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为并行推理版本适配v4 (#2307)
* 适配v4版本 * 适配v4版本 * modified: GPT_SoVITS/inference_webui_fast.py * 合并main分支 * fallback config * modified: GPT_SoVITS/TTS_infer_pack/TTS.py * fix bug * modified: GPT_SoVITS/TTS_infer_pack/TTS.py * modified: GPT_SoVITS/inference_webui_fast.py
This commit is contained in:
ChasonJiang
GitHub
parent
bc2fe5ec86
commit
e0f2818df7
@ -25,7 +25,7 @@ from AR.models.t2s_lightning_module import Text2SemanticLightningModule
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from BigVGAN.bigvgan import BigVGAN
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from feature_extractor.cnhubert import CNHubert
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from module.mel_processing import mel_spectrogram_torch, spectrogram_torch
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from module.models import SynthesizerTrn, SynthesizerTrnV3,Generator
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from module.models import SynthesizerTrn, SynthesizerTrnV3, Generator
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from peft import LoraConfig, get_peft_model
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from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new
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from transformers import AutoModelForMaskedLM, AutoTokenizer
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@ -66,6 +66,7 @@ mel_fn = lambda x: mel_spectrogram_torch(
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"center": False,
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},
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)
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mel_fn_v4 = lambda x: mel_spectrogram_torch(
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x,
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**{
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@ -105,7 +106,7 @@ def speed_change(input_audio: np.ndarray, speed: float, sr: int):
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resample_transform_dict = {}
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def resample(audio_tensor, sr0,sr1, device):
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def resample(audio_tensor, sr0, sr1, device):
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global resample_transform_dict
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key="%s-%s"%(sr0,sr1)
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if key not in resample_transform_dict:
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@ -144,6 +145,52 @@ class DictToAttrRecursive(dict):
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class NO_PROMPT_ERROR(Exception):
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pass
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# configs/tts_infer.yaml
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"""
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custom:
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bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
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cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
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device: cpu
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is_half: false
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t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
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vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
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version: v2
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v1:
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bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
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cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
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device: cpu
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is_half: false
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t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
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vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
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version: v1
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v2:
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bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
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cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
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device: cpu
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is_half: false
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t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
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vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
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version: v2
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v3:
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bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
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cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
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device: cpu
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is_half: false
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t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
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vits_weights_path: GPT_SoVITS/pretrained_models/s2Gv3.pth
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version: v3
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v4:
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bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
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cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
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device: cpu
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is_half: false
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t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
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version: v4
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vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth
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"""
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def set_seed(seed: int):
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seed = int(seed)
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seed = seed if seed != -1 else random.randint(0, 2**32 - 1)
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@ -201,10 +248,11 @@ class TTS_Config:
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"is_half": False,
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"version": "v4",
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"t2s_weights_path": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
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"vits_weights_path": "GPT_SoVITS/pretrained_models/s2Gv3.pth",
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"vits_weights_path": "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
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"cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base",
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"bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large",
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},
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}
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configs: dict = None
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v1_languages: list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"]
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@ -261,7 +309,7 @@ class TTS_Config:
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self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path", None)
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self.languages = self.v1_languages if self.version == "v1" else self.v2_languages
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self.is_v3_synthesizer: bool = False
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self.use_vocoder: bool = False
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if (self.t2s_weights_path in [None, ""]) or (not os.path.exists(self.t2s_weights_path)):
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self.t2s_weights_path = self.default_configs[version]["t2s_weights_path"]
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@ -341,7 +389,7 @@ class TTS_Config:
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def __eq__(self, other):
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return isinstance(other, TTS_Config) and self.configs_path == other.configs_path
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from inference_webui import v3v4set
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class TTS:
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def __init__(self, configs: Union[dict, str, TTS_Config]):
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if isinstance(configs, TTS_Config):
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@ -354,10 +402,18 @@ class TTS:
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self.bert_tokenizer: AutoTokenizer = None
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self.bert_model: AutoModelForMaskedLM = None
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self.cnhuhbert_model: CNHubert = None
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self.vocoder_model = None
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self.vocoder = None
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self.sr_model: AP_BWE = None
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self.sr_model_not_exist: bool = False
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self.vocoder_configs: dict = {
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"sr": None,
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"T_ref": None,
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"T_chunk": None,
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"upsample_rate": None,
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"overlapped_len": None,
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}
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self._init_models()
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self.text_preprocessor: TextPreprocessor = TextPreprocessor(
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@ -376,6 +432,7 @@ class TTS:
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"aux_ref_audio_paths": [],
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}
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self.stop_flag: bool = False
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self.precision: torch.dtype = torch.float16 if self.configs.is_half else torch.float32
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@ -408,7 +465,6 @@ class TTS:
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def init_vits_weights(self, weights_path: str):
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self.configs.vits_weights_path = weights_path
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version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(weights_path)
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print(self.configs.default_configs)
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path_sovits = self.configs.default_configs[model_version]["vits_weights_path"]
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if if_lora_v3 == True and os.path.exists(path_sovits) == False:
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@ -442,23 +498,23 @@ class TTS:
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# print(f"model_version:{model_version}")
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# print(f'hps["model"]["version"]:{hps["model"]["version"]}')
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if model_version not in v3v4set:
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if model_version not in ["v3", "v4"]:
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vits_model = SynthesizerTrn(
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self.configs.filter_length // 2 + 1,
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self.configs.segment_size // self.configs.hop_length,
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n_speakers=self.configs.n_speakers,
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**kwargs,
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)
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self.configs.is_v3_synthesizer = False
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self.configs.use_vocoder = False
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else:
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self.configs.is_v3_synthesizer = kwargs["version"]=model_version
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vits_model = SynthesizerTrnV3(
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self.configs.filter_length // 2 + 1,
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self.configs.segment_size // self.configs.hop_length,
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n_speakers=self.configs.n_speakers,
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**kwargs,
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)
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self.init_vocoder()
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self.configs.use_vocoder = True
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self.init_vocoder(model_version)
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if "pretrained" not in weights_path and hasattr(vits_model, "enc_q"):
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del vits_model.enc_q
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@ -507,36 +563,64 @@ class TTS:
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if self.configs.is_half and str(self.configs.device) != "cpu":
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self.t2s_model = self.t2s_model.half()
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def init_vocoder(self):
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if self.vocoder_model is not None:
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return
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if self.configs.is_v3_synthesizer=="v3":
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self.vocoder_model = BigVGAN.from_pretrained(
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def init_vocoder(self, version: str):
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if version == "v3":
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if self.vocoder is not None and self.vocoder.__class__.__name__ == "BigVGAN":
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return
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if self.vocoder is not None:
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self.vocoder.cpu()
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del self.vocoder
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self.empty_cache()
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self.vocoder = BigVGAN.from_pretrained(
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"%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,),
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use_cuda_kernel=False,
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) # if True, RuntimeError: Ninja is required to load C++ extensions
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# remove weight norm in the model and set to eval mode
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self.vocoder_model.remove_weight_norm()
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self.vocoder_model = self.vocoder_model.eval()
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else:
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self.vocoder_model = Generator(
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initial_channel=100,
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resblock="1",
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resblock_kernel_sizes=[3, 7, 11],
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resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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upsample_rates=[10, 6, 2, 2, 2],
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upsample_initial_channel=512,
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upsample_kernel_sizes=[20, 12, 4, 4, 4],
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gin_channels=0, is_bias=True
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)
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self.vocoder_model.eval()
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self.vocoder_model.remove_weight_norm()
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self.vocoder.remove_weight_norm()
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self.vocoder_configs["sr"] = 24000
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self.vocoder_configs["T_ref"] = 468
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self.vocoder_configs["T_chunk"] = 934
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self.vocoder_configs["upsample_rate"] = 256
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self.vocoder_configs["overlapped_len"] = 12
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elif version == "v4":
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if self.vocoder is not None and self.vocoder.__class__.__name__ == "Generator":
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return
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if self.vocoder is not None:
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self.vocoder.cpu()
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del self.vocoder
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self.empty_cache()
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self.vocoder = Generator(
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initial_channel=100,
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resblock="1",
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resblock_kernel_sizes=[3, 7, 11],
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resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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upsample_rates=[10, 6, 2, 2, 2],
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upsample_initial_channel=512,
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upsample_kernel_sizes=[20, 12, 4, 4, 4],
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gin_channels=0, is_bias=True
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)
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self.vocoder.remove_weight_norm()
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state_dict_g = torch.load("%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu")
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print("loading v4 vocoder", self.vocoder_model.load_state_dict(state_dict_g))
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print("loading vocoder",self.vocoder.load_state_dict(state_dict_g))
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self.vocoder_configs["sr"] = 48000
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self.vocoder_configs["T_ref"] = 500
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self.vocoder_configs["T_chunk"] = 1000
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self.vocoder_configs["upsample_rate"] = 480
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self.vocoder_configs["overlapped_len"] = 12
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self.vocoder = self.vocoder.eval()
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if self.configs.is_half == True:
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self.vocoder_model = self.vocoder_model.half().to(self.configs.device)
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self.vocoder = self.vocoder.half().to(self.configs.device)
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else:
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self.vocoder_model = self.vocoder_model.to(self.configs.device)
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self.vocoder = self.vocoder.to(self.configs.device)
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def init_sr_model(self):
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if self.sr_model is not None:
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@ -572,8 +656,8 @@ class TTS:
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self.bert_model = self.bert_model.half()
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if self.cnhuhbert_model is not None:
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self.cnhuhbert_model = self.cnhuhbert_model.half()
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if self.vocoder_model is not None:
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self.vocoder_model = self.vocoder_model.half()
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if self.vocoder is not None:
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self.vocoder = self.vocoder.half()
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else:
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if self.t2s_model is not None:
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self.t2s_model = self.t2s_model.float()
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@ -583,8 +667,8 @@ class TTS:
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self.bert_model = self.bert_model.float()
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if self.cnhuhbert_model is not None:
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self.cnhuhbert_model = self.cnhuhbert_model.float()
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if self.vocoder_model is not None:
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self.vocoder_model = self.vocoder_model.float()
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if self.vocoder is not None:
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self.vocoder = self.vocoder.float()
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def set_device(self, device: torch.device, save: bool = True):
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"""
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@ -603,8 +687,8 @@ class TTS:
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self.bert_model = self.bert_model.to(device)
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if self.cnhuhbert_model is not None:
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self.cnhuhbert_model = self.cnhuhbert_model.to(device)
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if self.vocoder_model is not None:
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self.vocoder_model = self.vocoder_model.to(device)
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if self.vocoder is not None:
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self.vocoder = self.vocoder.to(device)
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if self.sr_model is not None:
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self.sr_model = self.sr_model.to(device)
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@ -915,13 +999,13 @@ class TTS:
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split_bucket = False
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print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理"))
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if split_bucket and speed_factor == 1.0 and not (self.configs.is_v3_synthesizer!=False and parallel_infer):
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if split_bucket and speed_factor == 1.0 and not (self.configs.use_vocoder and parallel_infer):
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print(i18n("分桶处理模式已开启"))
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elif speed_factor != 1.0:
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print(i18n("语速调节不支持分桶处理，已自动关闭分桶处理"))
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split_bucket = False
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elif self.configs.is_v3_synthesizer!=False and parallel_infer:
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print(i18n("当开启并行推理模式时，SoVits V3V4模型不支持分桶处理，已自动关闭分桶处理"))
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elif self.configs.use_vocoder and parallel_infer:
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print(i18n("当开启并行推理模式时，SoVits V3/4模型不支持分桶处理，已自动关闭分桶处理"))
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split_bucket = False
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else:
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print(i18n("分桶处理模式已关闭"))
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@ -938,7 +1022,7 @@ class TTS:
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if not no_prompt_text:
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assert prompt_lang in self.configs.languages
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if no_prompt_text and self.configs.is_v3_synthesizer!=False:
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if no_prompt_text and self.configs.use_vocoder:
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raise NO_PROMPT_ERROR("prompt_text cannot be empty when using SoVITS_V3")
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if ref_audio_path in [None, ""] and (
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@ -1046,12 +1130,7 @@ class TTS:
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t_34 = 0.0
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t_45 = 0.0
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audio = []
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if self.configs.is_v3_synthesizer==False:
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output_sr = 32000
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elif self.configs.is_v3_synthesizer == "v3":
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output_sr = 24000
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else:
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output_sr = 48000 # v4
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output_sr = self.configs.sampling_rate if not self.configs.use_vocoder else self.vocoder_configs["sr"]
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for item in data:
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t3 = time.perf_counter()
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if return_fragment:
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@ -1113,7 +1192,7 @@ class TTS:
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# pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec
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# ))
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print(f"############ {i18n('合成音频')} ############")
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if not self.configs.is_v3_synthesizer:
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if not self.configs.use_vocoder:
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if speed_factor == 1.0:
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print(f"{i18n('并行合成中')}...")
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# ## vits并行推理 method 2
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@ -1150,8 +1229,8 @@ class TTS:
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else:
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if parallel_infer:
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print(f"{i18n('并行合成中')}...")
