misc

codes/models/tacotron2/text/__init__.py

@@ -62,6 +62,10 @@ def tacotron_symbols():
   return list(_symbol_to_id.keys())
 
 
+def tacotron_symbol_mapping():
+  return _symbol_to_id.copy()
+
+
 def _clean_text(text, cleaner_names):
   for name in cleaner_names:
     cleaner = getattr(cleaners, name)

codes/train.py

@@ -317,7 +317,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/train_wav2vec_mass_large_/train_wav2vec_mass.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/debug_diffusion_tts7.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()

codes/trainer/eval/eval_wer.py

@@ -131,7 +131,7 @@ if __name__ == '__main__':
         #'kenlm_path': 'Y:\\bookscorpus-5gram\\5gram.bin',
     }
     model = Wav2VecWrapper(vocab_size=148, basis_model='facebook/wav2vec2-large-robust-ft-libri-960h', freeze_transformer=True, checkpointing_enabled=False)
-    weights = torch.load('D:\\dlas\\experiments\\train_wav2vec_mass_large2\\models\\17000_wav2vec.pth')
+    weights = torch.load('D:\\dlas\\experiments\\train_wav2vec_mass_large2\\models\\22500_wav2vec.pth')
     model.load_state_dict(weights)
     model = model.cuda()
     eval = WerEvaluator(model, opt_eval, env)