GPT-SoVITS/GPT_SoVITS/inference_webui.py

import argparse
import contextlib
import logging
import os
import re
import traceback
import warnings
from functools import partial
from pathlib import Path
from time import time as ttime
from typing import Any

import gradio as gr
import librosa
import numpy as np
import psutil
import torch
import torchaudio
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForMaskedLM, AutoTokenizer

from config import (
    change_choices,
    get_dtype,
    get_weights_names,
    pretrained_sovits_name,
)
from config import (
    infer_device as default_device,
)
from GPT_SoVITS.Accelerate import MLX, PyTorch, T2SEngineProtocol, T2SRequest, backends
from GPT_SoVITS.Accelerate.logger import console
from GPT_SoVITS.feature_extractor import cnhubert
from GPT_SoVITS.module.mel_processing import mel_spectrogram_torch, spectrogram_torch
from GPT_SoVITS.module.models import Generator, SynthesizerTrn, SynthesizerTrnV3
from GPT_SoVITS.process_ckpt import inspect_version
from GPT_SoVITS.sv import SV
from GPT_SoVITS.text import cleaned_text_to_sequence
from GPT_SoVITS.text.cleaner import clean_text
from GPT_SoVITS.text.LangSegmenter import LangSegmenter
from tools.assets import css, js, top_html
from tools.i18n.i18n import I18nAuto, scan_language_list
from tools.my_utils import DictToAttrRecursive

with contextlib.suppress(ImportError):
    import mlx.utils as mxutils

warnings.filterwarnings(
    "ignore", message="MPS: The constant padding of more than 3 dimensions is not currently supported natively."
)
warnings.filterwarnings("ignore", message=".*ComplexHalf support is experimental.*")

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)

os.environ["TOKENIZERS_PARALLELISM"] = "false"


def set_high_priority():
    if os.name != "nt":
        return
    p = psutil.Process(os.getpid())
    with contextlib.suppress(psutil.AccessDenied):
        p.nice(psutil.HIGH_PRIORITY_CLASS)
        print("已将进程优先级设为 High")


_LANG_RE = re.compile(r"^[a-z]{2}[_-][A-Z]{2}$")


def lang_type(text: str) -> str:
    if text == "Auto":
        return text
    if not _LANG_RE.match(text):
        raise argparse.ArgumentTypeError(f"Unspported Format: {text}, Expected ll_CC/ll-CC")
    ll, cc = re.split(r"[_-]", text)
    language = f"{ll}_{cc}"
    if language in scan_language_list():
        return language
    else:
        return "Auto"


def build_parser() -> argparse.ArgumentParser:
    p = argparse.ArgumentParser(
        prog="inference_webui",
        description=f"PYTHONPATH=. python -s GPT_SoVITS/inference_webui.py zh_CN -b {backends[-1]}",
    )
    p.add_argument(
        "language",
        nargs="?",
        default="Auto",
        type=lang_type,
        help="Language Code, Such as zh_CN, en-US",
    )
    p.add_argument(
        "--backends",
        "-b",
        choices=backends,
        default=backends[-1],
        help="AR Inference Backend",
        required=False,
    )
    p.add_argument(
        "--device",
        "-d",
        default=str(default_device),
        help="Inference Device",
        required=False,
    )
    p.add_argument(
        "--port",
        "-p",
        default=9872,
        type=int,
        help="WebUI Binding Port",
        required=False,
    )
    p.add_argument(
        "--share",
        "-s",
        default=False,
        action="store_true",
        help="Gradio Share Link",
        required=False,
    )
    p.add_argument(
        "--cnhubert",
        default="GPT_SoVITS/pretrained_models/chinese-hubert-base",
        help="CNHuBERT Pretrain",
        required=False,
    )
    p.add_argument(
        "--bert",
        default="GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
        help="BERT Pretrain",
        required=False,
    )
    p.add_argument(
        "--gpt",
        default="",
        help="GPT Model",
        required=False,
    )
    p.add_argument(
        "--sovits",
        default="",
        help="SoVITS Model",
        required=False,
    )

