api.py

"""
# api.py usage

` python api.py -dr "123.wav" -dt "一二三。" -dl "zh" `

## 执行参数:

`-s` - `SoVITS模型路径, 可在 config.py 中指定`
`-g` - `GPT模型路径, 可在 config.py 中指定`

调用请求缺少参考音频时使用
`-dr` - `默认参考音频路径`
`-dt` - `默认参考音频文本`
`-dl` - `默认参考音频语种, "中文","英文","日文","韩文","粤语,"zh","en","ja","ko","yue"`

`-d` - `推理设备, "cuda","cpu"`
`-a` - `绑定地址, 默认"127.0.0.1"`
`-p` - `绑定端口, 默认9880, 可在 config.py 中指定`
`-fp` - `覆盖 config.py 使用全精度`
`-hp` - `覆盖 config.py 使用半精度`
`-sm` - `流式返回模式, 默认不启用, "close","c", "normal","n", "keepalive","k"`
·-mt` - `返回的音频编码格式, 流式默认ogg, 非流式默认wav, "wav", "ogg", "aac"`
·-st` - `返回的音频数据类型, 默认int16, "int16", "int32"`
·-cp` - `文本切分符号设定, 默认为空, 以",.，。"字符串的方式传入`

`-hb` - `cnhubert路径`
`-b` - `bert路径`

## 调用:

### 推理

endpoint: `/`

使用执行参数指定的参考音频:
GET:
    `http://127.0.0.1:9880?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
POST:
```json
{
    "text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
    "text_language": "zh"
}
```

使用执行参数指定的参考音频并设定分割符号:
GET:
    `http://127.0.0.1:9880?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh&cut_punc=，。`
POST:
```json
{
    "text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
    "text_language": "zh",
    "cut_punc": "，。",
}
```

手动指定当次推理所使用的参考音频:
GET:
    `http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
POST:
```json
{
    "refer_wav_path": "123.wav",
    "prompt_text": "一二三。",
    "prompt_language": "zh",
    "text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
    "text_language": "zh"
}
```

RESP:
成功: 直接返回 wav 音频流， http code 200
失败: 返回包含错误信息的 json, http code 400

手动指定当次推理所使用的参考音频，并提供参数:
GET:
    `http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh&top_k=20&top_p=0.6&temperature=0.6&speed=1&inp_refs="456.wav"&inp_refs="789.wav"`
POST:
```json
{
    "refer_wav_path": "123.wav",
    "prompt_text": "一二三。",
    "prompt_language": "zh",
    "text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
    "text_language": "zh",
    "top_k": 20,
    "top_p": 0.6,
    "temperature": 0.6,
    "speed": 1,
    "inp_refs": ["456.wav","789.wav"]
}
```

RESP:
成功: 直接返回 wav 音频流， http code 200
失败: 返回包含错误信息的 json, http code 400


### 更换默认参考音频

endpoint: `/change_refer`

key与推理端一样

GET:
    `http://127.0.0.1:9880/change_refer?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh`
POST:
```json
{
    "refer_wav_path": "123.wav",
    "prompt_text": "一二三。",
    "prompt_language": "zh"
}
```

RESP:
成功: json, http code 200
失败: json, 400


### 命令控制

endpoint: `/control`

command:
"restart": 重新运行
"exit": 结束运行

GET:
    `http://127.0.0.1:9880/control?command=restart`
POST:
```json
{
    "command": "restart"
}
```

RESP: 无

"""

import argparse
import os
import re
import sys

now_dir = os.getcwd()
sys.path.append(now_dir)
sys.path.append("%s/GPT_SoVITS" % (now_dir))

import signal
from text.LangSegmenter import LangSegmenter
from time import time as ttime
import torch
import torchaudio
import librosa
import soundfile as sf
from fastapi import FastAPI, Request, Query
from fastapi.responses import StreamingResponse, JSONResponse
import uvicorn
from transformers import AutoModelForMaskedLM, AutoTokenizer
import numpy as np
from feature_extractor import cnhubert
from io import BytesIO
from module.models import SynthesizerTrn, SynthesizerTrnV3
from peft import LoraConfig, get_peft_model
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
from text import cleaned_text_to_sequence
from text.cleaner import clean_text
from module.mel_processing import spectrogram_torch
import config as global_config
import logging
import subprocess


