'''
按中英混合识别
按日英混合识别
多语种启动切分识别语种
全部按中文识别
全部按英文识别
全部按日文识别
'''
import logging
import traceback

logging.getLogger("markdown_it").setLevel(logging.ERROR)
logging.getLogger("urllib3").setLevel(logging.ERROR)
logging.getLogger("httpcore").setLevel(logging.ERROR)
logging.getLogger("httpx").setLevel(logging.ERROR)
logging.getLogger("asyncio").setLevel(logging.ERROR)
logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
logging.getLogger("multipart.multipart").setLevel(logging.ERROR)
import gradio.analytics as analytics
analytics.version_check = lambda:None
analytics.get_local_ip_address= lambda :"127.0.0.1"##不干掉本地联不通亚马逊的get_local_ip服务器
import nltk
nltk.download('averaged_perceptron_tagger_eng')
import LangSegment, os, re, sys, json
import pdb
import spaces
import torch

version="v2"#os.environ.get("version","v2")
cnhubert_base_path = os.environ.get(
    "cnhubert_base_path", "pretrained_models/chinese-hubert-base"
)
bert_path = os.environ.get(
    "bert_path", "pretrained_models/chinese-roberta-wwm-ext-large"
)

punctuation = set(['!', '?', '…', ',', '.', '-'," "])
import gradio as gr
from transformers import AutoModelForMaskedLM, AutoTokenizer
import numpy as np
import librosa
from feature_extractor import cnhubert

cnhubert.cnhubert_base_path = cnhubert_base_path

from module.models import SynthesizerTrn
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from text import cleaned_text_to_sequence
from text.cleaner import clean_text
from time import time as ttime
from module.mel_processing import spectrogram_torch
from tools.my_utils import load_audio
from tools.i18n.i18n import I18nAuto, scan_language_list

# language=os.environ.get("language","Auto")
# language=sys.argv[-1] if sys.argv[-1] in scan_language_list() else language
i18n = I18nAuto(language="Auto")

# os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'  # 确保直接启动推理UI时也能够设置。

if torch.cuda.is_available():
    device = "cuda"
    is_half = True  # eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
else:
    device = "cpu"
    is_half=False

dict_language_v1 = {
    i18n("中文"): "all_zh",#全部按中文识别
    i18n("英文"): "en",#全部按英文识别#######不变
    i18n("日文"): "all_ja",#全部按日文识别
    i18n("中英混合"): "zh",#按中英混合识别####不变
    i18n("日英混合"): "ja",#按日英混合识别####不变
    i18n("多语种混合"): "auto",#多语种启动切分识别语种
}
dict_language_v2 = {
    i18n("中文"): "all_zh",#全部按中文识别
    i18n("英文"): "en",#全部按英文识别#######不变
    i18n("日文"): "all_ja",#全部按日文识别
    i18n("粤语"): "all_yue",#全部按中文识别
    i18n("韩文"): "all_ko",#全部按韩文识别
    i18n("中英混合"): "zh",#按中英混合识别####不变
    i18n("日英混合"): "ja",#按日英混合识别####不变
    i18n("粤英混合"): "yue",#按粤英混合识别####不变
    i18n("韩英混合"): "ko",#按韩英混合识别####不变
    i18n("多语种混合"): "auto",#多语种启动切分识别语种
    i18n("多语种混合(粤语)"): "auto_yue",#多语种启动切分识别语种
}
dict_language = dict_language_v1 if version =='v1' else dict_language_v2

tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
if is_half == True:
    bert_model = bert_model.half().to(device)
else:
    bert_model = bert_model.to(device)


def get_bert_feature(text, word2ph):
    with torch.no_grad():
        inputs = tokenizer(text, return_tensors="pt")
        for i in inputs:
            inputs[i] = inputs[i].to(device)
        res = bert_model(**inputs, output_hidden_states=True)
        res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
    assert len(word2ph) == len(text)
    phone_level_feature = []
    for i in range(len(word2ph)):
        repeat_feature = res[i].repeat(word2ph[i], 1)
        phone_level_feature.append(repeat_feature)
    phone_level_feature = torch.cat(phone_level_feature, dim=0)
    return phone_level_feature.T


