Conformer CTC occasionally returns transcripts with multiple consecutive spaces
While analysing datasets via [SpeechDataExplorer](https://github.com/NVIDIA/NeMo/tree/main/tools/speech_data_explorer) I discovered that transcripts (in our case generated with Conformer CTC BPE) occasionally contain multiple consecutive spaces. This influences the computation of CER, but is also very confusing because the `text diff` section in SDE does not show any visual differences. The `text diff` also fails to show differences for UTF-8 symbols that appear in different versions in text and pred_text (composed and decomposed form), but the difference is correctly taken into account when computing WER and CER, hence this becomes confusing because there is no visual difference between the two transcripts. 

Tracking down the source for multiple consecutive spaces I see that this can occur if a `blank_id` token separates consecutive space characters: https://github.com/NVIDIA/NeMo/blob/91e6c2552a9ca8d00bf8ce758adea68d9c49f803/nemo/collections/asr/metrics/wer.py#L162-L165

Perhaps following Huggingface `tokenizer.decode` a flag `clean_up_tokenization_spaces` with the default value of `True` could be added to `decode_tokens_to_str` and `decode_ids_to_tokens`. The sole purpose of this flag would be to clean up the resulting data of multiple consecutive space symbols. If you agree I can make a PR. 