Update app.py

app.py

@@ -1,6 +1,7 @@
 
 import time
 import os
+import spaces
 import warnings
 warnings.filterwarnings("ignore")
 from pydub import AudioSegment
@@ -20,6 +21,7 @@ torch_dtype = torch.float32
 
 asr = pipeline("automatic-speech-recognition", model="NbAiLabBeta/nb-whisper-large", device=device, torch_dtype=torch_dtype)
 
+@spaces.GPU(queue=True)
 def transcribe_audio(audio_file):
     if audio_file.endswith(".m4a"):
         audio_file = convert_to_wav(audio_file)
@@ -59,7 +61,6 @@ def transcribe(audio_file):
     text = transcription
     return text, result
 
-
 def clean_text(text):
     text = re.sub(r'https?:\/\/.*[\r\n]*', '', str(text), flags=re.MULTILINE)
     text = re.sub(r'\<a href', ' ', str(text))
@@ -91,10 +92,11 @@ def preprocess_text(text):
 
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-summarization_model = AutoModelForSeq2SeqLM.from_pretrained("t5-base", return_dict=True, torch_dtype=torch.float16)
+summarization_model = AutoModelForSeq2SeqLM.from_pretrained("t5-base", device=device, return_dict=True, torch_dtype=torch.float16)
 summarization_tokenizer = AutoTokenizer.from_pretrained("t5-base")
 summarization_model.to(device)
 
+@spaces.GPU(queue=True)
 def summarize_text(text):
     preprocessed_text = preprocess_text(text)
     if preprocessed_text is None: