inference_webui.py

import os

os.makedirs("pretrained_models", exist_ok=True)
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="lj1995/GPT-SoVITS",
    repo_type="model",
    allow_patterns="chinese*",
    local_dir="pretrained_models",
)
snapshot_download(
    repo_id="lj1995/GPT-SoVITS",
    repo_type="model",
    allow_patterns="s1v3.ckpt",
    local_dir="pretrained_models",
)
snapshot_download(
    repo_id="lj1995/GPT-SoVITS",
    repo_type="model",
    allow_patterns="sv*",
    local_dir="pretrained_models",
)
snapshot_download(
    repo_id="lj1995/GPT-SoVITS",
    repo_type="model",
    allow_patterns="v2Pro/s2Gv2ProPlus.pth",
    local_dir="pretrained_models",
)
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)
logging.getLogger("python_multipart.multipart").setLevel(logging.ERROR)
logging.getLogger("split_lang.split.splitter").setLevel(logging.ERROR)

import nltk
import torchaudio

from text.LangSegmenter import LangSegmenter

nltk.download("averaged_perceptron_tagger_eng")
import json
import os
import pdb
import re
import sys
import threading

import LangSegment
import spaces
import torch

lock = threading.Lock()

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
import gradio.themes as themes
import librosa
import numpy as np
from gradio.themes.utils import fonts
from transformers import AutoModelForMaskedLM, AutoTokenizer

from feature_extractor import cnhubert

cnhubert.cnhubert_base_path = cnhubert_base_path

from time import time as ttime

from AR.models.structs import T2SRequest
from AR.models.t2s_model_flash_attn import CUDAGraphRunner
from module.mel_processing import spectrogram_torch
from module.models import SynthesizerTrn
from text import cleaned_text_to_sequence
from text.cleaner import clean_text
from tools.i18n.i18n import I18nAuto, scan_language_list
from tools.my_utils import load_audio

# 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/v2Pro/s2Gv2ProPlus.pth")


def change_gpt_weights(gpt_path):
    global t2s_model, config
    dict_s1 = torch.load(gpt_path, map_location="cpu")
    config = dict_s1["config"]
    t2s_model = CUDAGraphRunner(
        CUDAGraphRunner.load_decoder(gpt_path), torch.device(device), torch.float16 if is_half else torch.float32
    )
    total = sum(p.numel() for p in t2s_model.decoder_model.parameters())
    print("Number of parameter: %.2fM" % (total / 1e6))


change_gpt_weights("pretrained_models/s1v3.ckpt")
from sv import SV

sv_cn_model = SV(device, is_half)

resample_transform_dict = {}


def resample(audio_tensor, sr0, sr1, device):
    global resample_transform_dict
    key = "%s-%s-%s" % (sr0, sr1, str(device))
    if key not in resample_transform_dict:
        resample_transform_dict[key] = torchaudio.transforms.Resample(sr0, sr1).to(device)
    return resample_transform_dict[key](audio_tensor)


def get_spepc(hps, filename, dtype, device, is_v2pro=False):
    sr1 = int(hps.data.sampling_rate)
    audio, sr0 = torchaudio.load(filename)
    if sr0 != sr1:
        audio = audio.to(device)
        if audio.shape[0] == 2:
            audio = audio.mean(0).unsqueeze(0)
        audio = resample(audio, sr0, sr1, device)
    else:
        audio = audio.to(device)
        if audio.shape[0] == 2:
            audio = audio.mean(0).unsqueeze(0)

    maxx = audio.abs().max()
    if maxx > 1:
        audio /= min(2, maxx)
    spec = spectrogram_torch(
        audio,
        hps.data.filter_length,
        hps.data.sampling_rate,
        hps.data.hop_length,
        hps.data.win_length,
        center=False,
    )
    spec = spec.to(dtype)
    if is_v2pro == True:
        audio = resample(audio, sr1, 16000, device).to(dtype)
    return spec, audio


def clean_text_inf(text, language, version):
    language = language.replace("all_", "")
    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, final=False):
    if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
        formattext = text
        while "  " in formattext:
            formattext = formattext.replace("  ", " ")
        if language == "all_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 == "all_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 = []
        if language == "auto":
            for tmp in LangSegmenter.getTexts(text):
                langlist.append(tmp["lang"])
                textlist.append(tmp["text"])
        elif language == "auto_yue":
            for tmp in LangSegmenter.getTexts(text):
                if tmp["lang"] == "zh":
                    tmp["lang"] = "yue"
                langlist.append(tmp["lang"])
                textlist.append(tmp["text"])
        else:
            for tmp in LangSegmenter.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)

    if not final and len(phones) < 6:
        return get_phones_and_bert("." + text, language, version, final=True)

