Update app.py

app.py

@@ -3,28 +3,32 @@ from transformers import pipeline
 
 
 
-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,
+device = "cuda:0" if torch.cuda.is_available() else "CPU"
+
+def transcribe(audio):
+    
+    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."
+
+    return prediction
+
+gradio_app = gr.Interface(
+    prediction,
+    inputs=gr.Image(label="Input", sources=['audio'], type="pil"),
+    outputs=[gr.Image(label="Ouput"), gr.Label(label="Result", num_top_classes=2)],
+    title="Transcribed",
 )
 
-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()