From 17832e5c4a38a84f8d091a6e77c9a8813ae4d430 Mon Sep 17 00:00:00 2001 From: chasonjiang <1440499136@qq.com> Date: Fri, 8 Mar 2024 23:41:59 +0800 Subject: [PATCH] =?UTF-8?q?=09=E5=BF=BD=E7=95=A5ffmpeg=20=20=20.gitignore?= =?UTF-8?q?=20=09=E4=BD=BFt2s=E6=A8=A1=E5=9E=8B=E6=94=AF=E6=8C=81=E6=89=B9?= =?UTF-8?q?=E9=87=8F=E6=8E=A8=E7=90=86:=20=20=20GPT=5FSoVITS/AR/models/t2s?= =?UTF-8?q?=5Fmodel.py=20=09=E4=BF=AE=E5=A4=8Dbatch=20bug=20=20=20GPT=5FSo?= =?UTF-8?q?VITS/AR/models/utils.py=20=20=20=20=20=E9=87=8D=E6=9E=84?= =?UTF-8?q?=E7=9A=84tts=20infer=20=20=20GPT=5FSoVITS/TTS=5Finfer=5Fpack/TT?= =?UTF-8?q?S.py=20=09=E6=96=87=E6=9C=AC=E9=A2=84=E5=A4=84=E7=90=86?= =?UTF-8?q?=E6=A8=A1=E5=9D=97=20=20=20GPT=5FSoVITS/TTS=5Finfer=5Fpack/Text?= =?UTF-8?q?Preprocessor.py=20=09new=20file=20=20=20GPT=5FSoVITS/TTS=5Finfe?= =?UTF-8?q?r=5Fpack/=5F=5Finit=5F=5F.py=20=09=E6=96=87=E6=9C=AC=E6=8B=86?= =?UTF-8?q?=E5=88=86=E6=96=B9=E6=B3=95=E6=A8=A1=E5=9D=97=20=20=20GPT=5FSoV?= =?UTF-8?q?ITS/TTS=5Finfer=5Fpack/text=5Fsegmentation=5Fmethod.py=20=09tts?= =?UTF-8?q?=20infer=E9=85=8D=E7=BD=AE=E6=96=87=E4=BB=B6=20=20=20GPT=5FSoVI?= =?UTF-8?q?TS/configs/tts=5Finfer.yaml=20=09modified=20=20=20GPT=5FSoVITS/?= =?UTF-8?q?feature=5Fextractor/cnhubert.py=20=09modified=20=20=20GPT=5FSoV?= =?UTF-8?q?ITS/inference=5Fgui.py=20=09=E9=87=8D=E6=9E=84=E7=9A=84webui=20?= =?UTF-8?q?=20=20GPT=5FSoVITS/inference=5Fwebui.py=20=09new=20file=20=20?= =?UTF-8?q?=20GPT=5FSoVITS/inference=5Fwebui=5Fold.py?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 3 +- GPT_SoVITS/AR/models/t2s_model.py | 53 +- GPT_SoVITS/AR/models/utils.py | 12 +- GPT_SoVITS/TTS_infer_pack/TTS.py | 546 +++++++++++++++ GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py | 176 +++++ GPT_SoVITS/TTS_infer_pack/__init__.py | 1 + .../text_segmentation_method.py | 126 ++++ GPT_SoVITS/configs/tts_infer.yaml | 14 + GPT_SoVITS/feature_extractor/cnhubert.py | 9 +- GPT_SoVITS/inference_gui.py | 2 +- GPT_SoVITS/inference_webui.py | 516 ++------------- GPT_SoVITS/inference_webui_old.py | 622 ++++++++++++++++++ 12 files changed, 1588 insertions(+), 492 deletions(-) create mode 100644 GPT_SoVITS/TTS_infer_pack/TTS.py create mode 100644 GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py create mode 100644 GPT_SoVITS/TTS_infer_pack/__init__.py create mode 100644 GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py create mode 100644 GPT_SoVITS/configs/tts_infer.yaml create mode 100644 GPT_SoVITS/inference_webui_old.py diff --git a/.gitignore b/.gitignore index 96e754a9..6f846a91 100644 --- a/.gitignore +++ b/.gitignore @@ -10,5 +10,6 @@ reference GPT_weights SoVITS_weights TEMP - +ffmpeg.exe +ffprobe.exe diff --git a/GPT_SoVITS/AR/models/t2s_model.py b/GPT_SoVITS/AR/models/t2s_model.py index c8ad3d82..8c31f12a 100644 --- a/GPT_SoVITS/AR/models/t2s_model.py +++ b/GPT_SoVITS/AR/models/t2s_model.py @@ -1,5 +1,4 @@ -# modified from https://github.com/yangdongchao/SoundStorm/blob/master/soundstorm/s1/AR/models/t2s_model.py -# reference: https://github.com/lifeiteng/vall-e +# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_model.py import torch from tqdm import tqdm @@ -386,7 +385,9 @@ def infer_panel( x.device ) - + y_list = [None]*y.shape[0] + batch_idx_map = list(range(y.shape[0])) + idx_list = [None]*y.shape[0] for idx in tqdm(range(1500)): xy_dec, _ = self.h((xy_pos, None), mask=xy_attn_mask, cache=cache) @@ -397,17 +398,45 @@ def infer_panel( if(idx==0):###第一次跑不能EOS否则没有了 logits = logits[:, :-1] ###刨除1024终止符号的概率 samples = sample( - logits[0], y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature - )[0].unsqueeze(0) + logits, y, top_k=top_k, top_p=top_p, repetition_penalty=1.35, temperature=temperature + )[0] # 本次生成的 semantic_ids 和之前的 y 构成新的 y # print(samples.shape)#[1,1]#第一个1是bs y = torch.concat([y, samples], dim=1) + # 移除已经生成完毕的序列 + reserved_idx_of_batch_for_y = None + if (self.EOS in torch.argmax(logits, dim=-1)) or \ + (self.EOS in samples[:, 0]): ###如果生成到EOS，则停止 + l = samples[:, 0]==self.EOS + removed_idx_of_batch_for_y = torch.where(l==True)[0].tolist() + reserved_idx_of_batch_for_y = torch.where(l==False)[0] + # batch_indexs = torch.tensor(batch_idx_map, device=y.device)[removed_idx_of_batch_for_y] + for i in removed_idx_of_batch_for_y: + batch_index = batch_idx_map[i] + idx_list[batch_index] = idx - 1 + y_list[batch_index] = y[i, :-1] + + batch_idx_map = [batch_idx_map[i] for i in reserved_idx_of_batch_for_y.tolist()] + + # 只保留未生成完毕的序列 + if reserved_idx_of_batch_for_y is not None: + # index = torch.LongTensor(batch_idx_map).to(y.device) + y = torch.index_select(y, dim=0, index=reserved_idx_of_batch_for_y) + if cache["y_emb"] is not None: + cache["y_emb"] = torch.index_select(cache["y_emb"], dim=0, index=reserved_idx_of_batch_for_y) + if cache["k"] is not None: + for i in range(self.num_layers): + # 因为kv转置了，所以batch dim是1 + cache["k"][i] = torch.index_select(cache["k"][i], dim=1, index=reserved_idx_of_batch_for_y) + cache["v"][i] = torch.index_select(cache["v"][i], dim=1, index=reserved_idx_of_batch_for_y) + + if early_stop_num != -1 and (y.shape[1] - prefix_len) > early_stop_num: print("use early stop num:", early_stop_num) stop = True - - if torch.argmax(logits, dim=-1)[0] == self.EOS or samples[0, 0] == self.EOS: + + if not (None in idx_list): # print(torch.argmax(logits, dim=-1)[0] == self.EOS, samples[0, 0] == self.EOS) stop = True if stop: @@ -443,6 +472,12 @@ def infer_panel( xy_attn_mask = torch.zeros( (1, x_len + y_len), dtype=torch.bool, device=xy_pos.device ) + + if (None in idx_list): + for i in range(x.shape[0]): + if idx_list[i] is None: + idx_list[i] = 1500-1 ###如果没有生成到EOS，就用最大长度代替 + if ref_free: - return y[:, :-1], 0 - return y[:, :-1], idx-1 + return y_list, [0]*x.shape[0] + return y_list, idx_list diff --git a/GPT_SoVITS/AR/models/utils.py b/GPT_SoVITS/AR/models/utils.py index 9678c7e1..34178fea 100644 --- a/GPT_SoVITS/AR/models/utils.py +++ b/GPT_SoVITS/AR/models/utils.py @@ -115,17 +115,17 @@ def logits_to_probs( top_p: Optional[int] = None, repetition_penalty: float = 1.0, ): - if previous_tokens is not None: - previous_tokens = previous_tokens.squeeze() + # if previous_tokens is not None: + # previous_tokens = previous_tokens.squeeze() # print(logits.shape,previous_tokens.shape) # pdb.set_trace() if previous_tokens is not None and repetition_penalty != 1.0: previous_tokens = previous_tokens.long() - score = torch.gather(logits, dim=0, index=previous_tokens) + score = torch.gather(logits, dim=1, index=previous_tokens) score = torch.where( score < 0, score * repetition_penalty, score / repetition_penalty ) - logits.scatter_(dim=0, index=previous_tokens, src=score) + logits.scatter_(dim=1, index=previous_tokens, src=score) if top_p is not None and top_p < 1.0: sorted_logits, sorted_indices = torch.sort(logits, descending=True) @@ -133,9 +133,9 @@ def logits_to_probs( torch.nn.functional.softmax(sorted_logits, dim=-1), dim=-1 ) sorted_indices_to_remove = cum_probs > top_p - sorted_indices_to_remove[0] = False # keep at least one option + sorted_indices_to_remove[:, 0] = False # keep at least one option indices_to_remove = sorted_indices_to_remove.scatter( - dim=0, index=sorted_indices, src=sorted_indices_to_remove + dim=1, index=sorted_indices, src=sorted_indices_to_remove ) logits = logits.masked_fill(indices_to_remove, -float("Inf")) diff --git a/GPT_SoVITS/TTS_infer_pack/TTS.py b/GPT_SoVITS/TTS_infer_pack/TTS.py new file mode 100644 index 00000000..9f98a246 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/TTS.py @@ -0,0 +1,546 @@ +import os, sys + +now_dir = os.getcwd() +sys.path.append(now_dir) +import os +from typing import Generator, List, Union +import numpy as np +import torch +import yaml +from transformers import AutoModelForMaskedLM, AutoTokenizer + +from AR.models.t2s_lightning_module import Text2SemanticLightningModule +from feature_extractor.cnhubert import CNHubert +from module.models import SynthesizerTrn +import librosa +from time import time as ttime +from tools.i18n.i18n import I18nAuto +from my_utils import load_audio +from module.mel_processing import spectrogram_torch +from .text_segmentation_method import splits +from .TextPreprocessor import TextPreprocessor +i18n = I18nAuto() + +# tts_infer.yaml +""" +default: + device: cpu + is_half: false + bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large + cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base + t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt + vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth + +custom: + device: cuda + is_half: true + bert_base_path: GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large + cnhuhbert_base_path: GPT_SoVITS/pretrained_models/chinese-hubert-base + t2s_weights_path: GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt + vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth + + + +""" + + + +class TTS_Config: + def __init__(self, configs: Union[dict, str]): + configs_base_path:str = "GPT_SoVITS/configs/" + os.makedirs(configs_base_path, exist_ok=True) + self.configs_path:str = os.path.join(configs_base_path, "tts_infer.yaml") + if isinstance(configs, str): + self.configs_path = configs + configs:dict = self._load_configs(configs) + + # assert isinstance(configs, dict) + self.default_configs:dict = configs.get("default", None) + if self.default_configs is None: + self.default_configs={ + "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", + "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base", + "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large" + } + self.configs:dict = configs.get("custom", self.default_configs) + + self.device = self.configs.get("device") + self.is_half = self.configs.get("is_half") + self.t2s_weights_path = self.configs.get("t2s_weights_path") + self.vits_weights_path = self.configs.get("vits_weights_path") + self.bert_base_path = self.configs.get("bert_base_path") + self.cnhuhbert_base_path = self.configs.get("cnhuhbert_base_path") + + + self.max_sec = None + self.hz:int = 50 + self.semantic_frame_rate:str = "25hz" + self.segment_size:int = 20480 + self.filter_length:int = 2048 + self.sampling_rate:int = 32000 + self.hop_length:int = 640 + self.win_length:int = 2048 + self.n_speakers:int = 300 + + self.langauges:list = ["auto", "en", "zh", "ja", "all_zh", "all_ja"] + + def _load_configs(self, configs_path: str)->dict: + with open(configs_path, 'r') as f: + configs = yaml.load(f, Loader=yaml.FullLoader) + + return configs + + + def save_configs(self, configs_path:str=None)->None: + configs={ + "default": { + "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", + "cnhuhbert_base_path": "GPT_SoVITS/pretrained_models/chinese-hubert-base", + "bert_base_path": "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large" + }, + "custom": { + "device": str(self.device), + "is_half": self.is_half, + "t2s_weights_path": self.t2s_weights_path, + "vits_weights_path": self.vits_weights_path, + "bert_base_path": self.bert_base_path, + "cnhuhbert_base_path": self.cnhuhbert_base_path + } + } + if configs_path is None: + configs_path = self.configs_path + with open(configs_path, 'w') as f: + yaml.dump(configs, f) + + +class TTS: + def __init__(self, configs: Union[dict, str, TTS_Config]): + if isinstance(configs, TTS_Config): + self.configs = configs + else: + self.configs:TTS_Config = TTS_Config(configs) + + self.t2s_model:Text2SemanticLightningModule = None + self.vits_model:SynthesizerTrn = None + self.bert_tokenizer:AutoTokenizer = None + self.bert_model:AutoModelForMaskedLM = None + self.cnhuhbert_model:CNHubert = None + + self._init_models() + + self.text_preprocessor:TextPreprocessor = \ + TextPreprocessor(self.bert_model, + self.bert_tokenizer, + self.configs.device) + + + self.prompt_cache:dict = { + "ref_audio_path":None, + "prompt_semantic":None, + "refer_spepc":None, + "prompt_text":None, + "prompt_lang":None, + "phones":None, + "bert_features":None, + "norm_text":None, + } + + def _init_models(self,): + 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) + + + + def init_cnhuhbert_weights(self, base_path: str): + self.cnhuhbert_model = CNHubert(base_path) + self.cnhuhbert_model.eval() + if self.configs.is_half == True: + self.cnhuhbert_model = self.cnhuhbert_model.half() + self.cnhuhbert_model = self.cnhuhbert_model.to(self.configs.device) + + + + def init_bert_weights(self, base_path: str): + self.bert_tokenizer = AutoTokenizer.from_pretrained(base_path) + self.bert_model = AutoModelForMaskedLM.from_pretrained(base_path) + if self.configs.is_half: + self.bert_model = self.bert_model.half() + self.bert_model = self.bert_model.to(self.configs.device) + + + + def init_vits_weights(self, weights_path: str): + self.configs.vits_weights_path = weights_path + self.configs.save_configs() + dict_s2 = torch.load(weights_path, map_location=self.configs.device) + hps = dict_s2["config"] + self.configs.filter_length = hps["data"]["filter_length"] + self.configs.segment_size = hps["train"]["segment_size"] + self.configs.sampling_rate = hps["data"]["sampling_rate"] + self.configs.hop_length = hps["data"]["hop_length"] + self.configs.win_length = hps["data"]["win_length"] + self.configs.n_speakers = hps["data"]["n_speakers"] + self.configs.semantic_frame_rate = "25hz" + kwargs = hps["model"] + vits_model = SynthesizerTrn( + self.configs.filter_length // 2 + 1, + self.configs.segment_size // self.configs.hop_length, + n_speakers=self.configs.n_speakers, + **kwargs + ) + # if ("pretrained" not in weights_path): + if hasattr(vits_model, "enc_q"): + del vits_model.enc_q + + if self.configs.is_half: + vits_model = vits_model.half() + vits_model = vits_model.to(self.configs.device) + vits_model.eval() + vits_model.load_state_dict(dict_s2["weight"], strict=False) + self.vits_model = vits_model + + + def init_t2s_weights(self, weights_path: str): + self.configs.t2s_weights_path = weights_path + self.configs.save_configs() + self.configs.hz = 50 + dict_s1 = torch.load(weights_path, map_location=self.configs.device) + config = dict_s1["config"] + self.configs.max_sec = config["data"]["max_sec"] + t2s_model = Text2SemanticLightningModule(config, "****", is_train=False) + t2s_model.load_state_dict(dict_s1["weight"]) + if self.configs.is_half: + t2s_model = t2s_model.half() + t2s_model = t2s_model.to(self.configs.device) + t2s_model.eval() + self.t2s_model = t2s_model + + def set_ref_audio(self, ref_audio_path:str): + self._set_prompt_semantic(ref_audio_path) + self._set_ref_spepc(ref_audio_path) + + def _set_ref_spepc(self, ref_audio_path): + audio = load_audio(ref_audio_path, int(self.configs.sampling_rate)) + audio = torch.FloatTensor(audio) + audio_norm = audio + audio_norm = audio_norm.unsqueeze(0) + spec = spectrogram_torch( + audio_norm, + self.configs.filter_length, + self.configs.sampling_rate, + self.configs.hop_length, + self.configs.win_length, + center=False, + ) + spec = spec.to(self.configs.device) + if self.configs.is_half: + spec = spec.half() + # self.refer_spepc = spec + self.prompt_cache["refer_spepc"] = spec + + + def _set_prompt_semantic(self, ref_wav_path:str): + zero_wav = np.zeros( + int(self.configs.sampling_rate * 0.3), + dtype=np.float16 if self.configs.is_half else np.float32, + ) + with torch.no_grad(): + wav16k, sr = librosa.load(ref_wav_path, sr=16000) + if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): + raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) + wav16k = torch.from_numpy(wav16k) + zero_wav_torch = torch.from_numpy(zero_wav) + wav16k = wav16k.to(self.configs.device) + zero_wav_torch = zero_wav_torch.to(self.configs.device) + if self.configs.is_half: + wav16k = wav16k.half() + zero_wav_torch = zero_wav_torch.half() + + wav16k = torch.cat([wav16k, zero_wav_torch]) + hubert_feature = self.cnhuhbert_model.model(wav16k.unsqueeze(0))[ + "last_hidden_state" + ].transpose( + 1, 2 + ) # .float() + codes = self.vits_model.extract_latent(hubert_feature) + + prompt_semantic = codes[0, 0].to(self.configs.device) + self.prompt_cache["prompt_semantic"] = prompt_semantic + + def batch_sequences(self, sequences: List[torch.Tensor], axis: int = 0, pad_value: int = 0): + seq = sequences[0] + ndim = seq.dim() + if axis < 0: + axis += ndim + dtype:torch.dtype = seq.dtype + pad_value = torch.tensor(pad_value, dtype=dtype) + seq_lengths = [seq.shape[axis] for seq in sequences] + max_length = max(seq_lengths) + + padded_sequences = [] + for seq, length in zip(sequences, seq_lengths): + padding = [0] * axis + [0, max_length - length] + [0] * (ndim - axis - 1) + padded_seq = torch.nn.functional.pad(seq, padding, value=pad_value) + padded_sequences.append(padded_seq) + batch = torch.stack(padded_sequences) + return batch + + def to_batch(self, data:list, prompt_data:dict=None, batch_size:int=5, threshold:float=0.75): + + _data:list = [] + index_and_len_list = [] + for idx, item in enumerate(data): + norm_text_len = len(item["norm_text"]) + index_and_len_list.append([idx, norm_text_len]) + + index_and_len_list.sort(key=lambda x: x[1]) + # index_and_len_batch_list = [index_and_len_list[idx:min(idx+batch_size,len(index_and_len_list))] for idx in range(0,len(index_and_len_list),batch_size)] + index_and_len_list = np.array(index_and_len_list, dtype=np.int64) + + # for batch_idx, index_and_len_batch in enumerate(index_and_len_batch_list): + + batch_index_list = [] + batch_index_list_len = 0 + pos = 0 + while pos =threshold) or (pos_end-pos==1): + batch_index=index_and_len_list[pos:pos_end, 0].tolist() + batch_index_list_len += len(batch_index) + batch_index_list.append(batch_index) + pos = pos_end + break + pos_end=pos_end-1 + + assert batch_index_list_len == len(data) + + for batch_idx, index_list in enumerate(batch_index_list): + item_list = [data[idx] for idx in index_list] + phones_list = [] + # bert_features_list = [] + all_phones_list = [] + all_bert_features_list = [] + norm_text_batch = [] + for item in item_list: + if prompt_data is not None: + all_bert_features = torch.cat([prompt_data["bert_features"].clone(), item["bert_features"]], 1) + all_phones = torch.LongTensor(prompt_data["phones"]+item["phones"]) + phones = torch.LongTensor(item["phones"]) + # norm_text = prompt_data["norm_text"]+item["norm_text"] + else: + all_bert_features = item["bert_features"] + phones = torch.LongTensor(item["phones"]) + all_phones = phones.clone() + # norm_text = item["norm_text"] + + phones_list.append(phones) + all_phones_list.append(all_phones) + all_bert_features_list.append(all_bert_features) + norm_text_batch.append(item["norm_text"]) + # phones_batch = phones_list + phones_batch = self.batch_sequences(phones_list, axis=0, pad_value=0) + all_phones_batch = self.batch_sequences(all_phones_list, axis=0, pad_value=0) + all_bert_features_batch = torch.FloatTensor(len(item_list), 1024, all_phones_batch.shape[-1]) + all_bert_features_batch.zero_() + + for idx, item in enumerate(all_bert_features_list): + if item != None: + all_bert_features_batch[idx, :, : item.shape[-1]] = item + + batch = { + "phones": phones_batch, + "all_phones": all_phones_batch, + "all_bert_features": all_bert_features_batch, + "norm_text": norm_text_batch + } + _data.append(batch) + + return _data, batch_index_list + + def recovery_order(self, data:list, batch_index_list:list)->list: + lenght = len(sum(batch_index_list, [])) + _data = [None]*lenght + for i, index_list in enumerate(batch_index_list): + for j, index in enumerate(index_list): + _data[index] = data[i][j] + return _data + + + + + def run(self, inputs:dict): + """ + Text to speech inference. + + Args: + inputs (dict): + { + "text": "", + "text_lang: "", + "ref_audio_path": "", + "prompt_text": "", + "prompt_lang": "", + "top_k": 5, + "top_p": 0.9, + "temperature": 0.6, + "text_split_method": "", + "batch_size": 1, + "batch_threshold": 0.75 + } + returns: + tulpe[int, np.ndarray]: sampling rate and audio data. + """ + text:str = inputs.get("text", "") + text_lang:str = inputs.get("text_lang", "") + ref_audio_path:str = inputs.get("ref_audio_path", "") + prompt_text:str = inputs.get("prompt_text", "") + prompt_lang:str = inputs.get("prompt_lang", "") + top_k:int = inputs.get("top_k", 20) + top_p:float = inputs.get("top_p", 0.9) + temperature:float = inputs.get("temperature", 0.6) + text_split_method:str = inputs.get("text_split_method", "") + batch_size = inputs.get("batch_size", 1) + batch_threshold = inputs.get("batch_threshold", 0.75) + + no_prompt_text = False + if prompt_text in [None, ""]: + no_prompt_text = True + + assert text_lang in self.configs.langauges + if not no_prompt_text: + assert prompt_lang in self.configs.langauges + + if ref_audio_path in [None, ""] and \ + ((self.prompt_cache["prompt_semantic"] is None) or (self.prompt_cache["refer_spepc"] is None)): + raise ValueError("ref_audio_path cannot be empty, when the reference audio is not set using set_ref_audio()") + + t0 = ttime() + if (ref_audio_path is not None) and (ref_audio_path != self.prompt_cache["ref_audio_path"]): + self.set_ref_audio(ref_audio_path) + + if not no_prompt_text: + prompt_text = prompt_text.strip("\n") + if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_lang != "en" else "." + print(i18n("实际输入的参考文本:"), prompt_text) + if self.prompt_cache["prompt_text"] != prompt_text: + self.prompt_cache["prompt_text"] = prompt_text + self.prompt_cache["prompt_lang"] = prompt_lang + phones, bert_features, norm_text = \ + self.text_preprocessor.segment_and_extract_feature_for_text( + prompt_text, + prompt_lang) + self.prompt_cache["phones"] = phones + self.prompt_cache["bert_features"] = bert_features + self.prompt_cache["norm_text"] = norm_text + + zero_wav = np.zeros( + int(self.configs.sampling_rate * 0.3), + dtype=np.float16 if self.configs.is_half else np.float32, + ) + + + data = self.text_preprocessor.preprocess(text, text_lang, text_split_method) + audio = [] + t1 = ttime() + data, batch_index_list = self.to_batch(data, + prompt_data=self.prompt_cache if not no_prompt_text else None, + batch_size=batch_size, + threshold=batch_threshold) + t2 = ttime() + zero_wav = torch.zeros( + int(self.configs.sampling_rate * 0.3), + dtype=torch.float16 if self.configs.is_half else torch.float32, + device=self.configs.device + ) + + t_34 = 0.0 + t_45 = 0.0 + for item in data: + t3 = ttime() + batch_phones = item["phones"] + all_phoneme_ids = item["all_phones"] + all_bert_features = item["all_bert_features"] + norm_text = item["norm_text"] + + # phones = phones.to(self.configs.device) + all_phoneme_ids = all_phoneme_ids.to(self.configs.device) + all_bert_features = all_bert_features.to(self.configs.device) + if self.configs.is_half: + all_bert_features = all_bert_features.half() + # all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]*all_phoneme_ids.shape[0], device=self.configs.device) + + print(i18n("前端处理后的文本(每句):"), norm_text) + if no_prompt_text : + prompt = None + else: + prompt = self.prompt_cache["prompt_semantic"].clone().repeat(all_phoneme_ids.shape[0], 1).to(self.configs.device) + + with torch.no_grad(): + # pred_semantic = t2s_model.model.infer( + pred_semantic_list, idx_list = self.t2s_model.model.infer_panel( + all_phoneme_ids, + None, + prompt, + all_bert_features, + # prompt_phone_len=ph_offset, + top_k=top_k, + top_p=top_p, + temperature=temperature, + early_stop_num=self.configs.hz * self.configs.max_sec, + ) + t4 = ttime() + t_34 += t4 - t3 + + refer_audio_spepc:torch.Tensor = self.prompt_cache["refer_spepc"].clone().to(self.configs.device) + if self.configs.is_half: + refer_audio_spepc = refer_audio_spepc.half() + + ## 直接对batch进行decode 生成的音频会有问题 + # pred_semantic_list = [item[-idx:] for item, idx in zip(pred_semantic_list, idx_list)] + # pred_semantic = self.batch_sequences(pred_semantic_list, axis=0, pad_value=0).unsqueeze(0) + # batch_phones = batch_phones.to(self.configs.device) + # batch_audio_fragment =(self.vits_model.decode( + # pred_semantic, batch_phones, refer_audio_spepc + # ).detach()[:, 0, :]) + # max_audio=torch.abs(batch_audio_fragment).max()#简单防止16bit爆音 + # if max_audio>1: batch_audio_fragment/=max_audio + # batch_audio_fragment = batch_audio_fragment.cpu().numpy() + + ## 改成串行处理 + batch_audio_fragment = [] + for i, idx in enumerate(idx_list): + phones = batch_phones[i].clone().unsqueeze(0).to(self.configs.device) + _pred_semantic = (pred_semantic_list[i][-idx:].unsqueeze(0).unsqueeze(0)) # .unsqueeze(0)#mq要多unsqueeze一次 + audio_fragment =(self.vits_model.decode( + _pred_semantic, phones, refer_audio_spepc + ).detach()[0, 0, :]) + max_audio=torch.abs(audio_fragment).max()#简单防止16bit爆音 + if max_audio>1: audio_fragment/=max_audio + audio_fragment = torch.cat([audio_fragment, zero_wav], dim=0) + batch_audio_fragment.append( + audio_fragment.cpu().numpy() + ) ###试试重建不带上prompt部分 + + audio.append(batch_audio_fragment) + # audio.append(zero_wav) + t5 = ttime() + t_45 += t5 - t4 + + audio = self.recovery_order(audio, batch_index_list) + print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t_34, t_45)) + yield self.configs.sampling_rate, (np.concatenate(audio, 0) * 32768).astype( + np.int16 + ) + \ No newline at end of file diff --git a/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py new file mode 100644 index 00000000..1504a534 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/TextPreprocessor.py @@ -0,0 +1,176 @@ + + +import re +import torch +import LangSegment +from typing import Dict, List, Tuple +from text.cleaner import clean_text +from text import cleaned_text_to_sequence +from transformers import AutoModelForMaskedLM, AutoTokenizer +from .text_segmentation_method import splits, get_method as get_seg_method + +# from tools.i18n.i18n import I18nAuto + +# i18n = I18nAuto() + +def get_first(text:str) -> str: + pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" + text = re.split(pattern, text)[0].strip() + return text + +def merge_short_text_in_array(texts:str, threshold:int) -> list: + if (len(texts)) < 2: + return texts + result = [] + text = "" + for ele in texts: + text += ele + if len(text) >= threshold: + result.append(text) + text = "" + if (len(text) > 0): + if len(result) == 0: + result.append(text) + else: + result[len(result) - 1] += text + return result + + +class TextPreprocessor: + def __init__(self, bert_model:AutoModelForMaskedLM, + tokenizer:AutoTokenizer, device:torch.device): + self.bert_model = bert_model + self.tokenizer = tokenizer + self.device = device + + def preprocess(self, text:str, lang:str, text_split_method:str)->List[Dict]: + texts = self.pre_seg_text(text, lang, text_split_method) + result = [] + for text in texts: + phones, bert_features, norm_text = self.segment_and_extract_feature_for_text(text, lang) + res={ + "phones": phones, + "bert_features": bert_features, + "norm_text": norm_text, + } + result.append(res) + return result + + def