diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index bc723b4..917bcbc 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -1,4 +1,5 @@ -import os,re,logging +import os, re, logging + logging.getLogger("markdown_it").setLevel(logging.ERROR) logging.getLogger("urllib3").setLevel(logging.ERROR) logging.getLogger("httpcore").setLevel(logging.ERROR) @@ -10,16 +11,16 @@ logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) import pdb if os.path.exists("./gweight.txt"): - with open("./gweight.txt", 'r',encoding="utf-8") as file: + with open("./gweight.txt", 'r', encoding="utf-8") as file: gweight_data = file.read() gpt_path = os.environ.get( - "gpt_path", gweight_data) + "gpt_path", gweight_data) else: gpt_path = os.environ.get( - "gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt") + "gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt") if os.path.exists("./sweight.txt"): - with open("./sweight.txt", 'r',encoding="utf-8") as file: + with open("./sweight.txt", 'r', encoding="utf-8") as file: sweight_data = file.read() sovits_path = os.environ.get("sovits_path", sweight_data) else: @@ -37,16 +38,17 @@ bert_path = os.environ.get( infer_ttswebui = os.environ.get("infer_ttswebui", 9872) infer_ttswebui = int(infer_ttswebui) is_share = os.environ.get("is_share", "False") -is_share=eval(is_share) +is_share = eval(is_share) 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 +import librosa, torch from feature_extractor import cnhubert -cnhubert.cnhubert_base_path=cnhubert_base_path + +cnhubert.cnhubert_base_path = cnhubert_base_path from module.models import SynthesizerTrn from AR.models.t2s_lightning_module import Text2SemanticLightningModule @@ -56,9 +58,10 @@ from time import time as ttime from module.mel_processing import spectrogram_torch from my_utils import load_audio from tools.i18n.i18n import I18nAuto + i18n = I18nAuto() -os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。 +os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1' # 确保直接启动推理UI时也能够设置。 if torch.cuda.is_available(): device = "cuda" @@ -74,6 +77,7 @@ if is_half == True: else: bert_model = bert_model.to(device) + def get_bert_feature(text, word2ph): with torch.no_grad(): inputs = tokenizer(text, return_tensors="pt") @@ -89,6 +93,7 @@ def get_bert_feature(text, word2ph): phone_level_feature = torch.cat(phone_level_feature, dim=0) return phone_level_feature.T + class DictToAttrRecursive(dict): def __init__(self, input_dict): super().__init__(input_dict) @@ -123,10 +128,11 @@ if is_half == True: else: ssl_model = ssl_model.to(device) + def change_sovits_weights(sovits_path): - global vq_model,hps - dict_s2=torch.load(sovits_path,map_location="cpu") - hps=dict_s2["config"] + global vq_model, hps + dict_s2 = torch.load(sovits_path, map_location="cpu") + hps = dict_s2["config"] hps = DictToAttrRecursive(hps) hps.model.semantic_frame_rate = "25hz" vq_model = SynthesizerTrn( @@ -135,7 +141,7 @@ def change_sovits_weights(sovits_path): n_speakers=hps.data.n_speakers, **hps.model ) - if("pretrained"not in sovits_path): + if ("pretrained" not in sovits_path): del vq_model.enc_q if is_half == True: vq_model = vq_model.half().to(device) @@ -143,11 +149,15 @@ def change_sovits_weights(sovits_path): vq_model = vq_model.to(device) vq_model.eval() print(vq_model.load_state_dict(dict_s2["weight"], strict=False)) - with open("./sweight.txt","w",encoding="utf-8")as f:f.write(sovits_path) + with open("./sweight.txt", "w", encoding="utf-8") as