Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+import soundfile as sf
+import tempfile
+
+# Load the ASR model from Hugging Face
+@st.cache_resource
+def load_model():
+    # Use Hugging Face's pipeline with your desired model
+    return pipeline("automatic-speech-recognition", model="fractalego/personal-speech-to-text-model")
+
+# Initialize the model pipeline
+pipe = load_model()
+
+# Streamlit UI
+st.title("Speech-to-Text Transcription App")
+st.write("Upload an audio file, and the AI model will transcribe it.")
+
+# Upload audio file
+uploaded_file = st.file_uploader("Choose an audio file", type=["wav", "mp3", "m4a"])
+
+if uploaded_file is not None:
+    st.audio(uploaded_file, format='audio/wav')
+    
+    # Save uploaded file to a temporary location
+    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+        temp_file.write(uploaded_file.read())
+        temp_file_path = temp_file.name
+    
+    # Read the audio file and transcribe
+    with st.spinner("Transcribing... Please wait..."):
+        transcription = pipe(temp_file_path)
+    
+    # Display the transcription result
+    st.subheader("Transcription")
+    st.write(transcription['text'])
+
+else:
+    st.info("Please upload an audio file to start transcription.")