Update inference_webui.py

GPT_SoVITS/inference_webui.py

@@ -1,16 +1,38 @@
-import os,re,logging
+'''
+按中英混合识别
+按日英混合识别
+多语种启动切分识别语种
+全部按中文识别
+全部按英文识别
+全部按日文识别
+'''
+import os, re, logging
+import LangSegment
 logging.getLogger("markdown_it").setLevel(logging.ERROR)
 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
+import torch
+
+device = "cpu"
+
+try:
+    import torch_musa
+    use_torch_musa = True
+except ImportError:
+    use_torch_musa = False
+if use_torch_musa:
+    if "_MUSA_VISIBLE_DEVICES" in os.environ:
+        os.environ["MUSA_VISIBLE_DEVICES"] = os.environ["_MUSA_VISIBLE_DEVICES"]
+    if torch.musa.is_available():
+        device = "musa"
 
 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)
@@ -19,7 +41,7 @@ else:
         "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:
@@ -29,24 +51,25 @@ else:
 # )
 # 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"
+    "cnhubert_base_path", "GPT_SoVITS/pretrained_models/chinese-hubert-base"
 )
 bert_path = os.environ.get(
-    "bert_path", "pretrained_models/chinese-roberta-wwm-ext-large"
+    "bert_path", "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
 )
 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"))
+is_half = eval(os.environ.get("is_half", "True")) and torch.cuda.is_available()
 import gradio as gr
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 import numpy as np
-import librosa,torch
+import librosa
 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
@@ -56,16 +79,13 @@ 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"
-elif torch.backends.mps.is_available():
-    device = "mps"
-else:
-    device = "cpu"
 
 tokenizer = AutoTokenizer.from_pretrained(bert_path)
 bert_model = AutoModelForMaskedLM.from_pretrained(bert_path)
@@ -74,6 +94,7 @@ 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 +110,7 @@ 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 +145,11 @@ if is_half == True:
 else:
     ssl_model = ssl_model.to(device)
 
+
 def change_sovits_weights(sovits_path):
-    global vq_model,hps
+    global vq_model, hps
-    dict_s2=torch.load(sovits_path,map_location="cpu")
+    dict_s2 = torch.load(sovits_path, map_location="cpu")
-    hps=dict_s2["config"]
+    hps = dict_s2["config"]
     hps = DictToAttrRecursive(hps)
     hps.model.semantic_frame_rate = "25hz"
     vq_model = SynthesizerTrn(
@@ -135,7 +158,7 @@ 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 +166,15 @@ 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 +187,12 @@ 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)
@@ -179,43 +209,26 @@ def get_spepc(hps, filename):
     return spec
 
 
-dict_language={
+dict_language = {
-    i18n("中文"):"zh",
+    i18n("中文"): "all_zh",#全部按中文识别
-    i18n("英文"):"en",
+    i18n("英文"): "en",#全部按英文识别#######不变
-    i18n("日文"):"ja"
+    i18n("日文"): "all_ja",#全部按日文识别
+    i18n("中英混合"): "zh",#按中英混合识别####不变
+    i18n("日英混合"): "ja",#按日英混合识别####不变
+    i18n("多语种混合"): "auto",#多语种启动切分识别语种
 }
 
 
-def splite_en_inf(sentence, language):
-    pattern = re.compile(r'[a-zA-Z. ]+')
-    textlist = []
-    langlist = []
-    pos = 0
-    for match in pattern.finditer(sentence):
-        start, end = match.span()
-        if start > pos:
-            textlist.append(sentence[pos:start])
-            langlist.append(language)
-        textlist.append(sentence[start:end])
-        langlist.append("en")
-        pos = end
-    if pos < len(sentence):
-        textlist.append(sentence[pos:])
-        langlist.append(language)
-
-    return textlist, langlist
-
-
 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
 
-
+dtype=torch.float16 if is_half == True else torch.float32
 def get_bert_inf(phones, word2ph, norm_text, language):
+    language=language.replace("all_","")
     if language == "zh":
-        bert = get_bert_feature(norm_text, word2ph).to(device)
+        bert = get_bert_feature(norm_text, word2ph).to(device)#.to(dtype)
     else:
         bert = torch.zeros(
             (1024, len(phones)),
@@ -225,54 +238,112 @@ def get_bert_inf(phones, word2ph, norm_text, language):
     return bert
 
