import LangSegment import numpy as np import librosa import torch import re, os import librosa from transformers import AutoModelForMaskedLM, AutoTokenizer import sys sys.path.append('GPT_SoVITS/') from text import cleaned_text_to_sequence from text.cleaner import clean_text from feature_extractor import cnhubert from my_utils import load_audio from module.mel_processing import spectrogram_torch from module.models import SynthesizerTrn from AR.models.t2s_lightning_module import Text2SemanticLightningModule from scipy.io.wavfile import write from time import time as ttime if torch.cuda.is_available(): device = "cuda" elif torch.backends.mps.is_available(): device = "mps" else: device = "cpu" is_half = True splits = {",", "。", "?", "!", ",", ".", "?", "!", "~", ":", ":", "—", "…", } if device == "cuda": gpu_name = torch.cuda.get_device_name(0) if ( ("16" in gpu_name and "V100" not in gpu_name.upper()) or "P40" in gpu_name.upper() or "P10" in gpu_name.upper() or "1060" in gpu_name or "1070" in gpu_name or "1080" in gpu_name ): is_half=False if device=="cpu": is_half=False dtype=torch.float16 if is_half == True else torch.float32 bert_path = os.environ.get( "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large" ) cnhubert_base_path = os.environ.get( "cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base" ) cnhubert.cnhubert_base_path = cnhubert_base_path 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) ssl_model = cnhubert.get_model() if is_half == True: ssl_model = ssl_model.half().to(device) else: ssl_model = ssl_model.to(device) 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 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") def clean_text_inf(text, language): phones, word2ph, norm_text = clean_text(text, language.replace("all_","")) phones = cleaned_text_to_sequence(phones) return phones, word2ph, norm_text 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 def splite_en_inf(sentence, language): pattern = re.compile(r'[a-zA-Z ]+') textlist = [] langlist = [] pos = 0 for match in pattern.finditer(sentence): start, end = match.span() if start > pos: textlist.append(sentence[pos:start]) langlist.append(language) textlist.append(sentence[start:end]) langlist.append("en") pos = end if pos < len(sentence): textlist.append(sentence[pos:]) langlist.append(language) # Merge punctuation into previous word for i in range(len(textlist)-1, 0, -1): if re.match(r'^[\W_]+$', textlist[i]): textlist[i-1] += textlist[i] del textlist[i] del langlist[i] # Merge consecutive words with the same language tag i = 0 while i < len(langlist) - 1: if langlist[i] == langlist[i+1]: textlist[i] += textlist[i+1] del textlist[i+1] del langlist[i+1] else: i += 1 return textlist, langlist def nonen_clean_text_inf(text, language): if(language!="auto"): textlist, langlist = splite_en_inf(text, language) else: textlist=[] langlist=[] for tmp in LangSegment.getTexts(text): langlist.append(tmp["lang"]) textlist.append(tmp["text"]) print(textlist) print(langlist) phones_list = [] word2ph_list = [] norm_text_list = [] for i in range(len(textlist)): lang = langlist[i] phones, word2ph, norm_text = clean_text_inf(textlist[i], lang) phones_list.append(phones) if lang == "zh": word2ph_list.append(word2ph) norm_text_list.append(norm_text) print(word2ph_list) phones = sum(phones_list, []) word2ph = sum(word2ph_list, []) norm_text = ' '.join(norm_text_list) return phones, word2ph, norm_text def nonen_get_bert_inf(text, language): if(language!="auto"): textlist, langlist = splite_en_inf(text, language) else: textlist=[] langlist=[] for tmp in LangSegment.getTexts(text): langlist.append(tmp["lang"]) textlist.append(tmp["text"]) print(textlist) print(langlist) bert_list = [] for i in range(len(textlist)): text = textlist[i] lang = langlist[i] phones, word2ph, norm_text = clean_text_inf(text, lang) bert = get_bert_inf(phones, word2ph, norm_text, lang) bert_list.append(bert) bert = torch.cat(bert_list, dim=1) return bert def get_first(text): pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" text = re.split(pattern, text)[0].strip() return text def get_cleaned_text_fianl(text,language): if language in {"en","all_zh","all_ja"}: phones, word2ph, norm_text = clean_text_inf(text, language) elif language in {"zh", "ja","auto"}: phones, word2ph, norm_text = nonen_clean_text_inf(text, language) return phones, word2ph, norm_text def get_bert_final(phones, word2ph, norm_text, text_language, device, text): if text_language == "en": bert = get_bert_inf(phones, word2ph, norm_text, text_language) elif text_language in {"zh", "ja","auto"}: bert = nonen_get_bert_inf(text, text_language) elif text_language == "all_zh": bert = get_bert_feature(norm_text, word2ph).to(device) else: bert = torch.zeros((1024, len(phones))).to(device) return bert 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) items = ["".join(group) for group in zip(items[::2], items[1::2])] opt = "\n".join(items) return opt class GPT_SoVITS: def __init__(self): self.model = None # is_half = True # device = "cuda" if torch.cuda.is_available() else "cpu" def load_model(self, gpt_path, sovits_path): self.hz = 50 dict_s1 = torch.load(gpt_path, map_location="cpu") self.config = dict_s1["config"] self.max_sec = self.config["data"]["max_sec"] t2s_model = Text2SemanticLightningModule(self.config, "****", is_train=False) t2s_model.load_state_dict(dict_s1["weight"]) if is_half == True: t2s_model = t2s_model.half() self.t2s_model = t2s_model.to(device) self.t2s_model.eval() total = sum([param.nelement() for param in t2s_model.parameters()]) print("Number of parameter: %.2fM" % (total / 1e6)) dict_s2 = torch.load(sovits_path, map_location="cpu") self.hps = dict_s2["config"] self.hps = DictToAttrRecursive(self.hps) self.hps.model.semantic_frame_rate = "25hz" vq_model = SynthesizerTrn( self.hps.data.filter_length // 2 + 1, self.hps.train.segment_size // self.hps.data.hop_length, n_speakers=self.hps.data.n_speakers, **self.hps.model ) if ("pretrained" not in sovits_path): del vq_model.enc_q if is_half == True: self.vq_model = vq_model.half().to(device) else: self.vq_model = vq_model.to(device) self.vq_model.eval() print(self.vq_model.load_state_dict(dict_s2["weight"], strict=False)) def predict(self, ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut="不切", save_path = 'vits_res.wav'): print(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut) return self.get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut, save_path) def get_tts_wav(self, ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut="不切", save_path = 'vits_res.wav'): t0 = ttime() prompt_text = prompt_text.strip("\n") if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "." 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("实际输入的参考文本:", prompt_text) print("实际输入的目标文本:", text) zero_wav = np.zeros( int(self.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("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 = self.vq_model.extract_latent(ssl_content) prompt_semantic = codes[0, 0] t1 = ttime() dict_language = { "中文": "all_zh",#全部按中文识别 "英文": "en",#全部按英文识别#######不变 "日文": "all_ja",#全部按日文识别 "中英混合": "zh",#按中英混合识别####不变 "日英混合": "ja",#按日英混合识别####不变 "多语种混合": "auto",#多语种启动切分识别语种 } prompt_language = dict_language[prompt_language] text_language = dict_language[text_language] phones1, word2ph1, norm_text1=get_cleaned_text_fianl(prompt_text, prompt_language) if (how_to_cut == "凑四句一切"): text = cut1(text) elif (how_to_cut == "凑50字一切"): text = cut2(text) elif (how_to_cut == "按中文句号。切"): text = cut3(text) elif (how_to_cut == "按英文句号.切"): text = cut4(text) elif (how_to_cut == "按标点符号切"): text = cut5(text) text = text.replace("\n\n", "\n").replace("\n\n", "\n").replace("\n\n", "\n") print("实际输入的目标文本(切句后):", text) texts = text.split("\n") audio_opt = [] bert1=get_bert_final(phones1, word2ph1, norm_text1, prompt_language, device, text).to(dtype) 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("实际输入的目标文本(每句):", text) phones2, word2ph2, norm_text2 = get_cleaned_text_fianl(text, text_language) bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device, text).to(dtype) bert = torch.cat([bert1, bert2], 1) all_phoneme_ids = torch.LongTensor(phones1 + 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 = self.t2s_model.model.infer_panel( all_phoneme_ids, all_phoneme_len, prompt, bert, # prompt_phone_len=ph_offset, top_k=self.config["inference"]["top_k"], early_stop_num=self.hz * self.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(self.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 = ( self.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)) print("%.3f\t%.3f\t%.3f\t%.3f" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3)) # yield self.hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype( # np.int16 # ) write(save_path, self.hps.data.sampling_rate, (np.concatenate(audio_opt, 0) * 32768).astype(np.int16)) return save_path if __name__ == "__main__": GPT_SoVITS_inference = GPT_SoVITS() gpt_path = "GPT_SoVITS/pretrained_models/Gnews-e15.ckpt" sovits_path = "GPT_SoVITS/pretrained_models/Gnews_e8_s96.pth" GPT_SoVITS_inference.load_model(gpt_path, sovits_path) ref_wav_path = "GPT_SoVITS/reference_wav/Gnews/Gnews.mp3_0000270720_0000424960.wav" # 参考音频的文本 from ASR import WhisperASR, FunASR asr = FunASR() prompt_text = "" prompt_text = asr.transcribe(ref_wav_path) prompt_language = "中文" text = "大家好,这是我语音克隆的声音,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." text_language = "中英混合" how_to_cut = "不切" # ["不切", "凑四句一切", "凑50字一切", "按中文句号。切", "按英文句号.切", "按标点符号切"] print("参考音频文本:", prompt_text) print("目标文本:", text) save_audio_file = "./result.wav" GPT_SoVITS_inference.predict(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut, save_audio_file)