class DictToAttrRecursive(dict):
    def __init__(self, input_dict):
        super().__init__(input_dict)
        for key, value in input_dict.items():
            if isinstance(value, dict):
                value = DictToAttrRecursive(value)
            self[key] = value
            setattr(self, key, value)

    def __getattr__(self, item):
        try:
            return self[item]
        except KeyError:
            raise AttributeError(f"Attribute {item} not found")

    def __setattr__(self, key, value):
        if isinstance(value, dict):
            value = DictToAttrRecursive(value)
        super(DictToAttrRecursive, self).__setitem__(key, value)
        super().__setattr__(key, value)

    def __delattr__(self, item):
        try:
            del self[item]
        except KeyError:
            raise AttributeError(f"Attribute {item} not found")


ssl_model = cnhubert.get_model()
if is_half == True:
    ssl_model = ssl_model.half().to(device)
else:
    ssl_model = ssl_model.to(device)


def change_sovits_weights(sovits_path,prompt_language=None,text_language=None):
    global vq_model, hps, version, dict_language
    dict_s2 = torch.load(sovits_path, map_location="cpu")
    hps = dict_s2["config"]
    hps = DictToAttrRecursive(hps)
    hps.model.semantic_frame_rate = "25hz"
    if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
        hps.model.version = "v1"
    else:
        hps.model.version = "v2"
    version = hps.model.version
    # print("sovits版本:",hps.model.version)
    vq_model = SynthesizerTrn(
        hps.data.filter_length // 2 + 1,
        hps.train.segment_size // hps.data.hop_length,
        n_speakers=hps.data.n_speakers,
        **hps.model
    )
    if ("pretrained" not in sovits_path):
        del vq_model.enc_q
    if is_half == True:
        vq_model = vq_model.half().to(device)
    else:
        vq_model = vq_model.to(device)
    vq_model.eval()
    print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
    dict_language = dict_language_v1 if version =='v1' else dict_language_v2
    if prompt_language is not None and text_language is not None:
        if prompt_language in list(dict_language.keys()):
            prompt_text_update, prompt_language_update = {'__type__':'update'},  {'__type__':'update', 'value':prompt_language}
        else:
            prompt_text_update = {'__type__':'update', 'value':''}
            prompt_language_update = {'__type__':'update', 'value':i18n("中文")}
        if text_language in list(dict_language.keys()):
            text_update, text_language_update = {'__type__':'update'}, {'__type__':'update', 'value':text_language}
        else:
            text_update = {'__type__':'update', 'value':''}
            text_language_update = {'__type__':'update', 'value':i18n("中文")}
        return  {'__type__':'update', 'choices':list(dict_language.keys())}, {'__type__':'update', 'choices':list(dict_language.keys())}, prompt_text_update, prompt_language_update, text_update, text_language_update



change_sovits_weights("pretrained_models/gsv-v2final-pretrained/s2G2333k.pth")


def change_gpt_weights(gpt_path):
    global hz, max_sec, t2s_model, config
    hz = 50
    dict_s1 = torch.load(gpt_path, map_location="cpu")
    config = dict_s1["config"]
    max_sec = config["data"]["max_sec"]
    t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
    t2s_model.load_state_dict(dict_s1["weight"])
    if is_half == True:
        t2s_model = t2s_model.half()
    t2s_model = t2s_model.to(device)
    t2s_model.eval()
    total = sum([param.nelement() for param in t2s_model.parameters()])
    print("Number of parameter: %.2fM" % (total / 1e6))


change_gpt_weights("pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt")


def get_spepc(hps, filename):
    audio = load_audio(filename, int(hps.data.sampling_rate))
    audio = torch.FloatTensor(audio)
    maxx=audio.abs().max()
    if(maxx>1):audio/=min(2,maxx)
    audio_norm = audio
    audio_norm = audio_norm.unsqueeze(0)
    spec = spectrogram_torch(
        audio_norm,
        hps.data.filter_length,
        hps.data.sampling_rate,
        hps.data.hop_length,
        hps.data.win_length,
        center=False,
    )
    return spec