pre_seg_text(self, text:str, lang:str, text_split_method:str): + text = text.strip("\n") + if (text[0] not in splits and len(get_first(text)) < 4): + text = "。" + text if lang != "en" else "." + text + # print(i18n("实际输入的目标文本:"), text) + + seg_method = get_seg_method(text_split_method) + text = seg_method(text) + + while "\n\n" in text: + text = text.replace("\n\n", "\n") + # print(i18n("实际输入的目标文本(切句后):"), text) + _texts = text.split("\n") + _texts = merge_short_text_in_array(_texts, 5) + texts = [] + for text in _texts: + # 解决输入目标文本的空行导致报错的问题 + if (len(text.strip()) == 0): + continue + if (text[-1] not in splits): text += "。" if lang != "en" else "." + texts.append(text) + + return texts + + def segment_and_extract_feature_for_text(self, texts:list, language:str)->Tuple[list, torch.Tensor, str]: + textlist, langlist = self.seg_text(texts, language) + phones, bert_features, norm_text = self.extract_bert_feature(textlist, langlist) + + return phones, bert_features, norm_text + + + def seg_text(self, text:str, language:str)->Tuple[list, list]: + + textlist=[] + langlist=[] + if language in ["auto", "zh", "ja"]: + # LangSegment.setfilters(["zh","ja","en","ko"]) + for tmp in LangSegment.getTexts(text): + if tmp["lang"] == "ko": + langlist.append("zh") + elif tmp["lang"] == "en": + langlist.append("en") + else: + # 因无法区别中日文汉字,以用户输入为准 + langlist.append(language if language!="auto" else tmp["lang"]) + textlist.append(tmp["text"]) + elif language == "en": + # LangSegment.setfilters(["en"]) + formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) + while " " in formattext: + formattext = formattext.replace(" ", " ") + textlist.append(formattext) + langlist.append("en") + + elif language in ["all_zh","all_ja"]: + formattext = text + while " " in formattext: + formattext = formattext.replace(" ", " ") + language = language.replace("all_","") + textlist.append(formattext) + langlist.append(language) + + else: + raise ValueError(f"language {language} not supported") + + return textlist, langlist + + + def extract_bert_feature(self, textlist:list, langlist:list): + phones_list = [] + bert_feature_list = [] + norm_text_list = [] + for i in range(len(textlist)): + lang = langlist[i] + phones, word2ph, norm_text = self.clean_text_inf(textlist[i], lang) + _bert_feature = self.get_bert_inf(phones, word2ph, norm_text, lang) + # phones_list.append(phones) + phones_list.extend(phones) + norm_text_list.append(norm_text) + bert_feature_list.append(_bert_feature) + bert_feature = torch.cat(bert_feature_list, dim=1) + # phones = sum(phones_list, []) + norm_text = ''.join(norm_text_list) + + return phones, bert_feature, norm_text + + + def get_bert_feature(self, text:str, word2ph:list)->torch.Tensor: + with torch.no_grad(): + inputs = self.tokenizer(text, return_tensors="pt") + for i in inputs: + inputs[i] = inputs[i].to(self.device) + res = self.bert_model(**inputs, output_hidden_states=True) + res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] + assert len(word2ph) == len(text) + phone_level_feature = [] + for i in range(len(word2ph)): + repeat_feature = res[i].repeat(word2ph[i], 1) + phone_level_feature.append(repeat_feature) + phone_level_feature = torch.cat(phone_level_feature, dim=0) + return phone_level_feature.T + + def clean_text_inf(self, text:str, language:str): + phones, word2ph, norm_text = clean_text(text, language) + phones = cleaned_text_to_sequence(phones) + return phones, word2ph, norm_text + + def get_bert_inf(self, phones:list, word2ph:list, norm_text:str, language:str): + language=language.replace("all_","") + if language == "zh": + feature = self.get_bert_feature(norm_text, word2ph).to(self.device) + else: + feature = torch.zeros( + (1024, len(phones)), + dtype=torch.float32, + ).to(self.device) + + return feature \ No newline at end of file diff --git a/GPT_SoVITS/TTS_infer_pack/__init__.py b/GPT_SoVITS/TTS_infer_pack/__init__.py new file mode 100644 index 00000000..74381982 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/__init__.py @@ -0,0 +1 @@ +from . import TTS, text_segmentation_method \ No newline at end of file diff --git a/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py b/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py new file mode 100644 index 00000000..7bc6b009 --- /dev/null +++ b/GPT_SoVITS/TTS_infer_pack/text_segmentation_method.py @@ -0,0 +1,126 @@ + + + + +import re +from typing import Callable +from tools.i18n.i18n import I18nAuto + +i18n = I18nAuto() + +METHODS = dict() + +def get_method(name:str)->Callable: + method = METHODS.get(name, None) + if method is None: + raise ValueError(f"Method {name} not found") + return method + +def register_method(name): + def decorator(func): + METHODS[name] = func + return func + return decorator + +splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } + + +def split(todo_text): + todo_text = todo_text.replace("……", "。").replace("——", "，") + if todo_text[-1] not in splits: + todo_text += "。" + i_split_head = i_split_tail = 0 + len_text = len(todo_text) + todo_texts = [] + while 1: + if i_split_head >= len_text: + break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入 + if todo_text[i_split_head] in splits: + i_split_head += 1 + todo_texts.append(todo_text[i_split_tail:i_split_head]) + i_split_tail = i_split_head + else: + i_split_head += 1 + return todo_texts + + +# 不切 +@register_method("cut0") +def cut0(inp): + return inp + + +# 凑四句一切 +@register_method("cut1") +def cut1(inp): + inp = inp.strip("\n") + inps = split(inp) + split_idx = list(range(0, len(inps), 4)) + split_idx[-1] = None + if len(split_idx) > 1: + opts = [] + for idx in range(len(split_idx) - 1): + opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]])) + else: + opts = [inp] + return "\n".join(opts) + +# 凑50字一切 +@register_method("cut2") +def cut2(inp): + inp = inp.strip("\n") + inps = split(inp) + if len(inps) < 2: + return inp + opts = [] + summ = 0 + tmp_str = "" + for i in range(len(inps)): + summ += len(inps[i]) + tmp_str += inps[i] + if summ > 50: + summ = 0 + opts.append(tmp_str) + tmp_str = "" + if tmp_str != "": + opts.append(tmp_str) + # print(opts) + if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起 + opts[-2] = opts[-2] + opts[-1] + opts = opts[:-1] + return "\n".join(opts) + +# 按中文句号。切 +@register_method("cut3") +def cut3(inp): + inp = inp.strip("\n") + return "\n".join(["%s" % item for item in inp.strip("。").split("。")]) + +#按英文句号.切 +@register_method("cut4") +def cut4(inp): + inp = inp.strip("\n") + return "\n".join(["%s" % item for item in inp.strip(".").split(".")]) + +# 按标点符号切 +# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py +@register_method("cut5") +def cut5(inp): + # if not re.search(r'[^\w\s]', inp[-1]): + # inp += '。' + inp = inp.strip("\n") + punds = r'[,.;?!、，。？！;：…]' + items = re.split(f'({punds})', inp) + mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])] + # 在句子不存在符号或句尾无符号的时候保证文本完整 + if len(items)%2 == 1: + mergeitems.append(items[-1]) + opt = "\n".join(mergeitems) + return opt + + + +if __name__ == '__main__': + method = get_method("cut1") + print(method("你好，我是小明。你好，我是小红。你好，我是小刚。你好，我是小张。")) + \ No newline at end of file diff --git a/GPT_SoVITS/configs/tts_infer.yaml b/GPT_SoVITS/configs/tts_infer.yaml new file mode 100644 index 00000000..5f56a4ec --- /dev/null +++ b/GPT_SoVITS/configs/tts_infer.yaml @@ -0,0 +1,14 @@ +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/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt + vits_weights_path: GPT_SoVITS/pretrained_models/s2G488k.pth +default: + 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 diff --git a/GPT_SoVITS/feature_extractor/cnhubert.py b/GPT_SoVITS/feature_extractor/cnhubert.py index dc155bdd..7dffbdb2 100644 --- a/GPT_SoVITS/feature_extractor/cnhubert.py +++ b/GPT_SoVITS/feature_extractor/cnhubert.py @@ -20,13 +20,16 @@ class CNHubert(nn.Module): - def __init__(self): + def __init__(self, base_path:str=None): super().__init__() - self.model = HubertModel.from_pretrained(cnhubert_base_path) + if base_path is None: + base_path = cnhubert_base_path + self.model = HubertModel.from_pretrained(base_path) self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( - cnhubert_base_path + base_path ) + def forward(self, x): input_values = self.feature_extractor( x, return_tensors="pt", sampling_rate=16000 diff --git a/GPT_SoVITS/inference_gui.py b/GPT_SoVITS/inference_gui.py index f6cfdc5e..830c66de 100644 --- a/GPT_SoVITS/inference_gui.py +++ b/GPT_SoVITS/inference_gui.py @@ -7,7 +7,7 @@ from tools.i18n.i18n import I18nAuto i18n = I18nAuto() -from GPT_SoVITS.inference_webui import change_gpt_weights, change_sovits_weights, get_tts_wav +from GPT_SoVITS.inference_webui_old import change_gpt_weights, change_sovits_weights, get_tts_wav class GPTSoVITSGUI(QMainWindow): diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index ee099627..68a2136a 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -7,7 +7,7 @@ 全部按日文识别 ''' import os, re, logging -import LangSegment + logging.getLogger("markdown_it").setLevel(logging.ERROR) logging.getLogger("urllib3").setLevel(logging.ERROR) logging.getLogger("httpcore").setLevel(logging.ERROR) @@ -17,32 +17,12 @@ logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) import pdb import torch +# modified from https://github.com/feng-yufei/shared_debugging_code/blob/main/model/t2s_lightning_module.py +import os, sys -if os.path.exists("./gweight.txt"): - with open("./gweight.txt", 'r', encoding="utf-8") as file: - gweight_data = file.read() - gpt_path = os.environ.get( - "gpt_path", gweight_data) -else: - gpt_path = os.environ.get( - "gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt") +now_dir = os.getcwd() +sys.path.append(now_dir) -if os.path.exists("./sweight.txt"): - with open("./sweight.txt", 'r', encoding="utf-8") as file: - sweight_data = file.read() - sovits_path = os.environ.get("sovits_path", sweight_data) -else: - sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth") -# gpt_path = os.environ.get( -# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" -# ) -# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth") -cnhubert_base_path = os.environ.get( - "cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base" -) -bert_path = os.environ.get( - "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large" -) infer_ttswebui = os.environ.get("infer_ttswebui", 9872) infer_ttswebui = int(infer_ttswebui) is_share = os.environ.get("is_share", "False") @@ -51,22 +31,9 @@ os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] is_half = eval(os.environ.get("is_half", "True")) and not torch.backends.mps.is_available() import gradio as gr -from transformers import AutoModelForMaskedLM, AutoTokenizer -import numpy as np -import librosa -from feature_extractor import cnhubert - -cnhubert.cnhubert_base_path = cnhubert_base_path - -from module.models import SynthesizerTrn -from AR.models.t2s_lightning_module import Text2SemanticLightningModule -from text import cleaned_text_to_sequence -from text.cleaner import clean_text -from time import time as ttime -from module.mel_processing import spectrogram_torch -from my_utils import load_audio +from TTS_infer_pack.TTS import TTS, TTS_Config +from TTS_infer_pack.text_segmentation_method import cut1, cut2, cut3, cut4, cut5 from tools.i18n.i18n import I18nAuto - i18n = I18nAuto() os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。 @@ -76,128 +43,6 @@ else: device = "cpu" -tokenizer = AutoTokenizer.from_pretrained(bert_path) -bert_model = AutoModelForMaskedLM.from_pretrained(bert_path) -if is_half == True: - bert_model = bert_model.half().to(device) -else: - bert_model = bert_model.to(device) - - -def get_bert_feature(text, word2ph): - with torch.no_grad(): - inputs = tokenizer(text, return_tensors="pt") - for i in inputs: - inputs[i] = inputs[i].to(device) - res = bert_model(**inputs, output_hidden_states=True) - res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] - assert len(word2ph) == len(text) - phone_level_feature = [] - for i in range(len(word2ph)): - repeat_feature = res[i].repeat(word2ph[i], 1) - phone_level_feature.append(repeat_feature) - phone_level_feature = torch.cat(phone_level_feature, dim=0) - return phone_level_feature.T - - -class DictToAttrRecursive(dict): - def __init__(self, input_dict): - super().__init__(input_dict) - for key, value in input_dict.items(): - if isinstance(value, dict): - value = DictToAttrRecursive(value) - self[key] = value - setattr(self, key, value) - - def __getattr__(self, item): - try: - return self[item] - except KeyError: - raise AttributeError(f"Attribute {item} not found") - - def __setattr__(self, key, value): - if isinstance(value, dict): - value = DictToAttrRecursive(value) - super(DictToAttrRecursive, self).__setitem__(key, value) - super().__setattr__(key, value) - - def __delattr__(self, item): - try: - del self[item] - except KeyError: - raise AttributeError(f"Attribute {item} not found") - - -ssl_model = cnhubert.get_model() -if is_half == True: - ssl_model = ssl_model.half().to(device) -else: - ssl_model = ssl_model.to(device) - - -def change_sovits_weights(sovits_path): - global vq_model, hps - dict_s2 = torch.load(sovits_path, map_location="cpu") - hps = dict_s2["config"] - hps = DictToAttrRecursive(hps) - hps.model.semantic_frame_rate = "25hz" - vq_model = SynthesizerTrn( - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model - ) - if ("pretrained" not in sovits_path): - del vq_model.enc_q - if is_half == True: - vq_model = vq_model.half().to(device) - else: - vq_model = vq_model.to(device) - vq_model.eval() - print(vq_model.load_state_dict(dict_s2["weight"], strict=False)) - with open("./sweight.txt", "w", encoding="utf-8") as f: - f.write(sovits_path) - - -change_sovits_weights(sovits_path) - - -def change_gpt_weights(gpt_path): - global hz, max_sec, t2s_model, config - hz = 50 - dict_s1 = torch.load(gpt_path, map_location="cpu") - config = dict_s1["config"] - max_sec = config["data"]["max_sec"] - t2s_model = Text2SemanticLightningModule(config, "****", is_train=False) - t2s_model.load_state_dict(dict_s1["weight"]) - if is_half == True: - t2s_model = t2s_model.half() - t2s_model = t2s_model.to(device) - t2s_model.eval() - total = sum([param.nelement() for param in t2s_model.parameters()]) - print("Number of parameter: %.2fM" % (total / 1e6)) - with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path) - - -change_gpt_weights(gpt_path) - - -def get_spepc(hps, filename): - audio = load_audio(filename, int(hps.data.sampling_rate)) - audio = torch.FloatTensor(audio) - audio_norm = audio - audio_norm = audio_norm.unsqueeze(0) - spec = spectrogram_torch( - audio_norm, - hps.data.filter_length, - hps.data.sampling_rate, - hps.data.hop_length, - hps.data.win_length, - center=False, - ) - return spec - - dict_language = { i18n("中文"): "all_zh",#全部按中文识别 i18n("英文"): "en",#全部按英文识别#######不变 @@ -207,313 +52,36 @@ def get_spepc(hps, filename): i18n("多语种混合"): "auto",#多语种启动切分识别语种 } +cut_method = { + i18n("不切"):"cut0", + i18n("凑四句一切"): "cut1", + i18n("凑50字一切"): "cut2", + i18n("按中文句号。切"): "cut3", + i18n("按英文句号.切"): "cut4", + i18n("按标点符号切"): "cut5", +} -def clean_text_inf(text, language): - phones, word2ph, norm_text = clean_text(text, language) - phones = cleaned_text_to_sequence(phones) - return phones, word2ph, norm_text - -dtype=torch.float16 if is_half == True else torch.float32 -def get_bert_inf(phones, word2ph, norm_text, language): - language=language.replace("all_","") - if language == "zh": - bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype) - else: - bert = torch.zeros( - (1024, len(phones)), - dtype=torch.float16 if is_half == True else torch.float32, - ).to(device) - - return bert - - -splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } - - -def get_first(text): - pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" - text = re.split(pattern, text)[0].strip() - return text - - -def get_phones_and_bert(text,language): - if language in {"en","all_zh","all_ja"}: - language = language.replace("all_","") - if language == "en": - LangSegment.setfilters(["en"]) - formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) - else: - # 因无法区别中日文汉字,以用户输入为准 - formattext = text - while " " in formattext: - formattext = formattext.replace(" ", " ") - phones, word2ph, norm_text = clean_text_inf(formattext, language) - if language == "zh": - bert = get_bert_feature(norm_text, word2ph).to(device) - else: - bert = torch.zeros( - (1024, len(phones)), - dtype=torch.float16 if is_half == True else torch.float32, - ).to(device) - elif language in {"zh", "ja","auto"}: - textlist=[] - langlist=[] - LangSegment.setfilters(["zh","ja","en","ko"]) - if language == "auto": - for tmp in LangSegment.getTexts(text): - if tmp["lang"] == "ko": - langlist.append("zh") - textlist.append(tmp["text"]) - else: - langlist.append(tmp["lang"]) - textlist.append(tmp["text"]) - else: - for tmp in LangSegment.getTexts(text): - if tmp["lang"] == "en": - langlist.append(tmp["lang"]) - else: - # 因无法区别中日文汉字,以用户输入为准 - langlist.append(language) - textlist.append(tmp["text"]) - print(textlist) - print(langlist) - phones_list = [] - bert_list = [] - norm_text_list = [] - for i in range(len(textlist)): - lang = langlist[i] - phones, word2ph, norm_text = clean_text_inf(textlist[i], lang) - bert = get_bert_inf(phones, word2ph, norm_text, lang) - phones_list.append(phones) - norm_text_list.append(norm_text) - bert_list.append(bert) - bert = torch.cat(bert_list, dim=1) - phones = sum(phones_list, []) - norm_text = ''.join(norm_text_list) - - return phones,bert.to(dtype),norm_text - - -def merge_short_text_in_array(texts, threshold): - if (len(texts)) < 2: - return texts - result = [] - text = "" - for ele in texts: - text += ele - if len(text) >= threshold: - result.append(text) - text = "" - if (len(text) > 0): - if len(result) == 0: - result.append(text) - else: - result[len(result) - 1] += text - return result - -def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False): - if prompt_text is None or len(prompt_text) == 0: - ref_free = True - t0 = ttime() - prompt_language = dict_language[prompt_language] - text_language = dict_language[text_language] - if not ref_free: - prompt_text = prompt_text.strip("\n") - if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "." - print(i18n("实际输入的参考文本:"), prompt_text) - text = text.strip("\n") - if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text - - print(i18n("实际输入的目标文本:"), text) - zero_wav = np.zeros( - int(hps.data.sampling_rate * 0.3), - dtype=np.float16 if is_half == True else np.float32, - ) - with torch.no_grad(): - wav16k, sr = librosa.load(ref_wav_path, sr=16000) - if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): - raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) - wav16k = torch.from_numpy(wav16k) - zero_wav_torch = torch.from_numpy(zero_wav) - if is_half == True: - wav16k = wav16k.half().to(device) - zero_wav_torch = zero_wav_torch.half().to(device) - else: - wav16k = wav16k.to(device) - zero_wav_torch = zero_wav_torch.to(device) - wav16k = torch.cat([wav16k, zero_wav_torch]) - ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ - "last_hidden_state" - ].transpose( - 1, 2 - ) # .float() - codes = vq_model.extract_latent(ssl_content) - - prompt_semantic = codes[0, 0] - t1 = ttime() - - if (how_to_cut == i18n("凑四句一切")): - text = cut1(text) - elif (how_to_cut == i18n("凑50字一切")): - text = cut2(text) - elif (how_to_cut == i18n("按中文句号。切")): - text = cut3(text) - elif (how_to_cut == i18n("按英文句号.切")): - text = cut4(text) - elif (how_to_cut == i18n("按标点符号切")): - text = cut5(text) - while "\n\n" in text: - text = text.replace("\n\n", "\n") - print(i18n("实际输入的目标文本(切句后):"), text) - texts = text.split("\n") - texts = merge_short_text_in_array(texts, 5) - audio_opt = [] - if not ref_free: - phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language) - - for text in texts: - # 解决输入目标文本的空行导致报错的问题 - if (len(text.strip()) == 0): - continue - if (text[-1] not in splits): text += "。" if text_language != "en" else "." - print(i18n("实际输入的目标文本(每句):"), text) - phones2,bert2,norm_text2=get_phones_and_bert(text, text_language) - print(i18n("前端处理后的文本(每句):"), norm_text2) - if not ref_free: - bert = torch.cat([bert1, bert2], 1) - all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0) - else: - bert = bert2 - all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0) - - bert = bert.to(device).unsqueeze(0) - all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device) - prompt = prompt_semantic.unsqueeze(0).to(device) - t2 = ttime() - with torch.no_grad(): - # pred_semantic = t2s_model.model.infer( - pred_semantic, idx = t2s_model.model.infer_panel( - all_phoneme_ids, - all_phoneme_len, - None if ref_free else prompt, - bert, - # prompt_phone_len=ph_offset, - top_k=top_k, - top_p=top_p, - temperature=temperature, - early_stop_num=hz * max_sec, - ) - t3 = ttime() - # print(pred_semantic.shape,idx) - pred_semantic = pred_semantic[:, -idx:].unsqueeze( - 0 - ) # .unsqueeze(0)#mq要多unsqueeze一次 - refer = get_spepc(hps, ref_wav_path) # .to(device) - if is_half == True: - refer = refer.half().to(device) - else: - refer = refer.to(device) - # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0] - audio = ( - vq_model.decode( - pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer - ) - .detach() - .cpu() - .numpy()[0, 0] - ) ###试试重建不带上prompt部分 - max_audio=np.abs(audio).max()#简单防止16bit爆音 - if max_audio>1:audio/=max_audio - audio_opt.append(audio) - audio_opt.append(zero_wav) - t4 = ttime() - print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) - yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype( - np.int16 - ) - - -def split(todo_text): - todo_text = todo_text.replace("……", "。").replace("——", "，") - if todo_text[-1] not in splits: - todo_text += "。" - i_split_head = i_split_tail = 0 - len_text = len(todo_text) - todo_texts = [] - while 1: - if i_split_head >= len_text: - break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入 - if todo_text[i_split_head] in splits: - i_split_head += 1 - todo_texts.append(todo_text[i_split_tail:i_split_head]) - i_split_tail = i_split_head - else: - i_split_head += 1 - return todo_texts - - -def cut1(inp): - inp = inp.strip("\n") - inps = split(inp) - split_idx = list(range(0, len(inps), 4)) - split_idx[-1] = None - if len(split_idx) > 1: - opts = [] - for idx in range(len(split_idx) - 1): - opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]])) - else: - opts = [inp] - return "\n".join(opts) - - -def cut2(inp): - inp = inp.strip("\n") - inps = split(inp) - if len(inps) < 2: - return inp - opts = [] - summ = 0 - tmp_str = "" - for i in range(len(inps)): - summ += len(inps[i]) - tmp_str += inps[i] - if summ > 50: - summ = 0 - opts.append(tmp_str) - tmp_str = "" - if tmp_str != "": - opts.append(tmp_str) - # print(opts) - if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起 - opts[-2] = opts[-2] + opts[-1] - opts = opts[:-1] - return "\n".join(opts) - - -def cut3(inp): - inp = inp.strip("\n") - return "\n".join(["%s" % item for item in inp.strip("。").split("。")]) - - -def cut4(inp): - inp = inp.strip("\n") - return "\n".join(["%s" % item for item in inp.strip(".").split(".")]) - - -# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py -def cut5(inp): - # if not re.search(r'[^\w\s]', inp[-1]): - # inp += '。' - inp = inp.strip("\n") - punds = r'[,.;?!、，。？！;：…]' - items = re.split(f'({punds})', inp) - mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])] - # 在句子不存在符号或句尾无符号的时候保证文本完整 - if len(items)%2 == 1: - mergeitems.append(items[-1]) - opt = "\n".join(mergeitems) - return opt - +tts_config = TTS_Config("GPT_SoVITS/configs/tts_infer.yaml") +tts_config.device = device +tts_config.is_half = is_half +tts_pipline = TTS(tts_config) +gpt_path = tts_config.t2s_weights_path +sovits_path = tts_config.vits_weights_path + +def inference(text, text_lang, ref_audio_path, prompt_text, prompt_lang, top_k, top_p, temperature, text_split_method, batch_size): + inputs={ + "text": text, + "text_lang": dict_language[text_lang], + "ref_audio_path": ref_audio_path, + "prompt_text": prompt_text, + "prompt_lang": dict_language[prompt_lang], + "top_k": top_k, + "top_p": top_p, + "temperature": temperature, + "text_split_method": cut_method[text_split_method], + "batch_size":int(batch_size), + } + yield next(tts_pipline.run(inputs)) def custom_sort_key(s): # 使用正则表达式提取字符串中的数字部分和非数字部分 @@ -559,8 +127,8 @@ def get_weights_names(): SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True) refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary") refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]) - SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], []) - GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], []) + SoVITS_dropdown.change(tts_pipline.init_vits_weights, [SoVITS_dropdown], []) + GPT_dropdown.change(tts_pipline.init_t2s_weights, [GPT_dropdown], []) gr.Markdown(value=i18n("*请上传并填写参考信息")) with gr.Row(): inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath") @@ -585,15 +153,19 @@ def get_weights_names(): ) with gr.Row(): gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)：")) + batch_size = gr.Slider(minimum=1,maximum=20,step=1,label=i18n("batch_size"),value=1,interactive=True) top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True) top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True) temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True) inference_button = gr.Button(i18n("合成语音"), variant="primary") output = gr.Audio(label=i18n("输出的语音")) + + + inference_button.click( - get_tts_wav, - [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free], + inference, + [text,text_language, inp_ref, prompt_text, prompt_language, top_k, top_p, temperature, how_to_cut, batch_size], [output], ) diff --git a/GPT_SoVITS/inference_webui_old.py b/GPT_SoVITS/inference_webui_old.py new file mode 100644 index 00000000..ee099627 --- /dev/null +++ b/GPT_SoVITS/inference_webui_old.py @@ -0,0 +1,622 @@ +''' +按中英混合识别 +按日英混合识别 +多语种启动切分识别语种 +全部按中文识别 +全部按英文识别 +全部按日文识别 +''' +import os, re, logging +import LangSegment +logging.getLogger("markdown_it").setLevel(logging.ERROR) +logging.getLogger("urllib3").setLevel(logging.ERROR) +logging.getLogger("httpcore").setLevel(logging.ERROR) +logging.getLogger("httpx").setLevel(logging.ERROR) +logging.getLogger("asyncio").setLevel(logging.ERROR) +logging.getLogger("charset_normalizer").setLevel(logging.ERROR) +logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) +import pdb +import torch + +if os.path.exists("./gweight.txt"): + with open("./gweight.txt", 'r', encoding="utf-8") as file: + gweight_data = file.read() + gpt_path = os.environ.get( + "gpt_path", gweight_data) +else: + gpt_path = os.environ.get( + "gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt") + +if