Update app.py

app.py

@@ -1,11 +1,9 @@
 import torch
 import gradio as gr
 from transformers import pipeline
-from transformers.pipelines.audio_utils import ffmpeg_read
 
 MODEL_NAME = "openai/whisper-large-v3"
 BATCH_SIZE = 8
-FILE_LIMIT_MB = 1000
 
 device = 0 if torch.cuda.is_available() else "cpu"
 
@@ -16,29 +14,26 @@ pipe = pipeline(
     device=device,
 )
 
-def transcribe(inputs, task):
-    if inputs is None:
+def transcribe(audio, task):
+    if audio is None:
         raise gr.Error("No audio file submitted! Please upload an audio file before submitting your request.")
 
-    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
+    text = pipe(audio, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"]
     return text
 
 demo = gr.Interface(
     fn=transcribe,
     inputs=[
-        gr.Audio(source="upload", type="filepath", optional=True, label="Audio file"),
-        gr.Radio(["transcribe", "translate"], label="Task", default="transcribe"),
+        gr.Audio(type="filepath", label="Audio file"),
+        gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"),
     ],
     outputs="text",
-    layout="horizontal",
-    theme="huggingface",
     title="Whisper Large V3: Transcribe Audio",
     description=(
         "Transcribe audio files with the click of a button! This demo uses the OpenAI Whisper"
         f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files"
         " of arbitrary length."
     ),
-    allow_flagging="never",
 )
 
 demo.launch(enable_queue=True)