 
-def nonen_clean_text_inf(text, language):
+splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
-    textlist, langlist = splite_en_inf(text, language)
+
+
+def get_first(text):
+    pattern = "[" + "".join(re.escape(sep) for sep in splits) + "]"
+    text = re.split(pattern, text)[0].strip()
+    return text
+
+
+def get_phones_and_bert(text,language):
+    if language in {"en","all_zh","all_ja"}:
+        language = language.replace("all_","")
+        if language == "en":
+            LangSegment.setfilters(["en"])
+            formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
+        else:
+            # 因无法区别中日文汉字,以用户输入为准
+            formattext = text
+        while "  " in formattext:
+            formattext = formattext.replace("  ", " ")
+        phones, word2ph, norm_text = clean_text_inf(formattext, 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)
+    elif language in {"zh", "ja","auto"}:
+        textlist=[]
+        langlist=[]
+        LangSegment.setfilters(["zh","ja","en","ko"])
+        if language == "auto":
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "ko":
+                    langlist.append("zh")
+                    textlist.append(tmp["text"])
+                else:
+                    langlist.append(tmp["lang"])
+                    textlist.append(tmp["text"])
+        else:
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "en":
+                    langlist.append(tmp["lang"])
+                else:
+                    # 因无法区别中日文汉字,以用户输入为准
+                    langlist.append(language)
+                textlist.append(tmp["text"])
+        print(textlist)
+        print(langlist)
         phones_list = []
-    word2ph_list = []
+        bert_list = []
         norm_text_list = []
         for i in range(len(textlist)):
             lang = langlist[i]
             phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
-        phones_list.append(phones)
-        if lang == "en" or "ja":
-            pass
-        else:
-            word2ph_list.append(word2ph)
-        norm_text_list.append(norm_text)
-    print(word2ph_list)
-    phones = sum(phones_list, [])
-    word2ph = sum(word2ph_list, [])
-    norm_text = ' '.join(norm_text_list)
-
-    return phones, word2ph, norm_text
-
-
-def nonen_get_bert_inf(text, language):
-    textlist, langlist = splite_en_inf(text, language)
-    print(textlist)
-    print(langlist)
-    bert_list = []
-    for i in range(len(textlist)):
-        text = textlist[i]
-        lang = langlist[i]
-        phones, word2ph, norm_text = clean_text_inf(text, lang)
             bert = get_bert_inf(phones, word2ph, norm_text, lang)
+            phones_list.append(phones)
+            norm_text_list.append(norm_text)
             bert_list.append(bert)
         bert = torch.cat(bert_list, dim=1)
+        phones = sum(phones_list, [])
+        norm_text = ''.join(norm_text_list)
 
-    return bert
+    return phones,bert.to(dtype),norm_text
 
-#i18n("不切"),i18n("凑五句一切"),i18n("凑50字一切"),i18n("按中文句号。切"),i18n("按英文句号.切")
+
-def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,how_to_cut=i18n("不切")):
+def merge_short_text_in_array(texts, threshold):
+    if (len(texts)) < 2:
+        return texts
+    result = []
+    text = ""
+    for ele in texts:
+        text += ele
+        if len(text) >= threshold:
+            result.append(text)
+            text = ""
+    if (len(text) > 0):
+        if len(result) == 0:
+            result.append(text)
+        else:
+            result[len(result) - 1] += text
+    return result
+
+def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language, how_to_cut=i18n("不切"), top_k=20, top_p=0.6, temperature=0.6, ref_free = False):
+    if prompt_text is None or len(prompt_text) == 0:
+        ref_free = True
     t0 = ttime()
+    prompt_language = dict_language[prompt_language]
+    text_language = dict_language[text_language]
+    if not ref_free:
         prompt_text = prompt_text.strip("\n")
-    prompt_language, text = prompt_language, text.strip("\n")
+        if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
+        print(i18n("实际输入的参考文本:"), prompt_text)
+    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):
+            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)
         if is_half == True:
@@ -281,52 +352,51 @@ 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(
             1, 2
         )  # .float()
         codes = vq_model.extract_latent(ssl_content)
+   
         prompt_semantic = codes[0, 0]
     t1 = ttime()
-    prompt_language = dict_language[prompt_language]
-    text_language = dict_language[text_language]
 
