Attribute `batch_dim_index` is not working in `WER`.
Currently `WER` works incorrectly if `batch_dim_index=1`.

The outputs for `batch_dim_index=1` and `batch_dim_index=0` in the following example have to be equal.
Code to reproduce the error:
```python
from string import ascii_lowercase
from typing import List

import torch

from nemo.collections.asr.metrics.wer import WER


def string_to_ctc_tensor(txt: str, vocabulary: List[str]) -> torch.Tensor:
    # This function emulates how CTC output could like for txt
    blank_id = len(vocabulary)
    char_to_ind = dict([(vocabulary[i], i) for i in range(len(vocabulary))])
    string_in_id_form = [char_to_ind[c] for c in txt]
    ctc_list = []
    prev_id = -1
    for c in string_in_id_form:
        # when character is repeated we need to insert CTC blank symbol
        if c != prev_id:
            ctc_list.append(c)
        else:
            ctc_list.append(blank_id)
            ctc_list.append(c)
        prev_id = c
    return torch.Tensor(ctc_list).unsqueeze(0)


def reference_string_to_tensor(txt: str, vocabulary: List[str]) -> torch.Tensor:
    # Reference tensors aren't produced by CTC logic
    char_to_ind = dict([(vocabulary[i], i) for i in range(len(vocabulary))])
    string_in_id_form = [char_to_ind[c] for c in txt]
    return torch.Tensor(string_in_id_form).unsqueeze(0)


vocabulary = [" "] + list(ascii_lowercase) + ["'"]
wer = WER(vocabulary=vocabulary, batch_dim_index=1, use_cer=False, ctc_decode=True)

prediction, reference = ['a b c', 'a c c']
predictions_tensor = string_to_ctc_tensor(prediction, vocabulary)
targets_tensor = reference_string_to_tensor(reference, vocabulary)
targets_tensor.transpose_(0, 1)
predictions_tensor.transpose_(0, 1)
wer(predictions=predictions_tensor, targets=targets_tensor, target_lengths=torch.tensor([len(reference)]))
print("WER for batch_dim_index=1:", wer.compute()[0])

vocabulary = [" "] + list(ascii_lowercase) + ["'"]
wer = WER(vocabulary=vocabulary, batch_dim_index=0, use_cer=False, ctc_decode=True)

prediction, reference = ['a b c', 'a c c']
predictions_tensor = string_to_ctc_tensor(prediction, vocabulary)
targets_tensor = reference_string_to_tensor(reference, vocabulary)
wer(predictions=predictions_tensor, targets=targets_tensor, target_lengths=torch.tensor([len(reference)]))
print("WER for batch_dim_index=0:", wer.compute()[0])
```
Output:
```
[NeMo I 2021-10-31 02:33:21 wer:225] 
    
[NeMo I 2021-10-31 02:33:21 wer:226] reference:a
[NeMo I 2021-10-31 02:33:21 wer:227] predicted:a
WER for batch_dim_index=1: tensor(0.)
[NeMo I 2021-10-31 02:33:21 wer:225] 
    
[NeMo I 2021-10-31 02:33:21 wer:226] reference:a c c
[NeMo I 2021-10-31 02:33:21 wer:227] predicted:a b c
WER for batch_dim_index=0: tensor(0.3333)
```
Also updated docstrings.