def load_model(self, modelsize=None, cache_dir=None, model_dir=None):

         from faster_whisper import WhisperModel

         if model_dir is not None:

             logger.debug(f"Loading whisper model from model_dir {model_dir}. modelsize and cache_dir parameters are not used.")

             model_size_or_path = model_dir

     parser.add_argument('--vad', action="store_true", default=False, help='Use VAD = voice activity detection, with the default parameters.')

     parser.add_argument('--buffer_trimming', type=str, default="segment", choices=["sentence", "segment"],help='Buffer trimming strategy -- trim completed sentences marked with punctuation mark and detected by sentence segmenter, or the completed segments returned by Whisper. Sentence segmenter must be installed for "sentence" option.')

     parser.add_argument('--buffer_trimming_sec', type=float, default=15, help='Buffer trimming length threshold in seconds. If buffer length is longer, trimming sentence/segment is triggered.')

 def asr_factory(args, logfile=sys.stderr):

     """

         logger.error("No or one option from --offline and --comp_unaware are available, not both. Exiting.")

         sys.exit(1)

     audio_path = args.audio_path

     SAMPLING_RATE = 16000

 import numpy as np

 logger = logging.getLogger(__name__)

 parser = argparse.ArgumentParser()

 # server options

 parser.add_argument("--warmup-file", type=str, dest="warmup_file", 

         help="The path to a speech audio wav file to warm up Whisper so that the very first chunk processing is fast. It can be e.g. https://github.com/ggerganov/whisper.cpp/raw/master/samples/jfk.wav .")

 # options from whisper_online

 add_shared_args(parser)

 args = parser.parse_args()

 if args.log_level:

                         level=getattr(logging, args.log_level))

 # setting whisper object by args 

     if os.path.isfile(args.warmup_file):

         a = load_audio_chunk(args.warmup_file,0,1)

         asr.transcribe(a)

     else:

 else:

 ######### Server objects