import os import json import math import torch from torch import nn from torch.nn import functional as F from torch.utils.data import DataLoader import commons import utils from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate from models import SynthesizerTrn from text.symbols import symbols from text import text_to_sequence, cleaned_text_to_sequence from text.cleaners import japanese_cleaners from scipy.io.wavfile import write def get_text(text, hps): text_norm = text_to_sequence(text, hps.data.text_cleaners) if hps.data.add_blank: text_norm = commons.intersperse(text_norm, 0) text_norm = torch.LongTensor(text_norm) # print(text_norm.shape) return text_norm hps = utils.get_hparams_from_file("/mnt/vits_koni/configs/japanese_base.json") net_g = SynthesizerTrn( len(symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, **hps.model).cuda() _ = net_g.eval() _ = utils.load_checkpoint("/mnt/vits_koni/MyDrive/japanese_base/G_42000.pth", net_g, None) def tts(text): if len(text) > 150: return "Error: Text is too long", None stn_tst = get_text(text, hps) with torch.no_grad(): x_tst = stn_tst.cuda().unsqueeze(0) x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda() # print(stn_tst.size()) audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=2)[0][ 0, 0].data.cpu().float().numpy() return hps.data.sampling_rate, audio sampling_rate, infer_audio = tts("にーまーまーすーろーぁ") write("/mnt/vits_koni/MyDrive/japanese_base/inferwav/konitest3.wav", sampling_rate, infer_audio) print("1")