    return p


args = build_parser().parse_args()

hps: Any = None
vq_model: SynthesizerTrn | SynthesizerTrnV3 | None = None
t2s_engine: T2SEngineProtocol | None = None

version = model_version = "v2"
path_sovits_v3 = pretrained_sovits_name["v3"]
path_sovits_v4 = pretrained_sovits_name["v4"]
is_exist_s2gv3 = os.path.exists(path_sovits_v3)
is_exist_s2gv4 = os.path.exists(path_sovits_v4)

cnhubert_base_path = str(args.cnhubert)
bert_path = str(args.bert)
infer_ttswebui = int(args.port)
is_share = bool(args.share)


i18n = I18nAuto(language=args.language)
ar_backend: str = args.backends
change_choices_i18n = partial(change_choices, i18n=i18n)

SoVITS_names, GPT_names = get_weights_names(i18n)


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
    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
    i18n("日英混合"): "ja",  # 按日英混合识别
    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
    i18n("日文"): "all_ja",  # 全部按日文识别
    i18n("粤语"): "all_yue",  # 全部按粤语识别
    i18n("韩文"): "all_ko",  # 全部按韩文识别
    i18n("中英混合"): "zh",
    i18n("日英混合"): "ja",
    i18n("粤英混合"): "yue",
    i18n("韩英混合"): "ko",
    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

punctuation = set(["!", "?", "…", ",", ".", "-", " "])
splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…"}
v3v4set = {"v3", "v4"}

infer_device = torch.device(args.device)
device = infer_device if infer_device.type == "cuda" else torch.device("cpu")

dtype = get_dtype(device.index)
is_half = dtype == torch.float16

tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path).to(infer_device, dtype)

cnhubert.cnhubert_base_path = cnhubert_base_path
ssl_model = cnhubert.get_model().to(infer_device, dtype)

spec_min = -12
spec_max = 2


def norm_spec(x):
    return (x - spec_min) / (spec_max - spec_min) * 2 - 1


def denorm_spec(x):
    return (x + 1) / 2 * (spec_max - spec_min) + spec_min


def mel_fn(x):
    return mel_spectrogram_torch(
        y=x,
        n_fft=1024,
        num_mels=100,
        sampling_rate=24000,
        hop_size=256,
        win_size=1024,
        fmin=0,
        fmax=None,
        center=False,
    )


def mel_fn_v4(x):
    return mel_spectrogram_torch(
        y=x,
        n_fft=1280,
        num_mels=100,
        sampling_rate=32000,
        hop_size=320,
        win_size=1280,
        fmin=0,
        fmax=None,
        center=False,
    )


gpt_path = str(args.gpt) or GPT_names[0][-1]
sovits_path = str(args.sovits) or SoVITS_names[0][-1]


def get_bert_feature(text, word2ph):
    inputs = tokenizer(text, return_tensors="pt")
    for i in inputs:
        inputs[i] = inputs[i].to(infer_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_t = torch.cat(phone_level_feature, dim=0)
    return phone_level_feature_t.T


def change_sovits_weights(sovits_path, prompt_language=None, text_language=None):
    global vq_model, hps, version, model_version, dict_language
    model_version, version, is_lora, hps, dict_s2 = inspect_version(sovits_path)
    print(sovits_path, version, model_version, is_lora)
    is_exist = is_exist_s2gv3 if model_version == "v3" else is_exist_s2gv4
    path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
    if is_lora is True and is_exist is False:
        info = f"{path_sovits} SoVITS {model_version} {i18n('底模缺失，无法加载相应 LoRA 权重')}"
        gr.Warning(info)
        raise FileNotFoundError(info)
    dict_language = dict_language_v1 if version == "v1" else dict_language_v2
    visible_sample_steps = visible_inp_refs = None
    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 = gr.skip(), gr.update(choices=list(dict_language.keys()))
        else:
            prompt_text_update = gr.update(value="")
            prompt_language_update = gr.update(value=i18n("中文"), choices=list(dict_language.keys()))
        if text_language in list(dict_language.keys()):
            text_update, text_language_update = gr.skip(), gr.skip()
        else:
            text_update = gr.update(value="")
            text_language_update = gr.update(value=i18n("中文"), choices=list(dict_language.keys()))

        if model_version in v3v4set:
            visible_sample_steps = True
            visible_inp_refs = False
        else:
            visible_sample_steps = False
            visible_inp_refs = True
        yield (
            prompt_text_update,
            prompt_language_update,
            text_update,
            text_language_update,
            gr.update(
                visible=visible_sample_steps,
                value=32 if model_version == "v3" else 8,
                choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
            ),
            gr.update(visible=visible_inp_refs),
            gr.update(value=False, interactive=True if model_version not in v3v4set else False),
            gr.update(visible=True if model_version == "v3" else False),
            gr.update(value=i18n("模型加载中，请等待"), interactive=False),
        )

    hps = DictToAttrRecursive(hps)
    hps.model.semantic_frame_rate = "25hz"
    hps.model.version = model_version
    if model_version not in v3v4set:
        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,
        )
    else:
        vq_model = SynthesizerTrnV3(
            hps.data.filter_length // 2 + 1,
            hps.train.segment_size // hps.data.hop_length,
            n_speakers=hps.data.n_speakers,
            **hps.model,
        ).eval()

    if "pretrained" not in sovits_path:
        if hasattr(vq_model, "enc_q"):
            del vq_model.enc_q

    if is_lora is False:
        console.print(f">> loading sovits_{model_version}", vq_model.load_state_dict(dict_s2["weight"], strict=False))
    else:
        path_sovits = path_sovits_v3 if model_version == "v3" else path_sovits_v4
        console.print(f">> loading sovits_{model_version}spretrained_G")
        dict_pretrain = torch.load(path_sovits)["weight"]
        console.print(f">> loading sovits_{model_version}_lora{model_version}")
        dict_pretrain.update(dict_s2["weight"])
        state_dict = dict_pretrain
        lora_rank = dict_s2["lora_rank"]
        lora_config = LoraConfig(
            target_modules=["to_k", "to_q", "to_v", "to_out.0"],
            r=lora_rank,
            lora_alpha=lora_rank,
            init_lora_weights=True,
        )
        vq_model.cfm = get_peft_model(vq_model.cfm, lora_config)  # type: ignore
        vq_model.load_state_dict(state_dict, strict=False)
        vq_model.cfm = vq_model.cfm.merge_and_unload()  # pyright: ignore[reportAttributeAccessIssue, reportCallIssue]
        vq_model.eval()

    vq_model = vq_model.to(infer_device, dtype)

    yield (
        gr.skip(),
        gr.skip(),
        gr.skip(),
        gr.skip(),
        gr.skip(),
        gr.skip(),
        gr.skip(),
        gr.skip(),
        gr.update(value=i18n("合成语音"), interactive=True),
    )


with contextlib.suppress(UnboundLocalError):
    next(change_sovits_weights(sovits_path))


def change_gpt_weights(gpt_path):
    global t2s_engine, config

    if "mlx" in ar_backend.lower():
        t2s_engine = MLX.T2SEngineMLX(
            MLX.T2SEngineMLX.load_decoder(Path(gpt_path), backend=ar_backend),
            "mx.gpu" if infer_device.type != "cpu" else "mx.cpu",
            dtype=dtype,
        )
        # t2s_engine.decoder_model.compile()
        total = sum((p[-1].size for p in mxutils.tree_flatten(t2s_engine.decoder_model.parameters())))  # type: ignore
    else:
        t2s_engine = PyTorch.T2SEngineTorch(
            PyTorch.T2SEngineTorch.load_decoder(Path(gpt_path), backend=ar_backend),
            device,
            dtype=dtype,
        )
        # t2s_engine.decoder_model.compile()
        total = sum(p.numel() for p in t2s_engine.decoder_model.parameters())
    console.print(">> Number of parameter: %.2fM" % (total / 1e6))


change_gpt_weights(gpt_path)


def clean_hifigan_model():
    global hifigan_model
    if hifigan_model:
        hifigan_model = hifigan_model.cpu()
        del hifigan_model
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        hifigan_model = None


def clean_bigvgan_model():
    global bigvgan_model
    if bigvgan_model:
        bigvgan_model = bigvgan_model.cpu()
        del bigvgan_model
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        bigvgan_model = None


def clean_sv_cn_model():
    global sv_cn_model
    if sv_cn_model:
        sv_cn_model.embedding_model = sv_cn_model.embedding_model.cpu()
        del sv_cn_model
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        sv_cn_model = None


def init_bigvgan():
    global bigvgan_model, hifigan_model, sv_cn_model
    from BigVGAN import bigvgan

    bigvgan_model = bigvgan.BigVGAN.from_pretrained(
        "./GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x",
        use_cuda_kernel=False,
    )  # if True, RuntimeError: Ninja is required to load C++ extensions
    # remove weight norm in the model and set to eval mode
    bigvgan_model.remove_weight_norm()
    bigvgan_model = bigvgan_model.to(infer_device, dtype).eval()
    clean_hifigan_model()
    clean_sv_cn_model()


def init_hifigan():
    global hifigan_model, bigvgan_model, sv_cn_model
    hifigan_model = Generator(
        initial_channel=100,
        resblock="1",
        resblock_kernel_sizes=[3, 7, 11],
        resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
        upsample_rates=[10, 6, 2, 2, 2],
        upsample_initial_channel=512,
        upsample_kernel_sizes=[20, 12, 4, 4, 4],
        gin_channels=0,
        is_bias=True,
    )
    hifigan_model.eval()
    hifigan_model.remove_weight_norm()
    state_dict_g = torch.load(
        "./GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth",
        map_location="cpu",
        weights_only=False,
    )
    console.print(">> loading vocoder", hifigan_model.load_state_dict(state_dict_g))
    clean_bigvgan_model()
    clean_sv_cn_model()

    hifigan_model = hifigan_model.to(infer_device, dtype)


def init_sv_cn():
    global hifigan_model, bigvgan_model, sv_cn_model
    sv_cn_model = SV(infer_device, is_half)
    clean_bigvgan_model()
    clean_hifigan_model()


bigvgan_model = hifigan_model = sv_cn_model = None
if model_version == "v3":
    init_bigvgan()
if model_version == "v4":
    init_hifigan()
if model_version in {"v2Pro", "v2ProPlus"}:
    init_sv_cn()

resample_transform_dict = {}


def resample(audio_tensor, sr0, sr1, device):
    global resample_transform_dict
    key = f"{sr0}-{sr1}-{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_with_torchcodec(filename)
    audio = audio.to(device)

    if sr0 != sr1:
        audio = resample(audio, sr0, sr1, device)
    if audio.shape[0] > 1:
        audio = audio.mean(0).unsqueeze(0)

    maxx = float(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 is 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


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 is True else torch.float32,
        ).to(device)

    return bert


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


def get_phones_and_bert(text, language, version, final=False):
    text = re.sub(r" {2,}", " ", text)
    textlist = []
    langlist = []
    if language == "all_zh":
        for tmp in LangSegmenter.getTexts(text, "zh"):
            langlist.append(tmp["lang"])
            textlist.append(tmp["text"])
    elif language == "all_yue":
        for tmp in LangSegmenter.getTexts(text, "zh"):
            if tmp["lang"] == "zh":
                tmp["lang"] = "yue"
            langlist.append(tmp["lang"])
            textlist.append(tmp["text"])
    elif language == "all_ja":
        for tmp in LangSegmenter.getTexts(text, "ja"):
            langlist.append(tmp["lang"])
            textlist.append(tmp["text"])
    elif language == "all_ko":
        for tmp in LangSegmenter.getTexts(text, "ko"):
            langlist.append(tmp["lang"])
            textlist.append(tmp["text"])
    elif language == "en":
        langlist.append("en")
        textlist.append(text)
    elif 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 langlist:
                if (tmp["lang"] == "en" and langlist[-1] == "en") or (tmp["lang"] != "en" and langlist[-1] != "en"):
                    textlist[-1] += tmp["text"]
                    continue
            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


