diff --git a/.compute b/.compute
index c4bf43c9..7978efd3 100644
--- a/.compute
+++ b/.compute
@@ -3,5 +3,5 @@ ls ${SHARED_DIR}/data/
 pip3 install https://download.pytorch.org/whl/cu100/torch-1.0.1.post2-cp36-cp36m-linux_x86_64.whl
 yes | apt-get install espeak 
 python3 setup.py develop
-# python3 distribute.py --config_path config_cluster.json  --data_path ${SHARED_DIR}/data/Blizzard/Nancy/  --restore_path ${USER_DIR}/best_model.pth.tar
-python3 distribute.py --config_path config_cluster.json  --data_path ${SHARED_DIR}/data/Blizzard/Nancy/
+python3 distribute.py --config_path config_cluster.json  --data_path ${SHARED_DIR}/data/Blizzard/Nancy/  --restore_path ${USER_DIR}/best_model_4258.pth.tar
+# python3 distribute.py --config_path config_cluster.json  --data_path ${SHARED_DIR}/data/Blizzard/Nancy/
diff --git a/config_cluster.json b/config_cluster.json
index d707b929..ae28c765 100644
--- a/config_cluster.json
+++ b/config_cluster.json
@@ -55,7 +55,7 @@
     "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
     "min_seq_len": 0,       // DATASET-RELATED: minimum text length to use in training
-    "max_seq_len": 1000,     // DATASET-RELATED: maximum text length
+    "max_seq_len": 50,     // DATASET-RELATED: maximum text length
     "output_path": "../keep/",      // DATASET-RELATED: 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.