mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-07 15:19:59 +08:00
714 lines
24 KiB
Python
714 lines
24 KiB
Python
"""
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# api.py usage
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` python api.py -dr "123.wav" -dt "一二三。" -dl "zh" `
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## 执行参数:
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`-s` - `SoVITS模型路径, 可在 config.py 中指定`
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`-g` - `GPT模型路径, 可在 config.py 中指定`
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调用请求缺少参考音频时使用
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`-dr` - `默认参考音频路径`
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`-dt` - `默认参考音频文本`
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`-dl` - `默认参考音频语种, "中文","英文","日文","zh","en","ja"`
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`-cut` - `默认切分方式,"凑四句一切","凑50字一切","按中文句号。切","按英文句号.切","按标点符号切",分别为cut1,cut2,cut3,cut4,cut5`
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`-d` - `推理设备, "cuda","cpu","mps"`
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`-a` - `绑定地址, 默认"127.0.0.1"`
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`-p` - `绑定端口, 默认9880, 可在 config.py 中指定`
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`-fp` - `覆盖 config.py 使用全精度`
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`-hp` - `覆盖 config.py 使用半精度`
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`-hb` - `cnhubert路径`
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`-b` - `bert路径`
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## 调用:
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### 推理
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endpoint: `/`
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使用执行参数指定的参考音频与切分方式:
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GET:
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`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`
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POST:
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```json
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{
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"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。",
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"text_language": "zh"
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}
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```
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手动指定当次推理所使用的参考音频与切分方式:
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GET:
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`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&how_to_cut=cut1`
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POST:
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```json
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{
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"refer_wav_path": "123.wav",
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"prompt_text": "一二三。",
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"prompt_language": "zh",
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"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。",
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"text_language": "zh"
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"how_to_cut"= "cut1"
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}
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```
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RESP:
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成功: 直接返回 wav 音频流， http code 200
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失败: 返回包含错误信息的 json, http code 400
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### 更换默认参考音频及切分方式
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endpoint: `/change_refer`
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key与推理端一样
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GET:
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`http://127.0.0.1:9880/change_refer?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&how_to_cut=cut1`
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POST:
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```json
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{
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"refer_wav_path": "123.wav",
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"prompt_text": "一二三。",
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"prompt_language": "zh"
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"how_to_cut": "cut1"
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}
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```
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RESP:
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成功: json, http code 200
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失败: json, 400
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### 命令控制
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endpoint: `/control`
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command:
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"restart": 重新运行
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"exit": 结束运行
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GET:
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`http://127.0.0.1:9880/control?command=restart`
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POST:
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```json
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{
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"command": "restart"
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}
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```
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RESP: 无
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"""
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import argparse
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import os,re
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import sys
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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sys.path.append("%s/GPT_SoVITS" % (now_dir))
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import signal
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from time import time as ttime
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import torch
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import librosa
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import soundfile as sf
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from fastapi import FastAPI, Request, HTTPException
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from fastapi.responses import StreamingResponse, JSONResponse
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import uvicorn
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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import numpy as np
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from feature_extractor import cnhubert
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from io import BytesIO
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from module.models import SynthesizerTrn
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from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from text import cleaned_text_to_sequence
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from text.cleaner import clean_text
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from module.mel_processing import spectrogram_torch
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from my_utils import load_audio
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import config as global_config
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g_config = global_config.Config()
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# AVAILABLE_COMPUTE = "cuda" if torch.cuda.is_available() else "cpu"
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parser = argparse.ArgumentParser(description="GPT-SoVITS api")
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parser.add_argument("-s", "--sovits_path", type=str, default=g_config.sovits_path, help="SoVITS模型路径")
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parser.add_argument("-g", "--gpt_path", type=str, default=g_config.gpt_path, help="GPT模型路径")
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parser.add_argument("-dr", "--default_refer_path", type=str, default="", help="默认参考音频路径")
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parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
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parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
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parser.add_argument("-cut", "--default_how_to_cut", type=str, default="cut1", help="默认切分方式")
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parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu / mps")
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parser.add_argument("-a", "--bind_addr", type=str, default="0.0.0.0", help="default: 0.0.0.0")
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parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
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parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
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parser.add_argument("-hp", "--half_precision", action="store_true", default=False, help="覆盖config.is_half为True, 使用半精度")
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# bool值的用法为 `python ./api.py -fp ...`
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# 此时 full_precision==True, half_precision==False
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parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
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parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, help="覆盖config.bert_path")
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args = parser.parse_args()
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sovits_path = args.sovits_path
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gpt_path = args.gpt_path
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splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
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class DefaultRefer:
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def __init__(self, path, text, language, how_to_cut):
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self.path = args.default_refer_path
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self.text = args.default_refer_text
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self.language = args.default_refer_language
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self.how_to_cut = args.default_how_to_cut
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def is_ready(self) -> bool:
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return is_full(self.path, self.text, self.language,self.how_to_cut)
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default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language, args.default_how_to_cut)
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device = args.device
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port = args.port
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host = args.bind_addr
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if sovits_path == "":
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sovits_path = g_config.pretrained_sovits_path
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print(f"[WARN] 未指定SoVITS模型路径, fallback后当前值: {sovits_path}")
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if gpt_path == "":
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gpt_path = g_config.pretrained_gpt_path
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print(f"[WARN] 未指定GPT模型路径, fallback后当前值: {gpt_path}")
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# 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
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if default_refer.path == "" or default_refer.text == "" or default_refer.language == "":
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default_refer.path, default_refer.text, default_refer.language = "", "", ""
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print("[INFO] 未指定默认参考音频")
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else:
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if (default_refer.how_to_cut == "cut1"):
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cut_info = "凑四句一切"
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elif (default_refer.how_to_cut == "cut2"):
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cut_info = "凑50字一切"
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elif (default_refer.how_to_cut == "cut3"):
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cut_info = "按中文句号。切"
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elif (default_refer.how_to_cut == "cut4"):
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cut_info = "按英文句号.切"
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elif (default_refer.how_to_cut == "cut5"):
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cut_info = "按标点符号切"
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print(f"[INFO] 默认参考音频路径: {default_refer.path}")
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print(f"[INFO] 默认参考音频文本: {default_refer.text}")
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print(f"[INFO] 默认参考音频语种: {default_refer.language}")
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print(f"[INFO] 默认切割方式: {cut_info}")
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is_half = g_config.is_half
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if args.full_precision:
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is_half = False
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if args.half_precision:
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is_half = True
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if args.full_precision and args.half_precision:
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is_half = g_config.is_half # 炒饭fallback
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print(f"[INFO] 半精: {is_half}")
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cnhubert_base_path = args.hubert_path
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bert_path = args.bert_path
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cnhubert.cnhubert_base_path = cnhubert_base_path
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
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if is_half:
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bert_model = bert_model.half().to(device)
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else:
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bert_model = bert_model.to(device)
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def is_empty(*items): # 任意一项不为空返回False
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for item in items:
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if item is not None and item != "":
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return False
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return True
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def is_full(*items): # 任意一项为空返回False
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for item in items:
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if item is None or item == "":
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return False
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return True
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def change_sovits_weights(sovits_path):
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global vq_model, hps
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dict_s2 = torch.load(sovits_path, map_location="cpu")
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hps = dict_s2["config"]
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hps = DictToAttrRecursive(hps)
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hps.model.semantic_frame_rate = "25hz"
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vq_model = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model
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)
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if ("pretrained" not in sovits_path):
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del vq_model.enc_q
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if is_half == True:
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vq_model = vq_model.half().to(device)
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else:
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vq_model = vq_model.to(device)
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vq_model.eval()
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print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
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with open("./sweight.txt", "w", encoding="utf-8") as f:
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f.write(sovits_path)
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def change_gpt_weights(gpt_path):
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global hz, max_sec, t2s_model, config
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hz = 50
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dict_s1 = torch.load(gpt_path, map_location="cpu")
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config = dict_s1["config"]
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max_sec = config["data"]["max_sec"]
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t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
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t2s_model.load_state_dict(dict_s1["weight"])
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if is_half == True:
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t2s_model = t2s_model.half()
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t2s_model = t2s_model.to(device)
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t2s_model.eval()
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total = sum([param.nelement() for param in t2s_model.parameters()])
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print("Number of parameter: %.2fM" % (total / 1e6))
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with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
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def get_bert_feature(text, word2ph):
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(device) #####输入是long不用管精度问题，精度随bert_model
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res = bert_model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
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assert len(word2ph) == len(text)
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phone_level_feature = []
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for i in range(len(word2ph)):
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repeat_feature = res[i].repeat(word2ph[i], 1)
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phone_level_feature.append(repeat_feature)
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phone_level_feature = torch.cat(phone_level_feature, dim=0)
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# if(is_half==True):phone_level_feature=phone_level_feature.half()
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return phone_level_feature.T
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n_semantic = 1024
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dict_s2 = torch.load(sovits_path, map_location="cpu")
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hps = dict_s2["config"]
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class DictToAttrRecursive:
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def __init__(self, input_dict):
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for key, value in input_dict.items():
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if isinstance(value, dict):
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# 如果值是字典，递归调用构造函数
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setattr(self, key, DictToAttrRecursive(value))
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else:
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setattr(self, key, value)
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hps = DictToAttrRecursive(hps)
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hps.model.semantic_frame_rate = "25hz"
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dict_s1 = torch.load(gpt_path, map_location="cpu")
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config = dict_s1["config"]
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ssl_model = cnhubert.get_model()
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if is_half:
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ssl_model = ssl_model.half().to(device)
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else:
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ssl_model = ssl_model.to(device)
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vq_model = SynthesizerTrn(
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hps.data.filter_length // 2 + 1,
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hps.train.segment_size // hps.data.hop_length,
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n_speakers=hps.data.n_speakers,
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**hps.model)
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if is_half:
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vq_model = vq_model.half().to(device)
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else:
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vq_model = vq_model.to(device)
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vq_model.eval()
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print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
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hz = 50
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max_sec = config['data']['max_sec']
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t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
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t2s_model.load_state_dict(dict_s1["weight"])
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if is_half:
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t2s_model = t2s_model.half()
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t2s_model = t2s_model.to(device)
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t2s_model.eval()
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total = sum([param.nelement() for param in t2s_model.parameters()])
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print("Number of parameter: %.2fM" % (total / 1e6))
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def get_spepc(hps, filename):
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audio = load_audio(filename, int(hps.data.sampling_rate))
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audio = torch.FloatTensor(audio)
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audio_norm = audio
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audio_norm = audio_norm.unsqueeze(0)
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spec = spectrogram_torch(audio_norm, hps.data.filter_length, hps.data.sampling_rate, hps.data.hop_length,
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hps.data.win_length, center=False)
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return spec
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dict_language = {
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"中文": "zh",
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"英文": "en",
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"日文": "ja",
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"ZH": "zh",
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"EN": "en",
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"JA": "ja",
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"zh": "zh",
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"en": "en",
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"ja": "ja"
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}
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def split(todo_text):
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todo_text = todo_text.replace("……", "。").replace("——", "，")
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if todo_text[-1] not in splits:
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todo_text += "。"
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i_split_head = i_split_tail = 0
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len_text = len(todo_text)
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todo_texts = []
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while 1:
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if i_split_head >= len_text:
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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
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if todo_text[i_split_head] in splits:
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i_split_head += 1
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todo_texts.append(todo_text[i_split_tail:i_split_head])
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i_split_tail = i_split_head
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else:
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i_split_head += 1
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return todo_texts
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def cut1(inp):
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inp = inp.strip("\n")
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inps = split(inp)
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split_idx = list(range(0, len(inps), 4))
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split_idx[-1] = None
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if len(split_idx) > 1:
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opts = []
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for idx in range(len(split_idx) - 1):
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opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
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else:
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opts = [inp]
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return "\n".join(opts)
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def cut2(inp):
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inp = inp.strip("\n")
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inps = split(inp)
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if len(inps) < 2:
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return inp
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opts = []
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summ = 0
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tmp_str = ""
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for i in range(len(inps)):
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summ += len(inps[i])
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tmp_str += inps[i]
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if summ > 50:
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summ = 0
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opts.append(tmp_str)
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tmp_str = ""
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if tmp_str != "":
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opts.append(tmp_str)
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# print(opts)
|
||
if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起
|
||
opts[-2] = opts[-2] + opts[-1]
|
||
opts = opts[:-1]
|
||
return "\n".join(opts)
|
||
|
||
|
||
def cut3(inp):
|
||
inp = inp.strip("\n")
|
||
return "\n".join(["%s" % item for item in inp.strip("。").split("。")])
|
||
|
||
|
||
def cut4(inp):
|
||
inp = inp.strip("\n")
|
||
return "\n".join(["%s" % item for item in inp.strip(".").split(".")])
|
||
|
||
|
||
# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py
|
||
def cut5(inp):
|
||
# if not re.search(r'[^\w\s]', inp[-1]):
|
||
# inp += '。'
|
||
inp = inp.strip("\n")
|
||
punds = r'[,.;?!、，。？！;：…]'
|
||
items = re.split(f'({punds})', inp)
|
||
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])
|
||
opt = "\n".join(mergeitems)
|
||
return opt
|
||
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, how_to_cut):
|
||
if is_empty(path, text, language, how_to_cut):
|
||
return JSONResponse({"code": 400, "message": '缺少任意一项以下参数: "path", "text", "language","how_to_cut"'}, status_code=400)
|
||
|
||
if path == "" or path is None:
|
||
path = default_refer.path
|
||
if text == "" or text is None:
|
||
text = default_refer.text
|
||
if language == "" or language is None:
|
||
language = default_refer.language
|
||
if how_to_cut == "" or how_to_cut is None:
|
||
how_to_cut = default_refer.how_to_cut
|
||
|
||
default_refer.path = path
|
||
default_refer.text = text
|
||
default_refer.language = language
|
||
default_refer.how_to_cut = how_to_cut
|
||
|
||
|
||
if (default_refer.how_to_cut == "cut1"):
|
||
cut_info = "凑四句一切"
|
||
elif (default_refer.how_to_cut == "cut2"):
|
||
cut_info = "凑50字一切"
|
||
elif (default_refer.how_to_cut == "cut3"):
|
||
cut_info = "按中文句号。切"
|
||
elif (default_refer.how_to_cut == "cut4"):
|
||
cut_info = "按英文句号.切"
|
||
elif (default_refer.how_to_cut == "cut5"):
|
||
cut_info = "按标点符号切"
|
||
|
||
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] 默认切割方式: {cut_info}")
|
||
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,how_to_cut):
|
||
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 (how_to_cut == "" or how_to_cut is None):
|
||
how_to_cut = default_refer.how_to_cut
|
||
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 (how_to_cut == "cut1"):
|
||
text = cut1(text) #凑四句一切
|
||
|
||
elif (how_to_cut == "cut2"):
|
||
text = cut2(text) #凑50字一切
|
||
|
||
elif (how_to_cut == "cut3"):
|
||
text = cut3(text) #按中文句号。切
|
||
|
||
elif (how_to_cut == "cut4"):
|
||
text = cut4(text) #按英文句号.切
|
||
|
||
elif (how_to_cut == "cut5"):
|
||
text = cut5(text) #按标点符号切
|
||
|
||
while "\n\n" in text:
|
||
text = text.replace("\n\n", "\n")
|
||
|
||
print("实际输入的目标文本(切句后):", text)
|
||
|
||
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()
|
||
|
||
#clark新增-----2024-02-21
|
||
#可在启动后动态修改模型，以此满足同一个api不同的朗读者请求
|
||
@app.post("/set_model")
|
||
async def set_model(request: Request):
|
||
json_post_raw = await request.json()
|
||
global gpt_path
|
||
gpt_path=json_post_raw.get("gpt_model_path")
|
||
global sovits_path
|
||
sovits_path=json_post_raw.get("sovits_model_path")
|
||
print("gptpath"+gpt_path+";vitspath"+sovits_path)
|
||
change_sovits_weights(sovits_path)
|
||
change_gpt_weights(gpt_path)
|
||
return "ok"
|
||
# 新增-----end------
|
||
|
||
@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"),
|
||
json_post_raw.get("how_to_cut")
|
||
)
|
||
|
||
|
||
@app.get("/change_refer")
|
||
async def change_refer(
|
||
refer_wav_path: str = None,
|
||
prompt_text: str = None,
|
||
prompt_language: str = None,
|
||
how_to_cut: str = None
|
||
):
|
||
return handle_change(refer_wav_path, prompt_text, prompt_language, how_to_cut)
|
||
|
||
|
||
|
||
@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("how_to_cut")
|
||
)
|
||
|
||
|
||
@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,
|
||
how_to_cut: str = None
|
||
):
|
||
return handle(refer_wav_path, prompt_text, prompt_language, text, text_language,how_to_cut)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
uvicorn.run(app, host=host, port=port, workers=1)
|