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| import os | |
| 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", "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 | |
| 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" | |
| 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) | |
| # 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) | |
| # if(is_half==True):phone_level_feature=phone_level_feature.half() | |
| return phone_level_feature.T | |
| n_semantic = 1024 | |
| dict_s2=torch.load(sovits_path,map_location="cpu") | |
| hps=dict_s2["config"] | |
| 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") | |
| hps = DictToAttrRecursive(hps) | |
| hps.model.semantic_frame_rate = "25hz" | |
| dict_s1 = torch.load(gpt_path, map_location="cpu") | |
| config = dict_s1["config"] | |
| ssl_model = cnhubert.get_model() | |
| if is_half == True: | |
| ssl_model = ssl_model.half().to(device) | |
| else: | |
| ssl_model = ssl_model.to(device) | |
| 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 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)) | |
| hz = 50 | |
| max_sec = config["data"]["max_sec"] | |
| # t2s_model = Text2SemanticLightningModule.load_from_checkpoint(checkpoint_path=gpt_path, config=config, map_location="cpu")#########todo | |
| t2s_model = Text2SemanticLightningModule(config, "ojbk", 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)) | |
| 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 get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language): | |
| t0 = ttime() | |
| prompt_text = prompt_text.strip("\n") | |
| prompt_language, text = prompt_language, text.strip("\n") | |
| with torch.no_grad(): | |
| wav16k, sr = librosa.load(ref_wav_path, sr=16000) # 派蒙 | |
| wav16k = torch.from_numpy(wav16k) | |
| if is_half == True: | |
| wav16k = wav16k.half().to(device) | |
| else: | |
| wav16k = wav16k.to(device) | |
| 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] | |
| phones1, word2ph1, norm_text1 = clean_text(prompt_text, prompt_language) | |
| phones1 = cleaned_text_to_sequence(phones1) | |
| texts = text.split("\n") | |
| audio_opt = [] | |
| zero_wav = np.zeros( | |
| int(hps.data.sampling_rate * 0.3), | |
| dtype=np.float16 if is_half == True else np.float32, | |
| ) | |
| for text in texts: | |
| phones2, word2ph2, norm_text2 = clean_text(text, text_language) | |
| phones2 = cleaned_text_to_sequence(phones2) | |
| if prompt_language == "zh": | |
| bert1 = get_bert_feature(norm_text1, word2ph1).to(device) | |
| else: | |
| bert1 = torch.zeros( | |
| (1024, len(phones1)), | |
| dtype=torch.float16 if is_half == True else torch.float32, | |
| ).to(device) | |
| if text_language == "zh": | |
| bert2 = get_bert_feature(norm_text2, word2ph2).to(device) | |
| else: | |
| bert2 = torch.zeros((1024, len(phones2))).to(bert1) | |
| 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("。")]) | |
| with gr.Blocks(title="GPT-SoVITS WebUI") as app: | |
| gr.Markdown( | |
| value="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. <br>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<b>LICENSE</b>." | |
| ) | |
| # with gr.Tabs(): | |
| # with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")): | |
| with gr.Group(): | |
| gr.Markdown(value="*请上传并填写参考信息") | |
| with gr.Row(): | |
| inp_ref = gr.Audio(label="请上传参考音频", type="filepath") | |
| prompt_text = gr.Textbox(label="参考音频的文本", value="") | |
| prompt_language = gr.Dropdown( | |
| label="参考音频的语种", 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, | |
| [inp_ref, prompt_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]) | |
| gr.Markdown(value="后续将支持混合语种编码文本输入。") | |
| app.queue(concurrency_count=511, max_size=1022).launch( | |
| server_name="0.0.0.0", | |
| inbrowser=True, | |
| server_port=infer_ttswebui, | |
| quiet=True, | |
| ) | |