Spaces:
Running
Running
| # Modified from https://github.com/RVC-Boss/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py | |
| import os | |
| gpt_path = os.environ.get( | |
| "gpt_path", "pretrained_models/jay-e15.ckpt" | |
| ) | |
| sovits_path = os.environ.get("sovits_path", "pretrained_models/jay_e10_s60.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" | |
| ) | |
| if "_CUDA_VISIBLE_DEVICES" in os.environ: | |
| os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"] | |
| import gradio as gr | |
| import librosa | |
| import numpy as np | |
| import torch | |
| from transformers import AutoModelForMaskedLM, AutoTokenizer | |
| from feature_extractor import cnhubert | |
| cnhubert.cnhubert_base_path = cnhubert_base_path | |
| from time import time as ttime | |
| import datetime | |
| from AR.models.t2s_lightning_module import Text2SemanticLightningModule | |
| from module.mel_processing import spectrogram_torch | |
| from module.models import SynthesizerTrn | |
| from my_utils import load_audio | |
| from text import cleaned_text_to_sequence | |
| from text.cleaner import clean_text | |
| 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) | |
| # 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: | |
| def __init__(self, input_dict): | |
| for key, value in input_dict.items(): | |
| if isinstance(value, dict): | |
| # 如果值是字典，递归调用构造函数 | |
| setattr(self, key, DictToAttrRecursive(value)) | |
| else: | |
| setattr(self, key, value) | |
| 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 = {"Chinese": "zh", "English": "en", "Japanese": "ja"} | |
| def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language): | |
| start_time = datetime.datetime.now() | |
| print(f"---START---{start_time}---") | |
| print(f"ref_wav_path: {ref_wav_path}") | |
| print(f"prompt_text: {prompt_text}") | |
| print(f"prompt_language: {prompt_language}") | |
| print(f"text: {text}") | |
| print(f"text_language: {text_language}") | |
| if len(prompt_text) > 100 or len(text) > 100: | |
| print("Input text is limited to 100 characters.") | |
| return "Input text is limited to 100 characters.", None | |
| t0 = ttime() | |
| prompt_text = prompt_text.strip("\n") | |
| prompt_language, text = prompt_language, text.strip("\n") | |
| with torch.no_grad(): | |
| wav16k, _ = librosa.load(ref_wav_path, sr=16000) # 派蒙 | |
| # length of wav16k in sec should be in 60s | |
| if len(wav16k) > 16000 * 60: | |
| print("Input audio is limited to 60 seconds.") | |
| return "Input audio is limited to 60 seconds.", None | |
| wav16k = wav16k[: int(hps.data.sampling_rate * max_sec)] | |
| 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() | |
| end_time = datetime.datetime.now() | |
| dur = end_time - start_time | |
| print( | |
| f"Success! total time: {dur.seconds:.3f} sec,\ndetail time: {t1 - t0:.3f}, {t2 - t1:.3f}, {t3 - t2:.3f}, {t4 - t3:.3f}" | |
| ) | |
| print(f"---END---{end_time}---") | |
| return ( | |
| f"Success! total time: {dur.seconds:.3f} sec,\ndetail time: {t1 - t0:.3f}, {t2 - t1:.3f}, {t3 - t2:.3f}, {t4 - t3:.3f}", | |
| ( | |
| hps.data.sampling_rate, | |
| (np.concatenate(audio_opt, 0) * 32768).astype(np.int16), | |
| ), | |
| ) | |
| with gr.Blocks(title="GPT-SoVITS Zero-shot TTS Demo") as app: | |
| gr.Markdown("# <center>🥳💕🎶 GPT-SoVITS 1分钟完美声音克隆，版本大升级！</center>") | |
| gr.Markdown("## <center>🌟 支持中日英三语自动标注及训练 + 中文方言训练！Powered by [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS)</center>") | |
| gr.Markdown("### <center>🌊 更多精彩应用，尽在[滔滔AI](http://www.talktalkai.com)；滔滔AI，为爱滔滔！💕</center>") | |
| gr.Markdown("## 请上传参考音频") | |
| with gr.Row(): | |
| inp_ref = gr.Audio(label="请上传数据集中的参考音频", type="filepath", value="jay_speech.wav_1361600_1516800.wav") | |
| prompt_text = gr.Textbox(label="参考音频对应的文字内容", value="你喜欢这样的东西，所以你我觉得人要有一技之长呢。") | |
| prompt_language = gr.Dropdown( | |
| label="参考音频的语言", | |
| choices=["Chinese", "English", "Japanese"], | |
| value="Chinese", | |
| ) | |
| gr.Markdown("## 开始真实拟声之旅吧！") | |
| with gr.Row(): | |
| text = gr.Textbox(label="需要合成的内容", lines=5) | |
| text_language = gr.Dropdown( | |
| label="合成内容的语言", | |
| choices=["Chinese", "English", "Japanese"], | |
| value="Chinese", | |
| ) | |
| inference_button = gr.Button("开始真实拟声吧！", variant="primary") | |
| with gr.Column(): | |
| info = gr.Textbox(label="Info", visible=False) | |
| output = gr.Audio(label="为您合成的专属音频") | |
| inference_button.click( | |
| get_tts_wav, | |
| [inp_ref, prompt_text, prompt_language, text, text_language], | |
| [info, output], | |
| ) | |
| gr.Markdown("### <center>注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。</center>") | |
| gr.HTML(''' | |
| <div class="footer"> | |
| <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘 | |
| </p> | |
| </div> | |
| ''') | |
| app.queue(max_size=10) | |
| app.launch(inbrowser=True) | |