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| 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/otto/otto-e10.ckpt" | |
| ) | |
| sovits_path = os.environ.get("sovits_path", "models/otto/otto_e39_s1638.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) | |
| 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 cut4(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/otto' | |
| text_to_audio_mappings, audio_to_text_mappings = load_audio_text_mappings(audio_folder_path, 'otto.list') | |
| with gr.Blocks(title="GPT-SoVITS WebUI") as app: | |
| gr.Markdown(value=""" | |
| # <center>【AI电棍】在线语音生成（GPT-SoVITS）\n | |
| ### <center>模型作者：Xz乔希 https://space.bilibili.com/5859321\n | |
| ### <center>【GPT-SoVITS】在线合集：https://www.modelscope.cn/studios/xzjosh/GPT-SoVITS\n | |
| ### <center>数据集下载：https://huggingface.co/datasets/XzJosh/audiodataset\n | |
| ### <center>声音归属：电棍otto https://space.bilibili.com/628845081\n | |
| ### <center>GPT-SoVITS项目：https://github.com/RVC-Boss/GPT-SoVITS\n | |
| ### <center>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 | |
| ### <center>⚠️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（不建议选较长的）") | |
| with gr.Row(): | |
| audio_select = gr.Dropdown(label="选择参考音频（必选）", 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，中英混合选中文，日英混合选日文，暂不支持中日混合。") | |
| 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" | |
| ) | |
| how_to_cut = gr.Radio( | |
| label=("自动切分（长文本建议切分）"), | |
| choices=[("不切"),("凑五句一切"),("凑50字一切"),("按中文句号。切"),("按英文句号.切"),], | |
| value=("不切"), | |
| interactive=True, | |
| ) | |
| 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,how_to_cut], | |
| [output], | |
| ) | |
| gr.Markdown(value="文本切分工具，需要复制。") | |
| 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") | |
| button4 = gr.Button("按英文句号.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]) | |
| button4.click(cut4, [text_inp], [text_opt]) | |
| app.queue(max_size=10) | |
| app.launch(inbrowser=True) | |