Create app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import torch
+from transformers import pipeline
+
+# -----------------------------
+# 1. Load Whisper model on CPU
+# -----------------------------
+MODEL_NAME = "Phonepadith/whisper-3-large-turbo-lao-finetuned"  # Change if needed
+
+device = "cpu"
+torch_dtype = torch.float32
+
+asr_pipeline = pipeline(
+    "automatic-speech-recognition",
+    model=MODEL_NAME,
+    tokenizer=MODEL_NAME,
+    feature_extractor=MODEL_NAME,
+    device=-1,  # Force CPU
+    torch_dtype=torch_dtype,
+)
+
+# -----------------------------
+# 2. Transcription function
+# -----------------------------
+def transcribe(audio_file):
+    if audio_file is None:
+        return "❌ Please upload an audio file."
+    try:
+        result = asr_pipeline(audio_file)
+        text = result["text"]
+        return f"🧩 Lao Transcription:\n\n{text}"
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# -----------------------------
+# 3. Gradio Interface
+# -----------------------------
+title = "🎧 Lao Whisper Test (CPU)"
+description = """
+ທົດສອບຮູບແບບຈຳລອງ Whisper ທີ່ປັບປຸງໃຫ້ຮັບຮູ້ພາສາລາວ 🇱🇦  
+Upload an audio file (`.wav`, `.mp3`, `.m4a`, `.flac`) and get the Lao transcription.
+"""
+
+iface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(type="filepath", label="🎵 Upload Lao Speech Audio"),
+    outputs=gr.Textbox(label="🧾 Transcription Result", lines=8),
+    title=title,
+    description=description,
+    allow_flagging="never",
+)
+
+if __name__ == "__main__":
+    iface.launch()