def clean_text_inf(text, language, version):
    phones, word2ph, norm_text = clean_text(text, language, version)
    phones = cleaned_text_to_sequence(phones, version)
    return phones, word2ph, norm_text

dtype=torch.float16 if is_half == True else torch.float32
def get_bert_inf(phones, word2ph, norm_text, language):
    language=language.replace("all_","")
    if language == "zh":
        bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
    else:
        bert = torch.zeros(
            (1024, len(phones)),
            dtype=torch.float16 if is_half == True else torch.float32,
        ).to(device)

    return bert


splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }


def get_first(text):
    pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
    text = re.split(pattern, text)[0].strip()
    return text

from text import chinese
def get_phones_and_bert(text,language,version):
    if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
        language = language.replace("all_","")
        if language == "en":
            LangSegment.setfilters(["en"])
            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
        else:
            # 因无法区别中日韩文汉字,以用户输入为准
            formattext = text
        while "  " in formattext:
            formattext = formattext.replace("  ", " ")
        if language == "zh":
            if re.search(r'[A-Za-z]', formattext):
                formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
                formattext = chinese.mix_text_normalize(formattext)
                return get_phones_and_bert(formattext,"zh",version)
            else:
                phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
                bert = get_bert_feature(norm_text, word2ph).to(device)
        elif language == "yue" and re.search(r'[A-Za-z]', formattext):
                formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
                formattext = chinese.mix_text_normalize(formattext)
                return get_phones_and_bert(formattext,"yue",version)
        else:
            phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
            bert = torch.zeros(
                (1024, len(phones)),
                dtype=torch.float16 if is_half == True else torch.float32,
            ).to(device)
    elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
        textlist=[]
        langlist=[]
        LangSegment.setfilters(["zh","ja","en","ko"])
        if language == "auto":
            for tmp in LangSegment.getTexts(text):
                langlist.append(tmp["lang"])
                textlist.append(tmp["text"])
        elif language == "auto_yue":
            for tmp in LangSegment.getTexts(text):
                if tmp["lang"] == "zh":
                    tmp["lang"] = "yue"
                langlist.append(tmp["lang"])
                textlist.append(tmp["text"])
        else:
            for tmp in LangSegment.getTexts(text):
                if tmp["lang"] == "en":
                    langlist.append(tmp["lang"])
                else:
                    # 因无法区别中日韩文汉字,以用户输入为准
                    langlist.append(language)
                textlist.append(tmp["text"])
        print(textlist)
        print(langlist)
        phones_list = []
        bert_list = []
        norm_text_list = []
        for i in range(len(textlist)):
            lang = langlist[i]
            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang, version)
            bert = get_bert_inf(phones, word2ph, norm_text, lang)
            phones_list.append(phones)
            norm_text_list.append(norm_text)
            bert_list.append(bert)
        bert = torch.cat(bert_list, dim=1)
        phones = sum(phones_list, [])
        norm_text = ''.join(norm_text_list)

    return phones,bert.to(dtype),norm_text


def merge_short_text_in_array(texts, threshold):
    if (len(texts)) < 2:
        return texts
    result = []
    text = ""
    for ele in texts:
        text += ele
        if len(text) >= threshold:
            result.append(text)
            text = ""
    if (len(text) > 0):
        if len(result) == 0:
            result.append(text)
        else:
            result[len(result) - 1] += text
    return result

