Add model_downsampling_factor to NFA cfg
> Thanks for this!
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> Actually, the story is that I used to require model_downsample_factor to be in the config, but then I made some logic to calculate it, so that the user doesn't need to worry about it. The logic is here: https://github.com/NVIDIA/NeMo/blob/b6c3c0d5d41d2c695e6a1b23aa573e867b0ec729/tools/nemo_forced_aligner/utils/data_prep.py#L822
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> But I forgot to update buffered case/buffered case was merged without realizing this was gone.
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> Could you please update the code so that we calculate model_downsample_factor for the buffered case also?

@erastorgueva-nv, in this case, it’s unlikely we can calculate the `model_downsampling_factor` for the bucketed chunked ASR scenario. The reason is that we need to know the number of tokens generated from inference, but to run the inference, we first need the `model_stride_in_sec`, which is computed as the `window_stride` multiplied by the `model_downsampling_factor`. Since the inference requires this value upfront, it seems unavoidable to include `model_downsampling_factor` in the configuration for the buffered case.

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The `model_downsampling_factor` parameter was missing in the configuration, which caused the `use_buffered_chunked_streaming` option to malfunction. This PR adds the missing parameter and updates the documentation accordingly.