From d5c6d608848fab1643605a635cac28ab0cd930d7 Mon Sep 17 00:00:00 2001
From: erogol <erogol@hotmail.com>
Date: Tue, 4 Aug 2020 22:22:35 +0200
Subject: [PATCH] synthesis update for glow tts

---
 TTS/tts/utils/synthesis.py | 26 +++++++++++++++++---------
 1 file changed, 17 insertions(+), 9 deletions(-)

diff --git a/TTS/tts/utils/synthesis.py b/TTS/tts/utils/synthesis.py
index 76ac7909..85eeec66 100644
--- a/TTS/tts/utils/synthesis.py
+++ b/TTS/tts/utils/synthesis.py
@@ -46,16 +46,24 @@ def compute_style_mel(style_wav, ap, cuda=False):
 
 
 def run_model_torch(model, inputs, CONFIG, truncated, speaker_id=None, style_mel=None, speaker_embeddings=None):
-    if CONFIG.use_gst:
-        decoder_output, postnet_output, alignments, stop_tokens = model.inference(
-            inputs, style_mel=style_mel, speaker_ids=speaker_id, speaker_embeddings=speaker_embeddings)
-    else:
-        if truncated:
-            decoder_output, postnet_output, alignments, stop_tokens = model.inference_truncated(
-                inputs, speaker_ids=speaker_id, speaker_embeddings=speaker_embeddings)
-        else:
+    if 'tacotron' in CONFIG.model.lower():
+        if CONFIG.use_gst:
             decoder_output, postnet_output, alignments, stop_tokens = model.inference(
-                inputs, speaker_ids=speaker_id, speaker_embeddings=speaker_embeddings)
+                inputs, style_mel=style_mel, speaker_ids=speaker_id, speaker_embeddings=speaker_embeddings)
+        else:
+            if truncated:
+                decoder_output, postnet_output, alignments, stop_tokens = model.inference_truncated(
+                    inputs, speaker_ids=speaker_id, speaker_embeddings=speaker_embeddings)
+            else:
+                decoder_output, postnet_output, alignments, stop_tokens = model.inference(
+                    inputs, speaker_ids=speaker_id, speaker_embeddings=speaker_embeddings)
+    elif 'glow' in CONFIG.model.lower():
+        inputs_lengths = torch.tensor(inputs.shape[1:2]).to(inputs.device)
+        postnet_output, _, _, _, alignments, _, _ = model.inference(inputs, inputs_lengths)
+        postnet_output = postnet_output.permute(0, 2, 1)
+        # these only belong to tacotron models.
+        decoder_output = None
+        stop_tokens = None
     return decoder_output, postnet_output, alignments, stop_tokens