class DefaultRefer:
    def __init__(self, path, text, language):
        self.path = args.default_refer_path
        self.text = args.default_refer_text
        self.language = args.default_refer_language

    def is_ready(self) -> bool:
        return is_full(self.path, self.text, self.language)


def is_empty(*items):  # 任意一项不为空返回False
    for item in items:
        if item is not None and item != "":
            return False
    return True


def is_full(*items):  # 任意一项为空返回False
    for item in items:
        if item is None or item == "":
            return False
    return True


def init_bigvgan():
    global bigvgan_model
    from BigVGAN import bigvgan

    bigvgan_model = bigvgan.BigVGAN.from_pretrained(
        "%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
        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.eval()
    if is_half == True:
        bigvgan_model = bigvgan_model.half().to(device)
    else:
        bigvgan_model = bigvgan_model.to(device)


resample_transform_dict = {}


def resample(audio_tensor, sr0):
    global resample_transform_dict
    if sr0 not in resample_transform_dict:
        resample_transform_dict[sr0] = torchaudio.transforms.Resample(sr0, 24000).to(device)
    return resample_transform_dict[sr0](audio_tensor)


from module.mel_processing import mel_spectrogram_torch

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


mel_fn = lambda x: mel_spectrogram_torch(
    x,
    **{
        "n_fft": 1024,
        "win_size": 1024,
        "hop_size": 256,
        "num_mels": 100,
        "sampling_rate": 24000,
        "fmin": 0,
        "fmax": None,
        "center": False,
    },
)


sr_model = None


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

        try:
            sr_model = AP_BWE(device, DictToAttrRecursive)
        except FileNotFoundError:
            logger.info("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")
            return audio.cpu().detach().numpy(), sr
    return sr_model(audio, sr)


class Speaker:
    def __init__(self, name, gpt, sovits, phones=None, bert=None, prompt=None):
        self.name = name
        self.sovits = sovits
        self.gpt = gpt
        self.phones = phones
        self.bert = bert
        self.prompt = prompt


speaker_list = {}


class Sovits:
    def __init__(self, vq_model, hps):
        self.vq_model = vq_model
        self.hps = hps


from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new


def get_sovits_weights(sovits_path):
    path_sovits_v3 = "GPT_SoVITS/pretrained_models/s2Gv3.pth"
    is_exist_s2gv3 = os.path.exists(path_sovits_v3)

    version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path)
    if if_lora_v3 == True and is_exist_s2gv3 == False:
        logger.info("SoVITS V3 底模缺失，无法加载相应 LoRA 权重")

    dict_s2 = load_sovits_new(sovits_path)
    hps = dict_s2["config"]
    hps = DictToAttrRecursive(hps)
    hps.model.semantic_frame_rate = "25hz"
    if "enc_p.text_embedding.weight" not in dict_s2["weight"]:
        hps.model.version = "v2"  # v3model,v2sybomls
    elif dict_s2["weight"]["enc_p.text_embedding.weight"].shape[0] == 322:
        hps.model.version = "v1"
    else:
        hps.model.version = "v2"

    if model_version == "v3":
        hps.model.version = "v3"

    model_params_dict = vars(hps.model)
    if model_version != "v3":
        vq_model = SynthesizerTrn(
            hps.data.filter_length // 2 + 1,
            hps.train.segment_size // hps.data.hop_length,
            n_speakers=hps.data.n_speakers,
            **model_params_dict,
        )
    else:
        vq_model = SynthesizerTrnV3(
            hps.data.filter_length // 2 + 1,
            hps.train.segment_size // hps.data.hop_length,
            n_speakers=hps.data.n_speakers,
            **model_params_dict,
        )
        init_bigvgan()
    model_version = hps.model.version
    logger.info(f"模型版本: {model_version}")
    if "pretrained" not in sovits_path:
        try:
            del vq_model.enc_q
        except:
            pass
    if is_half == True:
        vq_model = vq_model.half().to(device)
    else:
        vq_model = vq_model.to(device)
    vq_model.eval()
    if if_lora_v3 == False:
        vq_model.load_state_dict(dict_s2["weight"], strict=False)
    else:
        vq_model.load_state_dict(load_sovits_new(path_sovits_v3)["weight"], strict=False)
        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)
        vq_model.load_state_dict(dict_s2["weight"], strict=False)
        vq_model.cfm = vq_model.cfm.merge_and_unload()
        # torch.save(vq_model.state_dict(),"merge_win.pth")
        vq_model.eval()

    sovits = Sovits(vq_model, hps)
    return sovits


class Gpt:
    def __init__(self, max_sec, t2s_model):
        self.max_sec = max_sec
        self.t2s_model = t2s_model


global hz
hz = 50


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

    gpt = Gpt(max_sec, t2s_model)
    return gpt


def change_gpt_sovits_weights(gpt_path, sovits_path):
    try:
        gpt = get_gpt_weights(gpt_path)
        sovits = get_sovits_weights(sovits_path)
    except Exception as e:
        return JSONResponse({"code": 400, "message": str(e)}, status_code=400)

    speaker_list["default"] = Speaker(name="default", gpt=gpt, sovits=sovits)
    return JSONResponse({"code": 0, "message": "Success"}, status_code=200)


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)  #####输入是long不用管精度问题，精度随bert_model
        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)
    # if(is_half==True):phone_level_feature=phone_level_feature.half()
    return phone_level_feature.T


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

    return bert


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"])
        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(torch.float16 if is_half == True else torch.float32), norm_text


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")


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


def pack_audio(audio_bytes, data, rate):
    if media_type == "ogg":
        audio_bytes = pack_ogg(audio_bytes, data, rate)
    elif media_type == "aac":
        audio_bytes = pack_aac(audio_bytes, data, rate)
    else:
        # wav无法流式, 先暂存raw
        audio_bytes = pack_raw(audio_bytes, data, rate)

    return audio_bytes


def pack_ogg(audio_bytes, data, rate):
    # Author: AkagawaTsurunaki
    # Issue:
    #   Stack overflow probabilistically occurs
    #   when the function `sf_writef_short` of `libsndfile_64bit.dll` is called
    #   using the Python library `soundfile`
    # Note:
    #   This is an issue related to `libsndfile`, not this project itself.
    #   It happens when you generate a large audio tensor (about 499804 frames in my PC)
    #   and try to convert it to an ogg file.
    # Related:
    #   https://github.com/RVC-Boss/GPT-SoVITS/issues/1199
    #   https://github.com/libsndfile/libsndfile/issues/1023
    #   https://github.com/bastibe/python-soundfile/issues/396
    # Suggestion:
    #   Or split the whole audio data into smaller audio segment to avoid stack overflow?

    def handle_pack_ogg():
        with sf.SoundFile(audio_bytes, mode="w", samplerate=rate, channels=1, format="ogg") as audio_file:
            audio_file.write(data)

    import threading

    # See: https://docs.python.org/3/library/threading.html
    # The stack size of this thread is at least 32768
    # If stack overflow error still occurs, just modify the `stack_size`.
    # stack_size = n * 4096, where n should be a positive integer.
    # Here we chose n = 4096.
    stack_size = 4096 * 4096
    try:
        threading.stack_size(stack_size)
        pack_ogg_thread = threading.Thread(target=handle_pack_ogg)
        pack_ogg_thread.start()
        pack_ogg_thread.join()
    except RuntimeError as e:
        # If changing the thread stack size is unsupported, a RuntimeError is raised.
        print("RuntimeError: {}".format(e))
        print("Changing the thread stack size is unsupported.")
    except ValueError as e:
        # If the specified stack size is invalid, a ValueError is raised and the stack size is unmodified.
        print("ValueError: {}".format(e))
        print("The specified stack size is invalid.")

    return audio_bytes


def pack_raw(audio_bytes, data, rate):
    audio_bytes.write(data.tobytes())

    return audio_bytes


def pack_wav(audio_bytes, rate):
    if is_int32:
        data = np.frombuffer(audio_bytes.getvalue(), dtype=np.int32)
        wav_bytes = BytesIO()
        sf.write(wav_bytes, data, rate, format="WAV", subtype="PCM_32")
    else:
        data = np.frombuffer(audio_bytes.getvalue(), dtype=np.int16)
        wav_bytes = BytesIO()
        sf.write(wav_bytes, data, rate, format="WAV")
    return wav_bytes


def pack_aac(audio_bytes, data, rate):
    if is_int32:
        pcm = "s32le"
        bit_rate = "256k"
    else:
        pcm = "s16le"
        bit_rate = "128k"
    process = subprocess.Popen(
        [
            "ffmpeg",
            "-f",
            pcm,  # 输入16位有符号小端整数PCM
            "-ar",
            str(rate),  # 设置采样率
            "-ac",
            "1",  # 单声道
            "-i",
            "pipe:0",  # 从管道读取输入
            "-c:a",
            "aac",  # 音频编码器为AAC
            "-b:a",
            bit_rate,  # 比特率
            "-vn",  # 不包含视频
            "-f",
            "adts",  # 输出AAC数据流格式
            "pipe:1",  # 将输出写入管道
        ],
        stdin=subprocess.PIPE,
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE,
    )
    out, _ = process.communicate(input=data.tobytes())
    audio_bytes.write(out)

    return audio_bytes


def read_clean_buffer(audio_bytes):
    audio_chunk = audio_bytes.getvalue()
    audio_bytes.truncate(0)
    audio_bytes.seek(0)

    return audio_bytes, audio_chunk


def cut_text(text, punc):
    punc_list = [p for p in punc if p in {",", ".", ";", "?", "!", "、", "，", "。", "？", "！", "；", "：", "…"}]
    if len(punc_list) > 0:
        punds = r"[" + "".join(punc_list) + r"]"
        text = text.strip("\n")
        items = re.split(f"({punds})", text)
        mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
        # 在句子不存在符号或句尾无符号的时候保证文本完整
        if len(items) % 2 == 1:
            mergeitems.append(items[-1])
        text = "\n".join(mergeitems)

    while "\n\n" in text:
        text = text.replace("\n\n", "\n")

    return text


def only_punc(text):
    return not any(t.isalnum() or t.isalpha() for t in text)


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


def get_tts_wav(
    ref_wav_path,
    prompt_text,
    prompt_language,
    text,
    text_language,
    top_k=15,
    top_p=0.6,
    temperature=0.6,
    speed=1,
    inp_refs=None,
    sample_steps=32,
    if_sr=False,
    spk="default",
):
    infer_sovits = speaker_list[spk].sovits
    vq_model = infer_sovits.vq_model
    hps = infer_sovits.hps
    version = vq_model.version

    infer_gpt = speaker_list[spk].gpt
    t2s_model = infer_gpt.t2s_model
    max_sec = infer_gpt.max_sec

    t0 = ttime()
    prompt_text = prompt_text.strip("\n")
    if prompt_text[-1] not in splits:
        prompt_text += "。" if prompt_language != "en" else "."
    prompt_language, text = prompt_language, text.strip("\n")
    dtype = torch.float16 if is_half == True else torch.float32
    zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
    with torch.no_grad():
        wav16k, sr = librosa.load(ref_wav_path, sr=16000)
        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)

        if version != "v3":
            refers = []
            if inp_refs:
                for path in inp_refs:
                    try:
                        refer = get_spepc(hps, path).to(dtype).to(device)
                        refers.append(refer)
                    except Exception as e:
                        logger.error(e)
            if len(refers) == 0:
                refers = [get_spepc(hps, ref_wav_path).to(dtype).to(device)]
        else:
            refer = get_spepc(hps, ref_wav_path).to(device).to(dtype)

    t1 = ttime()
    # os.environ['version'] = version
    prompt_language = dict_language[prompt_language.lower()]
    text_language = dict_language[text_language.lower()]
    phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language, version)
    texts = text.split("\n")
    audio_bytes = BytesIO()

    for text in texts:
        # 简单防止纯符号引发参考音频泄露
        if only_punc(text):
            continue

        audio_opt = []
        if text[-1] not in splits:
            text += "。" if text_language != "en" else "."
        phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language, version)
        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()
        with torch.no_grad():
            pred_semantic, idx = t2s_model.model.infer_panel(
                all_phoneme_ids,
                all_phoneme_len,
                prompt,
                bert,
                # prompt_phone_len=ph_offset,
                top_k=top_k,
                top_p=top_p,
                temperature=temperature,
                early_stop_num=hz * max_sec,
            )
            pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
        t3 = ttime()

        if version != "v3":
            audio = (
                vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refers, speed=speed)
                .detach()
                .cpu()
                .numpy()[0, 0]
            )  ###试试重建不带上prompt部分
        else:
            phoneme_ids0 = torch.LongTensor(phones1).to(device).unsqueeze(0)
            phoneme_ids1 = torch.LongTensor(phones2).to(device).unsqueeze(0)
            # print(11111111, phoneme_ids0, phoneme_ids1)
            fea_ref, ge = vq_model.decode_encp(prompt.unsqueeze(0), phoneme_ids0, refer)
            ref_audio, sr = torchaudio.load(ref_wav_path)
            ref_audio = ref_audio.to(device).float()
            if ref_audio.shape[0] == 2:
                ref_audio = ref_audio.mean(0).unsqueeze(0)
            if sr != 24000:
                ref_audio = resample(ref_audio, sr)
            # print("ref_audio",ref_audio.abs().mean())
            mel2 = mel_fn(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]
            if T_min > 468:
                mel2 = mel2[:, :, -468:]
                fea_ref = fea_ref[:, :, -468:]
                T_min = 468
            chunk_len = 934 - T_min
            # print("fea_ref",fea_ref,fea_ref.shape)
            # print("mel2",mel2)
            mel2 = mel2.to(dtype)
            fea_todo, ge = vq_model.decode_encp(pred_semantic, phoneme_ids1, refer, ge, speed)
            # print("fea_todo",fea_todo)
            # print("ge",ge.abs().mean())
            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)
                # set_seed(123)
                cfm_res = vq_model.cfm.inference(
                    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:]
                # print("fea", fea)
                # print("mel2in", mel2)
                fea_ref = fea_todo_chunk[:, :, -T_min:]
                cfm_resss.append(cfm_res)
            cmf_res = torch.cat(cfm_resss, 2)
            cmf_res = denorm_spec(cmf_res)
            if bigvgan_model == None:
                init_bigvgan()
            with torch.inference_mode():
                wav_gen = bigvgan_model(cmf_res)
                audio = wav_gen[0][0].cpu().detach().numpy()

        max_audio = np.abs(audio).max()
        if max_audio > 1:
            audio /= max_audio
        audio_opt.append(audio)
        audio_opt.append(zero_wav)
        audio_opt = np.concatenate(audio_opt, 0)
        t4 = ttime()

        sr = hps.data.sampling_rate if version != "v3" else 24000
        if if_sr and sr == 24000:
            audio_opt = torch.from_numpy(audio_opt).float().to(device)
            audio_opt, sr = audio_sr(audio_opt.unsqueeze(0), sr)
            max_audio = np.abs(audio_opt).max()
            if max_audio > 1:
                audio_opt /= max_audio
            sr = 48000

        if is_int32:
            audio_bytes = pack_audio(audio_bytes, (audio_opt * 2147483647).astype(np.int32), sr)
        else:
            audio_bytes = pack_audio(audio_bytes, (audio_opt * 32768).astype(np.int16), sr)
        # logger.info("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
        if stream_mode == "normal":
            audio_bytes, audio_chunk = read_clean_buffer(audio_bytes)
            yield audio_chunk

    if not stream_mode == "normal":
        if media_type == "wav":
            sr = 48000 if if_sr else 24000
            sr = hps.data.sampling_rate if version != "v3" else sr
            audio_bytes = pack_wav(audio_bytes, sr)
        yield audio_bytes.getvalue()


def handle_control(command):
    if command == "restart":
        os.execl(g_config.python_exec, g_config.python_exec, *sys.argv)
    elif command == "exit":
        os.kill(os.getpid(), signal.SIGTERM)
        exit(0)


def handle_change(path, text, language):
    if is_empty(path, text, language):
        return JSONResponse(
            {"code": 400, "message": '缺少任意一项以下参数: "path", "text", "language"'}, status_code=400
        )

    if path != "" or path is not None:
        default_refer.path = path
    if text != "" or text is not None:
        default_refer.text = text
    if language != "" or language is not None:
        default_refer.language = language

    logger.info(f"当前默认参考音频路径: {default_refer.path}")
    logger.info(f"当前默认参考音频文本: {default_refer.text}")
    logger.info(f"当前默认参考音频语种: {default_refer.language}")
    logger.info(f"is_ready: {default_refer.is_ready()}")

    return JSONResponse({"code": 0, "message": "Success"}, status_code=200)


