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api.py
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api.py
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"""
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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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`-d` - `推理设备, "cuda","cpu"`
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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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`-sm` - `流式返回模式, 默认不启用, "close","c", "normal","n", "keepalive","k"`
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·-mt` - `返回的音频编码格式, 流式默认ogg, 非流式默认wav, "wav", "ogg", "aac"`
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·-cp` - `文本切分符号设定, 默认为空, 以",.，。"字符串的方式传入`
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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:
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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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使用执行参数指定的参考音频并设定分割符号:
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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&cut_punc=，。`
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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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"cut_punc": "，。",
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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:
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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`
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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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}
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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
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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`
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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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}
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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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import LangSegment
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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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import logging
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import subprocess
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class DefaultRefer:
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def __init__(self, path, text, language):
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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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def is_ready(self) -> bool:
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return is_full(self.path, self.text, self.language)
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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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model_params_dict = vars(hps.model)
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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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**model_params_dict
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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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vq_model.load_state_dict(dict_s2["weight"], strict=False)
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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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logger.info("Number of parameter: %.2fM" % (total / 1e6))
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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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def clean_text_inf(text, language):
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phones, word2ph, norm_text = clean_text(text, language)
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phones = cleaned_text_to_sequence(phones)
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return phones, word2ph, norm_text
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def get_bert_inf(phones, word2ph, norm_text, language):
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language=language.replace("all_","")
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if language == "zh":
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bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
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else:
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bert = torch.zeros(
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(1024, len(phones)),
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dtype=torch.float16 if is_half == True else torch.float32,
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).to(device)
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return bert
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def get_phones_and_bert(text,language):
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if language in {"en","all_zh","all_ja"}:
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language = language.replace("all_","")
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if language == "en":
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LangSegment.setfilters(["en"])
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formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
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else:
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# 因无法区别中日文汉字,以用户输入为准
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formattext = text
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while " " in formattext:
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formattext = formattext.replace(" ", " ")
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phones, word2ph, norm_text = clean_text_inf(formattext, language)
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if language == "zh":
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bert = get_bert_feature(norm_text, word2ph).to(device)
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else:
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bert = torch.zeros(
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(1024, len(phones)),
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dtype=torch.float16 if is_half == True else torch.float32,
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).to(device)
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elif language in {"zh", "ja","auto"}:
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textlist=[]
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langlist=[]
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LangSegment.setfilters(["zh","ja","en","ko"])
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if language == "auto":
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for tmp in LangSegment.getTexts(text):
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if tmp["lang"] == "ko":
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langlist.append("zh")
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textlist.append(tmp["text"])
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else:
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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else:
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for tmp in LangSegment.getTexts(text):
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if tmp["lang"] == "en":
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langlist.append(tmp["lang"])
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else:
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# 因无法区别中日文汉字,以用户输入为准
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langlist.append(language)
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textlist.append(tmp["text"])
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# logger.info(textlist)
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# logger.info(langlist)
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phones_list = []
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bert_list = []
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norm_text_list = []
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for i in range(len(textlist)):
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lang = langlist[i]
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phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
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bert = get_bert_inf(phones, word2ph, norm_text, lang)
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phones_list.append(phones)
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norm_text_list.append(norm_text)
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bert_list.append(bert)
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bert = torch.cat(bert_list, dim=1)
