Create app.py

app.py

@@ -0,0 +1,53 @@
+import whisper
+import gradio as gr
+import json
+import tempfile
+import os
+
+print("Loading Whisper model...")
+# Load model when space starts
+model = whisper.load_model("base")
+print("Model loaded successfully!")
+
+def transcribe_audio(audio_file):
+    """
+    Simple function to transcribe audio to text
+    """
+    try:
+        print(f"Processing audio file: {audio_file}")
+        
+        # Transcribe the audio
+        result = model.transcribe(audio_file)
+        
+        print("Transcription completed successfully!")
+        
+        # Return as JSON string
+        return json.dumps({
+            "text": result["text"],
+            "language": result["language"],
+            "status": "success"
+        })
+        
+    except Exception as e:
+        print(f"Error: {str(e)}")
+        return json.dumps({
+            "error": str(e),
+            "status": "failed"
+        })
+
+# Create a simple web interface
+demo = gr.Interface(
+    fn=transcribe_audio,
+    inputs=gr.Audio(sources=["upload"], type="filepath", label="Upload Audio File"),
+    outputs=gr.Textbox(label="Transcription Result", lines=5),
+    title=" Whisper Transcription API",
+    description="Upload an audio file to convert it to text using OpenAI's Whisper model.",
+    examples=[
+        ["https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac"],
+        ["https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/2.flac"]
+    ]
+)
+
+# Launch the application
+if __name__ == "__main__":
+    demo.launch(debug=True, share=True)