mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-06 03:57:44 +08:00
Merge branch 'main' into patch-4
This commit is contained in:
RVC-Boss
GitHub
commit
bbae213502
@ -4,12 +4,30 @@ logging.getLogger("urllib3").setLevel(logging.ERROR)
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logging.getLogger("httpcore").setLevel(logging.ERROR)
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logging.getLogger("httpx").setLevel(logging.ERROR)
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logging.getLogger("asyncio").setLevel(logging.ERROR)
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logging.getLogger("charset_normalizer").setLevel(logging.ERROR)
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logging.getLogger("torchaudio._extension").setLevel(logging.ERROR)
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import pdb
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gpt_path = os.environ.get(
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"gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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)
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sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth")
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if os.path.exists("./gweight.txt"):
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with open("./gweight.txt", 'r',encoding="utf-8") as file:
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gweight_data = file.read()
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gpt_path = os.environ.get(
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"gpt_path", gweight_data)
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else:
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gpt_path = os.environ.get(
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"gpt_path", "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt")
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if os.path.exists("./sweight.txt"):
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with open("./sweight.txt", 'r',encoding="utf-8") as file:
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sweight_data = file.read()
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sovits_path = os.environ.get("sovits_path", sweight_data)
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else:
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sovits_path = os.environ.get("sovits_path", "GPT_SoVITS/pretrained_models/s2G488k.pth")
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# gpt_path = os.environ.get(
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# "gpt_path", "pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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# )
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# sovits_path = os.environ.get("sovits_path", "pretrained_models/s2G488k.pth")
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cnhubert_base_path = os.environ.get(
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"cnhubert_base_path", "pretrained_models/chinese-hubert-base"
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)
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@ -60,7 +78,7 @@ def get_bert_feature(text, word2ph):
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with torch.no_grad():
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inputs = tokenizer(text, return_tensors="pt")
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for i in inputs:
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inputs[i] = inputs[i].to(device) #####输入是long不用管精度问题，精度随bert_model
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inputs[i] = inputs[i].to(device)
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res = bert_model(**inputs, output_hidden_states=True)
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res = torch.cat(res["hidden_states"][-3:-2], -1)[0].cpu()[1:-1]
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assert len(word2ph) == len(text)
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@ -124,6 +142,7 @@ def change_sovits_weights(sovits_path):
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vq_model = vq_model.to(device)
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vq_model.eval()
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print(vq_model.load_state_dict(dict_s2["weight"], strict=False))
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with open("./sweight.txt","w",encoding="utf-8")as f:f.write(sovits_path)
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change_sovits_weights(sovits_path)
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def change_gpt_weights(gpt_path):
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@ -140,6 +159,7 @@ def change_gpt_weights(gpt_path):
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t2s_model.eval()
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total = sum([param.nelement() for param in t2s_model.parameters()])
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print("Number of parameter: %.2fM" % (total / 1e6))
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with open("./gweight.txt","w",encoding="utf-8")as f:f.write(gpt_path)
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change_gpt_weights(gpt_path)
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def get_spepc(hps, filename):
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@ -188,19 +208,19 @@ def splite_en_inf(sentence, language):
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def clean_text_inf(text, language):
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phones, word2ph, norm_text = clean_text(text, language)
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phones = cleaned_text_to_sequence(phones)
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return phones, word2ph, norm_text
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def get_bert_inf(phones, word2ph, norm_text, language):
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if language == "zh":
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bert = get_bert_feature(norm_text, word2ph).to(device)
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else:
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bert = torch.zeros(
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(1024, len(phones)),
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dtype=torch.float16 if is_half == True else torch.float32,
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).to(device)
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(1024, len(phones)),
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dtype=torch.float16 if is_half == True else torch.float32,
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).to(device)
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return bert
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@ -213,7 +233,7 @@ def nonen_clean_text_inf(text, language):
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lang = langlist[i]
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phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
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phones_list.append(phones)
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if lang=="en" or "ja":
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if lang == "en" or "ja":
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pass
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else:
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word2ph_list.append(word2ph)
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@ -222,7 +242,7 @@ def nonen_clean_text_inf(text, language):
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phones = sum(phones_list, [])
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word2ph = sum(word2ph_list, [])
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norm_text = ' '.join(norm_text_list)
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return phones, word2ph, norm_text
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@ -238,7 +258,7 @@ def nonen_get_bert_inf(text, language):
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bert = get_bert_inf(phones, word2ph, norm_text, lang)
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bert_list.append(bert)
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bert = torch.cat(bert_list, dim=1)
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return bert
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@ -271,6 +291,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
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t1 = ttime()
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prompt_language = dict_language[prompt_language]
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text_language = dict_language[text_language]
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if prompt_language == "en":
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phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language)
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else:
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@ -281,21 +302,21 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language)
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bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language)
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else:
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bert1 = nonen_get_bert_inf(prompt_text, prompt_language)
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for text in texts:
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# 解决输入目标文本的空行导致报错的问题
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if (len(text.strip()) == 0):
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continue
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if text_language == "en":
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phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language)
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else:
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phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language)
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if text_language == "en":
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bert2 = get_bert_inf(phones2, word2ph2, norm_text2, text_language)
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else:
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bert2 = nonen_get_bert_inf(text, text_language)
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bert = torch.cat([bert1, bert2], 1)
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all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
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@ -52,39 +52,32 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
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paths = [path.name for path in paths]
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for path in paths:
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inp_path = os.path.join(inp_root, path)
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need_reformat = 1
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done = 0
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if(os.path.isfile(inp_path)==False):continue
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try:
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y, sr = librosa.load(inp_path, sr=None)
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info = sf.info(inp_path)
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channels = info.channels
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if channels == 2 and sr == 44100:
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need_reformat = 0
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pre_fun._path_audio_(
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inp_path, save_root_ins, save_root_vocal, format0, is_hp3=is_hp3
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)
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done = 1
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else:
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done = 0
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try:
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y, sr = librosa.load(inp_path, sr=None)
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info = sf.info(inp_path)
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channels = info.channels
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if channels == 2 and sr == 44100:
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need_reformat = 0
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pre_fun._path_audio_(
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inp_path, save_root_ins, save_root_vocal, format0, is_hp3=is_hp3
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)
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done = 1
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else:
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need_reformat = 1
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except:
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need_reformat = 1
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except:
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need_reformat = 1
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traceback.print_exc()
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if need_reformat == 1:
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tmp_path = "%s/%s.reformatted.wav" % (
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os.path.join(os.environ["TEMP"]),
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os.path.basename(inp_path),
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)
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y_resampled = librosa.resample(y, sr, 44100)
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sf.write(tmp_path, y_resampled, 44100, "PCM_16")
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inp_path = tmp_path
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try:
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if done == 0:
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pre_fun._path_audio_(
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inp_path, save_root_ins, save_root_vocal, format0
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traceback.print_exc()
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if need_reformat == 1:
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tmp_path = "%s/%s.reformatted.wav" % (
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os.path.join(os.environ["TEMP"]),
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os.path.basename(inp_path),
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)
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infos.append("%s->Success" % (os.path.basename(inp_path)))
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yield "\n".join(infos)
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except:
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y_resampled = librosa.resample(y, sr, 44100)
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sf.write(tmp_path, y_resampled, 44100, "PCM_16")
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inp_path = tmp_path
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try:
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if done == 0:
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pre_fun._path_audio_(
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@ -93,10 +86,21 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
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infos.append("%s->Success" % (os.path.basename(inp_path)))
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yield "\n".join(infos)
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except:
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infos.append(
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"%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
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)
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yield "\n".join(infos)
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try:
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if done == 0:
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pre_fun._path_audio_(
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inp_path, save_root_ins, save_root_vocal, format0
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)
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infos.append("%s->Success" % (os.path.basename(inp_path)))
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yield "\n".join(infos)
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except:
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infos.append(
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"%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
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)
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yield "\n".join(infos)
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except:
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infos.append("Oh my god. %s->%s"%(os.path.basename(inp_path), traceback.format_exc()))
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yield "\n".join(infos)
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except:
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infos.append(traceback.format_exc())
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yield "\n".join(infos)
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