diff --git a/esc50.py b/esc50.py
index e69de29..d9c08a3 100644
--- a/esc50.py
+++ b/esc50.py
@@ -0,0 +1,82 @@
+from torch.utils.data import Dataset
+from torchvision.datasets.utils import download_url
+from tqdm import tqdm
+import pandas as pd
+import os
+import torch.nn as nn
+import torch
+
+class AudioDataset(Dataset):
+    def __init__(self, root: str, download: bool = True):
+        self.root = os.path.expanduser(root)
+        if download:
+            self.download()
+
+    def __getitem__(self, index):
+        raise NotImplementedError
+
+    def download(self):
+        raise NotImplementedError
+
+    def __len__(self):
+        raise NotImplementedError
+
+
+class ESC50(AudioDataset):
+    base_folder = 'ESC-50-master'
+    url = "https://github.com/karolpiczak/ESC-50/archive/refs/heads/master.zip"
+    filename = "ESC-50-master.zip"
+    num_files_in_dir = 2000
+    audio_dir = 'audio'
+    label_col = 'category'
+    file_col = 'filename'
+    meta = {
+        'filename': os.path.join('meta','esc50.csv'),
+    }
+
+    def __init__(self, root, reading_transformations: nn.Module = None, download: bool = True):
+        super().__init__(root)
+        self._load_meta()
+
+        self.targets, self.audio_paths = [], []
+        self.pre_transformations = reading_transformations
+        print("Loading audio files")
+        # self.df['filename'] = os.path.join(self.root, self.base_folder, self.audio_dir) + os.sep + self.df['filename']
+        self.df['category'] = self.df['category'].str.replace('_',' ')
+
+        for _, row in tqdm(self.df.iterrows()):
+            file_path = os.path.join(self.root, self.base_folder, self.audio_dir, row[self.file_col])
+            self.targets.append(row[self.label_col])
+            self.audio_paths.append(file_path)
+
+    def _load_meta(self):
+        path = os.path.join(self.root, self.base_folder, self.meta['filename'])
+
+        self.df = pd.read_csv(path)
+        self.class_to_idx = {}
+        self.classes = [x.replace('_',' ') for x in sorted(self.df[self.label_col].unique())]
+        for i, category in enumerate(self.classes):
+            self.class_to_idx[category] = i
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        file_path, target = self.audio_paths[index], self.targets[index]
+        idx = torch.tensor(self.class_to_idx[target])
+        one_hot_target = torch.zeros(len(self.classes)).scatter_(0, idx, 1).reshape(1,-1)
+        return file_path, target, one_hot_target
+
+    def __len__(self):
+        return len(self.audio_paths)
+
+    def download(self):
+        download_url(self.url, self.root, self.filename)
+
+        # extract file
+        from zipfile import ZipFile
+        with ZipFile(os.path.join(self.root, self.filename), 'r') as zip:
+            zip.extractall(path=self.root)
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diff --git a/main.py b/main.py
index e69de29..15d470f 100644
--- a/main.py
+++ b/main.py
@@ -0,0 +1,28 @@
+from CLAP_API import CLAP
+from esc50 import ESC50
+import time
+import torch.nn.functional as F
+
+start_time = time.time()
+weights_path = 'C:\\Users\\sdeshmukh\\Desktop\\CLAP_package\\model\\new\\best.pth'
+clap_model = CLAP(weights_path, use_cuda=False)
+print("Finished loading CLAP. Total time: {}".format(time.time() - start_time))
+
+esc50_dataset = ESC50(root='data', download=False)
+x, target = esc50_dataset[1000]
+x = [x]
+y = esc50_dataset.classes
+
+print('Computing text embeddings')
+text_embeddings = clap_model.get_text_embeddings(y)
+print('Computing audio embeddings')
+audio_embeddings = clap_model.get_audio_embeddings(x, resample=True)
+similarity = clap_model.compute_similarity(audio_embeddings, text_embeddings)
+
+similarity = F.softmax(similarity, dim=1)
+values, indices = similarity[0].topk(5)
+# Print the result
+print("Ground Truth: {}".format(target))
+print("Top predictions:\n")
+for value, index in zip(values, indices):
+    print(f"{y[index]:>16s}: {100 * value.item():.2f}%")
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