diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py index 1b7ad11a..19b01116 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 +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 @@ -67,6 +67,20 @@ mel_fn = lambda x: mel_spectrogram_torch( }, ) +mel_fn_v4 = lambda x: mel_spectrogram_torch( + x, + **{ + "n_fft": 1280, + "win_size": 1280, + "hop_size": 320, + "num_mels": 100, + "sampling_rate": 32000, + "fmin": 0, + "fmax": None, + "center": False, + }, +) + def speed_change(input_audio: np.ndarray, speed: float, sr: int): # 将 NumPy 数组转换为原始 PCM 流 @@ -141,7 +155,7 @@ custom: 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 -default: +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 @@ -149,7 +163,7 @@ default: 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 -default_v2: +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 @@ -157,7 +171,7 @@ default_v2: 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 -default_v3: +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 @@ -165,6 +179,14 @@ default_v3: 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 """ @@ -220,6 +242,16 @@ class TTS_Config: "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base", "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large", }, + "v4": { + "device": "cpu", + "is_half": False, + "version": "v4", + "t2s_weights_path": "GPT_SoVITS/pretrained_models/s1v3.ckpt", + "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"] @@ -255,7 +287,7 @@ class TTS_Config: assert isinstance(configs, dict) version = configs.get("version", "v2").lower() - assert version in ["v1", "v2", "v3"] + assert version in ["v1", "v2", "v3", "v4"] self.default_configs[version] = configs.get(version, self.default_configs[version]) self.configs: dict = configs.get("custom", deepcopy(self.default_configs[version])) @@ -276,7 +308,7 @@ 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"] @@ -369,10 +401,18 @@ class TTS: self.bert_tokenizer: AutoTokenizer = None self.bert_model: AutoModelForMaskedLM = None self.cnhuhbert_model: CNHubert = None - self.bigvgan_model: BigVGAN = 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( @@ -391,6 +431,7 @@ 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 @@ -456,14 +497,14 @@ class TTS: # print(f"model_version:{model_version}") # print(f'hps["model"]["version"]:{hps["model"]["version"]}') - if model_version != "v3": + 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: vits_model = SynthesizerTrnV3( self.configs.filter_length // 2 + 1, @@ -471,8 +512,8 @@ class TTS: n_speakers=self.configs.n_speakers, **kwargs, ) - self.configs.is_v3_synthesizer = True - self.init_bigvgan() + 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 @@ -481,8 +522,9 @@ class TTS: f"Loading VITS weights from {weights_path}. {vits_model.load_state_dict(dict_s2['weight'], strict=False)}" ) else: + path_sovits = self.configs.default_configs[model_version]["vits_weights_path"] print( - f"Loading VITS pretrained weights from {weights_path}. {vits_model.load_state_dict(load_sovits_new(path_sovits_v3)['weight'], strict=False)}" + f"Loading VITS pretrained weights from {weights_path}. {vits_model.load_state_dict(load_sovits_new(path_sovits)['weight'], strict=False)}" ) lora_rank = dict_s2["lora_rank"] lora_config = LoraConfig( @@ -521,20 +563,64 @@ class TTS: if self.configs.is_half and str(self.configs.device) != "cpu": self.t2s_model = self.t2s_model.half() - def init_bigvgan(self): - if self.bigvgan_model is not None: - return - self.bigvgan_model = 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.bigvgan_model.remove_weight_norm() - self.bigvgan_model = self.bigvgan_model.eval() + 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.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 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.bigvgan_model = self.bigvgan_model.half().to(self.configs.device) + self.vocoder = self.vocoder.half().to(self.configs.device) else: - self.bigvgan_model = self.bigvgan_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: @@ -913,13 +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 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 and parallel_infer: - print(i18n("当开启并行推理模式时，SoVits V3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")) @@ -936,7 +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: + 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 ( @@ -1044,7 +1130,7 @@ class TTS: t_34 = 0.0 t_45 = 0.0 audio = [] - output_sr = self.configs.sampling_rate if not self.configs.is_v3_synthesizer else 24000 + 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: @@ -1106,7 +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 @@ -1143,7 +1229,7 @@ class TTS: else: if parallel_infer: print(f"{i18n('并行合成中')}...") - audio_fragments = self.v3_synthesis_batched_infer( + audio_fragments = self.useing_vocoder_synthesis_batched_infer( idx_list, pred_semantic_list, batch_phones, speed=speed_factor, sample_steps=sample_steps ) batch_audio_fragment.extend(audio_fragments) @@ -1153,7 +1239,7 @@ 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( + audio_fragment = self.useing_vocoder_synthesis( _pred_semantic, phones, speed=speed_factor, sample_steps=sample_steps ) batch_audio_fragment.append(audio_fragment) @@ -1169,7 +1255,7 @@ class TTS: speed_factor, False, fragment_interval, - super_sampling if self.configs.is_v3_synthesizer else False, + super_sampling if self.configs.use_vocoder and self.configs.version == "v3" else False, ) else: audio.append(batch_audio_fragment) @@ -1190,7 +1276,7 @@ class TTS: speed_factor, split_bucket, fragment_interval, - super_sampling if self.configs.is_v3_synthesizer else False, + super_sampling if self.configs.use_vocoder and self.configs.version == "v3" else False, ) except Exception as e: @@ -1272,7 +1358,7 @@ class TTS: return sr, audio - def v3_synthesis( + def useing_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) @@ -1285,19 +1371,23 @@ 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) - if ref_sr != 24000: + + tgt_sr = self.vocoder_configs["sr"] + if ref_sr != tgt_sr: ref_audio = resample(ref_audio, ref_sr, self.configs.device) - mel2 = mel_fn(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] - if T_min > 468: - mel2 = mel2[:, :, -468:] - fea_ref = fea_ref[:, :, -468:] - T_min = 468 - chunk_len = 934 - 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) @@ -1324,12 +1414,12 @@ class TTS: cfm_res = denorm_spec(cfm_res) with torch.inference_mode(): - wav_gen = self.bigvgan_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 useing_vocoder_synthesis_batched_infer( self, idx_list: List[int], semantic_tokens_list: List[torch.Tensor], @@ -1350,21 +1440,27 @@ class TTS: if ref_sr != 24000: ref_audio = resample(ref_audio, ref_sr, self.configs.device) - mel2 = mel_fn(ref_audio) + tgt_sr = self.vocoder_configs["sr"] + if ref_sr != tgt_sr: + ref_audio = resample(ref_audio, ref_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] - if T_min > 468: - mel2 = mel2[:, :, -468:] - fea_ref = fea_ref[:, :, -468:] - T_min = 468 - chunk_len = 934 - 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 = [] @@ -1413,11 +1509,11 @@ class TTS: pred_spec = denorm_spec(pred_spec) with torch.no_grad(): - wav_gen = self.bigvgan_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 344aae4b..20c41a20 100644 --- a/GPT_SoVITS/configs/tts_infer.yaml +++ b/GPT_SoVITS/configs/tts_infer.yaml @@ -30,3 +30,11 @@ v3: 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 837a2e49..99cd44d0 100644 --- a/GPT_SoVITS/inference_webui_fast.py +++ b/GPT_SoVITS/inference_webui_fast.py @@ -195,26 +195,31 @@ def change_choices(): path_sovits_v3 = "GPT_SoVITS/pretrained_models/s2Gv3.pth" +path_sovits_v4 = "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth" +is_exist_s2gv3 = os.path.exists(path_sovits_v3) +is_exist_s2gv4 = os.path.exists(path_sovits_v4) pretrained_sovits_name = [ "GPT_SoVITS/pretrained_models/s2G488k.pth", "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth", - path_sovits_v3, + "GPT_SoVITS/pretrained_models/s2Gv3.pth", + "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth", ] pretrained_gpt_name = [ "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt", "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt", "GPT_SoVITS/pretrained_models/s1v3.ckpt", + "GPT_SoVITS/pretrained_models/s1v3.ckpt", ] + _ = [[], []] -for i in range(3): +for i in range(4): if os.path.exists(pretrained_gpt_name[i]): _[0].append(pretrained_gpt_name[i]) if os.path.exists(pretrained_sovits_name[i]): _[-1].append(pretrained_sovits_name[i]) pretrained_gpt_name, pretrained_sovits_name = _ - if os.path.exists("./weight.json"): pass else: @@ -232,8 +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) @@ -257,13 +262,14 @@ SoVITS_names, GPT_names = get_weights_names(GPT_weight_root, SoVITS_weight_root) from process_ckpt import get_sovits_version_from_path_fast - +v3v4set={"v3","v4"} def change_sovits_weights(sovits_path, prompt_language=None, text_language=None): - global version, model_version, dict_language, if_lora_v3 + # global vq_model, hps, version, model_version, dict_language, if_lora_v3 version, model_version, if_lora_v3 = get_sovits_version_from_path_fast(sovits_path) - # print(sovits_path,version, model_version, if_lora_v3) - if if_lora_v3 and not os.path.exists(path_sovits_v3): - info = path_sovits_v3 + i18n("SoVITS V3 底模缺失，无法加载相应 LoRA 权重") + print(sovits_path,version, model_version, if_lora_v3) + is_exist=is_exist_s2gv3 if model_version=="v3"else is_exist_s2gv4 + if if_lora_v3 == True and is_exist == False: + info = "GPT_SoVITS/pretrained_models/s2Gv3.pth" + i18n("SoVITS V3 底模缺失，无法加载相应 LoRA 权重") gr.Warning(info) raise FileExistsError(info) dict_language = dict_language_v1 if version == "v1" else dict_language_v2 @@ -281,13 +287,12 @@ def change_sovits_weights(sovits_path, prompt_language=None, text_language=None) else: text_update = {"__type__": "update", "value": ""} text_language_update = {"__type__": "update", "value": i18n("中文")} - if model_version == "v3": + if model_version in v3v4set: visible_sample_steps = True visible_inp_refs = False 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())},