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api.py 半精/全精 控制

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謬紗特 2024-01-19 15:54:51 +08:00 committed by GitHub
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1 changed files with 15 additions and 6 deletions
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api.py
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@ -19,12 +19,13 @@ 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
from config import python_exec, infer_device, is_half, api_port
from config import python_exec, infer_device, api_port
from config import is_half as config_is_half
DEFAULT_PORT = api_port
DEFAULT_CNHUBERT = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
DEFAULT_BERT = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
DEFAULT_HALF = is_half
DEFAULT_HALF = config_is_half
DEFAULT_GPT = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
DEFAULT_SOVITS = "GPT_SoVITS/pretrained_models/s2G488k.pth"
@ -44,7 +45,8 @@ parser.add_argument("-dl", "--default_refer_language", type=str, default="", hel
parser.add_argument("-d", "--device", type=str, default=infer_device, help="cuda / cpu")
parser.add_argument("-p", "--port", type=int, default=DEFAULT_PORT, help="default: 9880")
parser.add_argument("-a", "--bind_addr", type=str, default="127.0.0.1", help="default: 127.0.0.1")
parser.add_argument("-hp", "--half_precision", action='store_true', default=False)
parser.add_argument("-fp", "--full_precision", action='store_true', default=False, help="覆盖config.py使用全精度")
parser.add_argument("-hp", "--half_precision", action='store_true', default=False, help="覆盖config.py使用半精度")
parser.add_argument("-hb", "--hubert_path", type=str, default=DEFAULT_CNHUBERT)
parser.add_argument("-b", "--bert_path", type=str, default=DEFAULT_BERT)
@ -62,7 +64,6 @@ has_preset = False
device = args.device
port = args.port
host = args.bind_addr
is_half = args.half_precision
cnhubert_base_path = args.hubert_path
bert_path = args.bert_path
@ -84,17 +85,25 @@ else:
print(f"[INFO] 默认参考音频语种: {default_refer_language}")
has_preset = True
is_half = DEFAULT_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 = DEFAULT_HALF # 炒饭fallback
print(f"[INFO] 半精: {is_half}")
cnhubert.cnhubert_base_path = cnhubert_base_path
tokenizer = AutoTokenizer.from_pretrained(bert_path)
bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
# bert_model = AutoModelForSequenceClassification.from_pretrained(bert_path, config=bert_path+"/config.json")
if (is_half == True):
bert_model = bert_model.half().to(device)
else:
bert_model = bert_model.to(device)
# bert_model=bert_model.to(device)
def get_bert_feature(text, word2ph):
with torch.no_grad():
inputs = tokenizer(text, return_tensors="pt")