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