Refactoring get phones and bert

GPT_SoVITS/inference_webui.py

@@ -209,54 +209,8 @@ dict_language = {
 }
 
 
-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)
-    # Merge punctuation into previous word
-    for i in range(len(textlist)-1, 0, -1):
-        if re.match(r'^[\W_]+$', textlist[i]):
-            textlist[i-1] += textlist[i]
-            del textlist[i]
-            del langlist[i]
-    # Merge consecutive words with the same language tag
-    i = 0
-    while i < len(langlist) - 1:
-        if langlist[i] == langlist[i+1]:
-            textlist[i] += textlist[i+1]
-            del textlist[i+1]
-            del langlist[i+1]
-        else:
-            i += 1
-
-    return textlist, langlist
-
-
 def clean_text_inf(text, language):
-    formattext = ""
-    language = language.replace("all_","")
-    for tmp in LangSegment.getTexts(text):
-        if language == "ja":
-            if tmp["lang"] == language or tmp["lang"] == "zh":
-                formattext += tmp["text"] + " "
-            continue
-        if tmp["lang"] == language:
-            formattext += tmp["text"] + " "
-    while "  " in formattext:
-        formattext = formattext.replace("  ", " ")
-    phones, word2ph, norm_text = clean_text(formattext, language)
+    phones, word2ph, norm_text = clean_text(text, language)
     phones = cleaned_text_to_sequence(phones)
     return phones, word2ph, norm_text
 
@@ -274,55 +228,6 @@ def get_bert_inf(phones, word2ph, norm_text, language):
     return bert
 
 
-def nonen_clean_text_inf(text, language):
-    if(language!="auto"):
-        textlist, langlist = splite_en_inf(text, language)
-    else:
-        textlist=[]
-        langlist=[]
-        for tmp in LangSegment.getTexts(text):
-            langlist.append(tmp["lang"])
-            textlist.append(tmp["text"])
-    phones_list = []
-    word2ph_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 == "zh":
-            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):
-    if(language!="auto"):
-        textlist, langlist = splite_en_inf(text, language)
-    else:
-        textlist=[]
-        langlist=[]
-        for tmp in LangSegment.getTexts(text):
-            langlist.append(tmp["lang"])
-            textlist.append(tmp["text"])
-    print(textlist)
-    print(langlist)
-    bert_list = []
-    for i in range(len(textlist)):
-        lang = langlist[i]
-        phones, word2ph, norm_text = clean_text_inf(textlist[i], lang)
-        bert = get_bert_inf(phones, word2ph, norm_text, lang)
-        bert_list.append(bert)
-    bert = torch.cat(bert_list, dim=1)
-
-    return bert
-
-
 splits = {"，", "。", "？", "！", ",", ".", "?", "!", "~", ":", "：", "—", "…", }
 
 
@@ -332,23 +237,59 @@ def get_first(text):
     return text
 
 
-def get_cleaned_text_final(text,language):
+def get_phones_and_bert(text,language):
     if language in {"en","all_zh","all_ja"}:
-        phones, word2ph, norm_text = clean_text_inf(text, language)
+        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"}:
-        phones, word2ph, norm_text = nonen_clean_text_inf(text, language)
-    return phones, word2ph, norm_text
+        textlist=[]
+        langlist=[]
+        LangSegment.setfilters(["zh","ja","en"])
+        if language == "auto":
+            for tmp in LangSegment.getTexts(text):
+                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 = []
+        bert_list = []
+        norm_text_list = []
+        for i in range(len(textlist)):
+            lang = langlist[i]
+            phones, word2ph, norm_text = clean_text_inf(textlist[i], 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 phones,bert.to(dtype),norm_text
 
-def get_bert_final(phones, word2ph, text,language,device):
-    if language == "en":
-        bert = get_bert_inf(phones, word2ph, text, language)
-    elif language in {"zh", "ja","auto"}:
-        bert = nonen_get_bert_inf(text, language)
-    elif language == "all_zh":
-        bert = get_bert_feature(text, word2ph).to(device)
-    else:
-        bert = torch.zeros((1024, len(phones))).to(device)
-    return bert
 
 def merge_short_text_in_array(texts, threshold):
     if (len(texts)) < 2:
@@ -425,8 +366,7 @@ 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, word2ph1, norm_text1=get_cleaned_text_final(prompt_text, prompt_language)
-        bert1=get_bert_final(phones1, word2ph1, norm_text1,prompt_language,device).to(dtype)
+        phones1,bert1,norm_text1=get_phones_and_bert(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
@@ -434,8 +374,7 @@ 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, word2ph2, norm_text2 = get_cleaned_text_final(text, text_language)
-        bert2 = get_bert_final(phones2, word2ph2, norm_text2, text_language, device).to(dtype)
+        phones2,bert2,norm_text2=get_phones_and_bert(text, text_language)
         if not ref_free:
             bert = torch.cat([bert1, bert2], 1)
             all_phoneme_ids = torch.LongTensor(phones1+phones2).to(device).unsqueeze(0)

GPT_SoVITS/text/chinese.py

@@ -30,7 +30,7 @@ rep_map = {
     "\n": ".",
     "·": ",",
     "、": ",",
-    # "...": "…",
+    "...": "…",
     "$": ".",
     "/": ",",
     "—": "-",

requirements.txt

@@ -23,5 +23,5 @@ PyYAML
 psutil
 jieba_fast
 jieba
-LangSegment
+LangSegment>=0.2.0
 Faster_Whisper