mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 04:22:46 +08:00
504 lines
17 KiB
Python
504 lines
17 KiB
Python
"""
|
||
# api.py usage
|
||
|
||
` python api.py -dr "123.wav" -dt "一二三。" -dl "zh" `
|
||
|
||
## 执行参数:
|
||
|
||
`-s` - `SoVITS模型路径, 可在 config.py 中指定`
|
||
`-g` - `GPT模型路径, 可在 config.py 中指定`
|
||
|
||
调用请求缺少参考音频时使用
|
||
`-dr` - `默认参考音频路径`
|
||
`-dt` - `默认参考音频文本`
|
||
`-dl` - `默认参考音频语种, "中文","英文","日文","zh","en","ja"`
|
||
|
||
`-d` - `推理设备, "cuda","cpu","mps"`
|
||
`-a` - `绑定地址, 默认"127.0.0.1"`
|
||
`-p` - `绑定端口, 默认9880, 可在 config.py 中指定`
|
||
`-fp` - `覆盖 config.py 使用全精度`
|
||
`-hp` - `覆盖 config.py 使用半精度`
|
||
|
||
`-hb` - `cnhubert路径`
|
||
`-b` - `bert路径`
|
||
|
||
## 调用:
|
||
|
||
### 推理
|
||
|
||
endpoint: `/`
|
||
|
||
使用执行参数指定的参考音频:
|
||
GET:
|
||
`http://127.0.0.1:9880?text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
|
||
POST:
|
||
```json
|
||
{
|
||
"text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
|
||
"text_language": "zh"
|
||
}
|
||
```
|
||
|
||
手动指定当次推理所使用的参考音频:
|
||
GET:
|
||
`http://127.0.0.1:9880?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh&text=先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。&text_language=zh`
|
||
POST:
|
||
```json
|
||
{
|
||
"refer_wav_path": "123.wav",
|
||
"prompt_text": "一二三。",
|
||
"prompt_language": "zh",
|
||
"text": "先帝创业未半而中道崩殂，今天下三分，益州疲弊，此诚危急存亡之秋也。",
|
||
"text_language": "zh"
|
||
}
|
||
```
|
||
|
||
RESP:
|
||
成功: 直接返回 wav 音频流， http code 200
|
||
失败: 返回包含错误信息的 json, http code 400
|
||
|
||
|
||
### 更换默认参考音频
|
||
|
||
endpoint: `/change_refer`
|
||
|
||
key与推理端一样
|
||
|
||
GET:
|
||
`http://127.0.0.1:9880/change_refer?refer_wav_path=123.wav&prompt_text=一二三。&prompt_language=zh`
|
||
POST:
|
||
```json
|
||
{
|
||
"refer_wav_path": "123.wav",
|
||
"prompt_text": "一二三。",
|
||
"prompt_language": "zh"
|
||
}
|
||
```
|
||
|
||
RESP:
|
||
成功: json, http code 200
|
||
失败: json, 400
|
||
|
||
|
||
### 命令控制
|
||
|
||
endpoint: `/control`
|
||
|
||
command:
|
||
"restart": 重新运行
|
||
"exit": 结束运行
|
||
|
||
GET:
|
||
`http://127.0.0.1:9880/control?command=restart`
|
||
POST:
|
||
```json
|
||
{
|
||
"command": "restart"
|
||
}
|
||
```
|
||
|
||
RESP: 无
|
||
|
||
"""
|
||
|
||
|
||
import argparse
|
||
import os
|
||
import signal
|
||
import sys
|
||
from time import time as ttime
|
||
import torch
|
||
import librosa
|
||
import soundfile as sf
|
||
from fastapi import FastAPI, Request, HTTPException
|
||
from fastapi.responses import StreamingResponse, JSONResponse
|
||
import uvicorn
|
||
from transformers import AutoModelForMaskedLM, AutoTokenizer
|
||
import numpy as np
|
||
from feature_extractor import cnhubert
|
||
from io import BytesIO
|
||
from module.models import SynthesizerTrn
|
||
from AR.models.t2s_lightning_module import Text2SemanticLightningModule
|
||
from text import cleaned_text_to_sequence
|
||
from text.cleaner import clean_text
|
||
from module.mel_processing import spectrogram_torch
|
||
from my_utils import load_audio
|
||
import config as global_config
|
||
|
||
g_config = global_config.Config()
|
||
|
||
# AVAILABLE_COMPUTE = "cuda" if torch.cuda.is_available() else "cpu"
|
||
|
||
parser = argparse.ArgumentParser(description="GPT-SoVITS api")