sr_model = None


def audio_sr(audio, sr):
    global sr_model
    if sr_model is None:
        from tools.audio_sr import AP_BWE

        try:
            sr_model = AP_BWE(infer_device, DictToAttrRecursive)
        except FileNotFoundError:
            gr.Warning(i18n("你没有下载超分模型的参数，因此不进行超分。如想超分请先参照教程把文件下载好"))
            return audio.cpu().numpy(), sr
    return sr_model(audio, sr)


cache: dict[int, Any] = {}


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=None,
    sample_steps=8,
    if_sr=False,
    pause_second=0.3,
):
    torch.set_grad_enabled(False)
    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
    if model_version in v3v4set:
        ref_free = False  # s2v3暂不支持ref_free
    else:
        if_sr = False
    if model_version not in {"v3", "v4", "v2Pro", "v2ProPlus"}:
        clean_bigvgan_model()
        clean_hifigan_model()
        clean_sv_cn_model()
    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 * pause_second),
        dtype=np.float16 if is_half is True else np.float32,
    )
    zero_wav_torch = torch.from_numpy(zero_wav)
    if is_half is True:
        zero_wav_torch = zero_wav_torch.half().to(infer_device)
    else:
        zero_wav_torch = zero_wav_torch.to(infer_device)
    if not ref_free:
        assert vq_model
        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_t = torch.from_numpy(wav16k)
        if is_half is True:
            wav16k_t = wav16k_t.half().to(infer_device)
        else:
            wav16k_t = wav16k_t.to(infer_device)
        wav16k_t = torch.cat([wav16k_t, zero_wav_torch])
        ssl_content = ssl_model.model(wav16k_t.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)
    else:
        prompt = torch.zeros((1, 0)).to(device, torch.int32)

    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")
    texts = text.split("\n")
    texts = process_text(texts)
    texts = merge_short_text_in_array(texts, 5)
    audio_opt = []
    # s2v3暂不支持ref_free
    if not ref_free:
        phones1, bert1, _ = get_phones_and_bert(prompt_text, prompt_language, version)
    else:
        phones1, bert1 = [], torch.zeros(1024, 0).to(device, dtype)

    infer_len: list[int] = []
    infer_time: list[float] = []
    assert vq_model

    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)

        bert = torch.cat([bert1, bert2], 1)
        all_phoneme_ids = torch.LongTensor(phones1 + 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()
        if i_text in cache and if_freeze is True:
            pred_semantic = cache[i_text]
        else:
            t2s_request = T2SRequest(
                [all_phoneme_ids.squeeze(0)],
                all_phoneme_len,
                prompt,
                [bert.squeeze(0)],
                valid_length=1,
                top_k=top_k,
                top_p=top_p,
                temperature=temperature,
                early_stop_num=1500,
                use_cuda_graph=torch.cuda.is_available(),
                # debug=True,
            )
            assert t2s_engine
            t2s_result = t2s_engine.generate(t2s_request)
            if t2s_result.exception is not None:
                console.print(t2s_result.traceback)
                raise RuntimeError()
            pred_semantic_list = t2s_result.result
            assert pred_semantic_list, t2s_result.traceback
            pred_semantic = pred_semantic_list[0].unsqueeze(0).to(infer_device)
            infer_len.append(pred_semantic.shape[-1])
            infer_time.append(t2s_result.infer_speed[-1])

            cache[i_text] = pred_semantic
        t3 = ttime()
        is_v2pro = model_version in {"v2Pro", "v2ProPlus"}

