| import matplotlib.pyplot as plt | |
| 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 | |
| import sys | |
| from subprocess import call | |
| def run_cmd(command): | |
| try: | |
| print(command) | |
| call(command, shell=True) | |
| except KeyboardInterrupt: | |
| print("Process interrupted") | |
| sys.exit(1) | |
| current = os.getcwd() | |
| print(current) | |
| full = current + "/monotonic_align" | |
| print(full) | |
| os.chdir(full) | |
| print(os.getcwd()) | |
| run_cmd("python3 setup.py build_ext --inplace") | |
| run_cmd("apt-get install espeak -y") | |
| os.chdir("..") | |
| print(os.getcwd()) | |
| from models import SynthesizerTrn | |
| from text.symbols import symbols | |
| from text.cleaners import japanese_phrase_cleaners | |
| from text import cleaned_text_to_sequence | |
| from scipy.io.wavfile import write | |
| import gradio as gr | |
| import scipy.io.wavfile | |
| import numpy as np | |
| import re | |
| jp_match = re.compile(r'^.*[ぁ-ヺ].*$') | |
| title = "VITS" | |
| description = "demo for VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. To use it, simply add your text, or click one of the examples to load them. Read more at the links below." | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2106.06103'>Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech</a> | <a href='https://github.com/jaywalnut310/vits'>Github Repo</a></p>" | |
| examples = [ | |
| ["原因不明の海面上昇によって、地表の多くが海に沈んだ近未来。"], | |
| ["幼い頃の事故によって片足を失った少年・斑鳩夏生は、都市での暮らしに見切りを付け、海辺の田舎町へと移り住んだ。"], | |
| ["身よりのない彼に遺されたのは、海洋地質学者だった祖母の船と潜水艇、そして借金。"], | |
| ["nanika acltara itsudemo hanashIte kudasai. gakuiNno kotojanaku, shijini kaNsuru kotodemo nanidemo."] | |
| ] | |
| hps = utils.get_hparams_from_file("./configs/ATR.json") | |
| net_g = SynthesizerTrn( | |
| len(symbols), | |
| hps.data.filter_length // 2 + 1, | |
| hps.train.segment_size // hps.data.hop_length, | |
| **hps.model) | |
| _ = net_g.eval() | |
| _ = utils.load_checkpoint("./G_172000.pth", net_g, None) | |
| def get_text(text, hps): | |
| text_norm = cleaned_text_to_sequence(text) | |
| if hps.data.add_blank: | |
| text_norm = commons.intersperse(text_norm, 0) | |
| text_norm = torch.LongTensor(text_norm) | |
| return text_norm | |
| def jtts(text): | |
| if jp_match.match(text): | |
| stn_tst = get_text(japanese_phrase_cleaners(text), hps) | |
| else: | |
| stn_tst = get_text(text, hps) | |
| with torch.no_grad(): | |
| x_tst = stn_tst.unsqueeze(0) | |
| x_tst_lengths = torch.LongTensor([stn_tst.size(0)]) | |
| audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.float().numpy() | |
| scipy.io.wavfile.write("out.wav", hps.data.sampling_rate, audio) | |
| return "./out.wav" | |
| if __name__ == '__main__': | |
| inputs = gr.inputs.Textbox(lines=5, label="Input Text") | |
| outputs = gr.outputs.Audio(label="Output Audio") | |
| gr.Interface(jtts, inputs, outputs, title=title, description=description, article=article, examples=examples).launch() |