##ref_wav_path+prompt_text+prompt_language+text(单个)+text_language+top_k+top_p+temperature
# cache_tokens={}#暂未实现清理机制
cache= {}
@torch.inference_mode()
@spaces.GPU
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False,speed=1,if_freeze=False,inp_refs=123):
    global cache
    if ref_wav_path:pass
    else:gr.Warning(i18n('请上传参考音频'))
    if text:pass
    else:gr.Warning(i18n('请填入推理文本'))
    t = []
    if prompt_text is None or len(prompt_text) == 0:
        ref_free = True
    t0 = ttime()
    prompt_language = dict_language[prompt_language]
    text_language = dict_language[text_language]


    if not ref_free:
        prompt_text = prompt_text.strip("\n")
        if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
        print(i18n("实际输入的参考文本:"), prompt_text)
    text = text.strip("\n")
    if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text

    print(i18n("实际输入的目标文本:"), text)
    zero_wav = np.zeros(
        int(hps.data.sampling_rate * 0.3),
        dtype=np.float16 if is_half == True else np.float32,
    )
    if not ref_free:
        with torch.no_grad():
            wav16k, sr = librosa.load(ref_wav_path, sr=16000)
            if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000):
                gr.Warning(i18n("参考音频在3~10秒范围外，请更换！"))
                raise OSError(i18n("参考音频在3~10秒范围外，请更换！"))
            wav16k = torch.from_numpy(wav16k)
            zero_wav_torch = torch.from_numpy(zero_wav)
            if is_half == True:
                wav16k = wav16k.half().to(device)
                zero_wav_torch = zero_wav_torch.half().to(device)
            else:
                wav16k = wav16k.to(device)
                zero_wav_torch = zero_wav_torch.to(device)
            wav16k = torch.cat([wav16k, zero_wav_torch])
            ssl_content = ssl_model.model(wav16k.unsqueeze(0))[
                "last_hidden_state"
            ].transpose(
                1, 2
            )  # .float()
            codes = vq_model.extract_latent(ssl_content)
            prompt_semantic = codes[0, 0]
            prompt = prompt_semantic.unsqueeze(0).to(device)

    t1 = ttime()
    t.append(t1-t0)

    if (how_to_cut == i18n("凑四句一切")):
        text = cut1(text)
    elif (how_to_cut == i18n("凑50字一切")):
        text = cut2(text)
    elif (how_to_cut == i18n("按中文句号。切")):
        text = cut3(text)
    elif (how_to_cut == i18n("按英文句号.切")):
        text = cut4(text)
    elif (how_to_cut == i18n("按标点符号切")):
        text = cut5(text)
    while "\n\n" in text:
        text = text.replace("\n\n", "\n")
    print(i18n("实际输入的目标文本(切句后):"), text)
    texts = text.split("\n")
    texts = process_text(texts)
    texts = merge_short_text_in_array(texts, 5)
    audio_opt = []
    if not ref_free:
        phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language, version)

    for i_text,text in enumerate(texts):
        # 解决输入目标文本的空行导致报错的问题
        if (len(text.strip()) == 0):
            continue
        if (text[-1] not in splits): text += "。" if text_language != "en" else "."
        print(i18n("实际输入的目标文本(每句):"), text)
        phones2,bert2,norm_text2=get_phones_and_bert(text, text_language, version)
        print(i18n("前端处理后的文本(每句):"), norm_text2)
        if not ref_free:
            bert = torch.cat([bert1, bert2], 1)
            all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
        else:
            bert = bert2
            all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)

        bert = bert.to(device).unsqueeze(0)
        all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)

        t2 = ttime()
        # cache_key="%s-%s-%s-%s-%s-%s-%s-%s"%(ref_wav_path,prompt_text,prompt_language,text,text_language,top_k,top_p,temperature)
        # print(cache.keys(),if_freeze)
        if(i_text in cache and if_freeze==True):pred_semantic=cache[i_text]
        else:
            with torch.no_grad():
                pred_semantic, idx = t2s_model.model.infer_panel(
                    all_phoneme_ids,
                    all_phoneme_len,
                    None if ref_free else prompt,
                    bert,
                    # prompt_phone_len=ph_offset,
                    top_k=top_k,
                    top_p=top_p,
                    temperature=temperature,
                    early_stop_num=hz * max_sec,
                )
                pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
                cache[i_text]=pred_semantic
        t3 = ttime()
        refers=[]
        if(inp_refs):
            for path in inp_refs:
                try:
                    refer = get_spepc(hps, path.name).to(dtype).to(device)
                    refers.append(refer)
                except:
                    traceback.print_exc()
        if(len(refers)==0):refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
        audio = (vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers,speed=speed).detach().cpu().numpy()[0, 0])
        max_audio=np.abs(audio).max()#简单防止16bit爆音
        if max_audio>1:audio/=max_audio
        audio_opt.append(audio)
        audio_opt.append(zero_wav)
        t4 = ttime()
        t.extend([t2 - t1,t3 - t2, t4 - t3])
        t1 = ttime()
    print("%.3f\t%.3f\t%.3f\t%.3f" %
           (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3]))
           )
    yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(
        np.int16
    )


def split(todo_text):
    todo_text = todo_text.replace("……", "。").replace("——", "，")
    if todo_text[-1] not in splits:
        todo_text += "。"
    i_split_head = i_split_tail = 0
    len_text = len(todo_text)
    todo_texts = []
    while 1:
        if i_split_head >= len_text:
            break  # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
        if todo_text[i_split_head] in splits:
            i_split_head += 1
            todo_texts.append(todo_text[i_split_tail:i_split_head])
            i_split_tail = i_split_head
        else:
            i_split_head += 1
    return todo_texts


def cut1(inp):
    inp = inp.strip("\n")
    inps = split(inp)
    split_idx = list(range(0, len(inps), 4))
    split_idx[-1] = None
    if len(split_idx) > 1:
        opts = []
        for idx in range(len(split_idx) - 1):
            opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
    else:
        opts = [inp]
    opts = [item for item in opts if not set(item).issubset(punctuation)]
    return "\n".join(opts)


def cut2(inp):
    inp = inp.strip("\n")
    inps = split(inp)
    if len(inps) < 2:
        return inp
    opts = []
    summ = 0
    tmp_str = ""
    for i in range(len(inps)):
        summ += len(inps[i])
        tmp_str += inps[i]
        if summ > 50:
            summ = 0
            opts.append(tmp_str)
            tmp_str = ""
    if tmp_str != "":
        opts.append(tmp_str)
    # print(opts)
    if len(opts) > 1 and len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
        opts[-2] = opts[-2] + opts[-1]
        opts = opts[:-1]
    opts = [item for item in opts if not set(item).issubset(punctuation)]
    return "\n".join(opts)


def cut3(inp):
    inp = inp.strip("\n")
    opts = ["%s" % item for item in inp.strip("。").split("。")]
    opts = [item for item in opts if not set(item).issubset(punctuation)]
    return  "\n".join(opts)

def cut4(inp):
    inp = inp.strip("\n")
    opts = ["%s" % item for item in inp.strip(".").split(".")]
    opts = [item for item in opts if not set(item).issubset(punctuation)]
    return "\n".join(opts)


# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
def cut5(inp):
    inp = inp.strip("\n")
    punds = {',', '.', ';', '?', '!', '、', '，', '。', '？', '！', ';', '：', '…'}
    mergeitems = []
    items = []

    for i, char in enumerate(inp):
        if char in punds:
            if char == '.' and i > 0 and i < len(inp) - 1 and inp[i - 1].isdigit() and inp[i + 1].isdigit():
                items.append(char)
            else:
                items.append(char)
                mergeitems.append("".join(items))
                items = []
        else:
            items.append(char)

    if items:
        mergeitems.append("".join(items))

    opt = [item for item in mergeitems if not set(item).issubset(punds)]
    return "\n".join(opt)


def custom_sort_key(s):
    # 使用正则表达式提取字符串中的数字部分和非数字部分
    parts = re.split('(\d+)', s)
    # 将数字部分转换为整数，非数字部分保持不变
    parts = [int(part) if part.isdigit() else part for part in parts]
    return parts

