Update src/streamlit_app.py

src/streamlit_app.py

@@ -112,22 +112,22 @@ if st.button("Analyze"):
             st.write("Exists:", os.path.exists(audio_path))
             # pass
 
-        with st.spinner("Transcribing with Whisper..."):
-            whisper_model = whisper.load_model("base")
-            result = whisper_model.transcribe(audio_path)
-            transcription = result['text']
+        # with st.spinner("Transcribing with Whisper..."):
+        #     whisper_model = whisper.load_model("base")
+        #     result = whisper_model.transcribe(audio_path)
+        #     transcription = result['text']
             # transcription = "Hello There"
             # pass
 
         with st.spinner("Classifying accent..."):
-            # model_name = "dima806/english_accents_classification"
-            # pipe = pipeline('audio-classification', model=model_name, device=0)  # GPU (device=0) or CPU (device=-1)
-            # accent_data = accent_classify(pipe, audio_path)
+            model_name = "dima806/english_accents_classification"
+            pipe = pipeline('audio-classification', model=model_name, device=-1)  # GPU (device=0) or CPU (device=-1)
+            accent_data = accent_classify(pipe, audio_path)
             
             # audio_df = split_audio(audio_path)
             # print(np.concatenate(audio_df["audio"][:50].to_list()))
          
-            accent_data = {"label": "us", "score": 0.9}
+            # accent_data = {"label": "us", "score": 0.9}
             accent = accent_data.get("label", "American")
             confidence = accent_data.get("score", 0.0)
             # pass
@@ -135,8 +135,8 @@ if st.button("Analyze"):
         st.success("Analysis Complete!")
         st.markdown(f"**Accent:** {accent}")
         st.markdown(f"**Confidence Score:** {confidence:.2f}%")
-        st.markdown("**Transcription:**")
-        st.text_area("Transcript", transcription, height=200)
+        # st.markdown("**Transcription:**")
+        # st.text_area("Transcript", transcription, height=200)
 
         # Cleanup
         os.remove(video_path)