From e0f2818df76fdda9bcd3db9963d8f3408e76397c Mon Sep 17 00:00:00 2001 From: ChasonJiang <46401978+ChasonJiang@users.noreply.github.com> Date: Mon, 21 Apr 2025 23:20:20 +0800 Subject: [PATCH] =?UTF-8?q?=E4=B8=BA=E5=B9=B6=E8=A1=8C=E6=8E=A8=E7=90=86?= =?UTF-8?q?=E7=89=88=E6=9C=AC=E9=80=82=E9=85=8Dv4=20(#2307)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 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 --- GPT_SoVITS/TTS_infer_pack/TTS.py | 255 +++++++++++++++++++---------- GPT_SoVITS/configs/tts_infer.yaml | 80 ++++----- GPT_SoVITS/inference_webui_fast.py | 9 +- 3 files changed, 213 insertions(+), 131 deletions(-) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index f49970c..736daf9 100644 --- a/GPT_SoVITS/TTS_infer_pack/TTS.py +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -25,7 +25,7 @@ from AR.models.t2s_lightning_module import Text2SemanticLightningModule from BigVGAN.bigvgan import BigVGAN from feature_extractor.cnhubert import CNHubert from module.mel_processing import mel_spectrogram_torch, spectrogram_torch -from module.models import SynthesizerTrn, SynthesizerTrnV3,Generator +from module.models import SynthesizerTrn, SynthesizerTrnV3, Generator from peft import LoraConfig, get_peft_model from process_ckpt import get_sovits_version_from_path_fast, load_sovits_new from transformers import AutoModelForMaskedLM, AutoTokenizer @@ -66,6 +66,7 @@ mel_fn = lambda x: mel_spectrogram_torch( "center": False, }, ) + mel_fn_v4 = lambda x: mel_spectrogram_torch( x, **{ @@ -105,7 +106,7 @@ def speed_change(input_audio: np.ndarray, speed: float, sr: int): resample_transform_dict = {} -def resample(audio_tensor, sr0,sr1, device): +def resample(audio_tensor, sr0, sr1, device): global resample_transform_dict key="%s-%s"%(sr0,sr1) if key not in resample_transform_dict: @@ -144,6 +145,52 @@ class DictToAttrRecursive(dict): class NO_PROMPT_ERROR(Exception): pass + +# configs/tts_infer.yaml +""" +custom: + 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 + vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth + version: v2 +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 + vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth + version: v1 +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 + vits_weights_path: GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth + version: v2 +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 + vits_weights_path: GPT_SoVITS/pretrained_models/s2Gv3.pth + version: v3 +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 +""" + + def set_seed(seed: int): seed = int(seed) seed = seed if seed != -1 else random.randint(0, 2**32 - 1) @@ -201,10 +248,11 @@ class TTS_Config: "is_half": False, "version": "v4", "t2s_weights_path": "GPT_SoVITS/pretrained_models/s1v3.ckpt", - "vits_weights_path": "GPT_SoVITS/pretrained_models/s2Gv3.pth", + "vits_weights_path": "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth", "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base", "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", }, + } configs: dict = None v1_languages: list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"] @@ -261,7 +309,7 @@ class TTS_Config: self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path", None) self.languages = self.v1_languages if self.version == "v1" else self.v2_languages - self.is_v3_synthesizer: bool = False + self.use_vocoder: bool = False if (self.t2s_weights_path in [None, ""]) or (not os.path.exists(self.t2s_weights_path)): self.t2s_weights_path = self.default_configs[version]["t2s_weights_path"] @@ -341,7 +389,7 @@ class TTS_Config: def __eq__(self, other): return isinstance(other, TTS_Config) and self.configs_path == other.configs_path -from inference_webui import v3v4set + class TTS: def __init__(self, configs: Union[dict, str, TTS_Config]): if isinstance(configs, TTS_Config): @@ -354,10 +402,18 @@ class TTS: self.bert_tokenizer: AutoTokenizer = None self.bert_model: AutoModelForMaskedLM = None self.cnhuhbert_model: CNHubert = None - self.vocoder_model = None + self.vocoder = None self.sr_model: AP_BWE = None self.sr_model_not_exist: bool = False + self.vocoder_configs: dict = { + "sr": None, + "T_ref": None, + "T_chunk": None, + "upsample_rate": None, + "overlapped_len": None, + } + self._init_models() self.text_preprocessor: TextPreprocessor = TextPreprocessor( @@ -376,6 +432,7 @@ class TTS: "aux_ref_audio_paths": [], } + self.stop_flag: bool = False self.precision: torch.dtype = torch.float16 if self.configs.is_half else torch.float32 @@ -408,7 +465,6 @@ class TTS: def init_vits_weights(self, weights_path: str): self.configs.vits_weights_path = weights_path version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(weights_path) - print(self.configs.default_configs) path_sovits = self.configs.default_configs[model_version]["vits_weights_path"] if if_lora_v3 == True and os.path.exists(path_sovits) == False: @@ -442,23 +498,23 @@ class TTS: # print(f"model_version:{model_version}") # print(f'hps["model"]["version"]:{hps["model"]["version"]}') - if model_version not in v3v4set: + if model_version not in ["v3", "v4"]: vits_model = SynthesizerTrn( self.configs.filter_length // 2 + 1, self.configs.segment_size // self.configs.hop_length, n_speakers=self.configs.n_speakers, **kwargs, ) - self.configs.is_v3_synthesizer = False + self.configs.use_vocoder = False else: - self.configs.is_v3_synthesizer = kwargs["version"]=model_version vits_model = SynthesizerTrnV3( self.configs.filter_length // 2 + 1, self.configs.segment_size // self.configs.hop_length, n_speakers=self.configs.n_speakers, **kwargs, ) - self.init_vocoder() + self.configs.use_vocoder = True + self.init_vocoder(model_version) if "pretrained" not in weights_path and hasattr(vits_model, "enc_q"): del vits_model.enc_q @@ -507,36 +563,64 @@ class TTS: if self.configs.is_half and str(self.configs.device) != "cpu": self.t2s_model = self.t2s_model.half() - def init_vocoder(self): - if self.vocoder_model is not None: - return - if self.configs.is_v3_synthesizer=="v3": - self.vocoder_model = BigVGAN.from_pretrained( + def init_vocoder(self, version: str): + if version == "v3": + if self.vocoder is not None and self.vocoder.__class__.__name__ == "BigVGAN": + return + if self.vocoder is not None: + 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, ) # if True, RuntimeError: Ninja is required to load C++ extensions # remove weight norm in the model and set to eval mode - self.vocoder_model.remove_weight_norm() - self.vocoder_model = self.vocoder_model.eval() - else: - self.vocoder_model = Generator( - initial_channel=100, - resblock="1", - resblock_kernel_sizes=[3, 7, 11], - resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]], - upsample_rates=[10, 6, 2, 2, 2], - upsample_initial_channel=512, - upsample_kernel_sizes=[20, 12, 4, 4, 4], - gin_channels=0, is_bias=True - ) - self.vocoder_model.eval() - self.vocoder_model.remove_weight_norm() + self.vocoder.remove_weight_norm() + + self.vocoder_configs["sr"] = 24000 + self.vocoder_configs["T_ref"] = 468 + self.vocoder_configs["T_chunk"] = 934 + self.vocoder_configs["upsample_rate"] = 256 + self.vocoder_configs["overlapped_len"] = 12 + + elif version == "v4": + if self.vocoder is not None and self.vocoder.__class__.__name__ == "Generator": + return + if self.vocoder is not None: + self.vocoder.cpu() + del self.vocoder + self.empty_cache() + + self.vocoder = Generator( + initial_channel=100, + resblock="1", + resblock_kernel_sizes=[3, 7, 11], + resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]], + upsample_rates=[10, 6, 2, 2, 2], + upsample_initial_channel=512, + upsample_kernel_sizes=[20, 12, 4, 4, 4], + 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 v4 vocoder", self.vocoder_model.load_state_dict(state_dict_g)) + print("loading vocoder",self.vocoder.load_state_dict(state_dict_g)) + + self.vocoder_configs["sr"] = 48000 + self.vocoder_configs["T_ref"] = 500 + self.vocoder_configs["T_chunk"] = 1000 + self.vocoder_configs["upsample_rate"] = 480 + self.vocoder_configs["overlapped_len"] = 12 + + + + + self.vocoder = self.vocoder.eval() if self.configs.is_half == True: - self.vocoder_model = self.vocoder_model.half().to(self.configs.device) + self.vocoder = self.vocoder.half().to(self.configs.device) else: - self.vocoder_model = self.vocoder_model.to(self.configs.device) + self.vocoder = self.vocoder.to(self.configs.device) def init_sr_model(self): if self.sr_model is not None: @@ -572,8 +656,8 @@ class TTS: self.bert_model = self.bert_model.half() if self.cnhuhbert_model is not None: self.cnhuhbert_model = self.cnhuhbert_model.half() - if self.vocoder_model is not None: - self.vocoder_model = self.vocoder_model.half() + if self.vocoder is not None: + self.vocoder = self.vocoder.half() else: if self.t2s_model is not None: self.t2s_model = self.t2s_model.float() @@ -583,8 +667,8 @@ class TTS: self.bert_model = self.bert_model.float() if self.cnhuhbert_model is not None: self.cnhuhbert_model = self.cnhuhbert_model.float() - if self.vocoder_model is not None: - self.vocoder_model = self.vocoder_model.float() + if self.vocoder is not None: + self.vocoder = self.vocoder.float() def set_device(self, device: torch.device, save: bool = True): """ @@ -603,8 +687,8 @@ class TTS: self.bert_model = self.bert_model.to(device) if self.cnhuhbert_model is not None: self.cnhuhbert_model = self.cnhuhbert_model.to(device) - if self.vocoder_model is not None: - self.vocoder_model = self.vocoder_model.to(device) + if self.vocoder is not None: + self.vocoder = self.vocoder.to(device) if self.sr_model is not None: self.sr_model = self.sr_model.to(device) @@ -915,13 +999,13 @@ class TTS: split_bucket = False print(i18n("分段返回模式不支持分桶处理，已自动关闭分桶处理")) - if split_bucket and speed_factor == 1.0 and not (self.configs.is_v3_synthesizer!=False and parallel_infer): + if split_bucket and speed_factor == 1.0 and not (self.configs.use_vocoder and parallel_infer): print(i18n("分桶处理模式已开启")) elif speed_factor != 1.0: print(i18n("语速调节不支持分桶处理，已自动关闭分桶处理")) split_bucket = False - elif self.configs.is_v3_synthesizer!=False and parallel_infer: - 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")) + elif self.configs.use_vocoder and parallel_infer: + 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")) split_bucket = False else: print(i18n("分桶处理模式已关闭")) @@ -938,7 +1022,7 @@ class TTS: if not no_prompt_text: assert prompt_lang in self.configs.languages - if no_prompt_text and self.configs.is_v3_synthesizer!=False: + if no_prompt_text and self.configs.use_vocoder: raise NO_PROMPT_ERROR("prompt_text cannot be empty when using SoVITS_V3") if ref_audio_path in [None, ""] and ( @@ -1046,12 +1130,7 @@ class TTS: t_34 = 0.0 t_45 = 0.0 audio = [] - if self.configs.is_v3_synthesizer==False: - output_sr = 32000 - elif self.configs.is_v3_synthesizer == "v3": - output_sr = 24000 - else: - output_sr = 48000 # v4 + output_sr = self.configs.sampling_rate if not self.configs.use_vocoder else self.vocoder_configs["sr"] for item in data: t3 = time.perf_counter() if return_fragment: @@ -1113,7 +1192,7 @@ class TTS: # pred_semantic, pred_semantic_len, batch_phones, batch_phones_len,refer_audio_spec # )) print(f"############ {i18n('合成音频')} ############") - if not self.configs.is_v3_synthesizer: + if not self.configs.use_vocoder: if speed_factor == 1.0: print(f"{i18n('并行合成中')}...") # ## vits并行推理 method 2 @@ -1150,8 +1229,8 @@ class TTS: else: if parallel_infer: print(f"{i18n('并行合成中')}...") - audio_fragments = self.v3_synthesis_batched_infer( - idx_list, pred_semantic_list, batch_phones, speed=speed_factor, sample_steps=sample_steps,model_version=self.configs.is_v3_synthesizer + audio_fragments = self.using_vocoder_synthesis_batched_infer( + idx_list, pred_semantic_list, batch_phones, speed=speed_factor, sample_steps=sample_steps ) batch_audio_fragment.extend(audio_fragments) else: @@ -1160,8 +1239,8 @@ class TTS: _pred_semantic = ( 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]: diff --git a/GPT_SoVITS/configs/tts_infer.yaml b/GPT_SoVITS/configs/tts_infer.yaml index 9c65842..20c41a2 100644 --- a/GPT_SoVITS/configs/tts_infer.yaml +++ b/GPT_SoVITS/configs/tts_infer.yaml @@ -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 diff --git a/GPT_SoVITS/inference_webui_fast.py b/GPT_SoVITS/inference_webui_fast.py index 148f6bf..0624113 100644 --- a/GPT_SoVITS/inference_webui_fast.py +++ b/GPT_SoVITS/inference_webui_fast.py @@ -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(