Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,27 +1,38 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
 import whisper
+import tempfile
 
 st.set_page_config(
-   page_title="IndicASR",
-   page_icon="✔️",
-   layout="wide",
+    page_title="IndicASR",
+    page_icon="✔️",
+    layout="wide",
 )
 
-st.markdown('''<h1><center><b><u>IndicASR</u></b><center></h1>''', unsafe_allow_html=True)
+st.markdown(
+    '''<h1><center><b><u>IndicASR</u></b></center></h1>''',
+    unsafe_allow_html=True
+)
 
 activity = ['Select your Language', 'English']
 choice = st.selectbox('How you want to proceed?', activity)
 
 if choice == 'English':
-    uploaded_file = st.file_uploader("Upload your Audio File", type=["mp3"])
+    uploaded_file = st.file_uploader(
+        "Upload your Audio File",
+        type=["mp3", "wav", "m4a"]
+    )
+
     if uploaded_file is not None:
+        # Save uploaded file to a temp location
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp_file:
+            tmp_file.write(uploaded_file.read())
+            audio_path = tmp_file.name
+
+        # Load Whisper model
         model = whisper.load_model("turbo")
-        result = model.transcribe(uploaded_file, verbose=False)
-        # print(result["text"])
-        with st.echo():
-            greeting = "Transcript:  "
-            st.write(greeting, result["text"])
+
+        # Transcribe
+        result = model.transcribe(audio_path, verbose=False)
+
+        st.subheader("Transcript")
+        st.write(result["text"])