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phones = sum(phones_list, [])
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norm_text = ''.join(norm_text_list)
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return phones,bert.to(torch.float16 if is_half == True else torch.float32),norm_text
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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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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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def pack_audio(audio_bytes, data, rate):
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if media_type == "ogg":
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audio_bytes = pack_ogg(audio_bytes, data, rate)
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elif media_type == "aac":
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audio_bytes = pack_aac(audio_bytes, data, rate)
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else:
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# wav无法流式, 先暂存raw
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audio_bytes = pack_raw(audio_bytes, data, rate)
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return audio_bytes
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def pack_ogg(audio_bytes, data, rate):
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with sf.SoundFile(audio_bytes, mode='w', samplerate=rate, channels=1, format='ogg') as audio_file:
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audio_file.write(data)
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return audio_bytes
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def pack_raw(audio_bytes, data, rate):
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audio_bytes.write(data.tobytes())
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return audio_bytes
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def pack_wav(audio_bytes, rate):
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data = np.frombuffer(audio_bytes.getvalue(),dtype=np.int16)
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wav_bytes = BytesIO()
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sf.write(wav_bytes, data, rate, format='wav')
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return wav_bytes
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def pack_aac(audio_bytes, data, rate):
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process = subprocess.Popen([
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'ffmpeg',
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'-f', 's16le', # 输入16位有符号小端整数PCM
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'-ar', str(rate), # 设置采样率
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'-ac', '1', # 单声道
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'-i', 'pipe:0', # 从管道读取输入
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'-c:a', 'aac', # 音频编码器为AAC
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'-b:a', '192k', # 比特率
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'-vn', # 不包含视频
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'-f', 'adts', # 输出AAC数据流格式
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'pipe:1' # 将输出写入管道
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], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
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out, _ = process.communicate(input=data.tobytes())
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audio_bytes.write(out)
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return audio_bytes
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def read_clean_buffer(audio_bytes):
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audio_chunk = audio_bytes.getvalue()
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audio_bytes.truncate(0)
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audio_bytes.seek(0)
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return audio_bytes, audio_chunk
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def cut_text(text, punc):
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punc_list = [p for p in punc if p in {",", ".", ";", "?", "!", "、", "，", "。", "？", "！", "；", "：", "…"}]
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if len(punc_list) > 0:
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punds = r"[" + "".join(punc_list) + r"]"
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text = text.strip("\n")
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items = re.split(f"({punds})", text)
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mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
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# 在句子不存在符号或句尾无符号的时候保证文本完整
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if len(items)%2 == 1:
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mergeitems.append(items[-1])
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text = "\n".join(mergeitems)
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while "\n\n" in text:
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text = text.replace("\n\n", "\n")
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return text
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def only_punc(text):
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return not any(t.isalnum() or t.isalpha() for t in text)
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
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t0 = ttime()
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prompt_text = prompt_text.strip("\n")
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prompt_language, text = prompt_language, text.strip("\n")
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zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
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with torch.no_grad():
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wav16k, sr = librosa.load(ref_wav_path, sr=16000)
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wav16k = torch.from_numpy(wav16k)
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zero_wav_torch = torch.from_numpy(zero_wav)
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if (is_half == True):
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wav16k = wav16k.half().to(device)
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zero_wav_torch = zero_wav_torch.half().to(device)
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else:
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wav16k = wav16k.to(device)
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zero_wav_torch = zero_wav_torch.to(device)
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||||
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.lower()]
|
||||
text_language = dict_language[text_language.lower()]
|
||||
phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language)
|
||||
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 = []
|
||||
phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language)
|
||||
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()
|
||||
audio_bytes = pack_audio(audio_bytes,(np.concatenate(audio_opt, 0) * 32768).astype(np.int16),hps.data.sampling_rate)
|
||||
# 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":
|
||||
audio_bytes = pack_wav(audio_bytes,hps.data.sampling_rate)
|
||||
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):
|
||||
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 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), media_type="audio/"+media_type)
|
||||
|
||||
|
||||
|
||||
|
||||
# --------------------------------
|
||||
# 初始化部分
|
||||
# --------------------------------
|
||||
dict_language = {
|
||||
"中文": "all_zh",
|
||||
"英文": "en",
|
||||
"日文": "all_ja",
|
||||
"中英混合": "zh",
|
||||
"日英混合": "ja",
|
||||
"多语种混合": "auto", #多语种启动切分识别语种
|
||||
"all_zh": "all_zh",
|
||||
"en": "en",
|
||||
"all_ja": "all_ja",
|
||||
"zh": "zh",
|
||||
"ja": "ja",
|
||||
"auto": "auto",
|
||||
}
|
||||
|
||||
# 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("-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}")
|
||||
|
||||
# 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_sovits_weights(sovits_path)
|
||||
change_gpt_weights(gpt_path)
|
||||
|
||||
|
||||
|
||||
|
||||
# --------------------------------
|
||||
# 接口部分
|
||||
# --------------------------------
|
||||
app = FastAPI()
|
||||
|
||||
@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")
|
||||
logger.info("gptpath"+gpt_path+";vitspath"+sovits_path)
|
||||
change_sovits_weights(sovits_path)
|
||||
change_gpt_weights(gpt_path)
|
||||
return "ok"
|
||||
|
||||
|
||||
@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"),
|
||||
)
|
||||
|
||||
|
||||
@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,
|
||||
):
|
||||
return handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cut_punc)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
uvicorn.run(app, host=host, port=port, workers=1)
|
||||
Loading…
x
Reference in New Issue
Block a user