use bare except

app.py

@@ -18,7 +18,7 @@ title = "Whisper JAX: The Fastest Whisper API ⚡️"
 
 description = """Whisper JAX is an optimised implementation of the [Whisper model](https://huggingface.co/openai/whisper-large-v2) by OpenAI. It runs on JAX with a TPU v4-8 in the backend. Compared to PyTorch on an A100 GPU, it is over [**70x faster**](https://github.com/sanchit-gandhi/whisper-jax#benchmarks), making it the fastest Whisper API available.
 
-Note that at peak times, you may find yourself in the queue for this demo. When you submit a request, your queue position will be shown in the top right-hand side of the demo pane. Once you reach the front of the queue, your audio file will be transcribed, with the progress displayed through a progress bar. 
+Note that at peak times, you may find yourself in the queue for this demo. When you submit a request, your queue position will be shown in the top right-hand side of the demo pane. Once you reach the front of the queue, your audio file will be sent to the TPU and then transcribed, with the progress displayed through a progress bar. 
 
 To skip the queue, you may wish to create your own inference endpoint, details for which can be found in the [Whisper JAX repository](https://github.com/sanchit-gandhi/whisper-jax#creating-an-endpoint).
 """
@@ -101,7 +101,9 @@ if __name__ == "__main__":
 
         dataloader = processor.preprocess_batch(inputs, chunk_length_s=CHUNK_LENGTH_S, batch_size=BATCH_SIZE)
         progress(0, desc="Sending audio to TPU...")
-        batch_id = np.random.randint(1000000)  # TODO(SG): swap to an iterator
+        batch_id = np.random.randint(
+            1000000
+        )  # TODO(SG): swap to an iterator - currently taking our 1 in a million chances
         pool.map(partial(send_chunks, batch_id=batch_id), dataloader)
 
         model_outputs = []
@@ -154,7 +156,7 @@ if __name__ == "__main__":
         try:
             yt = pytube.YouTube(yt_url)
             stream = yt.streams.filter(only_audio=True)[0]
-        except KeyError:
+        except:
             raise gr.Error("An error occurred while loading the YouTube video. Please try again.")
 
         if stream.filesize_mb > max_filesize: