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GPT-SoVITS/api.py

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"""
# 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"`
`-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",
"slice": "按标点符号切"
}
```
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
### 动态更换底模
endpoint: `/set_model`
GET:
`http://127.0.0.1:9880/set_model?gpt_model_path=GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt&sovits_model_path=GPT_SoVITS/pretrained_models/s2G488k.pth`
POST:
```json
{
"gpt_model_path": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
"sovits_model_path": "GPT_SoVITS/pretrained_models/s2G488k.pth"
}
```
RESP:
成功: json, http code 200
### 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, re
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
import LangSegment
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")
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")
#parser.add_argument("-sl", "--slice", type=str, default="No slice", help="文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。")
# 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 change_sovits_weights(sovits_path):
global vq_model, hps
dict_s2 = torch.load(sovits_path, map_location="cpu")
hps = dict_s2["config"]
hps = DictToAttrRecursive(hps)
hps.model.semantic_frame_rate = "25hz"
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 ("pretrained" not in sovits_path):
del vq_model.enc_q
if is_half == True:
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))
with open("./sweight.txt", "w", encoding="utf-8") as f:
f.write(sovits_path)
def change_gpt_weights(gpt_path):
global hz, max_sec, t2s_model, config
hz = 50
dict_s1 = torch.load(gpt_path, map_location="cpu")
config = dict_s1["config"]
max_sec = config["data"]["max_sec"]
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
t2s_model.load_state_dict(dict_s1["weight"])
if is_half == True:
t2s_model = t2s_model.half()
t2s_model = t2s_model.to(device)
t2s_model.eval()
total = sum([param.nelement() for param in t2s_model.parameters()])
print("Number of parameter: %.2fM" % (total / 1e6))
with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path)
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",
"auto": "auto",
"中英混合": "zh",
"日英混合": "ja",
"多语种混合": "auto"
}
slice_option = {
"凑四句一切": "凑四句一切",
"凑50字一切": "凑50字一切",
"按中文句号。切": "按中文句号。切",
"按英文句号.切": "按英文句号.切",
"按标点符号切": "按标点符号切",
"per 4 sentences": "凑四句一切",
"per 50 letters": "凑50字一切",
"per period": "按英文句号.切",
"per punctuation mark": "按标点符号切",
None: "No slice"
}
dtype=torch.float16 if is_half == True else torch.float32
def get_bert_inf(phones, word2ph, norm_text, language):
language=language.replace("all_","")
if language == "zh":
bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
else:
bert = torch.zeros(
(1024, len(phones)),
dtype=torch.float16 if is_half == True else torch.float32,
).to(device)
return bert
def clean_text_inf(text, language):
phones, word2ph, norm_text = clean_text(text, language)
phones = cleaned_text_to_sequence(phones)
return phones, word2ph, norm_text
# Mixed language support
def get_phones_and_bert(text,language):
if language in {"en","all_zh","all_ja"}:
language = language.replace("all_","")
if language == "en":
LangSegment.setfilters(["en"])
formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
else:
# 因无法区别中日文汉字,以用户输入为准
formattext = text
while " " in formattext:
formattext = formattext.replace(" ", " ")
phones, word2ph, norm_text = clean_text_inf(formattext, language)
if language == "zh":
bert = get_bert_feature(norm_text, word2ph).to(device)
else:
bert = torch.zeros(
(1024, len(phones)),
dtype=torch.float16 if is_half == True else torch.float32,
).to(device)
elif language in {"zh", "ja","auto"}:
textlist=[]
langlist=[]
LangSegment.setfilters(["zh","ja","en"])
if language == "auto":
for tmp in LangSegment.getTexts(text):
langlist.append(tmp["lang"])
textlist.append(tmp["text"])
else:
for tmp in LangSegment.getTexts(text):
if tmp["lang"] == "en":
langlist.append(tmp["lang"])
else:
# 因无法区别中日文汉字,以用户输入为准
langlist.append(language)
textlist.append(tmp["text"])
print(textlist)
print(langlist)
phones_list = []
bert_list = []
norm_text_list = []
for i in range(len(textlist)):
lang = langlist[i]
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
bert = get_bert_inf(phones, word2ph, norm_text, lang)
