diff --git a/ranker.py b/ranker.py
new file mode 100644
index 00000000..f17b0cab
--- /dev/null
+++ b/ranker.py
@@ -0,0 +1,94 @@
+# python c:/.../ImageBind/ranker.py -d ImageBind/.assets -i ImageBind/.assets/bird_audio.wav -n 3 > output.txt
+
+import argparse, os, pathlib
+import torch
+from ImageBind.models import imagebind_model
+from ImageBind.models.imagebind_model import ModalityType
+import ImageBind.data as data
+
+args = argparse.ArgumentParser()
+args.add_argument("-d") # Directory of the dataset
+args.add_argument("-i") # Item to find the similarities with
+args.add_argument("-m") # Model path
+args.add_argument("-n") # Top N items to return
+args = args.parse_args()
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+modalities = {
+    ".wav" : ModalityType.AUDIO,
+    ".mp3" : ModalityType.AUDIO,
+    ".png" : ModalityType.VISION,
+    ".jpeg" : ModalityType.VISION,
+    ".jpg" : ModalityType.VISION,
+    ".mp4" : ModalityType.VISION,
+    ".txt": ModalityType.TEXT
+}
+
+preprocess_fn = {
+    ModalityType.AUDIO: data.load_and_transform_audio_data,
+    ModalityType.VISION: data.load_and_transform_vision_data,
+    ModalityType.TEXT: data.load_and_transform_text,
+}
+
+inputs = {
+    ModalityType.AUDIO: [],
+    ModalityType.VISION: [],
+    ModalityType.TEXT: [],
+}
+
+files = inputs.copy()
+
+for item in os.listdir(args.d):
+    path = pathlib.PureWindowsPath(os.path.join(args.d,item))
+    modality = modalities[path.suffix]
+
+    if modality == ModalityType.TEXT:
+        with open(item, "r") as f:
+            texts = f.readlines()
+            texts = [t.removesuffix("\n") for t in texts]
+            texts = " ".join(texts)
+            if texts != "":
+                files[modality].append(path)
+                inputs[modality].append(texts)
+    else:
+        inputs[modality].append(os.path.join(args.d,item))
+        
+keys = list(inputs.keys())
+for modality in keys:
+    if inputs[modality] == []:
+        inputs.pop(modality)
+
+keys = list(inputs.keys())
+inputs = {modality: preprocess_fn[modality](inputs[modality], device) for modality in inputs.keys()}
+
+model = torch.load(args.m) if args.m is not None else imagebind_model.imagebind_huge(pretrained=True)
+model.eval()
+model.to(device)
+
+with torch.no_grad():
+    embeddings = model(inputs)
+
+main_input_path = pathlib.PureWindowsPath(args.i)
+main_input_modality = modalities[main_input_path.suffix]
+main_input = {main_input_modality: preprocess_fn[main_input_modality]([main_input_path], device)}
+
+with torch.no_grad():
+    main_embedding = model(main_input)
+
+sim_matrices = {modality: torch.softmax(main_embedding[main_input_modality] @ embeddings[modality].T, dim=-1) for modality in inputs.keys()}
+
+scores={}
+for modality in inputs.keys():
+    for i in range(len(inputs[modality])):
+        score = sim_matrices[modality][0][i]
+        file = files[modality][i]
+        scores[file] = score
+
+def get_top_files(dictionary, N):
+    sorted_items = sorted(dictionary.items(), key=lambda x: x[1], reverse=True)
+    top_items = sorted_items[:N]
+    top_strings = [(item[0], f"{item[1]*100} %") for item in top_items]
+    return top_strings
+
+print(*get_top_files(scores, int(args.n) if args.n is not None else 5), sep="\n")