Update functions.py

functions.py

@@ -29,7 +29,7 @@ margin-bottom: 2.5rem">{}</div> """
 
 @st.experimental_singleton(suppress_st_warning=True)
 def load_models():
-    asr_model = whisper.load_model("small")
+    #asr_model = whisper.load_model("small")
     #asr_pipe = pipeline("automatic-speech-recognition",model = "openai/whisper-small")
     q_model = ORTModelForSequenceClassification.from_pretrained("nickmuchi/quantized-optimum-finbert-tone")
     ner_model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
@@ -40,8 +40,14 @@ def load_models():
     ner_pipe = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, grouped_entities=True)
     cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')
     
-    return asr_model, sent_pipe, sum_pipe, ner_pipe, cross_encoder
+    return sent_pipe, sum_pipe, ner_pipe, cross_encoder
+
+@st.experimental_singleton(suppress_st_warning=True)
+def load_asr_model(asr_model_name):
+    asr_model = whisper.load(asr_model_name)
     
+    return asr_model
+        
 @st.experimental_singleton(suppress_st_warning=True)    
 def load_sbert(model_name):
     sbert = SentenceTransformer(model_name)
@@ -311,4 +317,4 @@ def fin_ext(text):
     return make_spans(text,results)
     
 nlp = get_spacy()    
-asr_model, sent_pipe, sum_pipe, ner_pipe, cross_encoder  = load_models()
+sent_pipe, sum_pipe, ner_pipe, cross_encoder  = load_models()