Update app.py

app.py

@@ -1,17 +1,24 @@
 # from: https://gradio.app/real_time_speech_recognition/
 
-from transformers import pipeline
+from transformers import pipeline, Wav2Vec2CTCTokenizer, Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM
 import pyctcdecode
 import kenlm
 import gradio as gr
 import librosa
 import os
+import time
 
 #Loading the model and the tokenizer
 token_key = os.environ.get("HUGGING_FACE_HUB_TOKEN")
 #model_name = "unilux/wav2vec-xls-r-Luxembourgish20-with-LM"
-model_name = "unilux/wav2vec-xlsr-300m-Luxembourgish"
-p = pipeline("automatic-speech-recognition", model=model_name, use_auth_token = token_key)
+model_name = "unilux/wav2vec-xlsr-300m-Luxembourgish-with-LM"
+tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(model_name, use_auth_token=token_key)
+model = Wav2Vec2ForCTC.from_pretrained(model_name, use_auth_token=token_key)
+processor = Wav2Vec2ProcessorWithLM.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)
+
+#p = pipeline("automatic-speech-recognition", model=model_name, use_auth_token = token_key)
 #p = pipeline("automatic-speech-recognition", model=model_name, use_auth_token = True)
 
 #tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)