Version Check (#1390)

* version check

* fix webui and symbols

* fix v1 language map

GPT_SoVITS/inference_webui.py

@@ -152,6 +152,11 @@ def change_sovits_weights(sovits_path):
     hps = dict_s2["config"]
     hps = DictToAttrRecursive(hps)
     hps.model.semantic_frame_rate = "25hz"
+    if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
+        hps.model.version = "v1"
+    else:
+        hps.model.version = "v2"
+    # print("sovits版本:",hps.model.version)
     vq_model = SynthesizerTrn(
         hps.data.filter_length // 2 + 1,
         hps.train.segment_size // hps.data.hop_length,
@@ -231,9 +236,9 @@ dict_language = {
 }
 
 
-def clean_text_inf(text, language):
+def clean_text_inf(text, language, version):
-    phones, word2ph, norm_text = clean_text(text, language)
+    phones, word2ph, norm_text = clean_text(text, language, version)
-    phones = cleaned_text_to_sequence(phones)
+    phones = cleaned_text_to_sequence(phones, version)
     return phones, word2ph, norm_text
 
 dtype=torch.float16 if is_half == True else torch.float32
@@ -259,7 +264,7 @@ def get_first(text):
     return text
 
 from text import chinese
-def get_phones_and_bert(text,language):
+def get_phones_and_bert(text,language,version):
     if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
         language = language.replace("all_","")
         if language == "en":
@@ -274,16 +279,16 @@ def get_phones_and_bert(text,language):
             if re.search(r'[A-Za-z]', formattext):
                 formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
                 formattext = chinese.text_normalize(formattext)
-                return get_phones_and_bert(formattext,"zh")
+                return get_phones_and_bert(formattext,"zh",version)
             else:
-                phones, word2ph, norm_text = clean_text_inf(formattext, language)
+                phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
                 bert = get_bert_feature(norm_text, word2ph).to(device)
         elif language == "yue" and re.search(r'[A-Za-z]', formattext):
                 formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
                 formattext = chinese.text_normalize(formattext)
-                return get_phones_and_bert(formattext,"yue")
+                return get_phones_and_bert(formattext,"yue",version)
         else:
-            phones, word2ph, norm_text = clean_text_inf(formattext, language)
+            phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
             bert = torch.zeros(
                 (1024, len(phones)),
                 dtype=torch.float16 if is_half == True else torch.float32,
@@ -317,7 +322,7 @@ def get_phones_and_bert(text,language):
         norm_text_list = []
         for i in range(len(textlist)):
             lang = langlist[i]
-            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
+            phones, word2ph, norm_text = clean_text_inf(textlist[i], lang, version)
             bert = get_bert_inf(phones, word2ph, norm_text, lang)
             phones_list.append(phones)
             norm_text_list.append(norm_text)
@@ -357,6 +362,9 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
     t0 = ttime()
     prompt_language = dict_language[prompt_language]
     text_language = dict_language[text_language]
+
+    version = vq_model.version
+
     if not ref_free:
         prompt_text = prompt_text.strip("\n")
         if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
@@ -413,7 +421,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
     texts = merge_short_text_in_array(texts, 5)
     audio_opt = []
     if not ref_free:
-        phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language)
+        phones1,bert1,norm_text1=get_phones_and_bert(prompt_text, prompt_language, version)
 
     for i_text,text in enumerate(texts):
         # 解决输入目标文本的空行导致报错的问题
@@ -421,7 +429,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
             continue
         if (text[-1] not in splits): text += "。" if text_language != "en" else "."
         print(i18n("实际输入的目标文本(每句):"), text)
-        phones2,bert2,norm_text2=get_phones_and_bert(text, text_language)
+        phones2,bert2,norm_text2=get_phones_and_bert(text, text_language, version)
         print(i18n("前端处理后的文本(每句):"), norm_text2)
         if not ref_free:
             bert = torch.cat([bert1, bert2], 1)

GPT_SoVITS/module/models.py

@@ -15,7 +15,9 @@ from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
 from module.commons import init_weights, get_padding
 from module.mrte_model import MRTE
 from module.quantize import ResidualVectorQuantizer
-from text import symbols
+# from text import symbols
+from text import symbols as symbols_v1
+from text import symbols2 as symbols_v2
 from torch.cuda.amp import autocast
 import contextlib
 
@@ -185,6 +187,7 @@ class TextEncoder(nn.Module):
         kernel_size,
         p_dropout,
         latent_channels=192,
+        version = "v2",
     ):
         super().__init__()
         self.out_channels = out_channels
@@ -195,6 +198,7 @@ class TextEncoder(nn.Module):
         self.kernel_size = kernel_size
         self.p_dropout = p_dropout
         self.latent_channels = latent_channels
+        self.version = version
 
         self.ssl_proj = nn.Conv1d(768, hidden_channels, 1)
 
@@ -210,6 +214,11 @@ class TextEncoder(nn.Module):
         self.encoder_text = attentions.Encoder(
             hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
         )
+
+        if self.version == "v1":
+            symbols = symbols_v1.symbols
+        else:
+            symbols = symbols_v2.symbols
         self.text_embedding = nn.Embedding(len(symbols), hidden_channels)
 
         self.mrte = MRTE()
@@ -827,6 +836,7 @@ class SynthesizerTrn(nn.Module):
         use_sdp=True,
         semantic_frame_rate=None,
         freeze_quantizer=None,
+        version = "v2",
         **kwargs
     ):
         super().__init__()
@@ -847,6 +857,7 @@ class SynthesizerTrn(nn.Module):
         self.segment_size = segment_size
         self.n_speakers = n_speakers
         self.gin_channels = gin_channels
+        self.version = version
 
         self.use_sdp = use_sdp
         self.enc_p = TextEncoder(
@@ -857,6 +868,7 @@ class SynthesizerTrn(nn.Module):
             n_layers,
             kernel_size,
             p_dropout,
+            version = version,
         )
         self.dec = Generator(
             inter_channels,
@@ -881,7 +893,7 @@ class SynthesizerTrn(nn.Module):
             inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels
         )
 
-        self.version=os.environ.get("version","v1")
+        # self.version=os.environ.get("version","v1")
         if(self.version=="v1"):
             self.ref_enc = modules.MelStyleEncoder(spec_channels, style_vector_dim=gin_channels)
         else:

GPT_SoVITS/text/__init__.py

@@ -1,18 +1,26 @@
 import os
-if os.environ.get("version","v1")=="v1":
+# if os.environ.get("version","v1")=="v1":
-  from text.symbols import symbols
+#   from text.symbols import symbols
-else:
+# else:
-  from text.symbols2 import symbols
+#   from text.symbols2 import symbols
+
+from text import symbols as symbols_v1
+from text import symbols2 as symbols_v2
 
-_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+_symbol_to_id_v1 = {s: i for i, s in enumerate(symbols_v1.symbols)}
+_symbol_to_id_v2 = {s: i for i, s in enumerate(symbols_v2.symbols)}
 
-def cleaned_text_to_sequence(cleaned_text):
+def cleaned_text_to_sequence(cleaned_text, version):
   '''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
     Args:
       text: string to convert to a sequence
     Returns:
       List of integers corresponding to the symbols in the text
   '''
-  phones = [_symbol_to_id[symbol] for symbol in cleaned_text]
+  if version == "v1":
+    phones = [_symbol_to_id_v1[symbol] for symbol in cleaned_text]
+  else:
+    phones = [_symbol_to_id_v2[symbol] for symbol in cleaned_text]
+
   return phones
 

GPT_SoVITS/text/cleaner.py

@@ -1,13 +1,17 @@
 from text import japanese, cleaned_text_to_sequence, english,korean,cantonese
 import os
-if os.environ.get("version","v1")=="v1":
+# if os.environ.get("version","v1")=="v1":
-    from text import chinese
+#     from text import chinese
-    from text.symbols import symbols
+#     from text.symbols import symbols
-else:
+# else:
-    from text import chinese2 as chinese
+#     from text import chinese2 as chinese
-    from text.symbols2 import symbols
+#     from text.symbols2 import symbols
+
+from text import symbols as symbols_v1
+from text import symbols2 as symbols_v2
+from text import chinese as chinese_v1
+from text import chinese2 as chinese_v2
 
-language_module_map = {"zh": chinese, "ja": japanese, "en": english, "ko": korean,"yue":cantonese}
 special = [
     # ("%", "zh", "SP"),
     ("￥", "zh", "SP2"),
@@ -16,13 +20,20 @@ special = [
 ]
 
 
-def clean_text(text, language):
+def clean_text(text, language, version):
+    if version == "v1":
+        symbols = symbols_v1.symbols
+        language_module_map = {"zh": chinese_v1, "ja": japanese, "en": english}
+    else:
+        symbols = symbols_v2.symbols
+        language_module_map = {"zh": chinese_v2, "ja": japanese, "en": english, "ko": korean,"yue":cantonese}
+
     if(language not in language_module_map):
         language="en"
         text=" "
     for special_s, special_l, target_symbol in special:
         if special_s in text and language == special_l:
-            return clean_special(text, language, special_s, target_symbol)
+            return clean_special(text, language, special_s, target_symbol, version)
     language_module = language_module_map[language]
     if hasattr(language_module,"text_normalize"):
         norm_text = language_module.text_normalize(text)
@@ -42,11 +53,18 @@ def clean_text(text, language):
         word2ph = None
 
     for ph in phones:
-        assert ph in symbols
+        phones = ['UNK' if ph not in symbols else ph for ph in phones]
     return phones, word2ph, norm_text
 
 
-def clean_special(text, language, special_s, target_symbol):
+def clean_special(text, language, special_s, target_symbol, version):
+    if version == "v1":
+        symbols = symbols_v1.symbols
+        language_module_map = {"zh": chinese_v1, "ja": japanese, "en": english}
+    else:
+        symbols = symbols_v2.symbols
+        language_module_map = {"zh": chinese_v2, "ja": japanese, "en": english, "ko": korean,"yue":cantonese}
+
     """
     特殊静音段sp符号处理
     """

GPT_SoVITS/text/english.py

@@ -6,9 +6,6 @@ from g2p_en import G2p
 
 from text.symbols import punctuation
 
-if os.environ.get("version","v1")=="v1":
-    from text.symbols import symbols
-else:
 from text.symbols2 import symbols
 
 import unicodedata

GPT_SoVITS/text/japanese.py

@@ -4,12 +4,6 @@ import sys
 
 import pyopenjtalk
 
-
-import os
-if os.environ.get("version","v1")=="v1":
-    from text.symbols import symbols
-else:
-    from text.symbols2 import symbols
 from text.symbols import punctuation
 # Regular expression matching Japanese without punctuation marks:
 _japanese_characters = re.compile(
@@ -61,12 +55,13 @@ def post_replace_ph(ph):
         "、": ",",
         "...": "…",
     }
+
     if ph in rep_map.keys():
         ph = rep_map[ph]
-    if ph in symbols:
+    # if ph in symbols:
-        return ph
+    #     return ph
-    if ph not in symbols:
+    # if ph not in symbols:
-        ph = "UNK"
+    #     ph = "UNK"
     return ph
 
 

GPT_SoVITS/text/korean.py

@@ -2,10 +2,7 @@ import re
 from jamo import h2j, j2hcj
 import ko_pron
 from g2pk2 import G2p
-import os
+
-if os.environ.get("version","v1")=="v1":
-    from text.symbols import symbols
-else:
 from text.symbols2 import symbols 
 
 # This is a list of Korean classifiers preceded by pure Korean numerals.

api.py

@@ -11,7 +11,7 @@
 调用请求缺少参考音频时使用
 `-dr` - `默认参考音频路径`
 `-dt` - `默认参考音频文本`
-`-dl` - `默认参考音频语种, "中文","英文","日文","zh","en","ja"`
+`-dl` - `默认参考音频语种, "中文","英文","日文","韩文","粤语,"zh","en","ja","ko","yue"`
 
 `-d` - `推理设备, "cuda","cpu"`
 `-a` - `绑定地址, 默认"127.0.0.1"`
@@ -201,6 +201,11 @@ def change_sovits_weights(sovits_path):
     hps = dict_s2["config"]
     hps = DictToAttrRecursive(hps)
     hps.model.semantic_frame_rate = "25hz"
+    if dict_s2['weight']['enc_p.text_embedding.weight'].shape[0] == 322:
+        hps.model.version = "v1"
+    else:
+        hps.model.version = "v2"
+    print("sovits版本:",hps.model.version)
     model_params_dict = vars(hps.model)
     vq_model = SynthesizerTrn(
         hps.data.filter_length // 2 + 1,
@@ -251,9 +256,9 @@ def get_bert_feature(text, word2ph):
     return phone_level_feature.T
 
 
-def clean_text_inf(text, language):
+def clean_text_inf(text, language, version):
-    phones, word2ph, norm_text = clean_text(text, language)
+    phones, word2ph, norm_text = clean_text(text, language, version)
-    phones = cleaned_text_to_sequence(phones)
+    phones = cleaned_text_to_sequence(phones, version)
     return phones, word2ph, norm_text
 
