def load_stt_model(sst_model_id: str, sst_adapter_id: str | None, sst_pipeline_kwargs: dict) -> Pipeline:
    model = AutoModelForSpeechSeq2Seq.from_pretrained(
        sst_model_id, 
        torch_dtype=torch_dtype, 
        low_cpu_mem_usage=True, 
        use_safetensors=True)
    if sst_adapter_id:
        print(f"Loading adapter {sst_adapter_id} for model {sst_model_id}")
        model.load_adapter(sst_adapter_id, "adapter")
        model.set_adapter("adapter")
    model.to(device)

    processor = AutoProcessor.from_pretrained(sst_model_id)
    # https://huggingface.co/docs/transformers/en/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline
    return pipeline(
        "automatic-speech-recognition",
        model=model,
        tokenizer=processor.tokenizer,
        feature_extractor=processor.feature_extractor,
        torch_dtype=torch_dtype,
        device=device,
        batch_size=1,
        **sst_pipeline_kwargs,
    )