app.py

import gradio as gr
from transformers import pipeline
import torch
import spaces

# Initialize model on CPU
model = pipeline(
    "automatic-speech-recognition",
    model="Aekanun/whisper-small-hi",
    device="cpu"
)

@spaces.GPU
def transcribe_speech(audio):
    """Speech transcription with GPU support"""
    try:
        if audio is None:
            return "กรุณาบันทึกเสียงก่อน"
            
        # Move model to GPU
        model.model = model.model.to("cuda")
        
        with torch.amp.autocast('cuda'):
            # Process audio with chunk length to handle long audio
            result = model(
                audio,
                batch_size=1,
                return_timestamps=True,  # แก้ error เรื่อง timestamps
                chunk_length_s=30        # แก้ error เรื่องความยาวเสียง
            )
            
            # Get text result
            text = result["text"] if isinstance(result, dict) else result
            
        # Move model back to CPU
        model.model = model.model.to("cpu")
        torch.cuda.empty_cache()
        
        return text
        
    except Exception as e:
        # Make sure model is back on CPU in case of error
        model.model = model.model.to("cpu")
        torch.cuda.empty_cache()
        return f"เกิดข้อผิดพลาด: {str(e)}"

# Create Gradio interface
demo = gr.Interface(
    fn=transcribe_speech,
    inputs=gr.Audio(type="filepath"),
    outputs=gr.Textbox(label="ข้อความ"),
    title="Thai Speech Transcription",
    description="บันทึกเสียงเพื่อแปลงเป็นข้อความภาษาไทย",
)

if __name__ == "__main__":
    demo.queue().launch(server_name="0.0.0.0")