def handle(
    refer_wav_path,
    prompt_text,
    prompt_language,
    text,
    text_language,
    cut_punc,
    top_k,
    top_p,
    temperature,
    speed,
    inp_refs,
    sample_steps,
    if_sr,
):
    if (
        refer_wav_path == ""
        or refer_wav_path is None
        or prompt_text == ""
        or prompt_text is None
        or prompt_language == ""
        or prompt_language is None
    ):
        refer_wav_path, prompt_text, prompt_language = (
            default_refer.path,
            default_refer.text,
            default_refer.language,
        )
        if not default_refer.is_ready():
            return JSONResponse({"code": 400, "message": "未指定参考音频且接口无预设"}, status_code=400)

    if sample_steps not in [4, 8, 16, 32]:
        sample_steps = 32

    if cut_punc == None:
        text = cut_text(text, default_cut_punc)
    else:
        text = cut_text(text, cut_punc)

    return StreamingResponse(
        get_tts_wav(
            refer_wav_path,
            prompt_text,
            prompt_language,
            text,
            text_language,
            top_k,
            top_p,
            temperature,
            speed,
            inp_refs,
            sample_steps,
            if_sr,
        ),
        media_type="audio/" + media_type,
    )


# --------------------------------
# 初始化部分
# --------------------------------
dict_language = {
    "中文": "all_zh",
    "粤语": "all_yue",
    "英文": "en",
    "日文": "all_ja",
    "韩文": "all_ko",
    "中英混合": "zh",
    "粤英混合": "yue",
    "日英混合": "ja",
    "韩英混合": "ko",
    "多语种混合": "auto",  # 多语种启动切分识别语种
    "多语种混合(粤语)": "auto_yue",
    "all_zh": "all_zh",
    "all_yue": "all_yue",
    "en": "en",
    "all_ja": "all_ja",
    "all_ko": "all_ko",
    "zh": "zh",
    "yue": "yue",
    "ja": "ja",
    "ko": "ko",
    "auto": "auto",
    "auto_yue": "auto_yue",
}

# logger
logging.config.dictConfig(uvicorn.config.LOGGING_CONFIG)
logger = logging.getLogger("uvicorn")

# 获取配置
g_config = global_config.Config()

# 获取参数
parser = argparse.ArgumentParser(description="GPT-SoVITS api")

parser.add_argument("-s", "--sovits_path", type=str, default=g_config.sovits_path, help="SoVITS模型路径")
parser.add_argument("-g", "--gpt_path", type=str, default=g_config.gpt_path, help="GPT模型路径")
parser.add_argument("-dr", "--default_refer_path", type=str, default="", help="默认参考音频路径")
parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu")
parser.add_argument("-a", "--bind_addr", type=str, default="0.0.0.0", help="default: 0.0.0.0")
parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
parser.add_argument(
    "-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度"
)
parser.add_argument(
    "-hp", "--half_precision", action="store_true", default=False, help="覆盖config.is_half为True, 使用半精度"
)
# bool值的用法为 `python ./api.py -fp ...`
# 此时 full_precision==True, half_precision==False
parser.add_argument("-sm", "--stream_mode", type=str, default="close", help="流式返回模式, close / normal / keepalive")
parser.add_argument("-mt", "--media_type", type=str, default="wav", help="音频编码格式, wav / ogg / aac")
parser.add_argument("-st", "--sub_type", type=str, default="int16", help="音频数据类型, int16 / int32")
parser.add_argument("-cp", "--cut_punc", type=str, default="", help="文本切分符号设定, 符号范围,.;?!、，。？！；：…")
# 切割常用分句符为 `python ./api.py -cp ".?!。？！"`
parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, help="覆盖config.bert_path")

args = parser.parse_args()
sovits_path = args.sovits_path
gpt_path = args.gpt_path
device = args.device
port = args.port
host = args.bind_addr
cnhubert_base_path = args.hubert_path
bert_path = args.bert_path
default_cut_punc = args.cut_punc

# 应用参数配置
default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language)

# 模型路径检查
if sovits_path == "":
    sovits_path = g_config.pretrained_sovits_path
    logger.warn(f"未指定SoVITS模型路径, fallback后当前值: {sovits_path}")
if gpt_path == "":
    gpt_path = g_config.pretrained_gpt_path
    logger.warn(f"未指定GPT模型路径, fallback后当前值: {gpt_path}")

# 指定默认参考音频, 调用方 未提供/未给全 参考音频参数时使用
if default_refer.path == "" or default_refer.text == "" or default_refer.language == "":
    default_refer.path, default_refer.text, default_refer.language = "", "", ""
    logger.info("未指定默认参考音频")
else:
    logger.info(f"默认参考音频路径: {default_refer.path}")
    logger.info(f"默认参考音频文本: {default_refer.text}")
    logger.info(f"默认参考音频语种: {default_refer.language}")

