def from_config(cls, config: dict):
        """Loads model from config (randomized weights)"""
        if (
            config.get("target")
            == "nemo.collections.asr.models.rnnt_bpe_models.EncDecRNNTBPEModel"
            and config.get("model_defaults", {}).get("tdt_durations") is not None
        ):
            cfg = from_dict(ParakeetTDTArgs, config)
            model = ParakeetTDT(cfg)
        elif (
            config.get("target")
            == "nemo.collections.asr.models.hybrid_rnnt_ctc_bpe_models.EncDecHybridRNNTCTCBPEModel"
            and config.get("model_defaults", {}).get("tdt_durations") is not None
        ):
            cfg = from_dict(ParakeetTDTCTCArgs, config)
            model = ParakeetTDTCTC(cfg)
        elif (
            config.get("target")
            == "nemo.collections.asr.models.rnnt_bpe_models.EncDecRNNTBPEModel"
            and config.get("model_defaults", {}).get("tdt_durations") is None
        ):
            cfg = from_dict(ParakeetRNNTArgs, config)
            model = ParakeetRNNT(cfg)
        elif (
            config.get("target")
            == "nemo.collections.asr.models.ctc_bpe_models.EncDecCTCModelBPE"
        ):
            cfg = from_dict(ParakeetCTCArgs, config)
            model = ParakeetCTC(cfg)
        else:
            raise ValueError("Model is not supported yet!")

        model.eval()  # prevents layernorm not computing correctly on inference!

        return model
Loads model from config (randomized weights)
mlx_audio/stt/models/parakeet/parakeet.py