import os import sys # to avoid the modified user.pth file cnhubert_base_path = "GPT_SoVITS\pretrained_models\chinese-hubert-base" bert_path = "GPT_SoVITS\pretrained_models\chinese-roberta-wwm-ext-large" os.environ["version"] = 'v2' now_dir = os.getcwd() sys.path.insert(0, now_dir) import gradio as gr from transformers import AutoModelForMaskedLM, AutoTokenizer import numpy as np from pathlib import Path import os,librosa,torch, audiosegment from scipy.io.wavfile import write as wavwrite from GPT_SoVITS.feature_extractor import cnhubert cnhubert.cnhubert_base_path=cnhubert_base_path from GPT_SoVITS.module.models import SynthesizerTrn from GPT_SoVITS.AR.models.t2s_lightning_module import Text2SemanticLightningModule from GPT_SoVITS.text import cleaned_text_to_sequence from GPT_SoVITS.text.cleaner import clean_text from time import time as ttime from GPT_SoVITS.module.mel_processing import spectrogram_torch import tempfile from tools.my_utils import load_audio import os import json ################ End strange import and user.pth modification ################ # import pyopenjtalk # cwd = os.getcwd() # if os.path.exists(os.path.join(cwd,'user.dic')): # pyopenjtalk.update_global_jtalk_with_user_dict(os.path.join(cwd, 'user.dic')) 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" #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): # Bert(不是HuBERT的特征计算) 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 loaded_sovits_model = [] # [(path, dict, model)] loaded_gpt_model = [] ssl_model = cnhubert.get_model() if (is_half == True): ssl_model = ssl_model.half().to(device) else: ssl_model = ssl_model.to(device) def load_model(sovits_path, gpt_path): global ssl_model global loaded_sovits_model global loaded_gpt_model vq_model = None t2s_model = None dict_s2 = None dict_s1 = None hps = None for path, dict_s2_, model in loaded_sovits_model: if path == sovits_path: vq_model = model dict_s2 = dict_s2_ break for path, dict_s1_, model in loaded_gpt_model: if path == gpt_path: t2s_model = model dict_s1 = dict_s1_ break if dict_s2 is None: dict_s2 = torch.load(sovits_path, map_location="cpu") hps = dict_s2["config"] if dict_s1 is None: dict_s1 = torch.load(gpt_path, map_location="cpu") config = dict_s1["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" if not vq_model: 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) loaded_sovits_model.append((sovits_path, dict_s2, vq_model)) hz = 50 max_sec = config['data']['max_sec'] if not t2s_model: 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()]) loaded_gpt_model.append((gpt_path, dict_s1, t2s_model)) 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 = audio / np.max(np.abs(audio)) audio=torch.FloatTensor(audio) audio_norm = audio # audio_norm = audio / torch.max(torch.abs(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, target_phone, text_language, target_text = None): t0 = ttime() prompt_text=prompt_text.strip() prompt_language=prompt_language with torch.no_grad(): wav16k, sr = librosa.load(ref_wav_path, sr=16000, mono=False) direction = np.array([1,1]) if wav16k.ndim == 2: power = np.sum(np.abs(wav16k) ** 2, axis=1) direction = power / np.sum(power) wav16k = (wav16k[0] + wav16k[1]) / 2 # # maxx=0.95 # tmp_max = np.abs(wav16k).max() # alpha=0.5 # wav16k = (wav16k / tmp_max * (maxx * alpha*32768)) + ((1 - alpha)*32768) * wav16k #在这里归一化 #print(max(np.abs(wav16k))) #wav16k = wav16k / np.max(np.abs(wav16k)) #print(max(np.abs(wav16k))) # 添加0.3s的静音 wav16k = np.concatenate([wav16k, np.zeros(int(hps.data.sampling_rate * 0.3)),]) wav16k = torch.from_numpy(wav16k) wav16k = wav16k.float() 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((2, int(hps.data.sampling_rate*0.3)),dtype=np.float16 if is_half==True else np.float32) phones = get_phone_from_str_list(target_phone, text_language) for phones2 in phones: if(len(phones2) == 0): continue if(len(phones2) == 1 and phones2[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) 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) 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 # direction乘上,变双通道 # 强制0.5 direction = np.array([1, 1]) audio = np.expand_dims(audio, 0) * direction[:, np.newaxis] audio_opt.append(audio) audio_opt.append(zero_wav) t4 = ttime() audio = (hps.data.sampling_rate,(np.concatenate(audio_opt, axis=1)*32768).astype(np.int16).T) prefix_1 = prompt_text[:8].replace(" ", "_").replace("\n", "_").replace("?","_").replace("!","_").replace(",","_") prefix_2 = target_text[:8].replace(" ", "_").replace("\n", "_").replace("?","_").replace("!","_").replace(",","_") filename = tempfile.mktemp(suffix=".wav",prefix=f"{prefix_1}_{prefix_2}_") #audiosegment.from_numpy_array(audio[1].T, framerate=audio[0]).export(filename, format="WAV") wavwrite(filename, audio[0], audio[1]) return "Success", audio, filename return tts_fn def get_str_list_from_phone(text, text_language): # raw文本过g2p得到音素列表,再转成字符串 # 注意,这里的text是一个段落,可能包含多个句子 # 段落间\n分割,音素间空格分割 print(text) texts=text.split("\n") phone_list = [] for text in texts: phones2, word2ph2, norm_text2 = clean_text(text, text_language) phone_list.append(" ".join(phones2)) return "\n".join(phone_list) def get_phone_from_str_list(str_list:str, language:str = 'ja'): # 从音素字符串中得到音素列表 # 注意,这里的text是一个段落,可能包含多个句子 # 段落间\n分割,音素间空格分割 sentences = str_list.split("\n") phones = [] for sentence in sentences: phones.append(sentence.split(" ")) return phones 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 = json.load(open("./models/models_info.json", "r", encoding="utf-8")) 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 = {} transcript_path = info["transcript_path"] path = os.path.dirname(transcript_path) with open(transcript_path, 'r', encoding='utf-8') as file: for line in file: line = line.strip().replace("\\", "/") items = line.split("|") wav,t = items[0], items[-1] wav = os.path.basename(wav) transcripts[t] = os.path.join(os.path.join(path,"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 Demo\n" ) 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>' '</div>') with gr.Row(): with gr.Column(): prompt_text = gr.Dropdown( label="Transcript of the Reference Audio", value=example_reference if example_reference in transcripts else list(transcripts.keys())[0], choices=list(transcripts.keys()) ) inp_ref_audio = gr.Audio( label="Reference Audio", type="filepath", interactive=False, value=transcripts[example_reference] if example_reference in transcripts else list(transcripts.values())[0] ) 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="私はお兄ちゃんのだいだいだーいすきな妹なんだから、言うことなんでも聞いてくれますよね!") text_language = gr.Dropdown( label="Language", choices=["ja"], value="ja" ) clean_button = gr.Button("Clean Text", variant="primary") inference_button = gr.Button("Generate", variant="primary") cleaned_text = gr.Textbox(label="Cleaned Text") output = gr.Audio(label="Output Audio") output_file = gr.File(label="Output Audio File") om = gr.Textbox(label="Output Message") clean_button.click( fn=get_str_list_from_phone, inputs=[text, text_language], outputs=[cleaned_text] ) inference_button.click( fn=tts_fn, inputs=[inp_ref_audio, prompt_text, prompt_language, cleaned_text, text_language, text], outputs=[om, output, output_file] ) app.launch(share=True)