diff --git a/volia/stats.py b/volia/stats.py
index c12c5cd..fe20213 100644
--- a/volia/stats.py
+++ b/volia/stats.py
@@ -9,30 +9,12 @@ import scipy.stats
 import pickle
 import matplotlib.pyplot as plt
 import matplotlib.colors as mcolors
-
+from utils import SubCommandRunner
 
 
 from cycler import cycler
 
-def stats():
-    print("Decisions")
-
-
-print(list(mcolors.TABLEAU_COLORS))
-
-
-if __name__ == "__main__":
-
-    # Parser
-    parser = argparse.ArgumentParser(description="")
-
-    # Arguments
-    parser.add_argument("--predictions", type=str, help="prediction file", required=True)
-    parser.add_argument("--labels", type=str, help="label file", required=True)
-    parser.add_argument("--labelencoder", type=str, help="label encode pickle file", required=True)
-    parser.add_argument("--outdir", type=str, help="output file", required=True)
-    
-    args = parser.parse_args()
+def pred_distribution(predictions: str, labels: str, labelencoder: str, outdir: str):
 
     predictions = core.data.read_id_values(args.predictions, float)
     labels = core.data.read_labels(args.labels)
@@ -108,7 +90,57 @@ if __name__ == "__main__":
         plt.savefig(os.path.join(args.outdir, f"{label}_prediction_cdf.pdf"))
         plt.clf()
     
+    print("Decisions")
+
+
+def utt2dur(utt2dur: str, labels: str):
+    if labels == None:
+        pass
+    else:
+        pass
+
+    durations = []
+    with open(utt2dur, "r") as f:
+        for line in f:
+            splited = line.replace("\n", "").split(" ")
+            durations.append(float(splited[1]))
+    
+    durations = np.asarray(durations, dtype=float)
+    print(durations.shape)
+    mean = np.mean(durations)
+    std = np.std(durations)
 
-    # TODO: 
-    # One graph for each label. Distribution of their predictions output are displayed.
-        
+    print(f"mean: {mean}")
+    print(f"std: {std}")
+
+
+if __name__ == "__main__":
+
+    # Parser
+    parser = argparse.ArgumentParser(description="Statistics")
+    subparsers = parser.add_subparsers(title="actions")
+
+    # pred-distribution
+    parser_pred_dist = subparsers.add_parser("pred-distribution", help="plot distributions of prediction through labels")
+    parser_pred_dist.add_argument("--predictions", type=str, help="prediction file", required=True)
+    parser_pred_dist.add_argument("--labels", type=str, help="label file", required=True)
+    parser_pred_dist.add_argument("--labelencoder", type=str, help="label encode pickle file", required=True)
+    parser_pred_dist.add_argument("--outdir", type=str, help="output file", required=True)
+    parser_pred_dist.set_defaults(which="pred_distribution")
+
+    # duration-stats
+    parser_utt2dur = subparsers.add_parser("utt2dur", help="distribution of utt2dur")
+    parser_utt2dur.add_argument("--utt2dur", type=str, help="utt2dur file", required=True)
+    parser_utt2dur.add_argument("--labels", type=str, default=None, help="labels file")
+    parser_utt2dur.set_defaults(which="utt2dur")
+
+    # Parse
+    args = parser.parse_args()
+
+    # Run commands
+    runner = SubCommandRunner({
+        "pred-distribution": pred_distribution,
+        "utt2dur": utt2dur
+    })
+
+    runner.run(args.which, args.__dict__, remove="which")
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