''' 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, )