-    if prompt_language == "en":
+    if (how_to_cut == i18n("凑四句一切")):
-        phones1, word2ph1, norm_text1 = clean_text_inf(prompt_text, prompt_language)
+        text = cut1(text)
-    else:
+    elif (how_to_cut == i18n("凑50字一切")):
-        phones1, word2ph1, norm_text1 = nonen_clean_text_inf(prompt_text, prompt_language)
+        text = cut2(text)
-    if(how_to_cut==i18n("凑五句一切")):text=cut1(text)
+    elif (how_to_cut == i18n("按中文句号。切")):
-    elif(how_to_cut==i18n("凑50字一切")):text=cut2(text)
+        text = cut3(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")):
-    elif(how_to_cut==i18n("按英文句号.切")):text=cut4(text)
+        text = cut4(text)
-    text = text.replace("\n\n","\n").replace("\n\n","\n").replace("\n\n","\n")
+    elif (how_to_cut == i18n("按标点符号切")):
-    if(text[-1]not in splits):text+="。"if text_language!="en"else "."
+        text = cut5(text)
-    texts=text.split("\n")
+    while "\n\n" in text:
+        text = text.replace("\n\n", "\n")
+    print(i18n("实际输入的目标文本(切句后):"), text)
+    texts = text.split("\n")
+    texts = merge_short_text_in_array(texts, 5)
     audio_opt = []
-    if prompt_language == "en":
+    if not ref_free:
-        bert1 = get_bert_inf(phones1, word2ph1, norm_text1, prompt_language)
+        phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, 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":
+        if (text[-1] not in splits): text += "。" if text_language != "en" else "."
-            phones2, word2ph2, norm_text2 = clean_text_inf(text, text_language)
+        print(i18n("实际输入的目标文本(每句):"), text)
-        else:
+        phones2,bert2,norm_text2=get_phones_and_bert(text, text_language)
-            phones2, word2ph2, norm_text2 = nonen_clean_text_inf(text, text_language)
+        print(i18n("前端处理后的文本(每句):"), norm_text2)
-
+        if not ref_free:
-        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)
+        else:
+            bert = bert2
+            all_phoneme_ids = torch.LongTensor(phones2).to(device).unsqueeze(0)
 
-        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)
@@ -336,10 +406,12 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
             pred_semantic, idx = t2s_model.model.infer_panel(
                 all_phoneme_ids,
                 all_phoneme_len,
-                prompt,
+                None if ref_free else prompt,
                 bert,
                 # prompt_phone_len=ph_offset,
-                top_k=config["inference"]["top_k"],
+                top_k=top_k,
+                top_p=top_p,
+                temperature=temperature,
                 early_stop_num=hz * max_sec,
             )
         t3 = ttime()
@@ -361,6 +433,8 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
                 .cpu()
                 .numpy()[0, 0]
         )  ###试试重建不带上prompt部分
+        max_audio=np.abs(audio).max()#简单防止16bit爆音
+        if max_audio>1:audio/=max_audio
         audio_opt.append(audio)
         audio_opt.append(zero_wav)
         t4 = ttime()
@@ -370,23 +444,6 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
     )
 
