colab_inference.py

#colab克隆并且安装完环境后启动该文件
import romajitable
import re
import numpy as np
import logging
logging.getLogger('numba').setLevel(logging.WARNING)
import IPython.display as ipd
import torch
import commons
import utils
from models import SynthesizerTrn
from text.symbols import symbols
from text import text_to_sequence
import gradio as gr
import time
def get_text(text, hps):
    text_norm = text_to_sequence(text, symbols, 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
dev = torch.device("cuda:0")
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 is_japanese(string):
        for ch in string:
            if ord(ch) > 0x3040 and ord(ch) < 0x30FF:
                return True
        return False
def is_english(string):
    import re
    pattern = re.compile('^[A-Za-z0-9.,:;!?()_*"\' ]+$')
    if pattern.fullmatch(string):
        return True
    else:
        return False
def sle(language,tts_input0):
    if language == "中文":
        tts_input1 = "[ZH]" + tts_input0.replace('\n','。').replace(' ',',') + "[ZH]"
        return tts_input1
    if language == "自动":
        tts_input1 = f"[JA]{tts_input0}[JA]" if is_japanese(tts_input0) else f"[ZH]{tts_input0}[ZH]"
        return tts_input1
    elif language == "日文":
        tts_input1 = "[JA]" + tts_input0.replace('\n','。').replace(' ',',') + "[JA]"
        return tts_input1
def extrac(text):
    text = re.sub("<[^>]*>","",text)
    result_list = re.split(r'\n', text)
    final_list = []
    for i in result_list:
        if is_english(i):
            i = romajitable.to_kana(i).katakana
        i = i.replace('\n','').replace(' ','')
        #Current length of single sentence: 20 
        if len(i)>1:
            if len(i) > 20:
                try:
                    cur_list = re.split(r'。|！', i)
                    for i in cur_list:
                        if len(i)>1:
                            final_list.append(i+'。')
                except:
                    pass
            else:
                final_list.append(i)
    final_list = [x for x in final_list if x != '']
    print(final_list)
    return final_list
def infer(language,text,speaker_id, n_scale= 0.667,n_scale_w = 0.8, l_scale = 1 ):
    speaker_id = int(selection(speaker_id))
    a = ['【','[','(','（']
    b = ['】',']',')','）']
    for i in a:
        text = text.replace(i,'<')
    for i in b:
        text = text.replace(i,'>')
    final_list = extrac(text.replace('“','').replace('”',''))
    audio_fin = []
    c = 0
    for sentence in final_list:
        c +=1
        try:
            stn_tst = get_text(sle(language,sentence), 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 = "第"+str(c)+"句的推理时间为："+str(t2-t1)+"s"
                print(spending_time)
                audio_fin.append(audio)
        except:
            print('存在非法字符')
    return (hps_ms.data.sampling_rate, np.concatenate(audio_fin))
lan = ["中文","日文","自动"]
idols = ["高咲侑","歩夢","かすみ","しずく","果林","愛","せつ菜","璃奈","栞子","エマ","ランジュ","ミア","派蒙"]
hps_ms = utils.get_hparams_from_file("lovelive/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("lovelive/G_525000.pth", net_g_ms)
app = gr.Blocks()
with app:
    with gr.Tabs():

        with gr.TabItem("虹团vits模型，现可按句分割实现长文本合成，可自行用export_to_onnx.py导出"):

            tts_input1 = gr.TextArea(label="如需实现快速合成，建议在colab上克隆后运行本仓库", value="为什么你会那么熟练啊？你和雪菜亲过多少次了？我想做只属于你一个人的学院偶像，所以，请只注视我一个人，好吗？【中文】\nなんでそんなに慣れてんだよっ？せつ菜と…何回キスしたんだよ？どこまであたしを置いてきぼりにすれば気が済むんだよ？[日文]\nI can't choose just one（English）")
            language = gr.Dropdown(label="选择语言，目前勉强可以做到自动识别",choices=lan, value="自动", interactive=True)
            para_input1 = gr.Slider(minimum= 0,maximum=1.0,label="更改噪声比例，以控制情感", value=0.667)
            para_input2 = gr.Slider(minimum= 0,maximum=1.0,label="更改噪声偏差，以控制音素长短", value=0.7)
            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="歩夢", 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()