updated the app file

app.py

@@ -1,54 +1,54 @@
-import streamlit as st
-from ced_model.feature_extraction_ced import CedFeatureExtractor
-from ced_model.modeling_ced import CedForAudioClassification
-import torchaudio
-import torch
-import os
-import soundfile as sf
-
-model_name = "mispeech/ced-tiny"
-feature_extractor = CedFeatureExtractor.from_pretrained(model_name)
-model = CedForAudioClassification.from_pretrained(model_name)
-
-st.title("Audio Classification App")
-st.subheader("Trained on 50 classes of ESC 50 dataset")
-st.write("Upload an audio file to predict its class.")
-
-audio_file = st.file_uploader("Upload Audio File", type=["wav"])
-
-if audio_file is not None:
-    st.write(f"Uploaded file: {audio_file.name}")
-    
-    try:
-        temp_file_path = "temp.wav"
-        with open(temp_file_path, "wb") as f:
-            f.write(audio_file.read())
-        
-        try:
-            audio, sampling_rate = torchaudio.load(temp_file_path)
-        except Exception:
-            st.warning("Fallback to soundfile for audio loading.")
-            audio_data, sampling_rate = sf.read(temp_file_path)
-            audio = torch.tensor(audio_data).unsqueeze(0)  
-
-        if sampling_rate != 16000:
-            st.warning("Resampling audio to 16000 Hz...")
-            resampler = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)
-            audio = resampler(audio)
-            sampling_rate = 16000
-        
-        inputs = feature_extractor(audio, sampling_rate=sampling_rate, return_tensors="pt")
-        
-        with torch.no_grad():
-            logits = model(**inputs).logits
-        
-        predicted_class_id = torch.argmax(logits, dim=-1).item()
-        predicted_label = model.config.id2label[predicted_class_id]
-        
-        st.success(f"Predicted Class: {predicted_label}")
-        
-        os.remove(temp_file_path)
-    except Exception as e:
-        st.error(f"An error occurred: {e}")
-else:
-    st.info("Please upload a .wav audio file to continue.")
+import streamlit as st
+from ced_model.feature_extraction_ced import CedFeatureExtractor
+from ced_model.modeling_ced import CedForAudioClassification
+import torchaudio
+import torch
+import os
+import soundfile as sf
+
+model_name = "mispeech/ced-tiny"
+feature_extractor = CedFeatureExtractor.from_pretrained(model_name)
+model = CedForAudioClassification.from_pretrained(model_name)
+
+st.title("Audio Classification App")
+st.subheader("Trained on 50 classes of ESC 50 dataset")
+st.write("Upload an audio file to predict its class.")
+
+audio_file = st.file_uploader("Upload Audio File", type=["wav","mp3","m4a"])
+
+if audio_file is not None:
+    st.write(f"Uploaded file: {audio_file.name}")
+    
+    try:
+        temp_file_path = "temp.wav"
+        with open(temp_file_path, "wb") as f:
+            f.write(audio_file.read())
+        
+        try:
+            audio, sampling_rate = torchaudio.load(temp_file_path)
+        except Exception:
+            st.warning("Fallback to soundfile for audio loading.")
+            audio_data, sampling_rate = sf.read(temp_file_path)
+            audio = torch.tensor(audio_data).unsqueeze(0)  
+
+        if sampling_rate != 16000:
+            st.warning("Resampling audio to 16000 Hz...")
+            resampler = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)
+            audio = resampler(audio)
+            sampling_rate = 16000
+        
+        inputs = feature_extractor(audio, sampling_rate=sampling_rate, return_tensors="pt")
+        
+        with torch.no_grad():
+            logits = model(**inputs).logits
+        
+        predicted_class_id = torch.argmax(logits, dim=-1).item()
+        predicted_label = model.config.id2label[predicted_class_id]
+        
+        st.success(f"Predicted Class: {predicted_label}")
+        
+        os.remove(temp_file_path)
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+else:
+    st.info("Please upload a .wav audio file to continue.")