Create app.py

app.py

@@ -0,0 +1,36 @@
+import gradio as gr
+import torch
+from transformers import pipeline
+
+MODEL = "openai/whisper-tiny"  # good balance of speed + accuracy
+
+# Load model once at startup
+device = 0 if torch.cuda.is_available() else -1
+
+transcriber = pipeline(
+    "automatic-speech-recognition",
+    model=MODEL,
+    device=device
+)
+
+def transcribe(audio):
+    if audio is None:
+        return "Please upload or record audio."
+
+    result = transcriber(audio)
+    return result["text"]
+
+
+demo = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(
+        sources=["microphone", "upload"],
+        type="filepath"
+    ),
+    outputs=gr.Textbox(label="Transcription"),
+    title="🎙 Voice to Text",
+    description="Upload or record audio. Powered by Whisper running locally in HF Spaces."
+)
+
+if __name__ == "__main__":
+    demo.launch()