 
@@ -269,36 +274,48 @@ def get_bert_inf(phones, word2ph, norm_text, language):
 
     return bert
 
-
+from text import chinese
-def get_phones_and_bert(text,language):
+def get_phones_and_bert(text,language,version):
-    if language in {"en","all_zh","all_ja"}:
+    if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
         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
+            # 因无法区别中日韩文汉字,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":
+            if re.search(r'[A-Za-z]', formattext):
+                formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
+                formattext = chinese.text_normalize(formattext)
+                return get_phones_and_bert(formattext,"zh",version)
+            else:
+                phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
                 bert = get_bert_feature(norm_text, word2ph).to(device)
+        elif language == "yue" and re.search(r'[A-Za-z]', formattext):
+                formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
+                formattext = chinese.text_normalize(formattext)
+                return get_phones_and_bert(formattext,"yue",version)
         else:
+            phones, word2ph, norm_text = clean_text_inf(formattext, language, version)
             bert = torch.zeros(
                 (1024, len(phones)),
                 dtype=torch.float16 if is_half == True else torch.float32,
             ).to(device)
-    elif language in {"zh", "ja","auto"}:
+    elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
         textlist=[]
         langlist=[]
         LangSegment.setfilters(["zh","ja","en","ko"])
         if language == "auto":
             for tmp in LangSegment.getTexts(text):
-                if tmp["lang"] == "ko":
+                langlist.append(tmp["lang"])
-                    langlist.append("zh")
                 textlist.append(tmp["text"])
-                else:
+        elif language == "auto_yue":
+            for tmp in LangSegment.getTexts(text):
+                if tmp["lang"] == "zh":
+                    tmp["lang"] = "yue"
                 langlist.append(tmp["lang"])
                 textlist.append(tmp["text"])
         else:
@@ -306,17 +323,15 @@ def get_phones_and_bert(text,language):
                 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
+                    # 因无法区别中日韩文汉字,以用户输入为准
                     langlist.append(language)
                 textlist.append(tmp["text"])
-        # logger.info(textlist)
-        # logger.info(langlist)
         phones_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, word2ph, norm_text = clean_text_inf(textlist[i], lang, version)
             bert = get_bert_inf(phones, word2ph, norm_text, lang)
             phones_list.append(phones)
             norm_text_list.append(norm_text)
@@ -328,15 +343,33 @@ def get_phones_and_bert(text,language):
     return phones,bert.to(torch.float16 if is_half == True else torch.float32),norm_text
 
 
-class DictToAttrRecursive:
+class DictToAttrRecursive(dict):
     def __init__(self, input_dict):
+        super().__init__(input_dict)
         for key, value in input_dict.items():
             if isinstance(value, dict):
-                # 如果值是字典，递归调用构造函数
+                value = DictToAttrRecursive(value)
-                setattr(self, key, DictToAttrRecursive(value))
+            self[key] = value
-            else:
             setattr(self, key, value)
 
+    def __getattr__(self, item):
+        try:
+            return self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
+    def __setattr__(self, key, value):
+        if isinstance(value, dict):
+            value = DictToAttrRecursive(value)
+        super(DictToAttrRecursive, self).__setitem__(key, value)
+        super().__setattr__(key, value)
+
+    def __delattr__(self, item):
+        try:
+            del self[item]
+        except KeyError:
+            raise AttributeError(f"Attribute {item} not found")
+
 
 def get_spepc(hps, filename):
     audio = load_audio(filename, int(hps.data.sampling_rate))
@@ -488,9 +521,10 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
         codes = vq_model.extract_latent(ssl_content)
         prompt_semantic = codes[0, 0]
     t1 = ttime()
+    version = vq_model.version
     prompt_language = dict_language[prompt_language.lower()]
     text_language = dict_language[text_language.lower()]
-    phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language)
+    phones1, bert1, norm_text1 = get_phones_and_bert(prompt_text, prompt_language, version)
     texts = text.split("\n")
     audio_bytes = BytesIO()
 
@@ -500,7 +534,7 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
             continue
 
         audio_opt = []
-        phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language)
+        phones2, bert2, norm_text2 = get_phones_and_bert(text, text_language, version)
         bert = torch.cat([bert1, bert2], 1)
 
         all_phoneme_ids = torch.LongTensor(phones1 + phones2).to(device).unsqueeze(0)
@@ -606,17 +640,27 @@ def handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cu
 # --------------------------------
 dict_language = {
     "中文": "all_zh",
+    "粤语": "all_yue",
     "英文": "en",
     "日文": "all_ja",
+    "韩文": "all_ko",
     "中英混合": "zh",
+    "粤英混合": "yue",
     "日英混合": "ja",
+    "韩英混合": "ko",
     "多语种混合": "auto",    #多语种启动切分识别语种
+    "多语种混合(粤语)": "auto_yue",
     "all_zh": "all_zh",
+    "all_yue": "all_yue",
     "en": "en",
     "all_ja": "all_ja",
+    "all_ko": "all_ko",
     "zh": "zh",
+    "yue": "yue",
     "ja": "ja",
+    "ko": "ko",
     "auto": "auto",
+    "auto_yue": "auto_yue",
 }
 
 # logger