class_docstr: A ready-to-use wrapper for Zero-Shot Multi-Speaker Tacotron2.
  For voice cloning: (text, reference_audio) -> (mel_spec).
  For generating a random speaker voice: (text) -> (mel_spec).
  
  
  Example
  -------
  >>> tmpdir_tts = getfixture('tmpdir') / "tts"
  >>> mstacotron2 = MSTacotron2.from_hparams(source="speechbrain/tts-mstacotron2-libritts", savedir=tmpdir_tts) # doctest: +SKIP
  >>> # Sample rate of the reference audio must be greater or equal to the sample rate of the speaker embedding model
  >>> reference_audio_path = "tests/samples/single-mic/example1.wav"
  >>> input_text = "Mary had a little lamb."
  >>> mel_output, mel_length, alignment = mstacotron2.clone_voice(input_text, reference_audio_path) # doctest: +SKIP
  >>> # One can combine the TTS model with a vocoder (that generates the final waveform)
  >>> # Intialize the Vocoder (HiFIGAN)
  >>> tmpdir_vocoder = getfixture('tmpdir') / "vocoder"
  >>> from speechbrain.inference.vocoders import HIFIGAN
  >>> hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-libritts-22050Hz", savedir=tmpdir_vocoder) # doctest: +SKIP
  >>> # Running the TTS
  >>> mel_output, mel_length, alignment = mstacotron2.clone_voice(input_text, reference_audio_path) # doctest: +SKIP
  >>> # Running Vocoder (spectrogram-to-waveform)
  >>> waveforms = hifi_gan.decode_batch(mel_output) # doctest: +SKIP
  >>> # For generating a random speaker voice, use the following
  >>> mel_output, mel_length, alignment = mstacotron2.generate_random_voice(input_text) # doctest: +SKIP
docstr: Encodes raw text into a tensor with a customer text-to-equence fuction
          
docstr_tok: ["Encodes","raw","text","into","a","tensor","with","a","customer","text-to-equence","fuction"]
code: def __text_to_seq(self, txt):
      """Encodes raw text into a tensor with a customer text-to-equence fuction"""
      sequence = text_to_sequence(txt, self.text_cleaners)
      return sequence, len(sequence)
  
imports: import re
  import logging
  import torch
  import torchaudio
  import random
  import speechbrain
  from speechbrain.utils.fetching import fetch
  from speechbrain.inference.interfaces import Pretrained
  from speechbrain.utils.text_to_sequence import text_to_sequence
  from speechbrain.inference.text import GraphemeToPhoneme
  from speechbrain.inference.encoders import MelSpectrogramEncoder
  from speechbrain.inference.classifiers import EncoderClassifier
label_desc: Use image node_id 2 for calling the MSTacotron2 obj's underlying member method code with example usage: obj.__text_to_seq(txt) and returns: sequence,  len