diff --git a/GPT_SoVITS/inference_webui.py b/GPT_SoVITS/inference_webui.py index 8e826fa..574b9d8 100644 --- a/GPT_SoVITS/inference_webui.py +++ b/GPT_SoVITS/inference_webui.py @@ -4,12 +4,30 @@ logging.getLogger("urllib3").setLevel(logging.ERROR) logging.getLogger("httpcore").setLevel(logging.ERROR) logging.getLogger("httpx").setLevel(logging.ERROR) logging.getLogger("asyncio").setLevel(logging.ERROR) + +logging.getLogger("charset_normalizer").setLevel(logging.ERROR) +logging.getLogger("torchaudio._extension").setLevel(logging.ERROR) import pdb -gpt_path = os.environ.get( - "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" -) -sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth") +if os.path.exists("./gweight.txt"): + with open("./gweight.txt", 'r',encoding="utf-8") as file: + gweight_data = file.read() + gpt_path = os.environ.get( + "gpt_path", gweight_data) +else: + gpt_path = os.environ.get( + "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: + sweight_data = file.read() + sovits_path = os.environ.get("sovits_path", sweight_data) +else: + sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth") +# gpt_path = os.environ.get( +# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt" +# ) +# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth") cnhubert_base_path = os.environ.get( "cnhubert_base_path", "pretrained_models/chinese-hubert-base" ) @@ -60,7 +78,7 @@ 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 + inputs[i] = inputs[i].to(device) 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) @@ -124,6 +142,7 @@ 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) change_sovits_weights(sovits_path) def change_gpt_weights(gpt_path): @@ -140,6 +159,7 @@ 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) change_gpt_weights(gpt_path) def get_spepc(hps, filename): @@ -188,19 +208,19 @@ def splite_en_inf(sentence, language): def clean_text_inf(text, language): phones, word2ph, norm_text = clean_text(text, language) phones = cleaned_text_to_sequence(phones) - + return phones, word2ph, norm_text - + def get_bert_inf(phones, word2ph, norm_text, language): if language == "zh": bert = get_bert_feature(norm_text, word2ph).to(device) else: bert = torch.zeros( - (1024, len(phones)), - dtype=torch.float16 if is_half == True else torch.float32, - ).to(device) - + (1024, len(phones)), + dtype=torch.float16 if is_half == True else torch.float32, + ).to(device) + return bert @@ -213,7 +233,7 @@ def nonen_clean_text_inf(text, language): lang = langlist[i] phones, word2ph, norm_text = clean_text_inf(textlist[i], lang) phones_list.append(phones) - if lang=="en" or "ja": + if lang == "en" or "ja": pass else: word2ph_list.append(word2ph) @@ -222,7 +242,7 @@ def nonen_clean_text_inf(text, language): phones = sum(phones_list, []) word2ph = sum(word2ph_list, []) norm_text = ' '.join(norm_text_list) - + return phones, word2ph, norm_text @@ -238,7 +258,7 @@ def nonen_get_bert_inf(text, language): bert = get_bert_inf(phones, word2ph, norm_text, lang) bert_list.append(bert) bert = torch.cat(bert_list, dim=1) - + return bert @@ -271,6 +291,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language) t1 = ttime() prompt_language = dict_language[prompt_language] text_language = dict_language[text_language] + if prompt_language == "en": phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language) else: @@ -281,21 +302,21 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language) bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language) else: bert1 = nonen_get_bert_inf(prompt_text, prompt_language) + for text in texts: # 解决输入目标文本的空行导致报错的问题 if (len(text.strip()) == 0): continue - if text_language == "en": phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language) else: phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language) - + if text_language == "en": bert2 = get_bert_inf(phones2, word2ph2, norm_text2, text_language) else: bert2 = nonen_get_bert_inf(text, text_language) - + bert = torch.cat([bert1, bert2], 1) all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0) diff --git a/tools/uvr5/webui.py b/tools/uvr5/webui.py index 71e7ebc..97170bf 100644 --- a/tools/uvr5/webui.py +++ b/tools/uvr5/webui.py @@ -52,39 +52,32 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format paths = [path.name for path in paths] for path in paths: inp_path = os.path.join(inp_root, path) - need_reformat = 1 - done = 0 + if(os.path.isfile(inp_path)==False):continue try: - y, sr = librosa.load(inp_path, sr=None) - info = sf.info(inp_path) - channels = info.channels - if channels == 2 and sr == 44100: - need_reformat = 0 - pre_fun._path_audio_( - inp_path, save_root_ins, save_root_vocal, format0, is_hp3=is_hp3 - ) - done = 1 - else: + done = 0 + try: + y, sr = librosa.load(inp_path, sr=None) + info = sf.info(inp_path) + channels = info.channels + if channels == 2 and sr == 44100: + need_reformat = 0 + pre_fun._path_audio_( + inp_path, save_root_ins, save_root_vocal, format0, is_hp3=is_hp3 + ) + done = 1 + else: + need_reformat = 1 + except: need_reformat = 1 - except: - need_reformat = 1 - traceback.print_exc() - if need_reformat == 1: - tmp_path = "%s/%s.reformatted.wav" % ( - os.path.join(os.environ["TEMP"]), - os.path.basename(inp_path), - ) - y_resampled = librosa.resample(y, sr, 44100) - sf.write(tmp_path, y_resampled, 44100, "PCM_16") - inp_path = tmp_path - try: - if done == 0: - pre_fun._path_audio_( - inp_path, save_root_ins, save_root_vocal, format0 + traceback.print_exc() + if need_reformat == 1: + tmp_path = "%s/%s.reformatted.wav" % ( + os.path.join(os.environ["TEMP"]), + os.path.basename(inp_path), ) - infos.append("%s->Success" % (os.path.basename(inp_path))) - yield "\n".join(infos) - except: + y_resampled = librosa.resample(y, sr, 44100) + sf.write(tmp_path, y_resampled, 44100, "PCM_16") + inp_path = tmp_path try: if done == 0: pre_fun._path_audio_( @@ -93,10 +86,21 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format infos.append("%s->Success" % (os.path.basename(inp_path))) yield "\n".join(infos) except: - infos.append( - "%s->%s" % (os.path.basename(inp_path), traceback.format_exc()) - ) - yield "\n".join(infos) + try: + if done == 0: + pre_fun._path_audio_( + inp_path, save_root_ins, save_root_vocal, format0 + ) + infos.append("%s->Success" % (os.path.basename(inp_path))) + yield "\n".join(infos) + except: + infos.append( + "%s->%s" % (os.path.basename(inp_path), traceback.format_exc()) + ) + yield "\n".join(infos) + except: + infos.append("Oh my god. %s->%s"%(os.path.basename(inp_path), traceback.format_exc())) + yield "\n".join(infos) except: infos.append(traceback.format_exc()) yield "\n".join(infos)