From fe531567f1293c1b1048e39dbc46985923be87a9 Mon Sep 17 00:00:00 2001 From: Jarod Mica Date: Fri, 23 May 2025 02:48:09 -0700 Subject: [PATCH] Fix package pathing issues --- GPT_SoVITS/TTS_infer_pack/TTS.py | 30 ++++++++++++++-------- GPT_SoVITS/f5_tts/model/backbones/dit.py | 2 +- GPT_SoVITS/f5_tts/model/backbones/mmdit.py | 2 +- GPT_SoVITS/f5_tts/model/backbones/unett.py | 2 +- GPT_SoVITS/module/models.py | 2 +- GPT_SoVITS/text/english.py | 2 +- 6 files changed, 25 insertions(+), 15 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index aff60cee..53f392b0 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -445,8 +445,9 @@ class TTS: def _init_models( self, ): - self.init_t2s_weights(self.configs.t2s_weights_path) - self.init_vits_weights(self.configs.vits_weights_path) + # Don't auto load the weights, load them independently not on instantiation + # self.init_t2s_weights(self.configs.t2s_weights_path) + # self.init_vits_weights(self.configs.vits_weights_path) self.init_bert_weights(self.configs.bert_base_path) self.init_cnhuhbert_weights(self.configs.cnhuhbert_base_path) # self.enable_half_precision(self.configs.is_half) @@ -468,10 +469,11 @@ class TTS: if self.configs.is_half and str(self.configs.device) != "cpu": self.bert_model = self.bert_model.half() - def init_vits_weights(self, weights_path: str): + def init_vits_weights(self, weights_path: str, vocoder_path: str = None): self.configs.vits_weights_path = weights_path version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(weights_path) path_sovits = self.configs.default_configs[model_version]["vits_weights_path"] + print(if_lora_v3) if if_lora_v3 == True and os.path.exists(path_sovits) == False: info = path_sovits + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重"%model_version) @@ -502,9 +504,10 @@ class TTS: self.configs.update_version(model_version) - # print(f"model_version:{model_version}") + print(f"model_version:{model_version}") # print(f'hps["model"]["version"]:{hps["model"]["version"]}') if model_version not in {"v3", "v4"}: + print("Using v2 model") vits_model = SynthesizerTrn( self.configs.filter_length // 2 + 1, self.configs.segment_size // self.configs.hop_length, @@ -513,6 +516,7 @@ class TTS: ) self.configs.use_vocoder = False else: + print("Using v3 model") kwargs["version"]=model_version vits_model = SynthesizerTrnV3( self.configs.filter_length // 2 + 1, @@ -520,8 +524,9 @@ class TTS: n_speakers=self.configs.n_speakers, **kwargs, ) + self.configs.use_vocoder = True - self.init_vocoder(model_version) + self.init_vocoder(model_version, vocoder_path) if "pretrained" not in weights_path and hasattr(vits_model, "enc_q"): del vits_model.enc_q @@ -570,7 +575,7 @@ class TTS: if self.configs.is_half and str(self.configs.device) != "cpu": self.t2s_model = self.t2s_model.half() - def init_vocoder(self, version: str): + def init_vocoder(self, version: str, vocoder_path: str = None): if version == "v3": if self.vocoder is not None and self.vocoder.__class__.