Add sentence splitting (#3227)

* Add sentence spliting

* update requirements

* update default args v2

* Add spanish

* Fix return gpt_latents

* Update requirements

* Fix requirements

TTS/tts/layers/xtts/tokenizer.py

@@ -1,17 +1,72 @@
-import json
 import os
 import re
-from functools import cached_property
-
-import pypinyin
 import torch
+import pypinyin
+import textwrap
+
+from functools import cached_property
 from hangul_romanize import Transliter
 from hangul_romanize.rule import academic
 from num2words import num2words
 from tokenizers import Tokenizer
 
 from TTS.tts.layers.xtts.zh_num2words import TextNorm as zh_num2words
 
+from spacy.lang.en import English
+from spacy.lang.zh import Chinese
+from spacy.lang.ja import Japanese
+from spacy.lang.ar import Arabic
+from spacy.lang.es import Spanish
+
+
+def get_spacy_lang(lang):
+    if lang == "zh":
+        return Chinese()
+    elif lang == "ja":
+        return Japanese()
+    elif lang == "ar":
+        return Arabic()
+    elif lang == "es":
+        return Spanish()
+    else:
+        # For most languages, Enlish does the job
+        return English()
+
+def split_sentence(text, lang, text_split_length=250):
+    """Preprocess the input text"""
+    text_splits = []
+    if text_split_length is not None and len(text) >= text_split_length:
+        text_splits.append("")
+        nlp = get_spacy_lang(lang)
+        nlp.add_pipe("sentencizer")
+        doc = nlp(text)
+        for sentence in doc.sents:
+            if len(text_splits[-1]) + len(str(sentence)) <= text_split_length:
+                # if the last sentence + the current sentence is less than the text_split_length
+                # then add the current sentence to the last sentence
+                text_splits[-1] += " " + str(sentence)
+                text_splits[-1] = text_splits[-1].lstrip()
+            elif len(str(sentence)) > text_split_length:
+                # if the current sentence is greater than the text_split_length
+                for line in textwrap.wrap(
+                    str(sentence),
+                    width=text_split_length,
+                    drop_whitespace=True,
+                    break_on_hyphens=False,
+                    tabsize=1,
+                ):
+                    text_splits.append(str(line))
+            else:
+                text_splits.append(str(sentence))
+
+        if len(text_splits) > 1:
+            if text_splits[0] == "":
+                del text_splits[0]
+    else:
+        text_splits = [text.lstrip()]
+
+    return text_splits
+
 _whitespace_re = re.compile(r"\s+")
 
 # List of (regular expression, replacement) pairs for abbreviations:
@@ -464,7 +519,7 @@ def _expand_number(m, lang="en"):
 
 
 def expand_numbers_multilingual(text, lang="en"):
-    if lang == "zh" or lang == "zh-cn":
+    if lang == "zh":
         text = zh_num2words()(text)
     else:
         if lang in ["en", "ru"]:
@@ -525,7 +580,7 @@ def japanese_cleaners(text, katsu):
     return text
 
 
-def korean_cleaners(text):
+def korean_transliterate(text):
     r = Transliter(academic)
     return r.translit(text)
 
@@ -546,7 +601,7 @@ def __init__(self, vocab_file=None):
             "it": 213,
             "pt": 203,
             "pl": 224,
-            "zh-cn": 82,
+            "zh": 82,
             "ar": 166,
             "cs": 186,
             "ru": 182,
@@ -571,19 +626,20 @@ def check_input_length(self, txt, lang):
             )
 
     def preprocess_text(self, txt, lang):
-        if lang in {"ar", "cs", "de", "en", "es", "fr", "hu", "it", "nl", "pl", "pt", "ru", "tr", "zh-cn", "zh-cn"}:
+        if lang in {"ar", "cs", "de", "en", "es", "fr", "hu", "it", "nl", "pl", "pt", "ru", "tr", "zh-cn", "ko"}:
             txt = multilingual_cleaners(txt, lang)
-            if lang in {"zh", "zh-cn"}:
+            if lang == "zh":
                 txt = chinese_transliterate(txt)
+            if lang == "ko":
+                txt = korean_transliterate(txt)
         elif lang == "ja":
             txt = japanese_cleaners(txt, self.katsu)
-        elif lang == "ko":
-            txt = korean_cleaners(txt)
         else:
             raise NotImplementedError(f"Language '{lang}' is not supported.")
         return txt
 
     def encode(self, txt, lang):
+        lang = lang.split("-")[0] # remove the region
         self.check_input_length(txt, lang)
         txt = self.preprocess_text(txt, lang)
         txt = f"[{lang}]{txt}"