], dim=-1), dim=-1, k=beam_width)
+            probabilities = (probabilities.repeat_interleave(beam_width, dim=0) * topk.values.flatten())
+            probabilities, sort_indices = torch.sort(probabilities, descending=True)
+            probabilities = probabilities[:beam_width]
+
+            mel_seq = mel_seq.repeat_interleave(beam_width, dim=0)
+            codes = topk.indices.flatten()
+            mel_seq = torch.cat([mel_seq, codes.unsqueeze(1)], dim=1)
+            mel_seq = mel_seq[sort_indices]
+            mel_seq = mel_seq[:beam_width]
+
+            if torch.all(torch.any(mel_seq == self.MEL_STOP_TOKEN, dim=1)):
+                break
+
+        if mel_seq.shape[1] >= self.max_mel_frames:
+            print("Warning! Encountered frame limit before a stop token. Output is likely wrong.")
+
+        # Format mel_seq so that the DVAE can actually use it (it is a two-tiered DVAE)
+        mel_seq = mel_seq[0, 1:-1].unsqueeze(0)  # Pick most likely outcome, remove first and last tokens, which were artificially added for GPT
+        mel_seq = mel_seq * (mel_seq < 512)      # The DVAE doesn't understand BOS/EOS/PAD tokens.
+
+        return mel_seq
+
 
 
 @register_model