 
-splits = {
-    "，",
-    "。",
-    "？",
-    "！",
-    ",",
-    ".",
-    "?",
-    "!",
-    "~",
-    ":",
-    "：",
-    "—",
-    "…",
-}  # 不考虑省略号
-
-
 def split(todo_text):
     todo_text = todo_text.replace("……", "。").replace("——", "，")
     if todo_text[-1] not in splits:
@@ -409,12 +466,12 @@ def split(todo_text):
 def cut1(inp):
     inp = inp.strip("\n")
     inps = split(inp)
-    split_idx = list(range(0, len(inps), 5))
+    split_idx = list(range(0, len(inps), 4))
     split_idx[-1] = None
     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)
@@ -424,7 +481,7 @@ def cut2(inp):
     inp = inp.strip("\n")
     inps = split(inp)
     if len(inps) < 2:
-        return [inp]
+        return inp
     opts = []
     summ = 0
     tmp_str = ""
@@ -437,7 +494,8 @@ def cut2(inp):
             tmp_str = ""
     if tmp_str != "":
         opts.append(tmp_str)
-    if len(opts[-1]) < 50:  ##如果最后一个太短了，和前一个合一起
+    # 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
         opts[-2] = opts[-2] + opts[-1]
         opts = opts[:-1]
     return "\n".join(opts)
@@ -445,10 +503,28 @@ def cut2(inp):
 
 def cut3(inp):
     inp = inp.strip("\n")
-    return "\n".join(["%s。" % item for item in inp.strip("。").split("。")])
+    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(".")])
+    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)
+    mergeitems = ["".join(group) for group in zip(items[::2], items[1::2])]
+    # 在句子不存在符号或句尾无符号的时候保证文本完整
+    if len(items)%2 == 1:
+        mergeitems.append(items[-1])
+    opt = "\n".join(mergeitems)
+    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
@@ -457,25 +533,31 @@ 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))
+        if name.endswith(".ckpt"): GPT_names.append("%s/%s" % (GPT_weight_root, name))
-    return SoVITS_names,GPT_names
+    return SoVITS_names, GPT_names
-SoVITS_names,GPT_names = get_weights_names()
+
+
+SoVITS_names, GPT_names = get_weights_names()
 
 with gr.Blocks(title="GPT-SoVITS WebUI") as app:
     gr.Markdown(
@@ -484,53 +566,63 @@ 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)
+            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)
+            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],[])
+            SoVITS_dropdown.change(change_sovits_weights, [SoVITS_dropdown], [])
-            GPT_dropdown.change(change_gpt_weights,[GPT_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("请上传参考音频"), type="filepath")
+            inp_ref = gr.Audio(label=i18n("请上传3~10秒内参考音频，超过会报错！"), type="filepath")
+            with gr.Column():
+                ref_text_free = gr.Checkbox(label=i18n("开启无参考文本模式。不填参考文本亦相当于开启。"), value=False, interactive=True, show_label=True)
+                gr.Markdown(i18n("使用无参考文本模式时建议使用微调的GPT，听不清参考音频说的啥(不晓得写啥)可以开，开启后无视填写的参考文本。"))
                 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"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], 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。"))
+        gr.Markdown(value=i18n("*请填写需要合成的目标文本和语种模式"))
         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"), i18n("中英混合"), i18n("日英混合"), i18n("多语种混合")], value=i18n("中文")
             )
             how_to_cut = gr.Radio(
                 label=i18n("怎么切"),
-                choices=[i18n("不切"),i18n("凑五句一切"),i18n("凑50字一切"),i18n("按中文句号。切"),i18n("按英文句号.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50字一切"),
+                value=i18n("凑四句一切"),
                 interactive=True,
             )
+            with gr.Row():
+                gr.Markdown(value=i18n("gpt采样参数(无参考文本时不要太低)："))
+                top_k = gr.Slider(minimum=1,maximum=100,step=1,label=i18n("top_k"),value=5,interactive=True)
+                top_p = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("top_p"),value=1,interactive=True)
+                temperature = gr.Slider(minimum=0,maximum=1,step=0.05,label=i18n("temperature"),value=1,interactive=True)
             inference_button = gr.Button(i18n("合成语音"), variant="primary")
             output = gr.Audio(label=i18n("输出的语音"))
 
         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, top_k, top_p, temperature, ref_text_free],
             [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("凑五句一切"), variant="primary")
+            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])
-        gr.Markdown(value=i18n("后续将支持混合语种编码文本输入。"))
+            button5.click(cut5, [text_inp], [text_opt])
+        gr.Markdown(value=i18n("后续将支持转音素、手工修改音素、语音合成分步执行。"))
 
 app.queue(concurrency_count=511, max_size=1022).launch(
     server_name="0.0.0.0",