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https://github.com/RVC-Boss/GPT-SoVITS.git
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config.py引入class, requirement补全, 自述追加已测试环境
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Miuzarte
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3
.gitignore
vendored
3
.gitignore
vendored
@ -1,2 +1,3 @@
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env
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runtime
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runtime
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.idea
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@ -36,9 +36,12 @@ https://github.com/RVC-Boss/GPT-SoVITS/assets/129054828/05bee1fa-bdd8-4d85-9350-
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If you are a Windows user (tested with win>=10) you can install directly via the prezip. Just download the [prezip](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-beta.7z?download=true), unzip it and double-click go-webui.bat to start GPT-SoVITS-WebUI.
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### Python and PyTorch Version
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### Tested Environments
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Tested with Python 3.9, PyTorch 2.0.1, and CUDA 11.
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- Python 3.9, PyTorch 2.0.1, CUDA 11
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- Python 3.10.13, PyTorch 2.1.2, CUDA 12.3
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_Note: numba==0.56.4 require py<3.11_
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### Quick Install with Conda
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@ -90,7 +93,7 @@ Download and place [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWeb
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### Pretrained Models
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Download pretrained models from [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) and place them in `GPT_SoVITS\pretrained_models`.
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Download pretrained models from [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) and place them in `GPT_SoVITS/pretrained_models`.
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For Chinese ASR (additionally), download models from [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files), and [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) and place them in `tools/damo_asr/models`.
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63
api.py
63
api.py
@ -19,40 +19,37 @@ 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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from config import python_exec, infer_device, is_half, api_port
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import config as global_config
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DEFAULT_PORT = api_port
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DEFAULT_CNHUBERT = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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DEFAULT_BERT = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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DEFAULT_HALF = is_half
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DEFAULT_GPT = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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DEFAULT_SOVITS = "GPT_SoVITS/pretrained_models/s2G488k.pth"
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g_config = global_config.Config()
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# AVAILABLE_COMPUTE = "cuda" if torch.cuda.is_available() else "cpu"
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parser = argparse.ArgumentParser(description="GPT-SoVITS api")
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parser.add_argument("-g", "--gpt_path", type=str, default="", help="GPT模型路径")
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parser.add_argument("-s", "--sovits_path", type=str, default="", help="SoVITS模型路径")
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parser.add_argument("-s", "--sovits_path", type=str, default=g_config.sovits_path, help="SoVITS模型路径")
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parser.add_argument("-g", "--gpt_path", type=str, default=g_config.gpt_path, help="GPT模型路径")
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parser.add_argument("-dr", "--default_refer_path", type=str, default="",
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help="默认参考音频路径, 请求缺少参考音频时调用")
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parser.add_argument("-dt", "--default_refer_text", type=str, default="", help="默认参考音频文本")
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parser.add_argument("-dl", "--default_refer_language", type=str, default="", help="默认参考音频语种")
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parser.add_argument("-d", "--device", type=str, default=infer_device, help="cuda / cpu")
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parser.add_argument("-p", "--port", type=int, default=DEFAULT_PORT, help="default: 9880")
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parser.add_argument("-d", "--device", type=str, default=g_config.infer_device, help="cuda / cpu")
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parser.add_argument("-p", "--port", type=int, default=g_config.api_port, help="default: 9880")
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parser.add_argument("-a", "--bind_addr", type=str, default="127.0.0.1", help="default: 127.0.0.1")
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parser.add_argument("-hp", "--half_precision", action='store_true', default=False)
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parser.add_argument("-fp", "--full_precision", action="store_true", default=False, help="覆盖config.is_half为False, 使用全精度")
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parser.add_argument("-hp", "--half_precision", action="store_true", default=False, help="覆盖config.is_half为True, 使用半精度")
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# bool值的用法为 `python ./api.py -fp ...`
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# 此时 full_precision==True, half_precision==False
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parser.add_argument("-hb", "--hubert_path", type=str, default=DEFAULT_CNHUBERT)
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parser.add_argument("-b", "--bert_path", type=str, default=DEFAULT_BERT)
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parser.add_argument("-hb", "--hubert_path", type=str, default=g_config.cnhubert_path, help="覆盖config.cnhubert_path")
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parser.add_argument("-b", "--bert_path", type=str, default=g_config.bert_path, help="覆盖config.bert_path")
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args = parser.parse_args()
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gpt_path = args.gpt_path
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sovits_path = args.sovits_path
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gpt_path = args.gpt_path
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default_refer_path = args.default_refer_path
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default_refer_text = args.default_refer_text
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@ -62,18 +59,15 @@ has_preset = False
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device = args.device
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port = args.port
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host = args.bind_addr
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is_half = args.half_precision
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cnhubert_base_path = args.hubert_path
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bert_path = args.bert_path
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if gpt_path == "":
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gpt_path = DEFAULT_GPT
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print("[WARN] 未指定GPT模型路径")
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if sovits_path == "":
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sovits_path = DEFAULT_SOVITS
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print("[WARN] 未指定SoVITS模型路径")
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sovits_path = g_config.pretrained_sovits_path
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print(f"[WARN] 未指定SoVITS模型路径, fallback后当前值: {sovits_path}")
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if gpt_path == "":
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gpt_path = g_config.pretrained_gpt_path
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print(f"[WARN] 未指定GPT模型路径, fallback后当前值: {gpt_path}")
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# 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
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if default_refer_path == "" or default_refer_text == "" or default_refer_language == "":
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default_refer_path, default_refer_text, default_refer_language = "", "", ""
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print("[INFO] 未指定默认参考音频")
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@ -84,17 +78,28 @@ else:
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print(f"[INFO] 默认参考音频语种: {default_refer_language}")
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has_preset = True
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is_half = g_config.is_half
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if args.full_precision:
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is_half = False
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if args.half_precision:
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is_half = True
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if args.full_precision and args.half_precision:
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is_half = g_config.is_half # 炒饭fallback
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print(f"[INFO] 半精: {is_half}")
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cnhubert_base_path = args.hubert_path
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bert_path = args.bert_path
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cnhubert.cnhubert_base_path = cnhubert_base_path
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tokenizer = AutoTokenizer.from_pretrained(bert_path)
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bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
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# bert_model = AutoModelForSequenceClassification.from_pretrained(bert_path, config=bert_path+"/config.json")
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if (is_half == True):
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if is_half:
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bert_model = bert_model.half().to(device)
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else:
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bert_model = bert_model.to(device)
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# bert_model=bert_model.to(device)
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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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@ -256,7 +261,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
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def handle(command, refer_wav_path, prompt_text, prompt_language, text, text_language):
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if command == "/restart":
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os.execl(python_exec, python_exec, *sys.argv)
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os.execl(g_config.python_exec, g_config.python_exec, *sys.argv)
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elif command == "/exit":
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os.kill(os.getpid(), signal.SIGTERM)
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exit(0)
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33
config.py
33
config.py
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import sys
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# 推理用的指定模型
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sovits_path = ""
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gpt_path = ""
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is_half = True
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cnhubert_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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bert_path = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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pretrained_sovits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
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pretrained_gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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exp_root = "logs"
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python_exec = sys.executable or "python"
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infer_device = "cuda"
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@ -11,3 +21,26 @@ webui_port_infer_tts = 9872
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webui_port_subfix = 9871
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api_port = 9880
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class Config:
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def __init__(self):
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self.sovits_path = sovits_path
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self.gpt_path = gpt_path
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self.is_half = is_half
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self.cnhubert_path = cnhubert_path
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self.bert_path = bert_path
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self.pretrained_sovits_path = pretrained_sovits_path
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self.pretrained_gpt_path = pretrained_gpt_path
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self.exp_root = exp_root
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self.python_exec = python_exec
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self.infer_device = infer_device
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self.webui_port_main = webui_port_main
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self.webui_port_uvr5 = webui_port_uvr5
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self.webui_port_infer_tts = webui_port_infer_tts
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self.webui_port_subfix = webui_port_subfix
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self.api_port = api_port
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