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
import librosa
import romajitable
from mel_processing import spectrogram_torch
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) > 50:
                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.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.symbols,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(is_transfer,original_speaker, target_speaker,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 ):
        text = check_text(text)
        repeat_time = int(repeat_time)
        original_speaker_id = selection(original_speaker)
        target_speaker_id = selection(target_speaker)
        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('”',''))
            split_list = []
            while len(final_list) > 0:
                split_list.append(final_list[:500])
                final_list = final_list[500:]
            c0 = 0
            for lists in split_list:
                audio_fin = []
                t = datetime.timedelta(seconds=0)
                c = 0
                f1 = open(audiopath.replace('.wav',str(c0)+".srt"),'w',encoding='utf-8')
                for sentence in lists:
                    try:
                        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([original_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')
                        if is_transfer:
                            with torch.no_grad():
                                y = torch.FloatTensor(audio)
                                y = y / max(-y.min(), y.max()) / 0.99
                                y = y.to(dev)
                                y = y.unsqueeze(0)
                                spec = spectrogram_torch(y, hps.data.filter_length,
                                                            hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
                                                            center=False).to(dev)
                                spec_lengths = torch.LongTensor([spec.size(-1)]).to(dev)
                                sid_src = torch.LongTensor([original_speaker_id]).to(dev)
                                sid_tgt = torch.LongTensor([target_speaker_id]).to(dev)
                                audio = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
                                    0, 0].data.cpu().float().numpy()
                            del y, spec, spec_lengths, sid_src, sid_tgt
                        audio_fin.append(audio)
                    except:
                        pass
                write(audiopath.replace('.wav',str(c0)+'.wav'),22050,np.concatenate(audio_fin))
                c0 += 1
            file_path = audiopath.replace('.wav',str(c0)+".srt")
            return history,file_path,(hps.data.sampling_rate, np.concatenate(audio_fin))
    return tts_fn

def create_vc_fn(net_g,hps):
    def vc_fn(text,language,n_scale,n_scale_w,l_scale,original_speaker, target_speaker, record_audio, upload_audio):
        input_audio = record_audio if record_audio is not None else upload_audio
        original_speaker_id = selection(original_speaker)
        target_speaker_id = selection(target_speaker)
        if input_audio is None:
            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([original_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()
                sampling_rate = hps.data.sampling_rate
        else:
            sampling_rate, audio = input_audio
            audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
        if len(audio.shape) > 1:
            audio = librosa.to_mono(audio.transpose(1, 0))
        if sampling_rate != hps.data.sampling_rate:
            audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate)
        with torch.no_grad():
            y = torch.FloatTensor(audio)
            y = y / max(-y.min(), y.max()) / 0.99
            y = y.to(dev)
            y = y.unsqueeze(0)
            spec = spectrogram_torch(y, hps.data.filter_length,
                                        hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length,
                                        center=False).to(dev)
            spec_lengths = torch.LongTensor([spec.size(-1)]).to(dev)
            sid_src = torch.LongTensor([original_speaker_id]).to(dev)
            sid_tgt = torch.LongTensor([target_speaker_id]).to(dev)
            audio = net_g.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][
                0, 0].data.cpu().float().numpy()
        del y, spec, spec_lengths, sid_src, sid_tgt
        return "Success", (hps.data.sampling_rate, audio)
    return vc_fn

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

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 == "派蒙":
        spk = 16
        return spk
    
    elif speaker == "c1":
        spk = 18
        return spk

    elif speaker == "c2":
        spk = 19
        return spk

    elif speaker == "華恋":
        spk = 21
        return spk

    elif speaker == "まひる":
        spk = 22
        return spk

    elif speaker == "なな":
        spk = 23
        return spk

    elif speaker == "クロディーヌ":
        spk = 24
        return spk

    elif speaker == "ひかり":
        spk = 25
        return spk

    elif speaker == "純那":
        spk = 26
        return spk

    elif speaker == "香子":
        spk = 27
        return spk

    elif speaker == "真矢":
        spk = 28
        return spk
    
    elif speaker == "双葉":
        spk = 29
        return spk
    
    elif speaker == "ミチル":
        spk = 30
        return spk
    
    elif speaker == "メイファン":
        spk = 31
        return spk
    
    elif speaker == "やちよ":
        spk = 32
        return spk
   
    elif speaker == "晶":
        spk = 33
        return spk
    
    elif speaker == "いちえ":
        spk = 34
        return spk
    
    elif speaker == "ゆゆ子":
        spk = 35
        return spk
    
    elif speaker == "塁":
        spk = 36
        return spk
    
    elif speaker == "珠緒":
        spk = 37
        return spk
    
    elif speaker == "あるる":
        spk = 38
        return spk
    
    elif speaker == "ララフィン":
        spk = 39
        return spk
    
    elif speaker == "美空":
        spk = 40
        return spk
    
    elif speaker == "静羽":
        spk = 41
        return spk
    
    else:
        return 0

def check_text(input):
    if isinstance(input, str):
        return input
    else:
        with open(input.name, "r", encoding="utf-8") as f:
            return f.read()