phones_list.append(phones)
norm_text_list.append(norm_text)
bert_list.append(bert)
bert = torch.cat(bert_list, dim=1)
phones = sum(phones_list, [])
norm_text = ''.join(norm_text_list)
return phones,bert.to(dtype),norm_text
def merge_short_text_in_array(texts, threshold):
if (len(texts)) < 2:
return texts
result = []
text = ""
for ele in texts:
text += ele
if len(text) >= threshold:
result.append(text)
text = ""
if (len(text) > 0):
if len(result) == 0:
result.append(text)
else:
result[len(result) - 1] += text
return result
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut):
# not supporting ref_free
t0 = ttime()
prompt_language = dict_language[prompt_language]
prompt_text = prompt_text.strip("\n")
if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
print(f"实际输入的参考文本: {prompt_text}")
text = text.strip("\n")
if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text
print(f"实际输入的目标文本: {text}")
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)
# neglected error checking for reference audio duration
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()
# 文本切句
# default to no slice if no argument is provided
how_to_cut = slice_option[how_to_cut]
print(f"[INFO] 文本切句選項: {how_to_cut}")
if (how_to_cut == "凑四句一切"):
text = cut1(text)
elif (how_to_cut == "凑50字一切"):
text = cut2(text)
elif (how_to_cut == "按英文句号.切"):
text = cut3(text)
elif (how_to_cut == "按英文句号.切"):
text = cut4(text)
elif (how_to_cut == "按标点符号切"):
text = cut5(text)
while "\n\n" in text:
text = text.replace("\n\n", "\n")
print(f"实际输入的目标文本(切句后): {text}")
texts = text.split("\n")
texts = merge_short_text_in_array(texts, 5)
audio_opt = []
phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
for text in texts:
# 解决输入目标文本的空行导致报错的问题
if (len(text.strip()) == 0):
continue
if (text[-1] not in splits): text += "。" if text_language != "en" else "."
print(f"实际输入的目标文本(每句): {text}")
phones2,bert2,norm_text2=get_phones_and_bert(text, text_language)
print(f"前端处理后的文本(每句): {norm_text2}")
bert = torch.cat([bert1, bert2], 1)
all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)
bert = bert.to(device).unsqueeze(0)
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
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
max_audio=np.abs(audio).max()#简单防止16bit爆音
if max_audio>1:audio/=max_audio
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
)
splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
def get_first(text):
pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
text = re.split(pattern, text)[0].strip()
return text
def split(todo_text):
todo_text = todo_text.replace("……", "。").replace("——", "，")
if todo_text[-1] not in splits:
todo_text += "。"
i_split_head = i_split_tail = 0
len_text = len(todo_text)
todo_texts = []
while 1:
if i_split_head >= len_text:
break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入
if todo_text[i_split_head] in splits:
i_split_head += 1
todo_texts.append(todo_text[i_split_tail:i_split_head])
i_split_tail = i_split_head
else:
i_split_head += 1
return todo_texts
def cut1(inp):
inp = inp.strip("\n")
inps = split(inp)
split_idx = list(range(0, len(inps), 4))
split_idx[-1] = None
if len(split_idx) > 1:
opts = []
for idx in range(len(split_idx) - 1):
opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]]))
else:
opts = [inp]
return "\n".join(opts)
def cut2(inp):
inp = inp.strip("\n")
inps = split(inp)
if len(inps) < 2:
return inp
opts = []
summ = 0
tmp_str = ""
for i in range(len(inps)):
summ += len(inps[i])
tmp_str += inps[i]
if summ > 50:
summ = 0
opts.append(tmp_str)
tmp_str = ""
if tmp_str != "":
opts.append(tmp_str)
# 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 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, slice):
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, slice
)
sampling_rate, audio_data = next(gen)
wav = BytesIO()
sf.write(wav, audio_data, sampling_rate, format="wav")
wav.seek(0)
torch.cuda.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")
)
@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("slice"),
)
@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,
slice: str = None
):
return handle(refer_wav_path, prompt_text, prompt_language, text, text_language, slice)
if __name__ == "__main__":
uvicorn.run(app, host=host, port=port, workers=1)
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