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audio_fragments = self.v3_synthesis_batched_infer(
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idx_list, pred_semantic_list, batch_phones, speed=speed_factor, sample_steps=sample_steps,model_version=self.configs.is_v3_synthesizer
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audio_fragments = self.using_vocoder_synthesis_batched_infer(
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idx_list, pred_semantic_list, batch_phones, speed=speed_factor, sample_steps=sample_steps
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)
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batch_audio_fragment.extend(audio_fragments)
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else:
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@ -1160,8 +1239,8 @@ class TTS:
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_pred_semantic = (
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pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)
|
||||
) # .unsqueeze(0)#mq要多unsqueeze一次
|
||||
audio_fragment = self.v3_synthesis(
|
||||
_pred_semantic, phones, speed=speed_factor, sample_steps=sample_steps,model_version=self.configs.is_v3_synthesizer
|
||||
audio_fragment = self.using_vocoder_synthesis(
|
||||
_pred_semantic, phones, speed=speed_factor, sample_steps=sample_steps
|
||||
)
|
||||
batch_audio_fragment.append(audio_fragment)
|
||||
|
||||
@ -1176,7 +1255,7 @@ class TTS:
|
||||
speed_factor,
|
||||
False,
|
||||
fragment_interval,
|
||||
super_sampling if self.configs.is_v3_synthesizer=="v3" else False,
|
||||
super_sampling if self.configs.use_vocoder and self.configs.version == "v3" else False,
|
||||
)
|
||||
else:
|
||||
audio.append(batch_audio_fragment)
|
||||
@ -1197,7 +1276,7 @@ class TTS:
|
||||
speed_factor,
|
||||
split_bucket,
|
||||
fragment_interval,
|
||||
super_sampling if self.configs.is_v3_synthesizer=="v3" else False,
|
||||
super_sampling if self.configs.use_vocoder and self.configs.version == "v3" else False,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
@ -1279,8 +1358,8 @@ class TTS:
|
||||
|
||||
return sr, audio
|
||||
|
||||
def v3_synthesis(
|
||||
self, semantic_tokens: torch.Tensor, phones: torch.Tensor, speed: float = 1.0, sample_steps: int = 32,model_version="v4"
|
||||
def using_vocoder_synthesis(
|
||||
self, semantic_tokens: torch.Tensor, phones: torch.Tensor, speed: float = 1.0, sample_steps: int = 32
|
||||
):
|
||||
prompt_semantic_tokens = self.prompt_cache["prompt_semantic"].unsqueeze(0).unsqueeze(0).to(self.configs.device)
|
||||
prompt_phones = torch.LongTensor(self.prompt_cache["phones"]).unsqueeze(0).to(self.configs.device)
|
||||
@ -1292,22 +1371,24 @@ class TTS:
|
||||
ref_audio = ref_audio.to(self.configs.device).float()
|
||||
if ref_audio.shape[0] == 2:
|
||||
ref_audio = ref_audio.mean(0).unsqueeze(0)
|
||||
tgt_sr = 24000 if model_version == "v3" else 32000
|
||||
if ref_sr != tgt_sr:
|
||||
ref_audio = resample(ref_audio, ref_sr,tgt_sr, self.configs.device)
|
||||
|
||||
mel2 = mel_fn(ref_audio) if model_version == "v3" else mel_fn_v4(ref_audio)
|
||||
# tgt_sr = self.vocoder_configs["sr"]
|
||||
tgt_sr = 24000 if self.configs.version == "v3" else 32000
|
||||
if ref_sr != tgt_sr:
|
||||
ref_audio = resample(ref_audio, ref_sr, tgt_sr, self.configs.device)
|
||||
|
||||
mel2 = mel_fn(ref_audio) if self.configs.version == "v3" else mel_fn_v4(ref_audio)
|
||||
mel2 = norm_spec(mel2)
|
||||
T_min = min(mel2.shape[2], fea_ref.shape[2])
|
||||
mel2 = mel2[:, :, :T_min]
|
||||
fea_ref = fea_ref[:, :, :T_min]
|
||||
Tref = 468 if model_version == "v3" else 500
|
||||
Tchunk = 934 if model_version == "v3" else 1000
|
||||
if T_min > Tref:
|
||||
mel2 = mel2[:, :, -Tref:]
|
||||
fea_ref = fea_ref[:, :, -Tref:]
|
||||
T_min = Tref
|
||||
chunk_len = Tchunk - T_min
