Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import torch
+import io
+
+# Load the ASR pipeline with Whisper model
+pipe = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")
+
+def transcribe_audio(audio_file):
+    # Load audio file
+    audio_bytes = audio_file.read()
+    audio = io.BytesIO(audio_bytes)
+    
+    # Transcribe audio
+    transcription = pipe(audio)
+    return transcription['text']
+
+# Streamlit UI
+st.title("Speech-to-Text Transcription App")
+st.write("Upload an audio file to transcribe its content into text.")
+
+uploaded_file = st.file_uploader("Choose an audio file...", type=["wav", "mp3", "flac"])
+
+if uploaded_file is not None:
+    with st.spinner("Transcribing..."):
+        text = transcribe_audio(uploaded_file)
+        st.subheader("Transcription Result:")
+        st.write(text)