partial commit

README.md

@@ -106,6 +106,10 @@ You can also enjoy Tensorboard, if you point the Tensorboard argument```--logdir
 ## Testing
 Best way to test your pre-trained network is to use Notebooks under ```notebooks``` folder.
 
+## Logging 
+# TODO
+TTS enables intense logging on Tensorboard. 
+
 ## What is new with TTS
 If you train TTS with LJSpeech dataset, you start to hear reasonable results after 12.5K iterations with batch size 32. This is the fastest training with character based methods up to our knowledge. Out implementation is also quite robust against long sentences.
 

config.json

@@ -49,7 +49,7 @@
     "dataset": "tweb",      // one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
     "min_seq_len": 0,       // minimum text length to use in training
     "max_seq_len": 300,     // maximum text length
-    "output_path": "../keep/",      // output path for all training outputs.
+    "output_path": "/media/erogol/data_ssd/Data/models/tweb_models/",      // output path for all training outputs.
     "num_loader_workers": 8,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
     "num_val_loader_workers": 4     // number of evaluation data loader processes.
 }