def process_text(texts):
    _text=[]
    if all(text in [None, " ", "\n",""] for text in texts):
        raise ValueError(i18n("请输入有效文本"))
    for text in texts:
        if text in  [None, " ", ""]:
            pass
        else:
            _text.append(text)
    return _text


def html_center(text, label='p'):
    return f"""<div style="text-align: center; margin: 100; padding: 50;">
                <{label} style="margin: 0; padding: 0;">{text}</{label}>
                </div>"""

def html_left(text, label='p'):
    return f"""<div style="text-align: left; margin: 0; padding: 0;">
                <{label} style="margin: 0; padding: 0;">{text}</{label}>
                </div>"""


with gr.Blocks(title="GPT-SoVITS WebUI") as app:
    gr.Markdown(
        value="""# GPT-SoVITS-v2 Zero-shot TTS demo
## https://github.com/RVC-Boss/GPT-SoVITS
Input 3 to 10s reference audio to guide the time-bre, speed, emotion of voice, and generate the speech you want by input the inference text. <br>
输入3至10秒的参考音频来引导待合成语音的音色、语速和情感，然后输入待合成目标文本，生成目标语音. <br>
Cross-lingual Support: Inference in languages different from the training dataset, currently supporting English, Japanese, Korean and Cantonese.<br>
目前支持中日英韩粤跨语种合成。<br>
This demo is open source under the MIT license. The author does not have any control over it. Users who use the software and distribute the sounds exported by the software are solely responsible. If you do not agree with this clause, you cannot use or reference any codes and files within this demo. <br>
本demo以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. 如不认可该条款, 则不能使用或引用该demo内的任何代码和文件.
"""
    )
    with gr.Group():
        gr.Markdown(html_center(i18n("*请上传并填写参考信息"),'h3'))
        with gr.Row():
            inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
            with gr.Column():
                ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True)
                gr.Markdown(html_left(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开。<br>开启后无视填写的参考文本。")))
                prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="", lines=3, max_lines=3)
            prompt_language = gr.Dropdown(
                label=i18n("参考音频的语种"), choices=list(dict_language.keys()), value=i18n("中文")
            )
            inp_refs = gr.File(label=i18n("可选项：通过拖拽多个文件上传多个参考音频（建议同性），平均融合他们的音色。如不填写此项，音色由左侧单个参考音频控制。"),file_count="multiple")
        gr.Markdown(html_center(i18n("*请填写需要合成的目标文本和语种模式"),'h3'))
        with gr.Row():
            with gr.Column():
                text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=26, max_lines=26)
            with gr.Column():
                text_language = gr.Dropdown(
                        label=i18n("需要合成的语种")+i18n(".限制范围越小判别效果越好。"), choices=list(dict_language.keys()), value=i18n("中文")
                    )
                how_to_cut = gr.Dropdown(
                        label=i18n("怎么切"),
                        choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ],
                        value=i18n("凑四句一切"),
                        interactive=True
                    )
                gr.Markdown(value=html_center(i18n("语速调整，高为更快")))
                if_freeze=gr.Checkbox(label=i18n("是否直接对上次合成结果调整语速和音色。防止随机性。"), value=False, interactive=True,show_label=True)
                speed = gr.Slider(minimum=0.6,maximum=1.65,step=0.05,label=i18n("语速"),value=1,interactive=True)
                gr.Markdown(html_center(i18n("GPT采样参数(无参考文本时不要太低。不懂就用默认)：")))
                top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=15,interactive=True)
                top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
                temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
        with gr.Row():
            inference_button = gr.Button(i18n("合成语音"), variant="primary", size='lg')
            output = gr.Audio(label=i18n("输出的语音"))

        inference_button.click(
            get_tts_wav,
            [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free,speed,if_freeze,inp_refs],
            [output],
        )

if __name__ == '__main__':
    # app.queue(concurrency_count=511, max_size=1022).launch(
    app.queue().launch(
        server_name="0.0.0.0",
        inbrowser=True,
        # share=True,
        # server_port=infer_ttswebui,
        quiet=True,
    )