|
||||
T_ref = self.vocoder_configs["T_ref"]
|
||||
T_chunk = self.vocoder_configs["T_chunk"]
|
||||
if T_min > T_ref:
|
||||
mel2 = mel2[:, :, -T_ref:]
|
||||
fea_ref = fea_ref[:, :, -T_ref:]
|
||||
T_min = T_ref
|
||||
chunk_len = T_chunk - T_min
|
||||
|
||||
mel2 = mel2.to(self.precision)
|
||||
fea_todo, ge = self.vits_model.decode_encp(semantic_tokens, phones, refer_audio_spec, ge, speed)
|
||||
@ -1334,18 +1415,18 @@ class TTS:
|
||||
cfm_res = denorm_spec(cfm_res)
|
||||
|
||||
with torch.inference_mode():
|
||||
wav_gen = self.vocoder_model(cfm_res)
|
||||
wav_gen = self.vocoder(cfm_res)
|
||||
audio = wav_gen[0][0] # .cpu().detach().numpy()
|
||||
|
||||
return audio
|
||||
|
||||
def v3_synthesis_batched_infer(
|
||||
def using_vocoder_synthesis_batched_infer(
|
||||
self,
|
||||
idx_list: List[int],
|
||||
semantic_tokens_list: List[torch.Tensor],
|
||||
batch_phones: List[torch.Tensor],
|
||||
speed: float = 1.0,
|
||||
sample_steps: int = 32,model_version="v4"
|
||||
sample_steps: int = 32,
|
||||
) -> List[torch.Tensor]:
|
||||
prompt_semantic_tokens = self.prompt_cache["prompt_semantic"].unsqueeze(0).unsqueeze(0).to(self.configs.device)
|
||||
prompt_phones = torch.LongTensor(self.prompt_cache["phones"]).unsqueeze(0).to(self.configs.device)
|
||||
@ -1357,27 +1438,29 @@ class TTS:
|
||||
ref_audio = ref_audio.to(self.configs.device).float()
|
||||
if ref_audio.shape[0] == 2:
|
||||
ref_audio = ref_audio.mean(0).unsqueeze(0)
|
||||
tgt_sr = 24000 if model_version == "v3" else 32000
|
||||
|
||||
# tgt_sr = self.vocoder_configs["sr"]
|
||||
tgt_sr = 24000 if self.configs.version == "v3" else 32000
|
||||
if ref_sr != tgt_sr:
|
||||
ref_audio = resample(ref_audio, ref_sr,tgt_sr, self.configs.device)
|
||||
ref_audio = resample(ref_audio, ref_sr, tgt_sr, self.configs.device)
|
||||
|
||||
mel2 = mel_fn(ref_audio) if model_version == "v3" else mel_fn_v4(ref_audio)
|
||||
mel2 = mel_fn(ref_audio) if self.configs.version == "v3" else mel_fn_v4(ref_audio)
|
||||
mel2 = norm_spec(mel2)
|
||||
T_min = min(mel2.shape[2], fea_ref.shape[2])
|
||||
mel2 = mel2[:, :, :T_min]
|
||||
fea_ref = fea_ref[:, :, :T_min]
|
||||
Tref = 468 if model_version == "v3" else 500
|
||||
Tchunk = 934 if model_version == "v3" else 1000
|
||||
if T_min > Tref:
|
||||
mel2 = mel2[:, :, -Tref:]
|
||||
fea_ref = fea_ref[:, :, -Tref:]
|
||||
T_min = Tref
|
||||
chunk_len = Tchunk - T_min
|
||||
T_ref = self.vocoder_configs["T_ref"]
|
||||
T_chunk = self.vocoder_configs["T_chunk"]
|
||||
if T_min > T_ref:
|
||||
mel2 = mel2[:, :, -T_ref:]
|
||||
fea_ref = fea_ref[:, :, -T_ref:]
|
||||
T_min = T_ref
|
||||
chunk_len = T_chunk - T_min
|
||||
|
||||
mel2 = mel2.to(self.precision)
|
||||
|
||||
# #### batched inference
|
||||
overlapped_len = 12
|
||||
overlapped_len = self.vocoder_configs["overlapped_len"]
|
||||
feat_chunks = []
|
||||
feat_lens = []
|
||||
feat_list = []
|
||||
@ -1426,11 +1509,11 @@ class TTS:
|
||||
pred_spec = denorm_spec(pred_spec)
|
||||
|
||||
with torch.no_grad():
|
||||
wav_gen = self.vocoder_model(pred_spec)
|
||||
wav_gen = self.vocoder(pred_spec)
|
||||
audio = wav_gen[0][0] # .cpu().detach().numpy()
|
||||
|
||||
audio_fragments = []
|
||||
upsample_rate = 256
|
||||
upsample_rate = self.vocoder_configs["upsample_rate"]
|
||||
pos = 0
|
||||
|
||||
while pos < audio.shape[-1]:
|
||||
|
||||
@ -1,40 +1,40 @@
|
||||
custom:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cuda
|
||||
is_half: true
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
|
||||
version: v4
|
||||
vits_weights_path: SoVITS_weights_v4/diangun1min_e2_s66_l32.pth
|
||||
v1:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cpu
|
||||
is_half: false
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
|
||||
version: v1
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
|
||||
