init

app.py

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+import streamlit as st
+import os
+import whisper
+import soundfile as sf
+
+# Assuming you have your .env file configured with necessary API keys or configurations
+# load_dotenv()
+
+# Initialize the model outside the main app function to load it only once
+model = whisper.load_model("base")
+
+def transcribe_audio(audio_file):
+    # Save the audio file to a temporary file
+    with open("temp_audio_file", "wb") as f:
+        f.write(audio_file.getbuffer())
+    
+    # Transcribe the audio file using the Whisper model
+    result = model.transcribe("temp_audio_file")
+    return result["text"]
+
+# Streamlit app
+def main():
+    st.title('USE ME TO TRANSCRIBE')
+    
+    # Audio file uploader
+    uploaded_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "m4a", "ogg", "flac"])
+
+    if uploaded_file is not None:
+        # Show a button to start the transcription process
+        if st.button('Transcribe'):
+            # Show a message while transcribing
+            with st.spinner('Transcribing...'):
+                text = transcribe_audio(uploaded_file)
+            
+            # Show the transcription
+            st.subheader('Transcription:')
+            st.write(text)
+        else:
+            st.write('Upload an audio file to get started.')
+
+if __name__ == "__main__":
+    main()