TypeError: Error instantiating 'nemo.collections.tts.torch.data.TTSDataset'
**Describe the bug**

When I was trying to train Fastpitch from scratch using `fastpitch_align_v1.05.yaml` config, after it spending over 1 hour trying to `Loading dataset`, it threw the error:
```
Traceback (most recent call last):
  File "examples/tts/fastpitch.py", line 28, in main
    model = FastPitchModel(cfg=cfg.model, trainer=trainer)
  File "/root/NeMo/nemo/collections/tts/models/fastpitch.py", line 81, in __init__
    super().__init__(cfg=cfg, trainer=trainer)
  File "/root/NeMo/nemo/core/classes/modelPT.py", line 138, in __init__
    self.setup_training_data(self._cfg.train_ds)
  File "/root/NeMo/nemo/collections/tts/models/fastpitch.py", line 453, in setup_training_data
    self._train_dl = self.__setup_dataloader_from_config(cfg)
  File "/root/NeMo/nemo/collections/tts/models/fastpitch.py", line 440, in __setup_dataloader_from_config
    dataset = instantiate(
  File "/root/miniconda3/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 180, in instantiate
    return instantiate_node(config, *args, recursive=_recursive_, convert=_convert_)
  File "/root/miniconda3/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 249, in instantiate_node
    return _call_target(_target_, *args, **kwargs)
  File "/root/miniconda3/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 64, in _call_target
    raise type(e)(
  File "/root/miniconda3/lib/python3.8/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 62, in _call_target
    return _target_(*args, **kwargs)
  File "/root/NeMo/nemo/collections/tts/torch/data.py", line 228, in __init__
    librosa.filters.mel(
TypeError: Error instantiating 'nemo.collections.tts.torch.data.TTSDataset' : mel() takes 0 positional arguments but 2 positional arguments (and 3 keyword-only arguments) were given
```

**Steps/Code to reproduce bug**

` python examples/tts/fastpitch.py --config-name=fastpitch_align_v1.05.yaml train_dataset=/root/autodl-tmp/nancy_train.json validation_datasets=/root/autodl-tmp/nancy_val.json sup_data_path=/root/autodl-tmp/nemo-training/nancy_22k/sup_data exp_manager.exp_dir=/root/autodl-tmp/nemo-training/nancy_22k exp_manager.resume_if_exists=True exp_manager.resume_ignore_no_checkpoint=True model.train_ds.dataloader_params.batch_size=24 pitch_mean=199.37802124023438 pitch_std=55.59949493408203`


**Expected behavior**

A clear and concise description of what you expected to happen.

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

 - Environment location: Cloud
 - Method of NeMo install:  from source


**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 20.04
- PyTorch version: 1.10
- Python version: 3.8

**Additional context**

Add any other context about the problem here.
Example: RTX 3090