# 获取半精度
is_half = g_config.is_half
if args.full_precision:
    is_half = False
if args.half_precision:
    is_half = True
if args.full_precision and args.half_precision:
    is_half = g_config.is_half  # 炒饭fallback
logger.info(f"半精: {is_half}")

# 流式返回模式
if args.stream_mode.lower() in ["normal", "n"]:
    stream_mode = "normal"
    logger.info("流式返回已开启")
else:
    stream_mode = "close"

# 音频编码格式
if args.media_type.lower() in ["aac", "ogg"]:
    media_type = args.media_type.lower()
elif stream_mode == "close":
    media_type = "wav"
else:
    media_type = "ogg"
logger.info(f"编码格式: {media_type}")

# 音频数据类型
if args.sub_type.lower() == "int32":
    is_int32 = True
    logger.info("数据类型: int32")
else:
    is_int32 = False
    logger.info("数据类型: int16")

# 初始化模型
cnhubert.cnhubert_base_path = cnhubert_base_path
tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
ssl_model = cnhubert.get_model()
if is_half:
    bert_model = bert_model.half().to(device)
    ssl_model = ssl_model.half().to(device)
else:
    bert_model = bert_model.to(device)
    ssl_model = ssl_model.to(device)
change_gpt_sovits_weights(gpt_path=gpt_path, sovits_path=sovits_path)


# --------------------------------
# 接口部分
# --------------------------------
app = FastAPI()


@app.post("/set_model")
async def set_model(request: Request):
    json_post_raw = await request.json()
    return change_gpt_sovits_weights(
        gpt_path=json_post_raw.get("gpt_model_path"), sovits_path=json_post_raw.get("sovits_model_path")
    )


@app.get("/set_model")
async def set_model(
    gpt_model_path: str = None,
    sovits_model_path: str = None,
):
    return change_gpt_sovits_weights(gpt_path=gpt_model_path, sovits_path=sovits_model_path)


@app.post("/control")
async def control(request: Request):
    json_post_raw = await request.json()
    return handle_control(json_post_raw.get("command"))


@app.get("/control")
async def control(command: str = None):
    return handle_control(command)


@app.post("/change_refer")
async def change_refer(request: Request):
    json_post_raw = await request.json()
    return handle_change(
        json_post_raw.get("refer_wav_path"), json_post_raw.get("prompt_text"), json_post_raw.get("prompt_language")
    )


@app.get("/change_refer")
async def change_refer(refer_wav_path: str = None, prompt_text: str = None, prompt_language: str = None):
    return handle_change(refer_wav_path, prompt_text, prompt_language)


@app.post("/")
async def tts_endpoint(request: Request):
    json_post_raw = await request.json()
    return handle(
        json_post_raw.get("refer_wav_path"),
        json_post_raw.get("prompt_text"),
        json_post_raw.get("prompt_language"),
        json_post_raw.get("text"),
        json_post_raw.get("text_language"),
        json_post_raw.get("cut_punc"),
        json_post_raw.get("top_k", 15),
        json_post_raw.get("top_p", 1.0),
        json_post_raw.get("temperature", 1.0),
        json_post_raw.get("speed", 1.0),
        json_post_raw.get("inp_refs", []),
        json_post_raw.get("sample_steps", 32),
        json_post_raw.get("if_sr", False),
    )


@app.get("/")
async def tts_endpoint(
    refer_wav_path: str = None,
    prompt_text: str = None,
    prompt_language: str = None,
    text: str = None,
    text_language: str = None,
    cut_punc: str = None,
    top_k: int = 15,
    top_p: float = 1.0,
    temperature: float = 1.0,
    speed: float = 1.0,
    inp_refs: list = Query(default=[]),
    sample_steps: int = 32,
    if_sr: bool = False,
):
    return handle(
        refer_wav_path,
        prompt_text,
        prompt_language,
        text,
        text_language,
        cut_punc,
        top_k,
        top_p,
        temperature,
        speed,
        inp_refs,
        sample_steps,
        if_sr,
    )


if __name__ == "__main__":
    uvicorn.run(app, host=host, port=port, workers=1)