import matplotlib.pyplot as plt import os import json import math import scipy 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.symbols1 import symbols1 from text import text_to_sequence from text import text_to_sequence1 from scipy.io.wavfile import write import io """ import argparse parser = argparse.ArgumentParser(description='查看传参') parser.add_argument("--text",type=str,default="你好。") parser.add_argument("--character",type=int,default=0) args = parser.parse_args() """ 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) return text_norm def get_text1(text, hps): text_norm = text_to_sequence1(text, hps.data.text_cleaners) if hps.data.add_blank: text_norm = commons.intersperse(text_norm, 0) text_norm = torch.LongTensor(text_norm) return text_norm hps = utils.get_hparams_from_file("./vits/configs/ys.json") hps1= utils.get_hparams_from_file("./vits/configs/bh3.json") net_g = SynthesizerTrn( len(symbols), hps.data.filter_length // 2 + 1, hps.train.segment_size // hps.data.hop_length, n_speakers=hps.data.n_speakers,# **hps.model).cuda() _ = net_g.eval() net_g1 = SynthesizerTrn( len(symbols1), hps1.data.filter_length // 2 + 1, hps1.train.segment_size // hps1.data.hop_length, n_speakers=hps1.data.n_speakers,# **hps1.model).cuda() _ = net_g1.eval() _ = utils.load_checkpoint("./vits/models/ys.pth", net_g, None) _ = utils.load_checkpoint("./vits/models/bh3.pth", net_g1, None) def ys(text,character): #text=args.text audio_bytes = io.BytesIO() 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() #character=args.character sid=torch.LongTensor([character]).cuda() audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, sid = sid, noise_scale_w=0.8, length_scale=1.2)[0][0,0].data.cpu().float().numpy() scipy.io.wavfile.write(audio_bytes, hps.data.sampling_rate, audio) return audio_bytes def bh3(text,character): audio_bytes = io.BytesIO() stn_tst = get_text1(text, hps1) with torch.no_grad(): x_tst = stn_tst.cuda().unsqueeze(0) x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda() #character=args.character sid=torch.LongTensor([character]).cuda() audio = net_g1.infer(x_tst, x_tst_lengths, noise_scale=.667, sid = sid, noise_scale_w=0.8, length_scale=1.2)[0][0,0].data.cpu().float().numpy() scipy.io.wavfile.write(audio_bytes, hps1.data.sampling_rate, audio) return audio_bytes