        sv_emb: list[torch.Tensor] = []
        if model_version not in v3v4set:
            refers = []
            if is_v2pro and sv_cn_model is None:
                init_sv_cn()
            if inp_refs:
                for path in inp_refs:
                    try:  # 这里加上提取sv的逻辑，要么一堆sv一堆refer，要么单个sv单个refer
                        refer, audio_tensor = get_spepc(hps, path.name, dtype, infer_device, is_v2pro)
                        refers.append(refer)
                        if is_v2pro:
                            assert sv_cn_model
                            sv_emb.append(sv_cn_model.compute_embedding(audio_tensor))
                    except Exception as e:
                        print(e)
                        traceback.print_exc()
            if len(refers) == 0:
                refers, audio_tensor = get_spepc(hps, ref_wav_path, dtype, infer_device, is_v2pro)
                refers = [refers]
                if is_v2pro:
                    assert sv_cn_model
                    sv_emb = [sv_cn_model.compute_embedding(audio_tensor)]
            if is_v2pro:
                audio = vq_model.decode(
                    pred_semantic,
                    torch.LongTensor(phones2).to(infer_device).unsqueeze(0),
                    refers,
                    speed=speed,
                    sv_emb=sv_emb,
                )[0][0]  # type: ignore
            else:
                audio = vq_model.decode(
                    pred_semantic,
                    torch.LongTensor(phones2).to(infer_device).unsqueeze(0),
                    refers,
                    speed=speed,
                )[0][0]  # type: ignore
        else:
            refer, audio_tensor = get_spepc(hps, ref_wav_path, dtype, infer_device)
            phoneme_ids0 = torch.LongTensor(phones1).to(infer_device).unsqueeze(0)
            phoneme_ids1 = torch.LongTensor(phones2).to(infer_device).unsqueeze(0)
            fea_ref, ge = vq_model.decode_encp(prompt.unsqueeze(0), phoneme_ids0, refer)  # type: ignore
            tgt_sr = 24000 if model_version == "v3" else 32000
            ref_audio, sr = torchaudio.load_with_torchcodec(ref_wav_path)
            ref_audio = ref_audio.to(infer_device)
            if sr != tgt_sr:
                ref_audio = resample(ref_audio, sr, tgt_sr, infer_device)
            if ref_audio.shape[0] > 1:
                ref_audio = ref_audio.mean(0).unsqueeze(0)
            mel2 = mel_fn(ref_audio) if model_version == "v3" else mel_fn_v4(ref_audio)
            mel2 = norm_spec(mel2)
            T_min = min(mel2.shape[2], fea_ref.shape[2])
            mel2 = mel2[:, :, :T_min]
            fea_ref = fea_ref[:, :, :T_min]
            Tref = 468 if model_version == "v3" else 500
            Tchunk = 934 if model_version == "v3" else 1000
            if T_min > Tref:
                mel2 = mel2[:, :, -Tref:]
                fea_ref = fea_ref[:, :, -Tref:]
                T_min = Tref
            chunk_len = Tchunk - T_min
            mel2 = mel2.to(dtype)
            fea_todo, ge = vq_model.decode_encp(pred_semantic, phoneme_ids1, refer, ge, speed)  # type: ignore
            cfm_resss = []
            idx = 0
            while 1:
                fea_todo_chunk = fea_todo[:, :, idx : idx + chunk_len]
                if fea_todo_chunk.shape[-1] == 0:
                    break
                idx += chunk_len
                fea = torch.cat([fea_ref, fea_todo_chunk], 2).transpose(2, 1)
                cfm_res = vq_model.cfm.inference(  # type: ignore
                    fea, torch.LongTensor([fea.size(1)]).to(fea.device), mel2, sample_steps, inference_cfg_rate=0
                )
                cfm_res = cfm_res[:, :, mel2.shape[2] :]
                mel2 = cfm_res[:, :, -T_min:]
                fea_ref = fea_todo_chunk[:, :, -T_min:]
                cfm_resss.append(cfm_res)
            cfm_res = torch.cat(cfm_resss, 2)
            cfm_res = denorm_spec(cfm_res)
            if model_version == "v3":
                if bigvgan_model is None:
                    init_bigvgan()
            else:  # v4
                if hifigan_model is None:
                    init_hifigan()
            vocoder_model = bigvgan_model if model_version == "v3" else hifigan_model
            with torch.inference_mode():
                wav_gen = vocoder_model(cfm_res)  # type: ignore
                audio = wav_gen[0][0]
        max_audio = torch.abs(audio).max()  # 简单防止16bit爆音
        if max_audio > 1:
            audio = audio / max_audio
        audio_opt.append(audio)
        audio_opt.append(zero_wav_torch)  # zero_wav
        t4 = ttime()
        t.extend([t2 - t1, t3 - t2, t4 - t3])
        t1 = ttime()