os.path.exists("./sweight.txt"): + with open("./sweight.txt", 'r', encoding="utf-8") as file: + sweight_data = file.read() + sovits_path = os.environ.get("sovits_path", sweight_data) +else: + sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth") +# gpt_path = os.environ.get( +# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" +# ) +# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth") +cnhubert_base_path = os.environ.get( + "cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base" +) +bert_path = os.environ.get( + "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large" +) +infer_ttswebui = os.environ.get("infer_ttswebui", 9872) +infer_ttswebui = int(infer_ttswebui) +is_share = os.environ.get("is_share", "False") +is_share = eval(is_share) +if "_CUDA_VISIBLE_DEVICES" in os.environ: + os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] +is_half = eval(os.environ.get("is_half", "True")) and not torch.backends.mps.is_available() +import gradio as gr +from transformers import AutoModelForMaskedLM, AutoTokenizer +import numpy as np +import librosa +from feature_extractor import cnhubert + +cnhubert.cnhubert_base_path = cnhubert_base_path + +from module.models import SynthesizerTrn +from AR.models.t2s_lightning_module import Text2SemanticLightningModule +from text import cleaned_text_to_sequence +from text.cleaner import clean_text +from time import time as ttime +from module.mel_processing import spectrogram_torch +from my_utils import load_audio +from tools.i18n.i18n import I18nAuto + +i18n = I18nAuto() + +os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。 + +if torch.cuda.is_available(): + device = "cuda" +else: + device = "cpu" + +tokenizer = AutoTokenizer.from_pretrained(bert_path) +bert_model = AutoModelForMaskedLM.from_pretrained(bert_path) +if is_half == True: + bert_model = bert_model.half().to(device) +else: + bert_model = bert_model.to(device) + + +def get_bert_feature(text, word2ph): + with torch.no_grad(): + inputs = tokenizer(text, return_tensors="pt") + for i in inputs: + inputs[i] = inputs[i].to(device) + res = bert_model(**inputs, output_hidden_states=True) + res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1] + assert len(word2ph) == len(text) + phone_level_feature = [] + for i in range(len(word2ph)): + repeat_feature = res[i].repeat(word2ph[i], 1) + phone_level_feature.append(repeat_feature) + phone_level_feature = torch.cat(phone_level_feature, dim=0) + return phone_level_feature.T + + +class DictToAttrRecursive(dict): + def __init__(self, input_dict): + super().__init__(input_dict) + for key, value in input_dict.items(): + if isinstance(value, dict): + value = DictToAttrRecursive(value) + self[key] = value + setattr(self, key, value) + + def __getattr__(self, item): + try: + return self[item] + except KeyError: + raise AttributeError(f"Attribute {item} not found") + + def __setattr__(self, key, value): + if isinstance(value, dict): + value = DictToAttrRecursive(value) + super(DictToAttrRecursive, self).__setitem__(key, value) + super().__setattr__(key, value) + + def __delattr__(self, item): + try: + del self[item] + except KeyError: + raise AttributeError(f"Attribute {item} not found") + + +ssl_model = cnhubert.get_model() +if is_half == True: + ssl_model = ssl_model.half().to(device) +else: + ssl_model = ssl_model.to(device) + + +def change_sovits_weights(sovits_path): + global vq_model, hps + dict_s2 = torch.load(sovits_path, map_location="cpu") + hps = dict_s2["config"] + hps = DictToAttrRecursive(hps) + hps.model.semantic_frame_rate = "25hz" + vq_model = SynthesizerTrn( + hps.data.filter_length // 2 + 1, + hps.train.segment_size // hps.data.hop_length, + n_speakers=hps.data.n_speakers, + **hps.model + ) + if ("pretrained" not in sovits_path): + del vq_model.enc_q + if is_half == True: + vq_model = vq_model.half().to(device) + else: + vq_model = vq_model.to(device) + vq_model.eval() + print(vq_model.load_state_dict(dict_s2["weight"], strict=False)) + with open("./sweight.txt", "w", encoding="utf-8") as f: + f.write(sovits_path) + + +change_sovits_weights(sovits_path) + + +def change_gpt_weights(gpt_path): + global hz, max_sec, t2s_model, config + hz = 50 + dict_s1 = torch.load(gpt_path, map_location="cpu") + config = dict_s1["config"] + max_sec = config["data"]["max_sec"] + t2s_model = Text2SemanticLightningModule(config, "****", is_train=False) + t2s_model.load_state_dict(dict_s1["weight"]) + if is_half == True: + t2s_model = t2s_model.half() + t2s_model = t2s_model.to(device) + t2s_model.eval() + total = sum([param.nelement() for param in t2s_model.parameters()]) + print("Number of parameter: %.2fM" % (total / 1e6)) + with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path) + + +change_gpt_weights(gpt_path) + + +def get_spepc(hps, filename): + audio = load_audio(filename, int(hps.data.sampling_rate)) + audio = torch.FloatTensor(audio) + audio_norm = audio + audio_norm = audio_norm.unsqueeze(0) + spec = spectrogram_torch( + audio_norm, + hps.data.filter_length, + hps.data.sampling_rate, + hps.data.hop_length, + hps.data.win_length, + center=False, + ) + return spec + + +dict_language = { + i18n("中文"): "all_zh",#全部按中文识别 + i18n("英文"): "en",#全部按英文识别#######不变 + i18n("日文"): "all_ja",#全部按日文识别 + i18n("中英混合"): "zh",#按中英混合识别####不变 + i18n("日英混合"): "ja",#按日英混合识别####不变 + i18n("多语种混合"): "auto",#多语种启动切分识别语种 +} + + +def clean_text_inf(text, language): + phones, word2ph, norm_text = clean_text(text, language) + phones = cleaned_text_to_sequence(phones) + return phones, word2ph, norm_text + +dtype=torch.float16 if is_half == True else torch.float32 +def get_bert_inf(phones, word2ph, norm_text, language): + language=language.replace("all_","") + if language == "zh": + bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype) + else: + bert = torch.zeros( + (1024, len(phones)), + dtype=torch.float16 if is_half == True else torch.float32, + ).to(device) + + return bert + + +splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } + + +def get_first(text): + pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" + text = re.split(pattern, text)[0].strip() + return text + + +def get_phones_and_bert(text,language): + if language in {"en","all_zh","all_ja"}: + language = language.replace("all_","") + if language == "en": + LangSegment.setfilters(["en"]) + formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text)) + else: + # 因无法区别中日文汉字,以用户输入为准 + formattext = text + while " " in formattext: + formattext = formattext.replace(" ", " ") + phones, word2ph, norm_text = clean_text_inf(formattext, language) + if language == "zh": + bert = get_bert_feature(norm_text, word2ph).to(device) + else: + bert = torch.zeros( + (1024, len(phones)), + dtype=torch.float16 if is_half == True else torch.float32, + ).to(device) + elif language in {"zh", "ja","auto"}: + textlist=[] + langlist=[] + LangSegment.setfilters(["zh","ja","en","ko"]) + if language == "auto": + for tmp in LangSegment.getTexts(text): + if tmp["lang"] == "ko": + langlist.append("zh") + textlist.append(tmp["text"]) + else: + langlist.append(tmp["lang"]) + textlist.append(tmp["text"]) + else: + for tmp in LangSegment.getTexts(text): + if tmp["lang"] == "en": + langlist.append(tmp["lang"]) + else: + # 因无法区别中日文汉字,以用户输入为准 + langlist.append(language) + textlist.append(tmp["text"]) + print(textlist) + print(langlist) + phones_list = [] + bert_list = [] + norm_text_list = [] + for i in range(len(textlist)): + lang = langlist[i] + phones, word2ph, norm_text = clean_text_inf(textlist[i], lang) + bert = get_bert_inf(phones, word2ph, norm_text, lang) + phones_list.append(phones) + norm_text_list.append(norm_text) + bert_list.append(bert) + bert = torch.cat(bert_list, dim=1) + phones = sum(phones_list, []) + norm_text = ''.join(norm_text_list) + + return phones,bert.to(dtype),norm_text + + +def merge_short_text_in_array(texts, threshold): + if (len(texts)) < 2: + return texts + result = [] + text = "" + for ele in texts: + text += ele + if len(text) >= threshold: + result.append(text) + text = "" + if (len(text) > 0): + if len(result) == 0: + result.append(text) + else: + result[len(result) - 1] += text + return result + +def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False): + if prompt_text is None or len(prompt_text) == 0: + ref_free = True + t0 = ttime() + prompt_language = dict_language[prompt_language] + text_language = dict_language[text_language] + if not ref_free: + prompt_text = prompt_text.strip("\n") + if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "." + print(i18n("实际输入的参考文本:"), prompt_text) + text = text.strip("\n") + if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text + + print(i18n("实际输入的目标文本:"), text) + zero_wav = np.zeros( + int(hps.data.sampling_rate * 0.3), + dtype=np.float16 if is_half == True else np.float32, + ) + with torch.no_grad(): + wav16k, sr = librosa.load(ref_wav_path, sr=16000) + if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): + raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) + wav16k = torch.from_numpy(wav16k) + zero_wav_torch = torch.from_numpy(zero_wav) + if is_half == True: + wav16k = wav16k.half().to(device) + zero_wav_torch = zero_wav_torch.half().to(device) + else: + wav16k = wav16k.to(device) + zero_wav_torch = zero_wav_torch.to(device) + wav16k = torch.cat([wav16k, zero_wav_torch]) + ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ + "last_hidden_state" + ].transpose( + 1, 2 + ) # .float() + codes = vq_model.extract_latent(ssl_content) + + prompt_semantic = codes[0, 0] + t1 = ttime() + + if (how_to_cut == i18n("凑四句一切")): + text = cut1(text) + elif (how_to_cut == i18n("凑50字一切")): + text = cut2(text) + elif (how_to_cut == i18n("按中文句号。