@Narsil  Yes, sure. Here is the code chunk which I was using before:
from espnet2.bin.tts_inference import Text2Speech
from espnet2.utils.types import str_or_none
import torch
import scipy.io.wavfile

tag = 'kan-bayashi/ljspeech_vits' 
vocoder_tag = "none"

text2speech = Text2Speech.from_pretrained(
    model_tag=str_or_none(tag),
    vocoder_tag=str_or_none(vocoder_tag),
    device="cpu",
    # Only for Tacotron 2 & Transformer
    threshold=0.5,
    # Only for Tacotron 2
    minlenratio=0.0,
    maxlenratio=10.0,
    use_att_constraint=False,
    backward_window=1,
    forward_window=3,
    # Only for FastSpeech & FastSpeech2 & VITS
    speed_control_alpha=1.0,
    # Only for VITS
    noise_scale=0.333,
    noise_scale_dur=0.333,
)

def get_audio_tts(text):
  with torch.no_grad():
    wav = text2speech(text)["wav"]
    scipy.io.wavfile.write("out.wav",text2speech.fs , wav.view(-1).cpu().numpy())
  return  "out.wav"

audio_file = get_audio_tts(generated_answer)