main.py

import logging
logging.getLogger('numba').setLevel(logging.WARNING)
logging.getLogger('matplotlib').setLevel(logging.WARNING)
logging.getLogger('urllib3').setLevel(logging.WARNING)
import json
import re
import numpy as np
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
import datetime
import os
import pickle
import openai
from scipy.io.wavfile import write
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 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 to_numpy(tensor: torch.Tensor):
    return tensor.detach().cpu().numpy() if tensor.requires_grad \
        else tensor.detach().numpy()

def chatgpt(text):
    messages = []
    try:
        if text != 'exist':
            with open('log.pickle', 'rb') as f:
                messages = pickle.load(f)
        messages.append({"role": "user", "content": text},)
        chat = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages)
        reply = chat.choices[0].message.content
        messages.append({"role": "assistant", "content": reply})
        print(messages[-1])
        if len(messages) == 12:
            messages[6:10] = messages[8:]
            del messages[-2:]
        with open('log.pickle', 'wb') as f:
            pickle.dump(messages, f)
        return reply
    except:
        messages.append({"role": "user", "content": text},)
        chat = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages)
        reply = chat.choices[0].message.content
        messages.append({"role": "assistant", "content": reply})
        print(messages[-1])
        if len(messages) == 12:
            messages[6:10] = messages[8:]
            del messages[-2:]
        with open('log.pickle', 'wb') as f:
            pickle.dump(messages, f)
        return reply

def get_symbols_from_json(path):
    assert os.path.isfile(path)
    with open(path, 'r') as f:
        data = json.load(f)
    return data['symbols']

def sle(language,text):
        text = text.replace('\n', ' ').replace('\r', '').replace(" ", "")
        if language == "中文":
            tts_input1 = "[ZH]" + text + "[ZH]"
            return tts_input1
        elif language == "自动":
            tts_input1 = f"[JA]{text}[JA]" if is_japanese(text) else f"[ZH]{text}[ZH]"
            return tts_input1
        elif language == "日文":
            tts_input1 = "[JA]" + text + "[JA]"
            return tts_input1
        elif language == "英文":
            tts_input1 = "[EN]" + text + "[EN]"
            return tts_input1
        elif language == "手动":
            return text

def get_text(text,hps_ms):
    text_norm = text_to_sequence(text,hps_ms.data.text_cleaners)
    if hps_ms.data.add_blank:
        text_norm = commons.intersperse(text_norm, 0)
    text_norm = torch.LongTensor(text_norm)
    return text_norm

def create_tts_fn(net_g,hps,speaker_id):
    speaker_id = int(speaker_id)
    def tts_fn(history,is_gpt,api_key,is_audio,audiopath,repeat_time,text, language, extract, n_scale= 0.667,n_scale_w = 0.8, l_scale = 1 ):
        repeat_time = int(repeat_time)
        if is_gpt:
            openai.api_key = api_key
            text = chatgpt(text)
            history[-1][1] = text
        if not extract:
            print(text)
            t1 = time.time()
            stn_tst = get_text(sle(language,text),hps)
            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)
                audio = net_g.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 = "推理时间为："+str(t2-t1)+"s"
                print(spending_time)
                file_path = "subtitles.srt"
            try:
                write(audiopath + '.wav',22050,audio)
                if is_audio:
                    for i in range(repeat_time):
                        cmd = 'ffmpeg -y -i ' +  audiopath + '.wav' + ' -ar 44100 '+ audiopath.replace('temp','temp'+str(i))
                        os.system(cmd)
            except:
                pass
            return history,file_path,(hps.data.sampling_rate,audio)
        else:
            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
            t = datetime.timedelta(seconds=0)
            f1 = open("subtitles.srt",'w',encoding='utf-8')
            for sentence in final_list:
                c +=1
                stn_tst = get_text(sle(language,sentence),hps)
                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.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)
                    time_start = str(t).split(".")[0] + "," + str(t.microseconds)[:3]
                    last_time = datetime.timedelta(seconds=len(audio)/float(22050))
                    t+=last_time
                    time_end = str(t).split(".")[0] + "," + str(t.microseconds)[:3]
                    print(time_end)
                    f1.write(str(c-1)+'\n'+time_start+' --> '+time_end+'\n'+sentence+'\n\n')
                    audio_fin.append(audio)
                try:
                    write(audiopath + '.wav',22050,np.concatenate(audio_fin))
                    if is_audio:
                        for i in range(repeat_time):
                            cmd = 'ffmpeg -y -i ' +  audiopath + '.wav' + ' -ar 44100 '+ audiopath.replace('temp','temp'+str(i))
                            os.system(cmd)
                    
