Update app.py

app.py

@@ -1,3 +1,38 @@
 import gradio as gr
+from transformers import pipeline
+import torch
+import os
+import tempfile
+import soundfile as sf
 
-gr.load("models/openai/whisper-small").launch()
+# Load the Whisper model once during startup
+device = 0 if torch.cuda.is_available() else -1
+asr_pipeline = pipeline(model="openai/whisper-small", device=device)
+
+# Function to handle the transcription process
+def transcribe_audio(audio_file):
+    # Create a temporary file to save the uploaded audio
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio_file:
+        temp_audio_file.write(audio_file.read())
+        temp_file_path = temp_audio_file.name
+
+    # Perform the transcription
+    transcription = asr_pipeline(temp_file_path)
+    
+    # Remove the temporary file
+    os.remove(temp_file_path)
+
+    # Return the transcription result
+    return transcription['text']
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=transcribe_audio,  # The function to call when audio is uploaded
+    inputs=gr.Audio(source="upload", type="file"),  # Input type: audio file
+    outputs="text",  # Output type: text (transcription)
+    title="Whisper Audio Transcription",  # Title of the Gradio interface
+    description="Upload an audio file to get a transcription using OpenAI's Whisper model"
+)
+
+# Launch the Gradio interface
+interface.launch()