import os,re,logging 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 gpt_path = os.environ.get( "gpt_path", "models/Taffy/Taffy-e5.ckpt" ) sovits_path = os.environ.get("sovits_path", "models/Taffy/Taffy_e20_s1020.pth") cnhubert_base_path = os.environ.get( "cnhubert_base_path", "pretrained_models/chinese-hubert-base" ) bert_path = os.environ.get( "bert_path", "pretrained_models/chinese-roberta-wwm-ext-large" ) infer_ttswebui = os.environ.get("infer_ttswebui", 9872) infer_ttswebui = int(infer_ttswebui) 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")) import gradio as gr from transformers import AutoModelForMaskedLM, AutoTokenizer import numpy as np import librosa,torch from feature_extractor import cnhubert cnhubert.cnhubert_base_path=cnhubert_base_path import ssl ssl._create_default_https_context = ssl._create_unverified_context import nltk nltk.download('cmudict') 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 device = "cuda" if torch.cuda.is_available() else "cpu" is_half = eval( os.environ.get("is_half", "True" if torch.cuda.is_available() else "False") ) 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) #####输入是long不用管精度问题,精度随bert_model 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)) 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)) 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={ ("中文"):"zh", ("英文"):"en", ("日文"):"ja" } 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) return textlist, langlist 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 def get_bert_inf(phones, word2ph, norm_text, 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) return bert def nonen_clean_text_inf(text, language): textlist, langlist = splite_en_inf(text, language) 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 == "en" or "ja": pass else: 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): textlist, langlist = splite_en_inf(text, language) 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_tts_wav(selected_text, prompt_text, prompt_language, text, text_language,how_to_cut=("不切")): ref_wav_path = text_to_audio_mappings.get(selected_text, "") if not ref_wav_path: print("Audio file not found for the selected text.") return t0 = ttime() prompt_text = prompt_text.strip("\n") prompt_language, text = prompt_language, text.strip("\n") 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) 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() prompt_language = dict_language[prompt_language] text_language = dict_language[text_language] if prompt_language == "en": phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language) else: phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language) if(how_to_cut==("凑五句一切")):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) text = text.replace("\n\n","\n").replace("\n\n","\n").replace("\n\n","\n") if(text[-1]not in splits):text+="。"if text_language!="en"else "." texts=text.split("\n") audio_opt = [] if prompt_language == "en": bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language) else: bert1 = nonen_get_bert_inf(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_language == "en": phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language) else: phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language) if text_language == "en": bert2 = get_bert_inf(phones2, word2ph2, norm_text2, text_language) else: bert2 = nonen_get_bert_inf(text, text_language) 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 = t2s_model.model.infer_panel( all_phoneme_ids, all_phoneme_len, prompt, bert, # prompt_phone_len=ph_offset, top_k=config["inference"]["top_k"], 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 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 ) splits = { ",", "。", "?", "!", ",", ".", "?", "!", "~", ":", ":", "—", "…", } # 不考虑省略号 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), 5)) 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) if 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 scan_audio_files(folder_path): """ 扫描指定文件夹获取音频文件列表 """ return [f for f in os.listdir(folder_path) if f.endswith('.wav')] def load_audio_text_mappings(folder_path, list_file_name): text_to_audio_mappings = {} audio_to_text_mappings = {} with open(os.path.join(folder_path, list_file_name), 'r', encoding='utf-8') as file: for line in file: parts = line.strip().split('|') if len(parts) >= 4: audio_file_name = parts[0] text = parts[3] audio_file_path = os.path.join(folder_path, audio_file_name) text_to_audio_mappings[text] = audio_file_path audio_to_text_mappings[audio_file_path] = text return text_to_audio_mappings, audio_to_text_mappings audio_folder_path = 'audio/Taffy' text_to_audio_mappings, audio_to_text_mappings = load_audio_text_mappings(audio_folder_path, 'Taffy.list') with gr.Blocks(title="GPT-SoVITS WebUI") as app: gr.Markdown(value=""" #
【AI塔菲】在线语音生成(GPT-SoVITS)\n ###
模型作者:Xz乔希 https://space.bilibili.com/5859321\n ###
GPT-SoVITS在线合集:https://www.modelscope.cn/studios/xzjosh/GPT-SoVITS\n ###
数据集下载:https://huggingface.co/datasets/XzJosh/audiodataset\n ###
声音归属:永雏塔菲 https://space.bilibili.com/1265680561\n ###
GPT-SoVITS项目:https://github.com/RVC-Boss/GPT-SoVITS\n ###
使用本模型请严格遵守法律法规!发布二创作品请标注本项目作者及链接、作品使用GPT-SoVITS AI生成!\n ###
⚠️在线端不稳定且生成速度较慢,强烈建议下载模型本地推理!\n """) # with gr.Tabs(): with gr.Group(): gr.Markdown(value="*参考音频选择(必选)") with gr.Row(): audio_select = gr.Dropdown(label="选择参考音频(不建议选较长的)", choices=list(text_to_audio_mappings.keys())) ref_audio = gr.Audio(label="参考音频试听") ref_text = gr.Textbox(label="参考音频文本") # 定义更新参考文本的函数 def update_ref_text_and_audio(selected_text): audio_path = text_to_audio_mappings.get(selected_text, "") return selected_text, audio_path # 绑定下拉菜单的变化到更新函数 audio_select.change(update_ref_text_and_audio, [audio_select], [ref_text, ref_audio]) # 其他 Gradio 组件和功能 prompt_language = gr.Dropdown( label="参考音频语种", choices=["中文", "英文", "日文"], value="中文" ) gr.Markdown(value="*请填写需要合成的目标文本") with gr.Row(): text = gr.Textbox(label="需要合成的文本", value="") text_language = gr.Dropdown( label="需要合成的语种", choices=["中文", "英文", "日文"], value="中文" ) inference_button = gr.Button("合成语音", variant="primary") output = gr.Audio(label="输出的语音") inference_button.click( get_tts_wav, [audio_select, ref_text, prompt_language, text, text_language], [output], ) gr.Markdown(value="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="需要合成的切分前文本", value="") button1 = gr.Button("凑五句一切", variant="primary") button2 = gr.Button("凑50字一切", variant="primary") button3 = gr.Button("按中文句号。切", variant="primary") text_opt = gr.Textbox(label="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]) app.queue(max_size=10) app.launch(inbrowser=True)