    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 = {}


@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 = torchaudio.load(ref_wav_path)
            wav16k=wav16k.to(device)
            if wav16k.shape[0] == 2:
                wav16k = wav16k.mean(0).unsqueeze(0)
            if sr!=16000:
                wav16k=resample(wav16k, sr, 16000, device)
            wav16k=wav16k[0]
            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！"))
            zero_wav_torch = torch.from_numpy(zero_wav)
            if is_half == True:
                wav16k = wav16k.half()
                zero_wav_torch = zero_wav_torch.half().to(device)
            else:
                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)

    infer_speed: list[float] = []

    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(), lock:
                t2s_request = T2SRequest(
                    [all_phoneme_ids.squeeze(0)],
                    all_phoneme_len,
                    all_phoneme_ids.new_zeros((1, 0)) if ref_free else prompt,
                    [bert.squeeze(0)],
                    valid_length=1,
                    top_k=top_k,
                    top_p=top_p,
                    temperature=temperature,
                    early_stop_num=1500,
                    use_cuda_graph=True,
                    # debug=True,
                )
                t2s_result = t2s_model.generate(t2s_request)

                if t2s_result.exception is not None:
                    print(t2s_result.exception)
                    print(t2s_result.traceback)
                    raise RuntimeError("")

                infer_speed.append(t2s_result.infer_speed)
                pred_semantic = t2s_result.result
                assert pred_semantic
                cache[i_text] = pred_semantic
        t3 = ttime()
        refers = []
        sv_emb = []
        if inp_refs:
            for path in inp_refs:
                try:
                    refer, audio_tensor = get_spepc(hps, path.name, dtype, device, is_v2pro=True)
                    refers.append(refer)
                    sv_emb.append(sv_cn_model.compute_embedding3(audio_tensor))
                except:
                    traceback.print_exc()
        if len(refers) == 0:
            refers, audio_tensor = get_spepc(hps, ref_wav_path, dtype, device, is_v2pro=True)
            refers = [refers]
            sv_emb = [sv_cn_model.compute_embedding3(audio_tensor)]
        audio = (
            vq_model.decode(
                pred_semantic[0].unsqueeze(0).unsqueeze(0),
                torch.LongTensor(phones2).to(device).unsqueeze(0),
                refers,
                speed=speed,
                sv_emb=sv_emb,
            )
            .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])))
    gr.Info(f"Infer Speed: {sum(infer_speed) / len(infer_speed):.2f} Token/s")
    gr.Info("%.3f\t%.3f\t%.3f\t%.3f" % (t[0], sum(t[1::3]), sum(t[2::3]), sum(t[3::3])), duration=4)
    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>"""


theme = themes.Soft(
    font=(
        "-apple-system",
        fonts.GoogleFont("Inter"),
        fonts.GoogleFont("Quicksand"),
        "ui-sans-serif",
        "sans-serif",
    )
)
theme.block_border_width = "1px"

with gr.Blocks(
    title="GPT-SoVITS WebUI",
    theme=theme,
    analytics_enabled=False,
) as app:
    gr.Markdown(
        value="""# GPT-SoVITS-ProPlus 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混合合成。<br>
If the quota has been reached, you can try the backup space at https://huggingface.co/spaces/XXXXRT/GPT-SoVITS-ProPlus<br>
如果遇到限额不够可以去备用空间https://huggingface.co/spaces/XXXXRT/GPT-SoVITS-ProPlus 试试<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.
"""
    )
    gr.Markdown(html_center(i18n("*请上传并填写参考信息"), "h3"))
    with gr.Row(equal_height=True):
        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,
            )
            prompt_text = gr.Textbox(
                label=i18n("参考音频的文本"),
                value="",
                lines=3,
                max_lines=3,
                info=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_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(equal_height=True):
        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(equal_height=True):
        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__":
    import tempfile
    import wave

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as temp_file:
        file_name = temp_file.name
        with wave.open(temp_file, "w") as wav_file:
            channels = 1
            sample_width = 2
            sample_rate = 44100
            duration = 5
            frequency = 440.0

            t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
            sine_wave = np.sin(2 * np.pi * frequency * t)  # Sine Wave
            int_wave = (sine_wave * 32767).astype(np.int16)

            wav_file.setnchannels(channels)  # pylint: disable=no-member
            wav_file.setsampwidth(sample_width)  # pylint: disable=no-member
            wav_file.setframerate(sample_rate)  # pylint: disable=no-member
            wav_file.writeframes(int_wave.tobytes())  # pylint: disable=no-member

            gen = get_tts_wav(
                ref_wav_path=file_name,
                prompt_text="",
                prompt_language=i18n("中文"),
                text="犯大吴疆土者,盛必击而破之,犯大吴疆土者,盛必击而破之,犯大吴疆土者,盛必击而破之,犯大吴疆土者,盛必击而破之.你好世界 Love you 世界へ 안녕하세요",
                text_language=i18n("多语种混合"),
                inp_refs=[],
            )
            next(gen)

    app.queue().launch(
        server_name="0.0.0.0",
        inbrowser=True,
        show_api=False,
        allowed_paths=["/"],
    )