Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import torch
+
+# Load the Whisper model
+model_id = "openai/whisper-tiny.en"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+pipe = pipeline("automatic-speech-recognition", model=model_id, device=device)
+
+def transcribe_audio(audio_file):
+    # Read audio file
+    audio_bytes = audio_file.read()
+    # Get transcription results
+    results = pipe(audio_bytes)
+    # Return the transcription
+    return results
+
+# Streamlit interface
+st.title("Speech to Text with Whisper")
+audio_file = st.file_uploader("Upload an audio file", type=['wav', 'mp3', 'ogg'])
+
+if audio_file is not None:
+    # Display a button to transcribe the audio
+    if st.button('Transcribe'):
+        with st.spinner(f'Transcribing audio...'):
+            transcription = transcribe_audio(audio_file)
+            st.text_area("Transcription", transcription['text'], height=150)