    audio_opt_t = torch.cat(audio_opt, 0)  # np.concatenate
    if model_version in {"v1", "v2", "v2Pro", "v2ProPlus"}:
        opt_sr = 32000
    elif model_version == "v3":
        opt_sr = 24000
    else:
        opt_sr = 48000  # v4
    if if_sr is True and opt_sr == 24000:
        print(">>", i18n("音频超分中"))
        audio_opt_n, opt_sr = audio_sr(audio_opt_t.unsqueeze(0), opt_sr)
        max_audio = np.abs(audio_opt_n).max()
        if max_audio > 1:
            audio_opt_n /= max_audio
    else:
        audio_opt_n = audio_opt_t.cpu().numpy()

    t0 = t[0]
    t1 = sum(t[1::3])
    t2 = sum(t[2::3])
    t3 = sum(t[3::3])

    infer_speed_avg = sum(infer_len) / sum(infer_time)
    rtf_value = sum(t) / (audio_opt_n.__len__() / opt_sr)

    console.print(f">> Time Stamps: {t0:.3f}\t{t1:.3f}\t{t2:.3f}\t{t3:.3f}")
    console.print(f">> Infer Speed: {infer_speed_avg:.2f} Token/s")
    console.print(f">> RTF: {rtf_value:.2f}")

    gr.Info(f"{infer_speed_avg:.2f} Token/s", title="Infer Speed")
    gr.Info(f"{rtf_value:.2f}", title="RTF")

    if torch.cuda.is_available():
        torch.cuda.empty_cache()

    yield opt_sr, (audio_opt_n * 32767).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[int | None] = list(range(0, len(inps) + 1, 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)
    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 = 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 = re.split(r"(?<!\d)\.(?!\d)", inp.strip("."))
    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 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", analytics_enabled=False, js=js, css=css) as app:
    gr.HTML(
        top_html.format(
            i18n("本软件以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.")
            + i18n("如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.")
        ),
        elem_classes="markdown",
    )
    gr.Markdown(html_center(i18n("模型切换"), "h3"))
    with gr.Row(equal_height=True):
        with gr.Column(scale=2):
            with gr.Row(equal_height=True):
                GPT_dropdown = gr.Dropdown(
                    label=i18n("GPT模型列表"),
                    choices=GPT_names,
                    value=gpt_path,
                    interactive=True,
                )
                SoVITS_dropdown = gr.Dropdown(
                    label=i18n("SoVITS模型列表"),
                    choices=SoVITS_names,
                    value=sovits_path,
                    interactive=True,
                )
        with gr.Column(scale=1):
            refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary", scale=14)
        refresh_button.click(fn=change_choices_i18n, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown])
    gr.Markdown(html_center(i18n("*请上传并填写参考信息"), "h3"))
    with gr.Row(equal_height=True):
        with gr.Column(scale=2):
            with gr.Row(equal_height=True):
                with gr.Column(scale=1):
                    inp_ref = gr.Audio(
                        label=i18n("请上传3~10秒内参考音频，超过会报错！"),
                        type="filepath",
                        sources="upload",
                        scale=13,
                        editable=False,
                        waveform_options={"show_recording_waveform": False},
                    )
                with gr.Column(scale=1):
                    gr.Markdown(
                        html_center(
                            i18n("使用无参考文本模式时建议使用微调的GPT")
                            + "<br>"
                            + i18n("听不清参考音频说的啥(不晓得写啥)可以开。开启后无视填写的参考文本。")
                        )
                    )
                    ref_text_free = gr.Checkbox(
                        label=i18n("开启无参考文本模式"),
                        info=i18n("不填参考文本亦相当于开启") + ", " + i18n("v3暂不支持该模式，使用了会报错。"),
                        value=False,
                        interactive=True if model_version not in v3v4set else False,
                        show_label=True,
                        scale=1,
                    )
                    prompt_language = gr.Dropdown(
                        label="",
                        info=i18n("参考音频的语种"),
                        choices=list(dict_language.keys()),
                        value=i18n("中文"),
                    )
                    prompt_text = gr.Textbox(label="", info=i18n("参考音频的文本"), value="", lines=3, max_lines=3)

