diff --git a/.compute b/.compute
index 47d3419d..c1957615 100644
--- a/.compute
+++ b/.compute
@@ -4,4 +4,4 @@ pip3 install https://download.pytorch.org/whl/cu100/torch-1.0.1.post2-cp36-cp36m
 yes | apt-get install espeak 
 python3 setup.py develop
 # python3 distribute.py --config_path config_cluster.json  --data_path ${SHARED_DIR}/data/keithito/LJSpeech-1.1/  --restore_path ${USER_DIR}/best_model.pth.tar
-python3 distribute.py --config_path config_cluster.json  --data_path ${SHARED_DIR}/data/keithito/LJSpeech-1.1/
\ No newline at end of file
+python3 train.py --config_path config.json  --data_path ${SHARED_DIR}/data/keithito/LJSpeech-1.1/
\ No newline at end of file
diff --git a/config.json b/config.json
index 037f8249..d07ea7d0 100644
--- a/config.json
+++ b/config.json
@@ -42,7 +42,7 @@
     "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention.
     "eval_batch_size":32,   
     "r": 5,                 // Number of frames to predict for step.
-    "wd": 0.00001,          // Weight decay weight.
+    "wd": 0.000001,          // Weight decay weight.
     "checkpoint": true,     // If true, it saves checkpoints per "save_step"
     "save_step": 5000,      // Number of training steps expected to save traning stats and checkpoints.
     "print_step": 50,       // Number of steps to log traning on console.
diff --git a/config_cluster.json b/config_cluster.json
index efe53c8a..073923be 100644
--- a/config_cluster.json
+++ b/config_cluster.json
@@ -1,6 +1,6 @@
 {
     "model_name": "tts-master",
-    "model_description": "tts master cluster test",
+    "model_description": "tts master with symbols update",
 
     "audio":{
         "audio_processor": "audio",     // to use dictate different audio processors, if available.
@@ -25,6 +25,11 @@
         "do_trim_silence": true  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
     },
 
+    "distributed":{
+        "backend": "nccl",
+        "url": "tcp:\/\/localhost:54321"
+    },
+
     "embedding_size": 256,  // Character embedding vector length. You don't need to change it in general.
     "text_cleaner": "phoneme_cleaners",
     "epochs": 1000,         // total number of epochs to train.
@@ -46,9 +51,9 @@
 
     "run_eval": true,
     "data_path": "/media/erogol/data_ssd/Data/LJSpeech-1.1",  // DATASET-RELATED: can overwritten from command argument
-    "meta_file_train": "prompts_train.data",      // DATASET-RELATED: metafile for training dataloader.
-    "meta_file_val": "prompts_val.data",    // DATASET-RELATED: metafile for evaluation dataloader.
-    "dataset": "nancy",      // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
+    "meta_file_train": "metadata_train.csv",      // DATASET-RELATED: metafile for training dataloader.
+    "meta_file_val": "metadata_val.csv",    // DATASET-RELATED: metafile for evaluation dataloader.
+    "dataset": "ljspeech",      // DATASET-RELATED: 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,       // DATASET-RELATED: minimum text length to use in training
     "max_seq_len": 300,     // DATASET-RELATED: maximum text length
     "output_path": "models/",      // DATASET-RELATED: output path for all training outputs.