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@ -1,11 +1,18 @@
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'''
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按中英混合识别
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按日英混合识别
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多语种启动切分识别语种
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全部按中文识别
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全部按英文识别
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全部按日文识别
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'''
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import os, re, logging
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import LangSegment
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logging.getLogger("markdown_it").setLevel(logging.ERROR)
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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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@ -193,9 +200,12 @@ def get_spepc(hps, filename):
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dict_language = {
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i18n("中文"): "zh",
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i18n("英文"): "en",
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i18n("日文"): "ja"
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i18n("中文"): "all_zh",#全部按中文识别
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i18n("英文"): "en",#全部按英文识别#######不变
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i18n("日文"): "all_ja",#全部按日文识别
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i18n("中英混合"): "zh",#按中英混合识别####不变
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i18n("日英混合"): "ja",#按日英混合识别####不变
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i18n("多语种混合"): "auto",#多语种启动切分识别语种
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}
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@ -235,15 +245,15 @@ 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, word2ph, norm_text = clean_text(text, language.replace("all_",""))
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phones = cleaned_text_to_sequence(phones)
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return phones, word2ph, norm_text
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dtype=torch.float16 if is_half == True else torch.float32
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def get_bert_inf(phones, word2ph, norm_text, language):
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language=language.replace("all_","")
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if language == "zh":
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bert = get_bert_feature(norm_text, word2ph).to(device)
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bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
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else:
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bert = torch.zeros(
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(1024, len(phones)),
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@ -254,7 +264,16 @@ def get_bert_inf(phones, word2ph, norm_text, language):
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def nonen_clean_text_inf(text, language):
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textlist, langlist = splite_en_inf(text, language)
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if(language!="auto"):
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textlist, langlist = splite_en_inf(text, language)
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else:
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textlist=[]
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langlist=[]
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for tmp in LangSegment.getTexts(text):
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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print(textlist)
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print(langlist)
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phones_list = []
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word2ph_list = []
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norm_text_list = []
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@ -262,9 +281,7 @@ 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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pass
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else:
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if lang == "zh":
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word2ph_list.append(word2ph)
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norm_text_list.append(norm_text)
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print(word2ph_list)
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@ -276,7 +293,14 @@ def nonen_clean_text_inf(text, language):
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def nonen_get_bert_inf(text, language):
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textlist, langlist = splite_en_inf(text, language)
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if(language!="auto"):
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textlist, langlist = splite_en_inf(text, language)
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else:
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textlist=[]
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langlist=[]
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for tmp in LangSegment.getTexts(text):
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langlist.append(tmp["lang"])
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textlist.append(tmp["text"])
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print(textlist)
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print(langlist)
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bert_list = []
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@ -300,6 +324,24 @@ def get_first(text):
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return text
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def get_cleaned_text_fianl(text,language):
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if language in {"en","all_zh","all_ja"}:
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phones, word2ph, norm_text = clean_text_inf(text, language)
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elif language in {"zh", "ja","auto"}:
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phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
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return phones, word2ph, norm_text
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def get_bert_final(phones, word2ph, norm_text,language,device):
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if text_language == "en":
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bert = get_bert_inf(phones, word2ph, norm_text, text_language)
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elif text_language in {"zh", "ja","auto"}:
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bert = nonen_get_bert_inf(text, text_language)
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elif text_language == "all_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((1024, len(phones))).to(device)
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return bert
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def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切")):
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t0 = ttime()
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prompt_text = prompt_text.strip("\n")
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@ -335,10 +377,9 @@ 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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phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language)
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phones1, word2ph1, norm_text1=get_cleaned_text_fianl(prompt_text, prompt_language)
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if (how_to_cut == i18n("凑四句一切")):
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text = cut1(text)
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elif (how_to_cut == i18n("凑50字一切")):
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@ -353,25 +394,16 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
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print(i18n("实际输入的目标文本(切句后):"), text)
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texts = text.split("\n")
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audio_opt = []
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if prompt_language == "en":
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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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bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
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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[-1] not in splits): text += "。" if text_language != "en" else "."
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print(i18n("实际输入的目标文本(每句):"), text)
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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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phones2, word2ph2, norm_text2 = get_cleaned_text_fianl(text, text_language)
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bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
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bert = torch.cat([bert1, bert2], 1)
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@ -557,7 +589,7 @@ with gr.Blocks(title="GPT-SoVITS WebUI") as app:
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with gr.Row():
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text = gr.Textbox(label=i18n("需要合成的文本"), value="")
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text_language = gr.Dropdown(
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label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文")], value=i18n("中文")
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label=i18n("需要合成的语种"), choices=[i18n("中文"), i18n("英文"), i18n("日文"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
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)
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how_to_cut = gr.Radio(
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label=i18n("怎么切"),
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