|
|
|
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 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): |
|
|
|
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() |
|
|
|
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() |
|
|
|
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 |
|
|
