STT_EN_FASTCONFORMER_TRANSDUCER_XLARG - Throws error for Tensor + List operation in confidence calculation
@Vladi-SmartAssets Hi,
I cannot reproduce the issue in the latest main.
What NeMo version are you using? This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days. This issue was closed because it has been inactive for 7 days since being marked as stale.
**Describe the bug**

TypeError: unsupported operand type(s) for +: 'Tensor' and 'list', occurs when wanting to extract the confidence levels for the STT FastConformer model. 

**Steps/Code to reproduce bug**

`class NemoModel(HuggingFaceBaseModel):

    def __init__(self, model_name, model_path):
        super().__init__(model_name)
        self.model_path = model_path
        self.model = None

    def load_model(self):
        self.model = nemo_asr.models.EncDecRNNTBPEModel.restore_from(self.model_path, map_location="mps")

    def predict(self, input_paths):

        confidence_cfg = ConfidenceConfig(
            preserve_frame_confidence=True,  # Internally set to true if preserve_token_confidence == True
            # or preserve_word_confidence == True
            preserve_token_confidence=True,  # Internally set to true if preserve_word_confidence == True
            preserve_word_confidence=True,
            aggregation="prod",  # How to aggregate frame scores to token scores and token scores to word scores
            exclude_blank=False,  # If true, only non-blank emissions contribute to confidence scores
            tdt_include_duration=False,  # If true, calculate duration confidence for the TDT models
            method_cfg=ConfidenceMethodConfig(  # Config for per-frame scores calculation (before aggregation)
                name="max_prob",  # Or "entropy" (default), which usually works better
                entropy_type="gibbs",  # Used only for name == "entropy". Recommended: "tsallis" (default) or "renyi"
                alpha=0.5,  # Low values (<1) increase sensitivity, high values decrease sensitivity
                entropy_norm="lin",  # How to normalize (map to [0,1]) entropy. Default: "exp"
            ),
        )
        self.model.change_decoding_strategy(RNNTDecodingConfig(fused_batch_size=-1, strategy="greedy_batch", confidence_cfg=confidence_cfg))

        transcriptions = self.model.transcribe(
            audio=input_paths, return_hypotheses=True
        )
        
        fastconformer_transcriptions = [x for x in transcriptions][0]

        return fastconformer_transcriptions
`
This when run with model.transcribe will throw the following error: 

`    for ts, te in zip(hyp.timestep, hyp.timestep[1:] + [len(hyp.frame_confidence)]):
TypeError: unsupported operand type(s) for +: 'Tensor' and 'list'`

**Expected behavior**

Expected behaviour is for the zip function to take the torch.tensor not the list, as hyp.timestamps is a Tensor and hyp.frame_confidence is a list of tensors. (Tensor[float], List[Tensor[float]]

**Environment overview (please complete the following information)**

 - Environment location: Docker
 - Method of NeMo install: pip install nemo 

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: MacOS 14.5 (23F79)
- PyTorch version: 2.3.1
- Python version: 3.10 

**Additional context**

Add any other context about the problem here.
Example: Using MPS 

Proposed solution: 
replace the following line 633 in nemo/collections/asr/parts/submodules/rnnt_decoding.py: 

` for ts, te in zip(hyp.timestep, hyp.timestep[1:] + [len(hyp.frame_confidence)]):`

with 

` for ts, te in zip(hyp.timestep, hyp.timestep[1:] + len(hyp.frame_confidence)):`
