From 5094499eba440efd41031ad8d7739c4b49c6045b Mon Sep 17 00:00:00 2001
From: vanIvan <alfa1211@gmail.com>
Date: Tue, 26 Jul 2022 16:05:11 +0300
Subject: [PATCH] Fix & update WaveRNN vocoder model (#1749)

* Fixes KeyError bug. Adding logging to dashboard.

* Make pep8 compliant

* Make style compliant

* Still fixing style
---
 TTS/vocoder/models/wavernn.py | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/TTS/vocoder/models/wavernn.py b/TTS/vocoder/models/wavernn.py
index 6686db45..e0a25e32 100644
--- a/TTS/vocoder/models/wavernn.py
+++ b/TTS/vocoder/models/wavernn.py
@@ -233,6 +233,7 @@ class Wavernn(BaseVocoder):
         else:
             raise RuntimeError("Unknown model mode value - ", self.args.mode)
 
+        self.ap = AudioProcessor(**config.audio.to_dict())
         self.aux_dims = self.args.res_out_dims // 4
 
         if self.args.use_upsample_net:
@@ -571,7 +572,7 @@ class Wavernn(BaseVocoder):
     def test(
         self, assets: Dict, test_loader: "DataLoader", output: Dict  # pylint: disable=unused-argument
     ) -> Tuple[Dict, Dict]:
-        ap = assets["audio_processor"]
+        ap = self.ap
         figures = {}
         audios = {}
         samples = test_loader.dataset.load_test_samples(1)
@@ -587,8 +588,16 @@ class Wavernn(BaseVocoder):
                 }
             )
             audios.update({f"test_{idx}/audio": y_hat})
+            # audios.update({f"real_{idx}/audio": y_hat})
         return figures, audios
 
+    def test_log(
+        self, outputs: Dict, logger: "Logger", assets: Dict, steps: int  # pylint: disable=unused-argument
+    ) -> Tuple[Dict, np.ndarray]:
+        figures, audios = outputs
+        logger.eval_figures(steps, figures)
+        logger.eval_audios(steps, audios, self.ap.sample_rate)
+
     @staticmethod
     def format_batch(batch: Dict) -> Dict:
         waveform = batch[0]
@@ -605,7 +614,7 @@ class Wavernn(BaseVocoder):
         verbose: bool,
         num_gpus: int,
     ):
-        ap = assets["audio_processor"]
+        ap = self.ap
         dataset = WaveRNNDataset(
             ap=ap,
             items=samples,