Update

app.py

@@ -28,12 +28,6 @@ models_paths = {
     "el-GR": "jonatasgrosman/wav2vec2-large-xlsr-53-greek",
 }
 
-# Initialize the models
-for lang_code in models_paths.keys():
-    models[lang_code] = {}
-    models[lang_code]["processor"] = Wav2Vec2Processor.from_pretrained(models_paths[lang_code])
-    models[lang_code]["model"] = Wav2Vec2ForCTC.from_pretrained(models_paths[lang_code])
-
 # Classifier Intent
 model_name = 'qanastek/XLMRoberta-Alexa-Intents-Classification'
 tokenizer_intent = AutoTokenizer.from_pretrained(model_name)
@@ -59,6 +53,11 @@ examples = [[e, e.split("=")[0].split("/")[-1]] for e in examples]
 def transcribe(audio_path, lang_code):
         
     speech_array, sampling_rate = librosa.load(audio_path, sr=16_000)
+
+    if lang_code not in models:
+        models[lang_code] = {}
+        models[lang_code]["processor"] = Wav2Vec2Processor.from_pretrained(models_paths[lang_code])
+        models[lang_code]["model"] = Wav2Vec2ForCTC.from_pretrained(models_paths[lang_code])
     
     # Load model
     processor_asr = models[lang_code]["processor"]
@@ -97,6 +96,7 @@ def getUniform(text):
 def predict(wav_file, lang_code):
 
     if lang_code not in models_paths.keys():
+        
         return {
             "The language code is unknown!"
         }