Cannot run Mixer TTS colab with Mixer-TTS-X model
Hi all. I'm struggling to run a training using Mixer-TTS-X model. I'm using the tutorial for training both Fastpitch and MixerTTS.

Modifications I've done:

```pretrained_model = "tts_en_lj_mixerttsx"```

Adding 'raw_texts' argument when generating a spectrogram:

```spectrogram = spec_gen.generate_spectrogram(tokens=tokens, raw_texts=["Hey, this produces speech!"])```

Correcting this:

```from nemo.collections.tts.torch.data import MixerTTSXDataset```

Just in case:
```
from nemo.collections.tts.torch.tts_data_types import LMTokens
from transformers.models.albert.tokenization_albert import AlbertTokenizer
```

add lm_tokenizer parameter here:

```
def pre_calculate_supplementary_data(sup_data_path, sup_data_types, text_tokenizer, text_normalizer, lm_tokenizer, text_normalizer_call_kwargs)
```

Getting the right config file:

```&& wget https://raw.githubusercontent.com/NVIDIA/NeMo/$BRANCH/examples/tts/conf/mixer-tts-x.yaml```

Creating the lm_tokenizer object:

```lm_tokenizer = LMTokens()```

And after running the command:
```
mixer_tts_sup_data_path = "mixer_tts_x_sup_data_folder"
sup_data_types = ["align_prior_matrix", "pitch", "lm_tokens"]

pitch_mean, pitch_std, pitch_min, pitch_max = pre_calculate_supplementary_data(
    mixer_tts_sup_data_path, sup_data_types, text_tokenizer, text_normalizer, lm_tokenizer, text_normalizer_call_kwargs
)
```

I get the following error:

```
[NeMo I 2022-08-24 22:00:27 data:216] Loading dataset from tests/data/asr/an4_train.json.
30it [00:00, 712.41it/s][NeMo I 2022-08-24 22:00:27 data:253] Loaded dataset with 30 files.
[NeMo I 2022-08-24 22:00:27 data:255] Dataset contains 0.02 hours.
[NeMo I 2022-08-24 22:00:27 data:357] Pruned 0 files. Final dataset contains 30 files
[NeMo I 2022-08-24 22:00:27 data:360] Pruned 0.00 hours. Final dataset contains 0.02 hours.

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
[<ipython-input-21-b87416bfd412>](https://localhost:8080/#) in <module>
      3 
      4 pitch_mean, pitch_std, pitch_min, pitch_max = pre_calculate_supplementary_data(
----> 5     mixer_tts_sup_data_path, sup_data_types, text_tokenizer, text_normalizer, lm_tokenizer, text_normalizer_call_kwargs
      6 )

3 frames
[<ipython-input-20-7893d3034131>](https://localhost:8080/#) in pre_calculate_supplementary_data(sup_data_path, sup_data_types, text_tokenizer, text_normalizer, lm_tokenizer, text_normalizer_call_kwargs)
     22             text_normalizer=text_normalizer,
     23             lm_tokenizer=lm_tokenizer,
---> 24             text_normalizer_call_kwargs=text_normalizer_call_kwargs
     25         ) 
     26         stage2dl[stage] = torch.utils.data.DataLoader(ds, batch_size=1, collate_fn=ds._collate_fn, num_workers=1)

[/usr/local/lib/python3.7/dist-packages/nemo/collections/tts/torch/data.py](https://localhost:8080/#) in __init__(self, **kwargs)
    759 class MixerTTSXDataset(TTSDataset):
    760     def __init__(self, **kwargs):
--> 761         super().__init__(**kwargs)
    762 
    763     def _albert(self):

[/usr/local/lib/python3.7/dist-packages/nemo/collections/tts/torch/data.py](https://localhost:8080/#) in __init__(self, manifest_filepath, sample_rate, text_tokenizer, tokens, text_normalizer, text_normalizer_call_kwargs, text_tokenizer_pad_id, sup_data_types, sup_data_path, max_duration, min_duration, ignore_file, trim, trim_ref, trim_top_db, trim_frame_length, trim_hop_length, n_fft, win_length, hop_length, window, n_mels, lowfreq, highfreq, **kwargs)
    323 
    324         for data_type in self.sup_data_types:
--> 325             getattr(self, f"add_{data_type.name}")(**kwargs)
    326 
    327     @staticmethod

[/usr/local/lib/python3.7/dist-packages/nemo/collections/tts/torch/data.py](https://localhost:8080/#) in add_lm_tokens(self, **kwargs)
    785 
    786     def add_lm_tokens(self, **kwargs):
--> 787         lm_model = "kwargs.pop('lm_model')"
    788 
    789         if lm_model == "albert":

KeyError: 'lm_model'
```

Any ideas are welcome.
Thanks,