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_list = ["ShojoKageki-Nijigasaki","ShojoKageki","Nijigasaki"]
    schools = []
    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)
    for i in models_info:
        checkpoint = models_info[i]["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)
        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)
        schools.append((i,create_vc_fn(net_g,hps)))
    with gr.Blocks() as app:
        with gr.Tabs():
            for (i,vc_fn) in schools:
                with gr.TabItem(i):
                    idols = ["派蒙"]
                    for (sid, name,  title, example, tts_fn) in models[schools_list.index(i)]:
                        idols.append(name)
                        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)
                                    input4 = gr.Slider(minimum=0, maximum=1.0, label="更改噪声比例(noise scale)，以控制情感", value=0.6)
                                    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=0.668)
                                    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 = '懂得都懂')   
                                with gr.Accordion(label="Advanced Setting", open=False):
                                    output2 = gr.outputs.File(label="字幕文件：subtitles.srt")
                                    audio_input1 = gr.Checkbox(value=False, label="保存路径")
                                    audio_input2 = gr.TextArea(label="音频路径",lines=1,value = 'D:/path/to/live2d/sounds/temp.wav')
                                    input3 = gr.Checkbox(value=False, label="长句切割(小说合成)")
                                    inputxt = gr.File(label="Text")
                                    is_transfer = gr.Checkbox(value=False, label="是否声线转化")
                                    source_speaker = gr.Dropdown(choices=idols, value=name, label="source speaker")
                                    target_speaker = gr.Dropdown(choices=idols, value=name, label="target speaker")
                                    btnbook = gr.Button("小说合成")  
                        btnVC.click(bot, inputs = [chatbot,input1], outputs = [chatbot]).then(
    tts_fn, inputs=[is_transfer,source_speaker,target_speaker,chatbot,api_input1,api_input2,audio_input1,audio_input2,audio_input3,input1,input2,input3,input4,input5,input6], outputs=[chatbot,output2,output1]
    )
                        btnbook.click(bot, inputs = [chatbot,inputxt], outputs = [chatbot]).then(
    tts_fn, inputs=[is_transfer,source_speaker,target_speaker,chatbot,api_input1,api_input2,audio_input1,audio_input2,audio_input3,inputxt,input2,input3,input4,input5,input6], outputs=[chatbot,output2,output1]
    )
                    with gr.Tab("Voice Conversion(类似sovits)"):
                        gr.Markdown("""
                                        声线转化，使用模型中的说话人作为音源时效果更佳
                        """)
                        with gr.Column():
                            with gr.Accordion(label="方法1:录制或上传声音,可进行歌声合成", open=False):
                                record_audio = gr.Audio(label="record your voice", source="microphone")
                                upload_audio = gr.Audio(label="or upload audio here", source="upload")
                            with gr.Accordion(label="方法2:由原说话人先进行tts后套娃，适用于合成中文等特殊场景", open=True):
                                text = gr.TextArea(label="Text", value='由源说话人进行语音转化',lines = 1)
                                language = gr.Dropdown(label="Language", choices=lan, value="自动", interactive=True)   
                                n_scale = gr.Slider(minimum=0, maximum=1.0, label="更改噪声比例(noise scale)，以控制情感", value=0.6)
                                n_scale_w = 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=0.668)
                                l_scale = gr.Slider(minimum=0.1, maximum=10, label="duration", value=1.1)
                            source_speaker = gr.Dropdown(choices=idols, value=idols[-2], label="source speaker")
                            target_speaker = gr.Dropdown(choices=idols, value=idols[-3], label="target speaker")
                        with gr.Column():
                            message_box = gr.Textbox(label="Message")
                            converted_audio = gr.Audio(label='converted audio')
                        btn = gr.Button("Convert!")
                        btn.click(vc_fn, inputs=[text,language,n_scale,n_scale_w,l_scale,source_speaker, target_speaker, record_audio, upload_audio],
                                outputs=[message_box, converted_audio])  
    app.launch()