Update app.py

app.py

@@ -1,18 +1,30 @@
 import gradio as gr
 from transformers import pipeline
 
-pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
 
-def predict(input_img):
-    predictions = pipeline(input_img)
-    return input_img, {p["label"]: p["score"] for p in predictions} 
 
-gradio_app = gr.Interface(
-    predict,
-    inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
-    title="Hot Dog? Or Not?",
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+pipe = pipeline(
+  "automatic-speech-recognition",
+  model="openai/whisper-small",
+  chunk_length_s=30,
+  device=device,
 )
 
+ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+sample = ds[0]["audio"]
+
+prediction = pipe(sample.copy(), batch_size=8)["text"]
+" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
+
+# we can also return timestamps for the predictions
+prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"]
+[{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.',
+  'timestamp': (0.0, 5.44)}]
+
+
+
+
 if __name__ == "__main__":
     gradio_app.launch()