Update app.py

app.py

@@ -14,6 +14,9 @@ word_tokenizer = hazm.WordTokenizer()
 
 tagger_path = hf_hub_download(repo_id="gyroing/HAZM_POS_TAGGER",  filename="pos_tagger.model")
 tagger = hazm.POSTagger(model=tagger_path)
+model_path = hf_hub_download(repo_id="gyroing/Persian-Piper-Model-gyro", filename="fa_IR-gyro-meduim.onnx")
+config_path = hf_hub_download(repo_id="gyroing/Persian-Piper-Model-gyro", filename="fa_IR-gyro-meduim.onnx.json")
+voice = PiperVoice.load(model_path, config_path)
 
 def preprocess_text(text: str) -> typing.List[typing.List[str]]:
         """Split/normalize text into sentences/words with hazm"""
@@ -43,10 +46,6 @@ def fix_words(words: typing.List[str]) -> typing.List[str]:
 def synthesize_speech(text):
 
 
-    model_path = hf_hub_download(repo_id="gyroing/Persian-Piper-Model-gyro", filename="fa_IR-gyro-meduim.onnx")
-    config_path = hf_hub_download(repo_id="gyroing/Persian-Piper-Model-gyro", filename="fa_IR-gyro-meduim.onnx.json")
-    voice = PiperVoice.load(model_path, config_path)
-
     # Create an in-memory buffer for the WAV file
     buffer = BytesIO()
     with wave.open(buffer, 'wb') as wav_file: