Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+# Load Whisper model and processor from Hugging Face
+processor = WhisperProcessor.from_pretrained("openai/whisper-base")
+model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base").to("cuda" if torch.cuda.is_available() else "cpu")
+
+def transcribe(audio):
+    try:
+        # Load audio
+        audio_input = processor(audio, sampling_rate=16000, return_tensors="pt")
+        
+        # Move to appropriate device
+        audio_input = audio_input.input_features.to(model.device)
+
+        # Generate transcription
+        predicted_ids = model.generate(audio_input)
+        transcription = processor.decode(predicted_ids[0], skip_special_tokens=True)
+        
+        return transcription
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.inputs.Audio(source="microphone", type="filepath"),
+    outputs="text",
+    title="Whisper Transcription",
+    description="Upload an audio file and get the transcription using Whisper model."
+)
+
+if __name__ == "__main__":
+    iface.launch()