切")): + text = cut3(text) + elif (how_to_cut == i18n("按英文句号.切")): + text = cut4(text) + elif (how_to_cut == i18n("按标点符号切")): + text = cut5(text) + while "\n\n" in text: + text = text.replace("\n\n", "\n") + print(i18n("实际输入的目标文本(切句后):"), text) + texts = text.split("\n") + texts = merge_short_text_in_array(texts, 5) + audio_opt = [] + if not ref_free: + phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language) + + for text in texts: + # 解决输入目标文本的空行导致报错的问题 + if (len(text.strip()) == 0): + continue + if (text[-1] not in splits): text += "。" if text_language != "en" else "." + print(i18n("实际输入的目标文本(每句):"), text) + phones2,bert2,norm_text2=get_phones_and_bert(text, text_language) + print(i18n("前端处理后的文本(每句):"), norm_text2) + if not ref_free: + bert = torch.cat([bert1, bert2], 1) + all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0) + else: + bert = bert2 + all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0) + + bert = bert.to(device).unsqueeze(0) + all_phoneme_len = torch.tensor([all_phoneme_ids.shape[-1]]).to(device) + prompt = prompt_semantic.unsqueeze(0).to(device) + t2 = ttime() + with torch.no_grad(): + # pred_semantic = t2s_model.model.infer( + pred_semantic, idx = t2s_model.model.infer_panel( + all_phoneme_ids, + all_phoneme_len, + None if ref_free else prompt, + bert, + # prompt_phone_len=ph_offset, + top_k=top_k, + top_p=top_p, + temperature=temperature, + early_stop_num=hz * max_sec, + ) + t3 = ttime() + # print(pred_semantic.shape,idx) + pred_semantic = pred_semantic[:, -idx:].unsqueeze( + 0 + ) # .unsqueeze(0)#mq要多unsqueeze一次 + refer = get_spepc(hps, ref_wav_path) # .to(device) + if is_half == True: + refer = refer.half().to(device) + else: + refer = refer.to(device) + # audio = vq_model.decode(pred_semantic, all_phoneme_ids, refer).detach().cpu().numpy()[0, 0] + audio = ( + vq_model.decode( + pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer + ) + .detach() + .cpu() + .numpy()[0, 0] + ) ###试试重建不带上prompt部分 + max_audio=np.abs(audio).max()#简单防止16bit爆音 + if max_audio>1:audio/=max_audio + audio_opt.append(audio) + audio_opt.append(zero_wav) + t4 = ttime() + print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) + yield hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype( + np.int16 + ) + + +def split(todo_text): + todo_text = todo_text.replace("……", "。").replace("——", "，") + if todo_text[-1] not in splits: + todo_text += "。" + i_split_head = i_split_tail = 0 + len_text = len(todo_text) + todo_texts = [] + while 1: + if i_split_head >= len_text: + break # 结尾一定有标点，所以直接跳出即可，最后一段在上次已加入 + if todo_text[i_split_head] in splits: + i_split_head += 1 + todo_texts.append(todo_text[i_split_tail:i_split_head]) + i_split_tail = i_split_head + else: + i_split_head += 1 + return todo_texts + + +def cut1(inp): + inp = inp.strip("\n") + inps = split(inp) + split_idx = list(range(0, len(inps), 4)) + split_idx[-1] = None + if len(split_idx) > 1: + opts = [] + for idx in range(len(split_idx) - 1): + opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]])) + else: + opts = [inp] + return "\n".join(opts) + + +def cut2(inp): + inp = inp.strip("\n") + inps = split(inp) + if len(inps) < 2: + return inp + opts = [] + summ = 0 + tmp_str = "" + for i in range(len(inps)): + summ += len(inps[i]) + tmp_str += inps[i] + if summ > 50: + summ = 0 + opts.append(tmp_str) + tmp_str = "" + if tmp_str != "": + opts.append(tmp_str) + # print(opts) + if len(opts) > 1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起 + opts[-2] = opts[-2] + opts[-1] + opts = opts[:-1] + return "\n".join(opts) + + +def cut3(inp): + inp = inp.strip("\n") + return "\n".join(["%s" % item for item in inp.strip("。").split("。")]) + + +def cut4(inp): + inp = inp.strip("\n") + return "\n".join(["%s" % item for item in inp.strip(".").split(".")]) + + +# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py +def cut5(inp): + # if not re.search(r'[^\w\s]', inp[-1]): + # inp += '。' + inp = inp.strip("\n") + punds = r'[,.;?!、，。？！;：…]' + items = re.split(f'({punds})', inp) + mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])] + # 在句子不存在符号或句尾无符号的时候保证文本完整 + if len(items)%2 == 1: + mergeitems.append(items[-1]) + opt = "\n".join(mergeitems) + return opt + + +def custom_sort_key(s): + # 使用正则表达式提取字符串中的数字部分和非数字部分 + parts = re.split('(\d+)', s) + # 将数字部分转换为整数，非数字部分保持不变 + parts = [int(part) if part.isdigit() else part for part in parts] + return parts + + +def change_choices(): + SoVITS_names, GPT_names = get_weights_names() + return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"} + + +pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth" +pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" +SoVITS_weight_root = "SoVITS_weights" +GPT_weight_root = "GPT_weights" +os.makedirs(SoVITS_weight_root, exist_ok=True) +os.makedirs(GPT_weight_root, exist_ok=True) + + +def get_weights_names(): + SoVITS_names = [pretrained_sovits_name] + for name in os.listdir(SoVITS_weight_root): + if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name)) + GPT_names = [pretrained_gpt_name] + for name in os.listdir(GPT_weight_root): + if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name)) + return SoVITS_names, GPT_names + + +SoVITS_names, GPT_names = get_weights_names() + +with gr.Blocks(title="GPT-SoVITS WebUI") as app: + gr.Markdown( + value=i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责.
如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录LICENSE.") + ) + with gr.Group(): + gr.Markdown(value=i18n("模型切换")) + with gr.Row(): + GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True) + SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True) + refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary") + refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]) + SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], []) + GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], []) + gr.Markdown(value=i18n("*请上传并填写参考信息")) + with gr.Row(): + inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath") + with gr.Column(): + ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True) + gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。")) + prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="") + prompt_language = gr.Dropdown( + label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文") + ) + gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式")) + with gr.Row(): + text = gr.Textbox(label=i18n("需要合成的文本"), value="") + text_language = gr.Dropdown( + label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文") + ) + how_to_cut = gr.Radio( + label=i18n("怎么切"), + choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ], + value=i18n("凑四句一切"), + interactive=True, + ) + with gr.Row(): + gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)：")) + top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True) + top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True) + temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True) + inference_button = gr.Button(i18n("合成语音"), variant="primary") + output = gr.Audio(label=i18n("输出的语音")) + + inference_button.click( + get_tts_wav, + [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut, top_k, top_p, temperature, ref_text_free], + [output], + ) + + gr.Markdown(value=i18n("文本切分工具。太长的文本合成出来效果不一定好，所以太长建议先切。合成会根据文本的换行分开合成再拼起来。")) + with gr.Row(): + text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="") + button1 = gr.Button(i18n("凑四句一切"), variant="primary") + button2 = gr.Button(i18n("凑50字一切"), variant="primary") + button3 = gr.Button(i18n("按中文句号。切"), variant="primary") + button4 = gr.Button(i18n("按英文句号.切"), variant="primary") + button5 = gr.Button(i18n("按标点符号切"), variant="primary") + text_opt = gr.Textbox(label=i18n("切分后文本"), value="") + button1.click(cut1, [text_inp], [text_opt]) + button2.click(cut2, [text_inp], [text_opt]) + button3.click(cut3, [text_inp], [text_opt]) + button4.click(cut4, [text_inp], [text_opt]) + button5.click(cut5, [text_inp], [text_opt]) + gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。")) + +app.queue(concurrency_count=511, max_size=1022).launch( + server_name="0.0.0.0", + inbrowser=True, + share=is_share, + server_port=infer_ttswebui, + quiet=True, +)