|
"""
|
|
# 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`
|
|
`-sm` - `流式返回模式, 默认不启用, "close","c", "normal","n", "keepalive","k"`
|
|
·-mt` - `返回的音频编码格式, 流式默认ogg, 非流式默认wav, "wav", "ogg", "aac"`
|
|
·-cp` - `文本切分符号设定, 默认为空, 以",.，。"字符串的方式传入`
|
|
|
|
`-hb` - `cnhubert路径`
|
|
`-b` - `bert路径`
|
|
|
|
## 调用:
|
|
|
|
### 推理
|
|
|
|
endpoint: `/`
|
|
|
|
使用执行参数指定的参考音频:
|
|
GET:
|
|
`http://127.0.0.1:9880?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
|
|
POST:
|
|
```json
|
|
{
|
|
"text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
|
|
"text_language": "zh"
|
|
}
|
|
```
|
|
|
|
使用执行参数指定的参考音频并设定分割符号:
|
|
GET:
|
|
`http://127.0.0.1:9880?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh&cut_punc=，。`
|
|
POST:
|
|
```json
|
|
{
|
|
"text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
|
|
"text_language": "zh",
|
|
"cut_punc": "，。",
|
|
}
|
|
```
|
|
|
|
手动指定当次推理所使用的参考音频:
|
|
GET:
|
|
`http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
|
|
POST:
|
|
```json
|
|
{
|
|
"refer_wav_path": "123.wav",
|
|
"prompt_text": "一二三。",
|
|
"prompt_language": "zh",
|
|
"text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
|
|
"text_language": "zh"
|
|
}
|
|
```
|
|
|
|
RESP:
|
|
成功: 直接返回 wav 音频流， http code 200
|
|
失败: 返回包含错误信息的 json, http code 400
|
|
|
|
|
|
### 更换默认参考音频
|
|
|
|
endpoint: `/change_refer`
|
|
|
|
key与推理端一样
|
|
|
|
GET:
|
|
`http://127.0.0.1:9880/change_refer?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh`
|
|
POST:
|
|
```json
|
|
{
|
|
"refer_wav_path": "123.wav",
|
|
"prompt_text": "一二三。",
|
|
"prompt_language": "zh"
|
|
}
|
|
```
|
|
|
|
RESP:
|
|
成功: json, http code 200
|
|
失败: json, 400
|
|
|
|
|
|
### 命令控制
|
|
|
|
endpoint: `/control`
|
|
|
|
command:
|
|
"restart": 重新运行
|
|
"exit": 结束运行
|
|
|
|
GET:
|
|
`http://127.0.0.1:9880/control?command=restart`
|
|
POST:
|
|
```json
|
|
{
|
|
"command": "restart"
|
|
}
|
|
```
|
|
|
|
RESP: 无
|
|
|
|
"""
|
|
|
|
|
|
import argparse
|
|
import os,re
|
|
import sys
|
|
import signal
|
|
import LangSegment
|
|
from time import time as ttime
|
|
import torch
|
|
import librosa
|
|
import soundfile as sf
|
|
from fastapi import FastAPI, Request, HTTPException
|
|
from fastapi.responses import StreamingResponse, JSONResponse
|
|
import uvicorn
|
|
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
|
import numpy as np
|
|
from feature_extractor import cnhubert
|
|
from io import BytesIO
|
|
from module.models import SynthesizerTrn
|
|
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
|
from text import cleaned_text_to_sequence
|
|
from text.cleaner import clean_text
|
|
from module.mel_processing import spectrogram_torch
|
|
from my_utils import load_audio
|
|
import config as global_config
|
|
import logging
|
|
import subprocess
|
|
|
|
|
|
class DefaultRefer:
|
|
def __init__(self, path, text, language):
|
|
self.path = args.default_refer_path
|
|
self.text = args.default_refer_text
|
|
self.language = args.default_refer_language
|
|
|
|
def is_ready(self) -> bool:
|
|
return is_full(self.path, self.text, self.language)
|
|
|
|
|
|
def is_empty(*items):
|
|
for item in items:
|
|
if item is not None and item != "":
|
|
return False
|
|
return True
|
|
|
|
|
|
def is_full(*items):
|
|
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()
|
|
vq_model.load_state_dict(dict_s2["weight"], strict=False)
|
|
|
|
|
|
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()])
|
|
logger.info("Number of parameter: %.2fM" % (total / 1e6))
|
|
|
|
|
|
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)
|
|
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)
|
|
|
|
return phone_level_feature.T
|
|
|
|
|
|
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
|
|
|
|
|
|
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)
|
|
else:
|
|
bert = torch.zeros(
|
|
(1024, len(phones)),
|
|
dtype=torch.float16 if is_half == True else torch.float32,
|
|
).to(device)
|
|
|
|
return bert
|
|
|
|
|
|
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:
|
|
|
|
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","ko"])
|
|
if language == "auto":
|
|
for tmp in LangSegment.getTexts(text):
|
|
if tmp["lang"] == "ko":
|
|
langlist.append("zh")
|
|
textlist.append(tmp["text"])
|
|
else:
|
|
langlist.append(tmp["lang"])
|
|
textlist.append(tmp["text"])
|
|
else:
|
|
for tmp in LangSegment.getTexts(text):
|
|
if tmp["lang"] == "en":
|
|
langlist.append(tmp["lang"])
|
|
else:
|
|
|
|
langlist.append(language)
|
|
textlist.append(tmp["text"])
|
|
|
|
|
|
phones_list = []
|
|
bert_list = []
|
|
norm_text_list = []
|
|
for i in range(len(textlist)):
|
|
lang = langlist[i]
|
|
phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
|
|
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(torch.float16 if is_half == True else torch.float32),norm_text
|
|
|
|
|
|
class DictToAttrRecursive:
|
|
def __init__(self, input_dict):
|
|
for key, value in input_dict.items():
|
|
if isinstance(value, dict):
|
|
|
|
setattr(self, key, DictToAttrRecursive(value))
|
|
else:
|
|
setattr(self, key, value)
|
|
|
|
|
|
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
|
|
|
|
|
|
def pack_audio(audio_bytes, data, rate):
|
|
if media_type == "ogg":
|
|
audio_bytes = pack_ogg(audio_bytes, data, rate)
|
|
elif media_type == "aac":
|
|
audio_bytes = pack_aac(audio_bytes, data, rate)
|
|
else:
|
|
|
|
audio_bytes = pack_raw(audio_bytes, data, rate)
|
|
|
|
return audio_bytes
|
|
|
|
|
|
def pack_ogg(audio_bytes, data, rate):
|
|
with sf.SoundFile(audio_bytes, mode='w', samplerate=rate, channels=1, format='ogg') as audio_file:
|
|
audio_file.write(data)
|
|
|
|
return audio_bytes
|
|
|
|
|
|
def pack_raw(audio_bytes, data, rate):
|
|
audio_bytes.write(data.tobytes())
|
|
|
|
return audio_bytes
|
|
|
|
|
|
def pack_wav(audio_bytes, rate):
|
|
data = np.frombuffer(audio_bytes.getvalue(),dtype=np.int16)
|
|
wav_bytes = BytesIO()
|
|
sf.write(wav_bytes, data, rate, format='wav')
|
|
|
|
return wav_bytes
|
|
|
|
|
|
def pack_aac(audio_bytes, data, rate):
|
|
process = subprocess.Popen([
|
|
'ffmpeg',
|
|
'-f', 's16le',
|
|
'-ar', str(rate),
|
|
'-ac', '1',
|
|
'-i', 'pipe:0',
|
|
'-c:a', 'aac',
|
|
'-b:a', '192k',
|
|
'-vn',
|
|
'-f', 'adts',
|
|
'pipe:1'
|
|
], stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
|
|
out, _ = process.communicate(input=data.tobytes())
|
|
audio_bytes.write(out)
|
|
|
|
return audio_bytes
|
|
|
|
|
|
def read_clean_buffer(audio_bytes):
|
|
audio_chunk = audio_bytes.getvalue()
|
|
audio_bytes.truncate(0)
|
|
audio_bytes.seek(0)
|
|
|
|
return audio_bytes, audio_chunk
|
|
|
|
|
|
def cut_text(text, punc):
|
|
punc_list = [p for p in punc if p in {",", ".", ";", "?", "!", "、", "，", "。", "？", "！", ";", "：", "…"}]
|
|
if len(punc_list) > 0:
|
|
punds = r"[" + "".join(punc_list) + r"]"
|
|
text = text.strip("\n")
|
|
items = re.split(f"({punds})", text)
|
|
mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
|
|
|
|
if len(items)%2 == 1:
|
|
mergeitems.append(items[-1])
|
|
text = "\n".join(mergeitems)
|
|
|
|
while "\n\n" in text:
|
|
text = text.replace("\n\n", "\n")
|
|
|
|
return text
|
|
|
|
|
|
def only_punc(text):
|
|
return not any(t.isalnum() or t.isalpha() for t in text)
|
|
|
|
|
|
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)
|
|
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:
|
|
|
|
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, idx = t2s_model.model.infer_panel(
|
|
all_phoneme_ids,
|
|
all_phoneme_len,
|
|
prompt,
|
|
bert,
|
|
|
|
top_k=config['inference']['top_k'],
|
|
early_stop_num=hz * max_sec)
|
|
t3 = ttime()
|
|
|
|
pred_semantic = pred_semantic[:, -idx:].unsqueeze(0)
|
|
refer = get_spepc(hps, ref_wav_path)
|
|
if (is_half == True):
|
|
refer = refer.half().to(device)
|
|
else:
|
|
refer = refer.to(device)
|
|
|
|
audio = \
|
|
vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
|
|
refer).detach().cpu().numpy()[
|
|
0, 0]
|
|
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)
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
now_dir = os.getcwd()
|
|
sys.path.append(now_dir)
|
|
sys.path.append("%s/GPT_SoVITS" % (now_dir))
|
|
|
|
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",
|
|
"all_zh": "all_zh",
|
|
"en": "en",
|
|
"all_ja": "all_ja",
|
|
"zh": "zh",
|
|
"ja": "ja",
|
|
"auto": "auto",
|
|
}
|
|
|
|
|
|
logging.config.dictConfig(uvicorn.config.LOGGING_CONFIG)
|
|
logger = logging.getLogger('uvicorn')
|
|
|
|
|
|
g_config = global_config.Config()
|
|
|
|
|
|
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("-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,.;?!、，。？！;：…")
|
|
|
|
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
|
|
|
|
|
|
default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language)
|
|
|
|
|
|
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}")
|
|
|
|
|
|
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}")
|
|
|
|
|
|
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
|
|
logger.info(f"半精: {is_half}")
|
|
|
|
|
|
if args.stream_mode.lower() in ["normal","n"]:
|
|
stream_mode = "normal"
|
|
logger.info("流式返回已开启")
|
|
else:
|
|
stream_mode = "close"
|
|
|
|
|
|
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}")
|
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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)
|
|
|
