diff --git a/CookieTTS/_2_ttm/VDVAETTS/model.py b/CookieTTS/_2_ttm/VDVAETTS/model.py
index dd60d35..f0681de 100644
--- a/CookieTTS/_2_ttm/VDVAETTS/model.py
+++ b/CookieTTS/_2_ttm/VDVAETTS/model.py
@@ -1443,10 +1443,10 @@ def update_device(self, **inputs):
                 outputs[key] = input
         return outputs
     
-    def inference(self, text_seq, text_lengths, speaker_id, torchmoji_hdn,
+    def inference(self, text_seq, text_lengths, speaker_id, torchmoji_hdn, multispeaker_mode,
                     char_sigma=1.0, frame_sigma=1.0,
                     bn_logdur=None, char_dur=None, gt_mel=None, alignment=None,
-                    mel_lengths=None,):# [B, enc_T], [B], [B], [B], [B, tm_dim]
+                    mel_lengths=None):# [B, enc_T], [B], [B], [B], [B, tm_dim]
         outputs = {}
         
         memory = []
@@ -1458,7 +1458,18 @@ def inference(self, text_seq, text_lengths, speaker_id, torchmoji_hdn,
         # (Speaker) speaker_id -> speaker_embed
         if hasattr(self, "speaker_embedding"):
             speaker_embed = self.speaker_embedding(speaker_id)# [B, embed]
-            outputs["speaker_embed"] = speaker_embed# [B, embed]
+            if multispeaker_mode == "hybrid_voices" and speaker_embed.shape[0] > 1:
+              splits = int(speaker_embed.shape[0] / 2)
+              mix_1, mix_2 = torch.split(speaker_embed, splits)
+              speaker_embed = torch.add(mix_1, mix_2)
+              speaker_embed = torch.div(speaker_embed, 2)
+              speaker_embed = speaker_embed.repeat(2, 1)
+            #outputs["speaker_embed"] = speaker_embed# [B, embed]
+            #speaker_embed_mix = self.speaker_embedding(speaker_mix)# [B, embed]
+            #outputs["speaker_embed_mix"] = speaker_embed_mix# [B, embed]
+            #print(speaker_embed_mix)
+            #speaker_embed = torch.div(torch.add(speaker_embed, speaker_embed_mix), 2)
+            outputs["speaker_embed"] = speaker_embed
         
         # (TorchMoji)
         if hasattr(self, 'tm_bn'):
diff --git a/CookieTTS/_2_ttm/VDVAETTS/train.py b/CookieTTS/_2_ttm/VDVAETTS/train.py
index 42aece9..49981bd 100644
--- a/CookieTTS/_2_ttm/VDVAETTS/train.py
+++ b/CookieTTS/_2_ttm/VDVAETTS/train.py
@@ -1005,4 +1005,3 @@ def train(args, rank, group_name, hparams):
         pass
     
     train(args, args.rank, args.group_name, hparams)
-
diff --git a/CookieTTS/_5_infer/VDVAETTS_server/templates/main.html b/CookieTTS/_5_infer/VDVAETTS_server/templates/main.html
index a767b71..f7f6c32 100644
--- a/CookieTTS/_5_infer/VDVAETTS_server/templates/main.html
+++ b/CookieTTS/_5_infer/VDVAETTS_server/templates/main.html
@@ -127,6 +127,7 @@ <h1 class="display-4">Text To Speech</h1>
                                 <option value="cycle_next" {{ 'selected' if multispeaker_mode == 'cycle_next' else '' }}>Cycle Next (go down the list, use the next speaker for each text then loop back to the top)</option>
                                 <option value="interleaved" {{ 'selected' if multispeaker_mode == 'interleaved' else '' }}>Interleaved (seperate speaker for each text segment, going from top to bottom)</option>
                                 <option value="not_interleaved" {{ 'selected' if multispeaker_mode == 'not_interleaved' else '' }}>Non-Interleaved (speaker going from top to bottom per clip attempt)</option>
+                                <option value="hybrid_voices" {{ 'selected' if multispeaker_mode == 'hybrid_voices' else '' }}>Hybrid Voices (Combine voices into one)</option>
                             </select></div>
                         <div class="form-group">
                             <label for="select">Global Sigma Scaler</label>
diff --git a/CookieTTS/_5_infer/VDVAETTS_server/text2speech.py b/CookieTTS/_5_infer/VDVAETTS_server/text2speech.py
index b8ed22f..e28cfd7 100644
--- a/CookieTTS/_5_infer/VDVAETTS_server/text2speech.py
+++ b/CookieTTS/_5_infer/VDVAETTS_server/text2speech.py
@@ -436,20 +436,25 @@ def shuffle_and_return():
                     speaker_names.append(speaker_names.pop(0))
                     return first_speaker
                 batch_speaker_names = [shuffle_and_return() for i in range(simultaneous_texts)]
+            elif multispeaker_mode == "hybrid_voices":
+                batch_speaker_names = speaker_names * -(-simultaneous_texts//len(speaker_names))
             else:
                 raise NotImplementedError
             
             if 0:# (optional) use different speaker list for text inside quotes
                 speaker_ids = [random.choice(speakers).split("|")[2] if ('"' in text) else random.choice(narrators).split("|")[2] for text in text_batch] # pick speaker if quotemark in text, else narrator
-            text_batch  = [text.replace('"',"") for text in text_batch] # remove quotes from text
+                text_batch  = [text.replace('"',"") for text in text_batch] # remove quotes from text
             
             if len(batch_speaker_names) > len(text_batch):
                 batch_speaker_names = batch_speaker_names[:len(text_batch)]
-                simultaneous_texts = len(text_batch)
+            simultaneous_texts = len(text_batch)
             
             # get speaker_ids (VDVAETTS)
             VDVAETTS_speaker_ids = [self.ttm_sp_name_lookup[speaker] for speaker in batch_speaker_names]
             VDVAETTS_speaker_ids = torch.LongTensor(VDVAETTS_speaker_ids).cuda().repeat_interleave(batch_size_per_text)
+            #VDVAETTS_speaker_mix = [44]
+            #print(VDVAETTS_speaker_mix)
+            #VDVAETTS_speaker_mix = torch.LongTensor(VDVAETTS_speaker_mix).cuda().repeat_interleave(batch_size_per_text)
             
             # get style input
             try:
@@ -503,7 +508,7 @@ def shuffle_and_return():
                 while np.amin(best_score) < target_score:
                     # run VDVAETTS
                     if status_updates: print("..", end='')
-                    outputs = self.VDVAETTS.inference(sequence, text_lengths.repeat_interleave(batch_size_per_text, dim=0), VDVAETTS_speaker_ids, style_input, char_sigma=char_sigma, frame_sigma=frame_sigma)
+                    outputs = self.VDVAETTS.inference(sequence, text_lengths.repeat_interleave(batch_size_per_text, dim=0), VDVAETTS_speaker_ids, style_input, multispeaker_mode, char_sigma=char_sigma, frame_sigma=frame_sigma)
                     batch_pred_mel = outputs['hifigan_inputs'] if self.MTW_conf.uses_latent_input else outputs['pred_mel']
                     
                     # metric for html side