|
||
|
||
parser.add_argument("-s", "--sovits_path", type=str, default=g_config.sovits_path, help="SoVITS模型路径")
|
||
parser.add_argument("-g", "--gpt_path", type=str, default=g_config.gpt_path, help="GPT模型路径")
|
||
|
||
parser.add_argument("-dr", "--default_refer_path", type=str, default="", help="默认参考音频路径")
|
||
parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
|
||
parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
|
||
|
||
parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu / mps")
|
||
parser.add_argument("-a", "--bind_addr", type=str, default="127.0.0.1", help="default: 127.0.0.1")
|
||
parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
|
||
parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
|
||
parser.add_argument("-hp", "--half_precision", action="store_true", default=False, help="覆盖config.is_half为True, 使用半精度")
|
||
# bool值的用法为 `python ./api.py -fp ...`
|
||
# 此时 full_precision==True, half_precision==False
|
||
|
||
parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
|
||
parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, help="覆盖config.bert_path")
|
||
|
||
args = parser.parse_args()
|
||
|
||
sovits_path = args.sovits_path
|
||
gpt_path = args.gpt_path
|
||
|
||
|
||
class DefaultRefer:
|
||
def __init__(self, path, text, language):
|
||
self.path = args.default_refer_path
|
||
self.text = args.default_refer_text
|
||
self.language = args.default_refer_language
|
||
|
||
def is_ready(self) -> bool:
|
||
return is_full(self.path, self.text, self.language)
|
||
|
||
|
||
default_refer = DefaultRefer(args.default_refer_path, args.default_refer_text, args.default_refer_language)
|
||
|
||
device = args.device
|
||
port = args.port
|
||
host = args.bind_addr
|
||
|
||
if sovits_path == "":
|
||
sovits_path = g_config.pretrained_sovits_path
|
||
print(f"[WARN] 未指定SoVITS模型路径, fallback后当前值: {sovits_path}")
|
||
if gpt_path == "":
|
||
gpt_path = g_config.pretrained_gpt_path
|
||
print(f"[WARN] 未指定GPT模型路径, fallback后当前值: {gpt_path}")
|
||
|
||
# 指定默认参考音频, 调用方 未提供/未给全 参考音频参数时使用
|
||
if default_refer.path == "" or default_refer.text == "" or default_refer.language == "":
|
||
default_refer.path, default_refer.text, default_refer.language = "", "", ""
|
||
print("[INFO] 未指定默认参考音频")
|
||
else:
|
||
print(f"[INFO] 默认参考音频路径: {default_refer.path}")
|
||
print(f"[INFO] 默认参考音频文本: {default_refer.text}")
|
||
print(f"[INFO] 默认参考音频语种: {default_refer.language}")
|
||
|
||
is_half = g_config.is_half
|
||
if args.full_precision:
|
||
is_half = False
|
||
if args.half_precision:
|
||
is_half = True
|
||
if args.full_precision and args.half_precision:
|
||
is_half = g_config.is_half # 炒饭fallback
|
||
|
||
print(f"[INFO] 半精: {is_half}")
|
||
|
||
cnhubert_base_path = args.hubert_path
|
||
bert_path = args.bert_path
|
||
|
||
cnhubert.cnhubert_base_path = cnhubert_base_path
|
||
tokenizer = AutoTokenizer.from_pretrained(bert_path)
|
||
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
|
||
if is_half:
|
||
bert_model = bert_model.half().to(device)
|
||
else:
|
||
bert_model = bert_model.to(device)
|
||
|
||
|
||
def is_empty(*items): # 任意一项不为空返回False
|
||
for item in items:
|
||
if item is not None and item != "":
|
||
return False
|
||
return True
|
||
|
||
|
||
def is_full(*items): # 任意一项为空返回False
|
||
for item in items:
|
||
if item is None or item == "":
|
||
return False
|
||
return True
|
||
|
||
|
||
def get_bert_feature(text, word2ph):
|
||
with torch.no_grad():
|
||
inputs = tokenizer(text, return_tensors="pt")
|
||
for i in inputs:
|
||
inputs[i] = inputs[i].to(device) #####输入是long不用管精度问题，精度随bert_model
|
||
res = bert_model(**inputs, output_hidden_states=True)
|
||
res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
|
||
assert len(word2ph) == len(text)
|
||
phone_level_feature = []
|
||
for i in range(len(word2ph)):
|
||
repeat_feature = res[i].repeat(word2ph[i], 1)
|
||
phone_level_feature.append(repeat_feature)
|
||
phone_level_feature = torch.cat(phone_level_feature, dim=0)
|
||
# if(is_half==True):phone_level_feature=phone_level_feature.half()
|
||
return phone_level_feature.T
|
||
|
||
|
||
n_semantic = 1024
|
||
dict_s2 = torch.load(sovits_path, map_location="cpu")
|
||
hps = dict_s2["config"]
|
||
|
||
|
||
class DictToAttrRecursive:
|
||
def __init__(self, input_dict):
|
||
for key, value in input_dict.items():
|
||
if isinstance(value, dict):
|
||
# 如果值是字典，递归调用构造函数
|
||
setattr(self, key, DictToAttrRecursive(value))
|
||
else:
|
||
setattr(self, key, value)
|
||
|
||
|
||
hps = DictToAttrRecursive(hps)
|
||
hps.model.semantic_frame_rate = "25hz"
|
||
dict_s1 = torch.load(gpt_path, map_location="cpu")
|
||
config = dict_s1["config"]
|
||
ssl_model = cnhubert.get_model()
|
||
if is_half:
|
||
ssl_model = ssl_model.half().to(device)
|
||
else:
|
||
ssl_model = ssl_model.to(device)
|
||
|
||
vq_model = SynthesizerTrn(
|
||
hps.data.filter_length // 2 + 1,
|
||
hps.train.segment_size // hps.data.hop_length,
|
||
n_speakers=hps.data.n_speakers,
|
||
**hps.model)
|
||
if is_half:
|
||
vq_model = vq_model.half().to(device)
|
||
else:
|
||
vq_model = vq_model.to(device)
|
||
vq_model.eval()
|
||
print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
|
||
hz = 50
|
||
max_sec = config['data']['max_sec']
|
||
t2s_model = Text2SemanticLightningModule(config, "****", is_train=False)
|
||
t2s_model.load_state_dict(dict_s1["weight"])
|
||
if is_half:
|
||
t2s_model = t2s_model.half()
|
||
t2s_model = t2s_model.to(device)
|
||
t2s_model.eval()
|
||
total = sum([param.nelement() for param in t2s_model.parameters()])
|
||
print("Number of parameter: %.2fM" % (total / 1e6))
|
||
|
||
|
||
def get_spepc(hps, filename):
|
||
audio = load_audio(filename, int(hps.data.sampling_rate))
|
||
audio = torch.FloatTensor(audio)
|
||
audio_norm = audio
|
||
audio_norm = audio_norm.unsqueeze(0)
|
||
spec = spectrogram_torch(audio_norm, hps.data.filter_length, hps.data.sampling_rate, hps.data.hop_length,
|
||
hps.data.win_length, center=False)
|
||
return spec
|
||
|
||
|
||
dict_language = {
|
||
"中文": "zh",
|
||
"英文": "en",
|
||
"日文": "ja",
|
||
"ZH": "zh",
|
||
"EN": "en",
|
||
"JA": "ja",
|
||
"zh": "zh",
|
||
"en": "en",
|
||
"ja": "ja"
|
||
}
|
||
|
||
|
||
def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language):
|
||
t0 = ttime()
|
||
prompt_text = prompt_text.strip("\n")
|
||
prompt_language, text = prompt_language, text.strip("\n")