                except:
                    pass
                
            file_path = "subtitles.srt"
            return history,file_path,(hps.data.sampling_rate, np.concatenate(audio_fin))
    return tts_fn

def bot(history,user_message):
    return history + [[user_message, None]]

if __name__ == '__main__':
    hps = utils.get_hparams_from_file('checkpoints/tmp/config.json')
    dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    models = []
    schools = ["Nijigasaki High School","Seisho-Nijigasaki(Recommend)","Seisho Music Academy","Rinmeikan Girls School","Frontier School of Arts","Siegfeld Institute of Music"]
    lan = ["中文","日文","自动","手动"]
    with open("checkpoints/info.json", "r", encoding="utf-8") as f:
        models_info = json.load(f)
    checkpoint = models_info['Seisho Music Academy']["checkpoint"]
    phone_dict = {
        symbol: i for i, symbol in enumerate(symbols)
    }
    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).to(dev)
    _ = net_g.eval()
    _ = utils.load_checkpoint(checkpoint, net_g)
    for i in models_info:
        school = models_info[i]
        speakers = school["speakers"]
        content = []
        for j in speakers:
            sid = int(speakers[j]['sid'])
            title = school
            example = speakers[j]['speech']
            name = speakers[j]["name"]
            content.append((sid, name, title, example, create_tts_fn(net_g,hps,sid)))
        models.append(content)
    
    with gr.Blocks() as app:
        with gr.Tabs():
            for i in schools:
                with gr.TabItem(i):
                    for (sid, name,  title, example, tts_fn) in models[schools.index(i)]:
                        with gr.TabItem(name):
                            with gr.Column():
                                with gr.Row():
                                    with gr.Row():
                                        gr.Markdown(
                                            '<div align="center">'
                                            f'<img style="width:auto;height:400px;" src="file/image/{name}.png">' 
                                            '</div>'
                                        )
                                    chatbot = gr.Chatbot()
                                with gr.Row():
                                    with gr.Column(scale=0.85):
                                        input1 = gr.TextArea(label="Text", value=example,lines = 1)    
                                    with gr.Column(scale=0.15, min_width=0):
                                        btnVC = gr.Button("Send")
                                output1 = gr.Audio(label="采样率22050")
                                with gr.Accordion(label="Setting", open=False):
                                    input2 = gr.Dropdown(label="Language", choices=lan, value="自动", interactive=True)
                                    input3 = gr.Checkbox(value=False, label="长句切割(小说合成)")
                                    input4 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声比例(noise scale)，以控制情感", value=0.567)
                                    input5 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声偏差(noise scale w)，以控制音素长短", value=0.7)
                                    input6 = gr.Slider(minimum=0.1, maximum=10, label="duration", value=1) 
                                with gr.Accordion(label="Advanced Setting", open=False):
                                    audio_input3 = gr.Dropdown(label="重复次数", choices=list(range(101)), value='0', interactive=True) 
                                    api_input1 = gr.Checkbox(value=False, label="接入chatgpt")
                                    api_input2 = gr.TextArea(label="api-key",lines=1,value = '见 https://openai.com/blog/openai-api')   
                                    output2 = gr.outputs.File(label="字幕文件：subtitles.srt")
                                    audio_input1 = gr.Checkbox(value=False, label="修改音频路径(live2d)")
                                    audio_input2 = gr.TextArea(label="音频路径",lines=1,value = '#参考 D:/app_develop/live2d_whole/2010002/sounds/temp.wav')
                                 
                        btnVC.click(bot, inputs = [chatbot,input1], outputs = [chatbot]).then(
    tts_fn, inputs=[chatbot,api_input1,api_input2,audio_input1,audio_input2,audio_input3,input1,input2,input3,input4,input5,input6], outputs=[chatbot,output2,output1]
    )
                
    app.launch()