frist commit

app.py

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+import gradio as gr
+from transformers import pipeline, WhisperProcessor, WhisperForConditionalGeneration
+
+# load mode
+model_name = "openai/whisper-medium"
+processor = WhisperProcessor.from_pretrained(model_name,language="lo")
+model = WhisperForConditionalGeneration.from_pretrained(model_dir)
+
+asr = pipeline(
+    "automatic-speech-recognition",
+    model= "LuoYiSULIXAY/whisper-lao-finetuned_laonlp",
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    device=-1  
+)
+
+def transcribe(audio):
+    result = asr(audio, generate_kwargs={"language": "lao", "task": "transcribe"})
+    return result["text"]
+
+demo = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(type="filepath",streaming=False),  # ✅ 正确写法
+    outputs="text",
+    title="Whisper Lao",
+    description="Realtime demo for Lao speech recognition using a fine-tuned Whisper model.",
+)
+
+demo.launch(share=True)

requirements.txt

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+gradio
+transformers
+torchaudio