|
||
zero_wav = np.zeros(int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32)
|
||
with torch.no_grad():
|
||
wav16k, sr = librosa.load(ref_wav_path, sr=16000)
|
||
wav16k = torch.from_numpy(wav16k)
|
||
zero_wav_torch = torch.from_numpy(zero_wav)
|
||
if (is_half == True):
|
||
wav16k = wav16k.half().to(device)
|
||
zero_wav_torch = zero_wav_torch.half().to(device)
|
||
else:
|
||
wav16k = wav16k.to(device)
|
||
zero_wav_torch = zero_wav_torch.to(device)
|
||
wav16k = torch.cat([wav16k, zero_wav_torch])
|
||
ssl_content = ssl_model.model(wav16k.unsqueeze(0))["last_hidden_state"].transpose(1, 2) # .float()
|
||
codes = vq_model.extract_latent(ssl_content)
|
||
prompt_semantic = codes[0, 0]
|
||
t1 = ttime()
|
||
prompt_language = dict_language[prompt_language]
|
||
text_language = dict_language[text_language]
|
||
phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language)
|
||
phones1 = cleaned_text_to_sequence(phones1)
|
||
texts = text.split("\n")
|
||
audio_opt = []
|
||
|
||
for text in texts:
|
||
phones2, word2ph2, norm_text2 = clean_text(text, text_language)
|
||
phones2 = cleaned_text_to_sequence(phones2)
|
||
if (prompt_language == "zh"):
|
||
bert1 = get_bert_feature(norm_text1, word2ph1).to(device)
|
||
else:
|
||
bert1 = torch.zeros((1024, len(phones1)), dtype=torch.float16 if is_half == True else torch.float32).to(
|
||
device)
|
||
if (text_language == "zh"):
|
||
bert2 = get_bert_feature(norm_text2, word2ph2).to(device)
|
||
else:
|
||
bert2 = torch.zeros((1024, len(phones2))).to(bert1)
|
||
bert = torch.cat([bert1, bert2], 1)
|
||
|
||
all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
|
||
bert = bert.to(device).unsqueeze(0)
|
||
all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device)
|
||
prompt = prompt_semantic.unsqueeze(0).to(device)
|
||
t2 = ttime()
|
||
with torch.no_grad():
|
||
# pred_semantic = t2s_model.model.infer(
|
||
pred_semantic, idx = t2s_model.model.infer_panel(
|
||
all_phoneme_ids,
|
||
all_phoneme_len,
|
||
prompt,
|
||
bert,
|
||
# prompt_phone_len=ph_offset,
|
||
top_k=config['inference']['top_k'],
|
||
early_stop_num=hz * max_sec)
|
||
t3 = ttime()
|
||
# print(pred_semantic.shape,idx)
|
||
pred_semantic = pred_semantic[:, -idx:].unsqueeze(0) # .unsqueeze(0)#mq要多unsqueeze一次
|
||
refer = get_spepc(hps, ref_wav_path) # .to(device)
|
||
if (is_half == True):
|
||
refer = refer.half().to(device)
|
||
else:
|
||
refer = refer.to(device)
|
||
# audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0]
|
||
audio = \
|
||
vq_model.decode(pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0),
|
||
refer).detach().cpu().numpy()[
|
||
0, 0] ###试试重建不带上prompt部分
|
||
audio_opt.append(audio)
|
||
audio_opt.append(zero_wav)
|
||
t4 = ttime()
|
||
print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
||
yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)
|
||
|
||
|
||
def handle_control(command):
|
||
if command == "restart":
|
||
os.execl(g_config.python_exec, g_config.python_exec, *sys.argv)
|
||
elif command == "exit":
|
||
os.kill(os.getpid(), signal.SIGTERM)
|
||
exit(0)
|
||
|
||
|
||
def handle_change(path, text, language):
|
||
if is_empty(path, text, language):
|