f: + f.write(sovits_path) + + change_sovits_weights(sovits_path) + def change_gpt_weights(gpt_path): - global hz,max_sec,t2s_model,config + global hz, max_sec, t2s_model, config hz = 50 dict_s1 = torch.load(gpt_path, map_location="cpu") config = dict_s1["config"] @@ -160,9 +170,12 @@ def change_gpt_weights(gpt_path): t2s_model.eval() total = sum([param.nelement() for param in t2s_model.parameters()]) print("Number of parameter: %.2fM" % (total / 1e6)) - with open("./gweight.txt","w",encoding="utf-8")as f:f.write(gpt_path) + with open("./gweight.txt", "w", encoding="utf-8") as f: f.write(gpt_path) + + change_gpt_weights(gpt_path) + def get_spepc(hps, filename): audio = load_audio(filename, int(hps.data.sampling_rate)) audio = torch.FloatTensor(audio) @@ -179,10 +192,10 @@ def get_spepc(hps, filename): return spec -dict_language={ - i18n("中文"):"zh", - i18n("英文"):"en", - i18n("日文"):"ja" +dict_language = { + i18n("中文"): "zh", + i18n("英文"): "en", + i18n("日文"): "ja" } @@ -262,27 +275,31 @@ def nonen_get_bert_inf(text, language): return bert -splits = {"，","。","？","！",",",".","?","!","~",":","：","—","…",} + +splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", } + + def get_first(text): pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]" text = re.split(pattern, text)[0].strip() return text -def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,how_to_cut=i18n("不切")): + +def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切")): t0 = ttime() prompt_text = prompt_text.strip("\n") - if(prompt_text[-1]not in splits):prompt_text+="。"if prompt_language!="en"else "." + if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "." text = text.strip("\n") - if(text[0]not in splits and len(get_first(text))<4):text="。"+text if text_language!="en"else "."+text - print(i18n("实际输入的参考文本:"),prompt_text) - print(i18n("实际输入的目标文本:"),text) + if (text[0] not in splits and len(get_first(text)) < 4): text = "。" + text if text_language != "en" else "." + text + print(i18n("实际输入的参考文本:"), prompt_text) + print(i18n("实际输入的目标文本:"), text) zero_wav = np.zeros( int(hps.data.sampling_rate * 0.3), dtype=np.float16 if is_half == True else np.float32, ) with torch.no_grad(): wav16k, sr = librosa.load(ref_wav_path, sr=16000) - if(wav16k.shape[0]>160000 or wav16k.shape[0]<48000): + if (wav16k.shape[0] > 160000 or wav16k.shape[0] < 48000): raise OSError(i18n("参考音频在3~10秒范围外，请更换！")) wav16k = torch.from_numpy(wav16k) zero_wav_torch = torch.from_numpy(zero_wav) @@ -292,7 +309,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, else: wav16k = wav16k.to(device) zero_wav_torch = zero_wav_torch.to(device) - wav16k=torch.cat([wav16k,zero_wav_torch]) + wav16k = torch.cat([wav16k, zero_wav_torch]) ssl_content = ssl_model.model(wav16k.unsqueeze(0))[ "last_hidden_state" ].transpose( @@ -307,13 +324,19 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language) else: phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language) - if(how_to_cut==i18n("凑四句一切")):text=cut1(text) - elif(how_to_cut==i18n("凑50字一切")):text=cut2(text) - elif(how_to_cut==i18n("按中文句号。切")):text=cut3(text) - elif(how_to_cut==i18n("按英文句号.切")):text=cut4(text) - text = text.replace("\n\n","\n").replace("\n\n","\n").replace("\n\n","\n") - print(i18n("实际输入的目标文本(切句后):"),text) - texts=text.split("\n") + if (how_to_cut == i18n("凑四句一切")): + text = cut1(text) + elif (how_to_cut == i18n("凑50字一切")): + text = cut2(text) + elif (how_to_cut == i18n("按中文句号。切")): + text = cut3(text) + elif (how_to_cut == i18n("按英文句号.切")): + text = cut4(text) + elif (how_to_cut == i18n("按标点符号切")): + text = cut5(text) + text = text.replace("\n\n", "\n").replace("\n\n", "\n").replace("\n\n", "\n") + print(i18n("实际输入的目标文本(切句后):"), text) + texts = text.split("\n") audio_opt = [] if prompt_language == "en": bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language) @@ -368,9 +391,9 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, vq_model.decode( pred_semantic, torch.LongTensor(phones2).to(device).unsqueeze(0), refer ) - .detach() - .cpu() - .numpy()[0, 0] + .detach() + .cpu() + .numpy()[0, 0] ) ###试试重建不带上prompt部分 audio_opt.append(audio) audio_opt.append(zero_wav) @@ -380,6 +403,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, np.int16 ) + def split(todo_text): todo_text = todo_text.replace("……", "。").replace("——", "，") if todo_text[-1] not in splits: @@ -407,7 +431,7 @@ def cut1(inp): 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]])) + opts.append("".join(inps[split_idx[idx]: split_idx[idx + 1]])) else: opts = [inp] return "\n".join(opts) @@ -431,7 +455,7 @@ def cut2(inp): if tmp_str != "": opts.append(tmp_str) # print(opts) - if len(opts)>1 and len(opts[-1]) < 50: ##如果最后一个太短了，和前一个合一起 + if len(opts) > 1 and 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) @@ -440,10 +464,25 @@ def cut2(inp): def cut3(inp): inp = inp.strip("\n") return "\n".join(["%s" % item for item in inp.strip("。").split("。")]) + + def cut4(inp): inp = inp.strip("\n") return "\n".join(["%s" % item for item in inp.strip(".").split(".")]) + +# contributed by https://github.com/AI-Hobbyist/GPT-SoVITS/blob/main/GPT_SoVITS/inference_webui.py +def cut5(inp): + # if not re.search(r'[^\w\s]', inp[-1]): + # inp += '。' + inp = inp.strip("\n") + punds = r'[,.;?!、，。？！;：]' + items = re.split(f'({punds})', inp) + items = ["".join(group) for group in zip(items[::2], items[1::2])] + opt = "\n".join(items) + return opt + + def custom_sort_key(s): # 使用正则表达式提取字符串中的数字部分和非数字部分 parts = re.split('(\d+)', s) @@ -451,25 +490,31 @@ def custom_sort_key(s): parts = [int(part) if part.isdigit() else part for part in parts] return parts + def change_choices(): SoVITS_names, GPT_names = get_weights_names() - return {"choices": sorted(SoVITS_names,key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names,key=custom_sort_key), "__type__": "update"} + return {"choices": sorted(SoVITS_names, key=custom_sort_key), "__type__": "update"}, {"choices": sorted(GPT_names, key=custom_sort_key), "__type__": "update"} + + +pretrained_sovits_name = "GPT_SoVITS/pretrained_models/s2G488k.pth" +pretrained_gpt_name = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" +SoVITS_weight_root = "SoVITS_weights" +GPT_weight_root = "GPT_weights" +os.makedirs(SoVITS_weight_root, exist_ok=True) +os.makedirs(GPT_weight_root, exist_ok=True) + -pretrained_sovits_name="GPT_SoVITS/pretrained_models/s2G488k.pth" -pretrained_gpt_name="GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" -SoVITS_weight_root="SoVITS_weights" -GPT_weight_root="GPT_weights" -os.makedirs(SoVITS_weight_root,exist_ok=True) -os.makedirs(GPT_weight_root,exist_ok=True) def get_weights_names(): SoVITS_names = [pretrained_sovits_name] for name in os.listdir(SoVITS_weight_root): - if name.endswith(".pth"):SoVITS_names.append("%s/%s"%(SoVITS_weight_root,name)) + if name.endswith(".pth"): SoVITS_names.append("%s/%s" % (SoVITS_weight_root, name)) GPT_names = [pretrained_gpt_name] for name in