__name__ == "BigVGAN": return @@ -578,7 +583,7 @@ class TTS: self.vocoder.cpu() del self.vocoder self.empty_cache() - + self.vocoder = BigVGAN.from_pretrained( "%s/GPT_SoVITS/pretrained_models/models--nvidia--bigvgan_v2_24khz_100band_256x" % (now_dir,), use_cuda_kernel=False, @@ -611,8 +616,13 @@ class TTS: gin_channels=0, is_bias=True ) self.vocoder.remove_weight_norm() - state_dict_g = torch.load("%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu") - print("loading vocoder",self.vocoder.load_state_dict(state_dict_g)) + print("loading vocoder",vocoder_path) + try: + state_dict_g = torch.load(vocoder_path, map_location="cpu") + self.vocoder.load_state_dict(state_dict_g) + except: + state_dict_g = torch.load("%s/GPT_SoVITS/pretrained_models/gsv-v4-pretrained/vocoder.pth" % (now_dir,), map_location="cpu") + print("loading vocoder",self.vocoder.load_state_dict(state_dict_g)) self.vocoder_configs["sr"] = 48000 self.vocoder_configs["T_ref"] = 500 @@ -988,7 +998,7 @@ class TTS: seed = inputs.get("seed", -1) seed = -1 if seed in ["", None] else seed actual_seed = set_seed(seed) - parallel_infer = inputs.get("parallel_infer", True) + parallel_infer = inputs.get("parallel_infer", False) repetition_penalty = inputs.get("repetition_penalty", 1.35) sample_steps = inputs.get("sample_steps", 32) super_sampling = inputs.get("super_sampling", False) diff --git a/GPT_SoVITS/f5_tts/model/backbones/dit.py b/GPT_SoVITS/f5_tts/model/backbones/dit.py index 7d98a858..105578ed 100644 --- a/GPT_SoVITS/f5_tts/model/backbones/dit.py +++ b/GPT_SoVITS/f5_tts/model/backbones/dit.py @@ -25,7 +25,7 @@ from GPT_SoVITS.f5_tts.model.modules import ( get_pos_embed_indices, ) -from module.commons import sequence_mask +from GPT_SoVITS.module.commons import sequence_mask class TextEmbedding(nn.Module): diff --git a/GPT_SoVITS/f5_tts/model/backbones/mmdit.py b/GPT_SoVITS/f5_tts/model/backbones/mmdit.py index 64c7ef18..cbb3fe39 100644 --- a/GPT_SoVITS/f5_tts/model/backbones/mmdit.py +++ b/GPT_SoVITS/f5_tts/model/backbones/mmdit.py @@ -14,7 +14,7 @@ from torch import nn from x_transformers.x_transformers import RotaryEmbedding -from f5_tts.model.modules import ( +from GPT_SoVITS.f5_tts.model.modules import ( TimestepEmbedding, ConvPositionEmbedding, MMDiTBlock, diff --git a/GPT_SoVITS/f5_tts/model/backbones/unett.py b/GPT_SoVITS/f5_tts/model/backbones/unett.py index acf649a5..81ddd435 100644 --- a/GPT_SoVITS/f5_tts/model/backbones/unett.py +++ b/GPT_SoVITS/f5_tts/model/backbones/unett.py @@ -17,7 +17,7 @@ import torch.nn.functional as F from x_transformers import RMSNorm from x_transformers.x_transformers import RotaryEmbedding -from f5_tts.model.modules import ( +from GPT_SoVITS.f5_tts.model.modules import ( TimestepEmbedding, ConvNeXtV2Block, ConvPositionEmbedding, diff --git a/GPT_SoVITS/module/models.py b/GPT_SoVITS/module/models.py index c5432287..a2e28864 100644 --- a/GPT_SoVITS/module/models.py +++ b/GPT_SoVITS/module/models.py @@ -10,7 +10,7 @@ from torch.nn import functional as F from GPT_SoVITS.module import commons from GPT_SoVITS.module import modules from GPT_SoVITS.module import attentions -from f5_tts.model import DiT +from GPT_SoVITS.f5_tts.model import DiT from torch.nn import Conv1d, ConvTranspose1d, Conv2d from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm from GPT_SoVITS.module.commons import init_weights, get_padding diff --git a/GPT_SoVITS/text/english.py b/GPT_SoVITS/text/english.py index 1ab530a1..879b251d 100644 --- a/GPT_SoVITS/text/english.py +++ b/GPT_SoVITS/text/english.py @@ -9,7 +9,7 @@ from GPT_SoVITS.text.symbols import punctuation from GPT_SoVITS.text.symbols2 import symbols from builtins import str as unicode -from text.en_normalization.expend import normalize +from GPT_SoVITS.text.en_normalization.expend import normalize from nltk.tokenize import TweetTokenizer word_tokenize = TweetTokenizer().tokenize