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| import os | |
| cnhubert_base_path = "pretrained_models/chinese-hubert-base" | |
| bert_path = "pretrained_models/chinese-roberta-wwm-ext-large" | |
| import re | |
| import gradio as gr | |
| from transformers import AutoModelForMaskedLM, AutoTokenizer | |
| import torch,numpy as np | |
| from pathlib import Path | |
| import os,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 | |
| import logging | |
| logging.getLogger('httpx').setLevel(logging.WARNING) | |
| logging.getLogger('httpcore').setLevel(logging.WARNING) | |
| logging.getLogger('multipart').setLevel(logging.WARNING) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| is_half = 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 | |
| def load_model(sovits_path, gpt_path): | |
| 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() | |
| 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)) | |
| return vq_model, ssl_model, t2s_model, hps, config, hz, max_sec | |
| 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 | |
| def create_tts_fn(vq_model, ssl_model, t2s_model, hps, config, hz, max_sec): | |
| def tts_fn(ref_wav_path, prompt_text, prompt_language, text, text_language): | |
| t0 = ttime() | |
| prompt_text=prompt_text.strip() | |
| prompt_language,text=prompt_language,text.strip(" ") | |
| print(text) | |
| 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() | |
| 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: | |
| if(len(text) == 0): | |
| continue | |
| 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() | |
| idx = 0 | |
| cnt = 0 | |
| while idx == 0 and cnt < 2: | |
| 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() | |
| cnt+=1 | |
| if idx == 0: | |
| return "Error: Generation failure: bad zero prediction.", None | |
| 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)) | |
| return "Success", (hps.data.sampling_rate,(np.concatenate(audio_opt,0)*32768).astype(np.int16)) | |
| return tts_fn | |
| 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 change_reference_audio(prompt_text, transcripts): | |
| return transcripts[prompt_text] | |
| models = [] | |
| models_info = { | |
| # "rice_shower": { | |
| # "gpt_weight": "umamusume/rice_shower/rice_shower-e15.ckpt", | |
| # "sovits_weight": "umamusume/rice_shower/rice_shower_e80_s3600.pth", | |
| # "title": "Umamusume-ライスシャワ", | |
| # "cover": "https://umamusume.jp/app/wp-content/uploads/2021/01/2108c5f47912c7fafdaaac66824f946d.png", | |
| # "example_reference": "トレーナーさんと、チームのみんなと一緒ならどこでも楽しい。 みんなの笑顔を見てると、ライスもうれしくて笑顔になるの。" | |
| # }, | |
| # "nice_nature":{ | |
| # "gpt_weight": "umamusume/nice_nature/nice_nature-e30.ckpt", | |
| # "sovits_weight": "umamusume/nice_nature/nice_nature_e80_s7440.pth", | |
| # "title": "Umamusume-ナイスネイチャ", | |
| # "cover": "https://umamusume.jp/app/wp-content/uploads/2021/01/6bc36e91836d4ec9bfe030b5b4e26e8b.png", | |
| # "example_reference": "ごめんごめん。そう、うちの学園の『主人公』たちのこと。 キラキラしまくってんのは、テイオーやマックイーンだけじゃない。" | |
| # }, | |
| # "satono_diamond":{ | |
| # "gpt_weight": "umamusume/satono_diamond/satono_diamond-e5.ckpt", | |
| # "sovits_weight": "umamusume/satono_diamond/satono_diamond_e120_s3840.pth", | |
| # "title": "Umamusume-サトノダイヤモンド", | |
| # "cover": "https://umamusume.jp/app/wp-content/uploads/2021/01/9ecb36a562e6b902b4e7bd9d06bd6dd6.png", | |
| # "example_reference": "たくさんの期待に全部応えて、私だけの走りを見せたい。 ――マックイーンさんみたいに！" | |
| # }, | |
| # "kitasan_black":{ | |
| # "gpt_weight": "umamusume/kitasan_black/kitasan_black-e5.ckpt", | |
| # "sovits_weight": "umamusume/kitasan_black/kitasan_black_e200_s7000.pth", | |
| # "title": "Umamusume-サトノダイヤモンド", | |
| # "cover": "https://umamusume.jp/app/wp-content/uploads/2021/01/f990f8130e35dab776a9a282284e92ef.png", | |
| # "example_reference": "大好きな友だちと一緒なら、なんだって嬉しいもん！ ダイヤちゃんと並んで走るの、楽しみだな～。" | |
| # }, | |
| "nimi_sora":{ | |
| "gpt_weight": "9nine/nimi_sora/nimi_sora-e5.ckpt", | |
| "sovits_weight": "9nine/nimi_sora/nimi_sora_e75_s22425.pth", | |
| "title": "9-nine-新海天", | |
| "cover": "https://9-nine-project.com/wp/wp-content/themes/nine-project/assets/images/pc/character/img_sora_01.png", | |
| "example_reference": "「は～……なるほど……。やっとわかってきた……。 あの地震で……っていうか、神社の宝物が壊れて、 超能力者が出現したってことね、この街に」" | |
| } | |
| } | |
| for i, info in models_info.items(): | |
| title = info['title'] | |
| cover = info['cover'] | |
| gpt_weight = info['gpt_weight'] | |
| sovits_weight = info['sovits_weight'] | |
| example_reference = info['example_reference'] | |
| transcripts = {} | |
| with open(f"9nine/{i}/transcript.txt", 'r', encoding='utf-8') as file: | |
| for line in file: | |
| line = line.strip().replace("\\", "/") | |
| wav,_,_, t = line.split("|") | |
| wav = os.path.basename(wav) | |
| transcripts[t] = os.path.join(f"9nine/{i}/reference_audio", wav) | |
| vq_model, ssl_model, t2s_model, hps, config, hz, max_sec = load_model(sovits_weight, gpt_weight) | |
| models.append( | |
| ( | |
| i, | |
| title, | |
| cover, | |
| transcripts, | |
| example_reference, | |
| create_tts_fn( | |
| vq_model, ssl_model, t2s_model, hps, config, hz, max_sec | |
| ) | |
| ) | |
| ) | |
| with gr.Blocks() as app: | |
| gr.Markdown( | |
| "# <center> GPT-SoVITS \n" | |
| "## <center> https://github.com/RVC-Boss/GPT-SoVITS\n" | |
| "## <center> GUI brought from https://huggingface.co/spaces/zomehwh/GPT-SoVITS-ba" | |
| ) | |
| with gr.Tabs(): | |
| for (name, title, cover, transcripts, example_reference, tts_fn) in models: | |
| with gr.TabItem(name): | |
| with gr.Row(): | |
| gr.Markdown( | |
| '<div align="center">' | |
| f'<a><strong>{title}</strong></a>' | |
| f'<img style="width:auto;height:300px;" src="{cover}">' if cover else "" | |
| '</div>') | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt_text = gr.Dropdown( | |
| label="Transcript of the Reference Audio", | |
| value=example_reference, | |
| choices=list(transcripts.keys()) | |
| ) | |
| inp_ref_audio = gr.Audio( | |
| label="Reference Audio", | |
| type="filepath", | |
| interactive=False, | |
| value=transcripts[example_reference] | |
| ) | |
| transcripts_state = gr.State(value=transcripts) | |
| prompt_text.change( | |
| fn=change_reference_audio, | |
| inputs=[prompt_text, transcripts_state], | |
| outputs=[inp_ref_audio] | |
| ) | |
| prompt_language = gr.State(value="ja") | |
| with gr.Column(): | |
| text = gr.Textbox(label="Input Text", value="お兄ちゃん大好き！…なんって絶対言わないからね！") | |
| text_language = gr.Dropdown( | |
| label="Language", | |
| choices=["zh", "en", "ja"], | |
| value="ja" | |
| ) | |
| inference_button = gr.Button("Generate", variant="primary") | |
| output = gr.Audio(label="Output Audio") | |
| om = gr.Textbox(label="Output Message") | |
| inference_button.click( | |
| fn=tts_fn, | |
| inputs=[inp_ref_audio, prompt_text, prompt_language, text, text_language], | |
| outputs=[om, output] | |
| ) | |
| app.queue().launch() |