||
return JSONResponse({"code": 400, "message": '缺少任意一项以下参数: "path", "text", "language"'}, status_code=400)
|
||
|
||
if path != "" or path is not None:
|
||
default_refer.path = path
|
||
if text != "" or text is not None:
|
||
default_refer.text = text
|
||
if language != "" or language is not None:
|
||
default_refer.language = language
|
||
|
||
print(f"[INFO] 当前默认参考音频路径: {default_refer.path}")
|
||
print(f"[INFO] 当前默认参考音频文本: {default_refer.text}")
|
||
print(f"[INFO] 当前默认参考音频语种: {default_refer.language}")
|
||
print(f"[INFO] is_ready: {default_refer.is_ready()}")
|
||
|
||
return JSONResponse({"code": 0, "message": "Success"}, status_code=200)
|
||
|
||
|
||
def handle(refer_wav_path, prompt_text, prompt_language, text, text_language):
|
||
if (
|
||
refer_wav_path == "" or refer_wav_path is None
|
||
or prompt_text == "" or prompt_text is None
|
||
or prompt_language == "" or prompt_language is None
|
||
):
|
||
refer_wav_path, prompt_text, prompt_language = (
|
||
default_refer.path,
|
||
default_refer.text,
|
||
default_refer.language,
|
||
)
|
||
if not default_refer.is_ready():
|
||
return JSONResponse({"code": 400, "message": "未指定参考音频且接口无预设"}, status_code=400)
|
||
|
||
with torch.no_grad():
|
||
gen = get_tts_wav(
|
||
refer_wav_path, prompt_text, prompt_language, text, text_language
|
||
)
|
||
sampling_rate, audio_data = next(gen)
|
||
|
||
wav = BytesIO()
|
||
sf.write(wav, audio_data, sampling_rate, format="wav")
|
||
wav.seek(0)
|
||
|
||
torch.cuda.empty_cache()
|
||
torch.mps.empty_cache()
|
||
return StreamingResponse(wav, media_type="audio/wav")
|
||
|
||
|
||
app = FastAPI()
|
||
|
||
|
||
@app.post("/control")
|
||
async def control(request: Request):
|
||
json_post_raw = await request.json()
|
||
return handle_control(json_post_raw.get("command"))
|
||
|
||
|
||
@app.get("/control")
|
||
async def control(command: str = None):
|
||
return handle_control(command)
|
||
|
||
|
||
@app.post("/change_refer")
|
||
async def change_refer(request: Request):
|
||
json_post_raw = await request.json()
|
||
return handle_change(
|
||
json_post_raw.get("refer_wav_path"),
|
||
json_post_raw.get("prompt_text"),
|
||
json_post_raw.get("prompt_language")
|
||
)
|
||
|
||
|
||
@app.get("/change_refer")
|
||
async def change_refer(
|
||
refer_wav_path: str = None,
|
||
prompt_text: str = None,
|
||
prompt_language: str = None
|
||
):
|
||
return handle_change(refer_wav_path, prompt_text, prompt_language)
|
||
|
||
|
||
@app.post("/")
|
||
async def tts_endpoint(request: Request):
|
||
json_post_raw = await request.json()
|
||
return handle(
|
||
json_post_raw.get("refer_wav_path"),
|
||
json_post_raw.get("prompt_text"),
|
||
json_post_raw.get("prompt_language"),
|
||
json_post_raw.get("text"),
|
||
json_post_raw.get("text_language"),
|
||
)
|
||
|
||
|
||
@app.get("/")
|
||
async def tts_endpoint(
|
||
refer_wav_path: str = None,
|
||
prompt_text: str = None,
|
||
prompt_language: str = None,
|
||
text: str = None,
|
||
text_language: str = None,
|
||
):
|
||
return handle(refer_wav_path, prompt_text, prompt_language, text, text_language)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
uvicorn.run(app, host=host, port=port, workers=1)
|