import os cnhubert_base_path = "pretrained_models/chinese-hubert-base" bert_path = "pretrained_models/chinese-roberta-wwm-ext-large" import gradio as gr from transformers import AutoModelForMaskedLM, AutoTokenizer import sys,torch,numpy as np from pathlib import Path import os,pdb,utils,librosa,math,traceback,requests,argparse,torch,multiprocessing,pandas as pd,torch.multiprocessing as mp,soundfile # torch.backends.cuda.sdp_kernel("flash") # torch.backends.cuda.enable_flash_sdp(True) # torch.backends.cuda.enable_mem_efficient_sdp(True) # Not avaliable if torch version is lower than 2.0 # torch.backends.cuda.enable_math_sdp(True) from random import shuffle from AR.utils import get_newest_ckpt from glob import glob from tqdm import tqdm from feature_extractor import cnhubert cnhubert.cnhubert_base_path=cnhubert_base_path from io import BytesIO from module.models import SynthesizerTrn from AR.models.t2s_lightning_module import Text2SemanticLightningModule from AR.utils.io import load_yaml_config from text import cleaned_text_to_sequence from text.cleaner import text_to_sequence, 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 = "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): 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() 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("\n") prompt_language,text=prompt_language,text.strip("\n") print(text) if len(text) > 50: return f"Error: Text is too long, ({len(text)}>50)", None 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: 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)) 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 = { "alice": { "gpt_weight": "blue_archive/alice/alice-e15.ckpt", "sovits_weight": "blue_archive/alice/alice_e8_s216.pth", "title": "Blue Archive-天童アリス", "cover": "https://pic.imgdb.cn/item/65a7dad6871b83018a48f494.png", "example_reference": "召喚にお応じろ!ゴーレムよ!主人の命令に従い!" }, "yuuka": { "gpt_weight": "blue_archive/yuuka/yuuka-e15.ckpt", "sovits_weight": "blue_archive/yuuka/yuuka_e8_s208.pth", "title": "Blue Archive-早瀬ユウカ", "cover": "https://pic.imgdb.cn/item/65a7da95871b83018a47a721.png", "example_reference": "せ~ん~せ~い~。もう少し頑張ってください!" }, "mika": { "gpt_weight": "blue_archive/mika/mika-e15.ckpt", "sovits_weight": "blue_archive/mika/mika_e8_s176.pth", "title": "Blue Archive-聖園ミカ", "cover": "https://pic.imgdb.cn/item/65a7daf6871b83018a499034.png", "example_reference": "あけましておめでとう、先生!こんな私だけど、今年もよろしくね☆" } } 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"blue_archive/{i}/reference_audio/transcript.txt", 'r', encoding='utf-8') as file: for line in file: line = line.strip() wav, t = line.split("|") transcripts[t] = os.path.join(f"blue_archive/{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( "#
GPT-SoVITS \n" "##
https://github.com/RVC-Boss/GPT-SoVITS\n" ) with gr.Tabs(): for (name, title, cover, transcripts, example_reference, tts_fn) in models: with gr.TabItem(name): with gr.Row(): gr.Markdown( '
' f'{title}' f'' if cover else "" '
') 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="はいきなり、春の嵐のように突然訪れた。") text_language = gr.Dropdown( label="Language", choices=["zh", "en", "ja"], value="ja" ) inference_button = gr.Button("Generate", variant="primary") om = gr.Textbox(label="Output Message") output = gr.Audio(label="Output Audio") inference_button.click( fn=tts_fn, inputs=[inp_ref_audio, prompt_text, prompt_language, text, text_language], outputs=[om, output] ) app.queue(concurrency_count=2).launch()