v2:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cpu
|
||||
is_half: false
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
|
||||
version: v2
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
|
||||
v3:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cpu
|
||||
is_half: false
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
|
||||
version: v3
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/s2Gv3.pth
|
||||
v4:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cpu
|
||||
is_half: false
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
|
||||
version: v4
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/s2Gv3.pth
|
||||
custom:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cuda
|
||||
is_half: true
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
|
||||
version: v2
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
|
||||
v1:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cpu
|
||||
is_half: false
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt
|
||||
version: v1
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth
|
||||
v2:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cpu
|
||||
is_half: false
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt
|
||||
version: v2
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth
|
||||
v3:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cpu
|
||||
is_half: false
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
|
||||
version: v3
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/s2Gv3.pth
|
||||
v4:
|
||||
bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large
|
||||
cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base
|
||||
device: cpu
|
||||
is_half: false
|
||||
t2s_weights_path: GPT_SoVITS/pretrained_models/s1v3.ckpt
|
||||
version: v4
|
||||
vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth
|
||||
|
||||
@ -211,6 +211,7 @@ pretrained_gpt_name = [
|
||||
"GPT_SoVITS/pretrained_models/s1v3.ckpt",
|
||||
]
|
||||
|
||||
|
||||
_ = [[], []]
|
||||
for i in range(4):
|
||||
if os.path.exists(pretrained_gpt_name[i]):
|
||||
@ -219,7 +220,6 @@ for i in range(4):
|
||||
_[-1].append(pretrained_sovits_name[i])
|
||||
pretrained_gpt_name, pretrained_sovits_name = _
|
||||
|
||||
|
||||
if os.path.exists("./weight.json"):
|
||||
pass
|
||||
else:
|
||||
@ -237,8 +237,8 @@ with open("./weight.json", "r", encoding="utf-8") as file:
|
||||
sovits_path = sovits_path[0]
|
||||
|
||||
|
||||
SoVITS_weight_root = ["SoVITS_weights", "SoVITS_weights_v2", "SoVITS_weights_v3"]
|
||||
GPT_weight_root = ["GPT_weights", "GPT_weights_v2", "GPT_weights_v3"]
|
||||
SoVITS_weight_root = ["SoVITS_weights", "SoVITS_weights_v2", "SoVITS_weights_v3", "SoVITS_weights_v4"]
|
||||
GPT_weight_root = ["GPT_weights", "GPT_weights_v2", "GPT_weights_v3", "GPT_weights_v4"]
|
||||
for path in SoVITS_weight_root + GPT_weight_root:
|
||||
os.makedirs(path, exist_ok=True)
|
||||
|
||||
@ -294,7 +294,6 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None)
|
||||
else:
|
||||
visible_sample_steps = False
|
||||
visible_inp_refs = True
|
||||
# prompt_language,text_language,prompt_text,prompt_language,text,text_language,inp_refs,ref_text_free,
|
||||
yield (
|
||||
{"__type__": "update", "choices": list(dict_language.keys())},
|
||||
{"__type__": "update", "choices": list(dict_language.keys())},
|
||||
@ -399,7 +398,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
|
||||
minimum=1, maximum=200, step=1, label=i18n("batch_size"), value=20, interactive=True
|
||||
)
|
||||
sample_steps = gr.Radio(
|
||||
label=i18n("采样步数(仅对V3生效)"), value=32, choices=[4, 8, 16, 32], visible=True
|
||||
label=i18n("采样步数(仅对V3/4生效)"), value=32, choices=[4, 8, 16, 32, 64, 128], visible=True
|
||||
)
|
||||
with gr.Row():
|
||||
fragment_interval = gr.Slider(
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user