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import time
import matplotlib.pyplot as plt
import IPython.display as ipd
import re
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 gradio as gr
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
import unicodedata
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)
return text_norm
def get_label(text, label):
if f'[{label}]' in text:
return True, text.replace(f'[{label}]', '')
else:
return False, text
def selection(speaker):
if speaker == "高咲侑":
spk = 0
return spk
elif speaker == "歩夢":
spk = 1
return spk
elif speaker == "かすみ":
spk = 2
return spk
elif speaker == "しずく":
spk = 3
return spk
elif speaker == "果林":
spk = 4
return spk
elif speaker == "愛":
spk = 5
return spk
elif speaker == "彼方":
spk = 6
return spk
elif speaker == "せつ菜":
spk = 7
return spk
elif speaker == "エマ":
spk = 8
return spk
elif speaker == "璃奈":
spk = 9
return spk
elif speaker == "栞子":
spk = 10
return spk
elif speaker == "ランジュ":
spk = 11
return spk
elif speaker == "ミア":
spk = 12
return spk
elif speaker == "三色绘恋1":
spk = 13
return spk
elif speaker == "三色绘恋2":
spk = 15
elif speaker == "派蒙":
spk = 16
return spk
def sle(language,tts_input0):
if language == "中文":
tts_input1 = "[ZH]" + tts_input0.replace('\n','。').replace(' ',',') + "[ZH]"
return tts_input1
if language == "英文":
tts_input1 = "[EN]" + tts_input0.replace('\n','.').replace(' ',',') + "[EN]"
return tts_input1
elif language == "日文":
tts_input1 = "[JA]" + tts_input0.replace('\n','。').replace(' ',',') + "[JA]"
return tts_input1
def infer(language,text,speaker_id, n_scale= 0.667,n_scale_w = 0.8, l_scale = 1 ):
speaker_id = int(selection(speaker_id))
stn_tst = get_text(sle(language,text), hps_ms)
with torch.no_grad():
x_tst = stn_tst.unsqueeze(0).to(dev)
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(dev)
sid = torch.LongTensor([speaker_id]).to(dev)
t1 = time.time()
audio = net_g_ms.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=n_scale, noise_scale_w=n_scale_w, length_scale=l_scale)[0][0,0].data.cpu().float().numpy()
t2 = time.time()
spending_time = "推理时间："+str(t2-t1)+"s"
print(spending_time)
return (hps_ms.data.sampling_rate, audio)
lan = ["中文","日文","英文"]
idols = ["高咲侑","歩夢","かすみ","しずく","果林","愛","彼方","せつ菜","璃奈","栞子","エマ","ランジュ","ミア","三色绘恋1","三色绘恋2","派蒙"]
Device = input("设置运行时类型")
if Device == "cpu":
dev = torch.device("cpu")
else:
dev = torch.device("cuda:0")
hps_ms = utils.get_hparams_from_file("C:/Users/24293/机器学习-/MB-iSTFT-VITS/logs/nijigaku/config.json")
net_g_ms = SynthesizerTrn(
len(symbols),
hps_ms.data.filter_length // 2 + 1,
hps_ms.train.segment_size // hps_ms.data.hop_length,
n_speakers=hps_ms.data.n_speakers,
**hps_ms.model).to(dev)
_ = net_g_ms.eval()
_ = utils.load_checkpoint("C:/Users/24293/机器学习-/MB-iSTFT-VITS/logs/nijigaku/G_1330000.pth", net_g_ms, None)
app = gr.Blocks()
with app:
with gr.Tabs():
with gr.TabItem("Basic"):
tts_input1 = gr.TextArea(label="旧模型", value="一次審査、二次審査、それぞれの欄に記入をお願いします。")
language = gr.Dropdown(label="选择语言",choices=lan, value="日文", interactive=True)
para_input1 = gr.Slider(minimum= 0.01,maximum=1.0,label="更改噪声比例", value=0.667)
para_input2 = gr.Slider(minimum= 0.01,maximum=1.0,label="更改噪声偏差", value=0.8)
para_input3 = gr.Slider(minimum= 0.1,maximum=10,label="更改时间比例", value=1)
tts_submit = gr.Button("Generate", variant="primary")
speaker1 = gr.Dropdown(label="选择说话人",choices=idols, value="かすみ", interactive=True)
tts_output2 = gr.Audio(label="Output")
tts_submit.click(infer, [language,tts_input1,speaker1,para_input1,para_input2,para_input3], [tts_output2])
#app.launch(share=True)
app.launch() |