diff --git a/README.md b/README.md
index f721ab0..586df14 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,7 @@
-# mayavoz
+<p align="center">
+  <img src="https://user-images.githubusercontent.com/25312635/195507951-fe64657c-9114-4d78-b04e-444e6d5bbcc4.png" />
+</p>
+
 mayavoz is a Pytorch-based opensource toolkit for speech enhancement. It is designed to save time for audio researchers. Is provides easy to use pretrained audio enhancement models and facilitates highly customisable custom model training .
 
 | **[Quick Start]()** | **[Installation]()** | **[Tutorials]()** | **[Available Recipes]()**
diff --git a/enhancer/loss.py b/enhancer/loss.py
index cdd15a5..5092656 100644
--- a/enhancer/loss.py
+++ b/enhancer/loss.py
@@ -1,5 +1,6 @@
 import logging
 
+import numpy as np
 import torch
 import torch.nn as nn
 from torchmetrics.audio.pesq import PerceptualEvaluationSpeechQuality
@@ -116,17 +117,24 @@ class Stoi:
 
 
 class Pesq:
-    def __init__(self, sr: int, mode="nb"):
+    def __init__(self, sr: int, mode="wb"):
 
-        self.pesq = PerceptualEvaluationSpeechQuality(fs=sr, mode=mode)
+        self.sr = sr
         self.name = "pesq"
+        self.mode = mode
+        self.pesq = PerceptualEvaluationSpeechQuality(fs=sr, mode=mode)
 
     def __call__(self, prediction: torch.Tensor, target: torch.Tensor):
-        try:
-            return self.pesq(prediction, target)
-        except Exception as e:
-            logging.warning(f"{e} error occured while calculating PESQ")
-            return torch.tensor(0.0)
+
+        pesq_values = []
+        for pred, target_ in zip(prediction, target):
+            try:
+                pesq_values.append(
+                    self.pesq(pred.squeeze(), target_.squeeze()).item()
+                )
+            except Exception as e:
+                logging.warning(f"{e} error occured while calculating PESQ")
+        return torch.tensor(np.mean(pesq_values))
 
 
 class LossWrapper(nn.Module):
@@ -177,7 +185,7 @@ class LossWrapper(nn.Module):
 LOSS_MAP = {
     "mae": mean_absolute_error,
     "mse": mean_squared_error,
-    "SI-SDR": Si_SDR,
+    "si-sdr": Si_SDR,
     "pesq": Pesq,
     "stoi": Stoi,
 }