Adding example

app.py

@@ -6,26 +6,25 @@ local_model_name = "wav2vec2_model_pipeline"
 speech_recognizer = pipeline("automatic-speech-recognition", model = local_model_name)
 
 def greet_and_transcribe(name, intensity, input_audio):
-    try:
-        input_audio_rs = librosa.resample(
-            input_audio[1].astype(float),
-            orig_sr=input_audio[0],
-            target_sr=speech_recognizer.feature_extractor.sampling_rate)
-        transcribed_audio = speech_recognizer(input_audio_rs)["text"]
-        return "Hello, " + name + "!" * int(intensity), transcribed_audio
-    except Exception as err:
-        return "Hello, " + name + "!" * int(intensity), err
+    input_audio_rs = librosa.resample(
+        input_audio[1].astype(float),
+        orig_sr=input_audio[0],
+        target_sr=speech_recognizer.feature_extractor.sampling_rate)
+    transcribed_audio = speech_recognizer(input_audio_rs)["text"]
+    return "Hello, " + name + "!" * int(intensity), transcribed_audio
 
 demo = gr.Interface(
     title="A simple audio transcribing model",
     description="This is an application to test gradio functionalities",
-
     fn=greet_and_transcribe,
     inputs=[
         gr.Text(placeholder="input your name here"),
         gr.Slider(minimum=1, maximum=5, value=3),
         gr.Audio()],
     outputs=[gr.Text(label="Greeting"), gr.Text(label="Transcribed output")],
-    allow_flagging="auto"
+    cache_examples="lazy",
+    allow_flagging="auto",
+    examples=[["Jacob", 3, "example_audio/conference.wav"]]
+    # article="<p style='text-align: center'><a href='https://tmabraham.github.io/blog/gradio_hf_spaces_tutorial' target='_blank'>Blog post</a></p>"
 )
 demo.launch(share=True)