        with gr.Column(scale=1):
            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。如是微调模型，建议参考音频全部在微调训练集音色内，底模不用管。"
                    ),
                    file_count="multiple",
                )
                if model_version not in v3v4set
                else 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。如是微调模型，建议参考音频全部在微调训练集音色内，底模不用管。"
                    ),
                    file_count="multiple",
                    visible=False,
                )
            )
            sample_steps = (
                gr.Radio(
                    label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),
                    value=32 if model_version == "v3" else 8,
                    choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
                    visible=True,
                )
                if model_version in v3v4set
                else gr.Radio(
                    label=i18n("采样步数,如果觉得电,提高试试,如果觉得慢,降低试试"),
                    choices=[4, 8, 16, 32, 64, 128] if model_version == "v3" else [4, 8, 16, 32],
                    visible=False,
                    value=32 if model_version == "v3" else 8,
                )
            )
            if_sr_Checkbox = gr.Checkbox(
                label=i18n("v3输出如果觉得闷可以试试开超分"),
                value=False,
                interactive=True,
                show_label=True,
                visible=False if model_version != "v3" else True,
            )
    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(scale=2):
            text = gr.Textbox(label=i18n("需要合成的文本"), value="", lines=30, max_lines=40)
        with gr.Column(scale=1):
            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"),
                scale=1,
            )
            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,
                scale=1,
            )
            if_freeze = gr.Checkbox(
                label=i18n("是否直接对上次合成结果调整语速和音色"),
                value=False,
                interactive=True,
                show_label=True,
                scale=1,
            )
            with gr.Row(equal_height=True):
                speed = gr.Slider(
                    minimum=0.6, maximum=1.65, step=0.05, label=i18n("语速"), value=1, interactive=True, scale=1
                )
                pause_second_slider = gr.Slider(
                    minimum=0.1,
                    maximum=0.5,
                    step=0.01,
                    label=i18n("句间停顿秒数"),
                    value=0.3,
                    interactive=True,
                    scale=1,
                )
            gr.Markdown(html_center(i18n("GPT采样参数(不懂就用默认):")))
            top_k = gr.Slider(minimum=1, maximum=100, step=1, label=i18n("top_k"), value=15, interactive=True, scale=1)
            top_p = gr.Slider(minimum=0, maximum=1, step=0.05, label=i18n("top_p"), value=1, interactive=True, scale=1)
            temperature = gr.Slider(
                minimum=0, maximum=1, step=0.05, label=i18n("temperature"), value=1, interactive=True, scale=1
            )
    with gr.Row(equal_height=True):
        with gr.Column(scale=2):
            inference_button = gr.Button(value=i18n("合成语音"), variant="primary", size="lg")
        with gr.Column(scale=1):
            output = gr.Audio(
                label=i18n("输出的语音"),
                waveform_options={"show_recording_waveform": False},
                editable=False,
            )

    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,
            sample_steps,
            if_sr_Checkbox,
            pause_second_slider,
        ],
        [output],
    )
    SoVITS_dropdown.change(
        change_sovits_weights,
        [SoVITS_dropdown, prompt_language, text_language],
        [
            prompt_text,
            prompt_language,
            text,
            text_language,
            sample_steps,
            inp_refs,
            ref_text_free,
            if_sr_Checkbox,
            inference_button,
        ],
    )
    GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], [])


if __name__ == "__main__":
    set_high_priority()
    app.queue(api_open=False, default_concurrency_limit=512, max_size=1024).launch(
        server_name="0.0.0.0",
        inbrowser=True,
        share=is_share,
        server_port=infer_ttswebui,
    )