Update app.py

app.py

@@ -38,7 +38,7 @@ projection = torch.nn.Linear(clip_embed, phi_embed).to(device)
 resblock = SimpleResBlock(phi_embed).to(device)
 phi_model = AutoModelForCausalLM.from_pretrained(phi_model_name,trust_remote_code=True).to(device)
 # Assuming you have defined 'device' and 'compute_type' elsewhere
-audio_model = whisperx.load_model("tiny", device, compute_type=compute_type, asr_options={'max_new_tokens': 2048, 'clip_timestamps': True, 'hallucination_silence_threshold': 0.25, 'hotwords': []})
+audio_model = whisperx.load_model("tiny", device, compute_type=compute_type, asr_options={'max_new_tokens': 2048, 'clip_timestamps': True, 'hallucination_silence_threshold': 0.25})
 
 # load weights
 model_to_merge = PeftModel.from_pretrained(phi_model,os.path.join(os.getcwd(), 'model_chkpt/lora_adaptor'))