Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load ASR pipeline
+asr_pipeline = pipeline(task="automatic-speech-recognition", model="openai/whisper-small") 
+# 🔹 Replace with your own model if you trained one, e.g., "Devion333/whisper-small-dv-syn"
+
+def transcribe(audio):
+    result = asr_pipeline(audio)
+    return result["text"]
+
+# Build Gradio app
+gradio_app = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(sources=["upload", "microphone"], type="filepath", label="Speak or Upload Audio"),
+    outputs=gr.Textbox(label="Transcription"),
+    title="Speech-to-Text (ASR)",
+    description="Upload an audio file or record speech and get the transcription using a Hugging Face ASR model."
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()