Create app.py

app.py

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+import gradio as gr
+import soundfile as sf
+from transformers import AutoProcessor, pipeline
+from optimum.intel.openvino import OVModelForSpeechSeq2Seq
+
+# Load model + processor
+model_id = "distil-whisper/distil-large-v2"
+processor = AutoProcessor.from_pretrained(model_id)
+ov_model = OVModelForSpeechSeq2Seq.from_pretrained(model_id, export=True)
+ov_model.generation_config.max_new_tokens = 128
+
+# Create HF pipeline
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=ov_model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    chunk_length_s=15,
+    batch_size=16,
+)
+
+# Transcription function
+def transcribe(audio):
+    audio_array, sampling_rate = sf.read(audio)
+    result = pipe(audio_array)
+    return result["text"]
+
+# Launch Gradio UI
+gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(type="filepath"),
+    outputs="text",
+    title="🧠 Distil-Whisper + OpenVINO ASR",
+    description="Upload audio to transcribe using Distil-Whisper accelerated with Intel OpenVINO.",
+).launch()