diff --git a/README.md b/README.md
index 7e0ec3a..25e8c6f 100644
--- a/README.md
+++ b/README.md
@@ -237,10 +237,13 @@ get_language_and_group, read_audio = utils
 files_dir = torch.hub.get_dir() + '/snakers4_silero-vad_master/files'
 
 wav = read_audio(f'{files_dir}/de.wav')
-language, language_group = get_language_and_group(wav, model, lang_dict, lang_group_dict)
+languages, language_groups = get_language_and_group(wav, model, lang_dict, lang_group_dict, top_n=2)
 
-pprint(f'Language: {language}')
-pprint(f'Language group: {language_group}')
+for i in languages:
+  pprint(f'Language: {i[0]} with prob {i[-1]}')
+
+for i in language_groups:
+  pprint(f'Language group: {i[0]} with prob {i[-1]}')
 ```
 
 ### ONNX
@@ -388,10 +391,13 @@ def validate_onnx(model, inputs):
 model = init_onnx_model(f'{files_dir}/lang_classifier_116.onnx')
 wav = read_audio(f'{files_dir}/de.wav')
 
-language, language_group = get_language_and_group(wav, model, lang_dict, lang_group_dict, run_function=validate_onnx)
+languages, language_groups = get_language_and_group(wav, model, lang_dict, lang_group_dict, top_n=2, run_function=validate_onnx)
 
-pprint(f'Language: {language}')
-pprint(f'Language group: {language_group}')
+for i in languages:
+  pprint(f'Language: {i[0]} with prob {i[-1]}')
+
+for i in language_groups:
+  pprint(f'Language group: {i[0]} with prob {i[-1]}')
 
 ```
 [![Open on Torch Hub](https://img.shields.io/badge/Torch-Hub-red?logo=pytorch&style=for-the-badge)](https://pytorch.org/hub/snakers4_silero-vad_language/)
diff --git a/utils_vad.py b/utils_vad.py
index ad54e25..f65a089 100644
--- a/utils_vad.py
+++ b/utils_vad.py
@@ -333,14 +333,26 @@ def get_language_and_group(wav: torch.Tensor,
                            model,
                            lang_dict: dict,
                            lang_group_dict: dict,
+                           top_n=1,
                            run_function=validate):
     wav = torch.unsqueeze(wav, dim=0)
     lang_logits, lang_group_logits = run_function(model, wav)
+    
+    softm = torch.softmax(lang_logits, dim=1).squeeze()
+    softm_group = torch.softmax(lang_group_logits, dim=1).squeeze()
+    
+    srtd = torch.argsort(softm, descending=True)
+    srtd_group = torch.argsort(softm_group, descending=True)
+    
+    outs = []
+    outs_group = []
+    for i in range(top_n):
+        prob = round(softm[srtd[i]].item(), 2)
+        prob_group = round(softm_group[srtd_group[i]].item(), 2)
+        outs.append((lang_dict[str(srtd[i].item())], prob))
+        outs_group.append((lang_group_dict[str(srtd_group[i].item())], prob_group))
 
-    lang_pred = torch.argmax(torch.softmax(lang_logits, dim=1), dim=1).item()   # from 0 to len(languages) - 1
-    lang_group_pred = torch.argmax(torch.softmax(lang_group_logits, dim=1), dim=1).item()
-
-    return lang_dict[str(lang_pred)], lang_group_dict[str(lang_group_pred)]
+    return outs, outs_group
 
 
 class VADiterator: