update to pipeline, model and processor

app.py

@@ -1,6 +1,6 @@
 # from: https://gradio.app/real_time_speech_recognition/
 
-from transformers import pipeline
+from transformers import pipeline, WhisperProcessor, WhisperForConditionalGeneration
 import torch
 import gradio as gr
 import librosa
@@ -15,10 +15,12 @@ print("key length:", len(token_key.strip()))
 model_name = "pgilles/whisper-large-v2-lb_cased_04"
 #model_name = "pgilles/whisper-large-10_Chamber" # model too bad
 
-model = WhisperForConditionalGeneration.from_pretrained(best_asr_model, use_auth_token=token_key)
+processor = WhisperProcessor.from_pretrained(model_name, language="lb", task="transcribe")
+tokenizer = processor.tokenizer
+model = WhisperForConditionalGeneration.from_pretrained(model_name, use_auth_token=token_key)
 #p = pipeline("automatic-speech-recognition", model=model, tokenizer=tokenizer, feature_extractor=processor.feature_extractor, decoder=processor.decoder, use_auth_token=token_key)
 
-pipe = pipeline("automatic-speech-recognition", model=model, device=0)
+pipe = pipeline("automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, decoder=processor.decoder, device=0)
 #pipe = pipeline("automatic-speech-recognition", model=model_name, device=0, use_auth_token=token_key)
 #pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language= "Luxembourgish", task="transcribe")
 #pipe.model.config.forced_decoder_ids = None