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",
)

# snapshot_download(
#     repo_id="Dylan916/gpt-sovits-zoengjyutgaai",
#     repo_type="model",
#     allow_patterns="SoVITS_weights_v4/zoengjyutgaai_e2_s534_l32.pth",
#     local_dir="pretrained_models",
# )

# snapshot_download(
#     repo_id="Dylan916/gpt-sovits-zoengjyutgaai",
#     repo_type="model",
#     allow_patterns="GPT_weights_v4/zoengjyutgaai-e15.ckpt",
#     local_dir="pretrained_models",
# )

snapshot_download(
    repo_id="Dylan916/gpt-sovits-zoengjyutgaai",
    repo_type="model",
    allow_patterns="GPT_weights_v2ProPlus/zoengjyutgaai-e15.ckpt",
    local_dir="pretrained_models",
)

snapshot_download(
    repo_id="Dylan916/gpt-sovits-zoengjyutgaai",
    repo_type="model",
    allow_patterns="SoVITS_weights_v2ProPlus/zoengjyutgaai_e8_s1016.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")
# change_sovits_weights("pretrained_models/SoVITS_weights_v4/zoengjyutgaai_e2_s534_l32.pth")
# change_sovits_weights("pretrained_models/SoVITS_weights_v2ProPlus/zoengjyutgaai_e8_s1016.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")
change_gpt_weights("pretrained_models/GPT_weights_v2ProPlus/zoengjyutgaai-e15.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 = 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)

    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:
    with gr.Row(equal_height=True):
        inp_ref = gr.Audio(value="./audio.opus", type="filepath", visible=False)
        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,
            visible=False,
            )
        prompt_text = gr.Textbox(
            label=i18n("参考音频的文本"),
            value="由東漢靈帝中平元年，即係公元一八四年，黃巾起義嗰陣開始。",
            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。"
                ),
            visible=False,
            )
        # 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,
        #         visible=False,
        #     )
        #     prompt_text = gr.Textbox(
        #         label=i18n("参考音频的文本"),
        #         value="由東漢靈帝中平元年，即係公元一八四年，黃巾起義嗰陣開始。",
        #         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。"
        #         ),
        #         visible=False,
        #     )
        prompt_language = gr.Dropdown(
            label=i18n("参考音频的语种"), choices=list(dict_language.keys()), value=i18n("粤语"), visible=False
        )
        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",
            visible=False,
        )
    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("粤语"),
                visible=False,
            )
            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("按标点符号切"),
                interactive=True,
                visible=False,
            )
            gr.Markdown(value=html_center(i18n("语速调整，高为更快")), visible=False)
            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,
                visible=False,
            )
            speed = gr.Slider(minimum=0.6, maximum=1.65, step=0.05, label=i18n("语速"), value=1, interactive=True, visible=False)
            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)：")), visible=False)
            top_k = gr.Slider(minimum=1, maximum=100, step=1, label=i18n("top_k"), value=15, interactive=True, visible=False)
            top_p = gr.Slider(minimum=0, maximum=1, step=0.05, label=i18n("top_p"), value=1, interactive=True, visible=False)
            temperature = gr.Slider(
                minimum=0, maximum=1, step=0.05, label=i18n("temperature"), value=1, interactive=True, visible=False
            )
            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],
                )
    # 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",
                text_language=i18n("中文"),
                inp_refs=[],
            )
            next(gen)

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