Add simple demo

app.py

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+import gradio as gr
+import numpy as np
+
+import onnx_asr
+
+models = {name: onnx_asr.load_model(name) for name in ["alphacep/vosk-model-ru", "alphacep/vosk-model-small-ru"]}
+
+
+def recoginize(audio: tuple[int, np.ndarray]):
+    sample_rate, waveform = audio
+    waveform = waveform.astype(np.float32) / 2 ** (8 * waveform.itemsize - 1)
+    return [[name, model.recognize(waveform, sample_rate=sample_rate)] for name, model in models.items()]
+
+
+demo = gr.Interface(
+    fn=recoginize,
+    inputs=[gr.Audio(min_length=1, max_length=10)],
+    outputs=[gr.Dataframe(headers=["Model", "result"], wrap=True, show_fullscreen_button=True)],
+    flagging_mode="never",
+)
+
+demo.launch()

requirements.txt

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+onnx-asr[cpu,hub]>=0.4.0