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","zh","en","ja"`



`-d` - `推理设备, "cuda","cpu","mps"`

`-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`



`-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"

}

```



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





### 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 sys

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

import signal
from time import time as ttime
import torch
import librosa
import soundfile as sf
from fastapi import FastAPI, Request, HTTPException
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
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
from my_utils import load_audio
import config as global_config

g_config = global_config.Config()

# AVAILABLE_COMPUTE = "cuda" if torch.cuda.is_available() else "cpu"

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 / mps")
parser.add_argument("-a", "--bind_addr", type=str, default="127.0.0.1", help="default: 127.0.0.1")
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("-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


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)


default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language)

device = args.device
port = args.port
host = args.bind_addr

if sovits_path == "":
    sovits_path = g_config.pretrained_sovits_path
    print(f"[WARN] 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
    print(f"[WARN] 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 = "", "", ""
    print("[INFO] 未指定默认参考音频")
else:
    print(f"[INFO] 默认参考音频路径: {default_refer.path}")
    print(f"[INFO] 默认参考音频文本: {default_refer.text}")
    print(f"[INFO] 默认参考音频语种: {default_refer.language}")

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

print(f"[INFO] 半精: {is_half}")

cnhubert_base_path = args.hubert_path
bert_path = args.bert_path

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


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 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


n_semantic = 1024
dict_s2 = torch.load(sovits_path, map_location="cpu")
hps = dict_s2["config"]


class DictToAttrRecursive:
    def __init__(self, input_dict):
        for key, value in input_dict.items():
            if isinstance(value, dict):
                # 如果值是字典，递归调用构造函数
                setattr(self, key, DictToAttrRecursive(value))
            else:
                setattr(self, key, value)


hps = DictToAttrRecursive(hps)
hps.model.semantic_frame_rate = "25hz"
dict_s1 = torch.load(gpt_path, map_location="cpu")
config = dict_s1["config"]
ssl_model = cnhubert.get_model()
if is_half:
    ssl_model = ssl_model.half().to(device)
else:
    ssl_model = ssl_model.to(device)

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


def get_spepc(hps, filename):
    audio = load_audio(filename, int(hps.data.sampling_rate))
    audio = torch.FloatTensor(audio)
    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


dict_language = {
    "中文": "zh",
    "英文": "en",
    "日文": "ja",
    "ZH": "zh",
    "EN": "en",
    "JA": "ja",
    "zh": "zh",
    "en": "en",
    "ja": "ja"
}


def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
    t0 = ttime()
    prompt_text = prompt_text.strip("\n")
    prompt_language, text = prompt_language, text.strip("\n")
    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]
    t1 = ttime()
    prompt_language = dict_language[prompt_language]
    text_language = dict_language[text_language]
    phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
    phones1 = cleaned_text_to_sequence(phones1)
    texts = text.split("\n")
    audio_opt = []

    for text in texts:
        phones2, word2ph2, norm_text2 = clean_text(text, text_language)
        phones2 = cleaned_text_to_sequence(phones2)
        if (prompt_language == "zh"):
            bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
        else:
            bert1 = torch.zeros((1024, len(phones1)), dtype=torch.float16 if is_half == True else torch.float32).to(
                device)
        if (text_language == "zh"):
            bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
        else:
            bert2 = torch.zeros((1024, len(phones2))).to(bert1)
        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)
        prompt = prompt_semantic.unsqueeze(0).to(device)
        t2 = ttime()
        with torch.no_grad():
            # pred_semantic = t2s_model.model.infer(
            pred_semantic, idx = t2s_model.model.infer_panel(
                all_phoneme_ids,
                all_phoneme_len,
                prompt,
                bert,
                # prompt_phone_len=ph_offset,
                top_k=config['inference']['top_k'],
                early_stop_num=hz * max_sec)
        t3 = ttime()
        # print(pred_semantic.shape,idx)
        pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)  # .unsqueeze(0)#mq要多unsqueeze一次
        refer = get_spepc(hps, ref_wav_path)  # .to(device)
        if (is_half == True):
            refer = refer.half().to(device)
        else:
            refer = refer.to(device)
        # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
        audio = \
            vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
                            refer).detach().cpu().numpy()[
                0, 0]  ###试试重建不带上prompt部分
        audio_opt.append(audio)
        audio_opt.append(zero_wav)
        t4 = ttime()
    print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
    yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)


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

    print(f"[INFO] 当前默认参考音频路径: {default_refer.path}")
    print(f"[INFO] 当前默认参考音频文本: {default_refer.text}")
    print(f"[INFO] 当前默认参考音频语种: {default_refer.language}")
    print(f"[INFO] 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):
    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)

    with torch.no_grad():
        gen = get_tts_wav(
            refer_wav_path, prompt_text, prompt_language, text, text_language
        )
        sampling_rate, audio_data = next(gen)

    wav = BytesIO()
    sf.write(wav, audio_data, sampling_rate, format="wav")
    wav.seek(0)

    torch.cuda.empty_cache()
    if device == "mps":
        print('executed torch.mps.empty_cache()')
        torch.mps.empty_cache()
    return StreamingResponse(wav, media_type="audio/wav")


app = FastAPI()


@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"),
    )


@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,

):
    return handle(refer_wav_path, prompt_text, prompt_language, text, text_language)


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