os.listdir(GPT_weight_root): - if name.endswith(".ckpt"): GPT_names.append("%s/%s"%(GPT_weight_root,name)) - return SoVITS_names,GPT_names -SoVITS_names,GPT_names = get_weights_names() + if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name)) + return SoVITS_names, GPT_names + + +SoVITS_names, GPT_names = get_weights_names() with gr.Blocks(title="GPT-SoVITS WebUI") as app: gr.Markdown( @@ -478,29 +523,29 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: with gr.Group(): gr.Markdown(value=i18n("模型切换")) with gr.Row(): - GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path,interactive=True) - SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path,interactive=True) + GPT_dropdown = gr.Dropdown(label=i18n("GPT模型列表"), choices=sorted(GPT_names, key=custom_sort_key), value=gpt_path, interactive=True) + SoVITS_dropdown = gr.Dropdown(label=i18n("SoVITS模型列表"), choices=sorted(SoVITS_names, key=custom_sort_key), value=sovits_path, interactive=True) refresh_button = gr.Button(i18n("刷新模型路径"), variant="primary") refresh_button.click(fn=change_choices, inputs=[], outputs=[SoVITS_dropdown, GPT_dropdown]) - SoVITS_dropdown.change(change_sovits_weights,[SoVITS_dropdown],[]) - GPT_dropdown.change(change_gpt_weights,[GPT_dropdown],[]) + SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], []) + GPT_dropdown.change(change_gpt_weights, [GPT_dropdown], []) gr.Markdown(value=i18n("*请上传并填写参考信息")) with gr.Row(): inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath") prompt_text = gr.Textbox(label=i18n("参考音频的文本"), value="") prompt_language = gr.Dropdown( - label=i18n("参考音频的语种"),choices=[i18n("中文"),i18n("英文"),i18n("日文")],value=i18n("中文") + label=i18n("参考音频的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文")], value=i18n("中文") ) gr.Markdown(value=i18n("*请填写需要合成的目标文本。中英混合选中文，日英混合选日文，中日混合暂不支持，非目标语言文本自动遗弃。")) with gr.Row(): text = gr.Textbox(label=i18n("需要合成的文本"), value="") text_language = gr.Dropdown( - label=i18n("需要合成的语种"),choices=[i18n("中文"),i18n("英文"),i18n("日文")],value=i18n("中文") + label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文")], value=i18n("中文") ) how_to_cut = gr.Radio( label=i18n("怎么切"), - choices=[i18n("不切"),i18n("凑四句一切"),i18n("凑50字一切"),i18n("按中文句号。切"),i18n("按英文句号.切"),], - value=i18n("凑50字一切"), + choices=[i18n("不切"), i18n("凑四句一切"), i18n("凑50字一切"), i18n("按中文句号。切"), i18n("按英文句号.切"), i18n("按标点符号切"), ], + value=i18n("凑四句一切"), interactive=True, ) inference_button = gr.Button(i18n("合成语音"), variant="primary") @@ -508,22 +553,24 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app: inference_button.click( get_tts_wav, - [inp_ref, prompt_text, prompt_language, text, text_language,how_to_cut], + [inp_ref, prompt_text, prompt_language, text, text_language, how_to_cut], [output], ) gr.Markdown(value=i18n("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=i18n("需要合成的切分前文本"),value="") + text_inp = gr.Textbox(label=i18n("需要合成的切分前文本"), value="") button1 = gr.Button(i18n("凑四句一切"), variant="primary") button2 = gr.Button(i18n("凑50字一切"), variant="primary") button3 = gr.Button(i18n("按中文句号。切"), variant="primary") button4 = gr.Button(i18n("按英文句号.切"), variant="primary") + button5 = gr.Button(i18n("按标点符号切"), variant="primary") text_opt = gr.Textbox(label=i18n("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]) button4.click(cut4, [text_inp], [text_opt]) + button5.click(cut5, [text_inp], [text_opt]) gr.Markdown(value=i18n("后续将支持混合语种编码文本输入。")) app.queue(concurrency_count=511, max_size=1022).launch(