tuna2134/sbv2-v2
1
0
Fork 0
Code Issues Pull Requests 1 Actions Packages Projects Releases Wiki Activity

Change to import inside if block

Browse Source
This commit is contained in:
litagin02
2024-02-23 16:49:11 +09:00
parent cb842faaf7
commit 049b0fc1d5
2 changed files with 16 additions and 28 deletions
Download Patch File Download Diff File Expand all files Collapse all files

5
text/__init__.py
View File

@@ -27,11 +27,6 @@ def get_bert(
assist_text_weight=0.7,
ignore_unknown=False,
):
# from .chinese_bert import get_bert_feature as zh_bert
# from .english_bert_mock import get_bert_feature as en_bert
# from .japanese_bert import get_bert_feature as jp_bert
# Change logic to import inside if condition to avoid unnecessary import
if language == "ZH":
from .chinese_bert import get_bert_feature as zh_bert

39
text/cleaner.py
View File

@@ -1,33 +1,26 @@
from text import chinese, japanese, english, cleaned_text_to_sequence
language_module_map = {"ZH": chinese, "JP": japanese, "EN": english}
def clean_text(text, language, use_jp_extra=True, ignore_unknown=False):
language_module = language_module_map[language]
norm_text = language_module.text_normalize(text)
if language == "JP":
# Changed to import inside if condition to avoid unnecessary import
if language == "ZH":
from . import chinese as language_module
norm_text = language_module.text_normalize(text)
phones, tones, word2ph = language_module.g2p(norm_text)
elif language == "EN":
from . import english as language_module
norm_text = language_module.text_normalize(text)
phones, tones, word2ph = language_module.g2p(norm_text)
elif language == "JP":
from . import japanese as language_module
norm_text = language_module.text_normalize(text)
phones, tones, word2ph = language_module.g2p(
norm_text, use_jp_extra, ignore_unknown=ignore_unknown
)
else:
phones, tones, word2ph = language_module.g2p(norm_text)
raise ValueError(f"Language {language} not supported")
return norm_text, phones, tones, word2ph
def clean_text_bert(text, language):
language_module = language_module_map[language]
norm_text = language_module.text_normalize(text)
phones, tones, word2ph = language_module.g2p(norm_text)
bert = language_module.get_bert_feature(norm_text, word2ph)
return phones, tones, bert
def text_to_sequence(text, language):
norm_text, phones, tones, word2ph = clean_text(text, language)
return cleaned_text_to_sequence(phones, tones, language)
if __name__ == "__main__":
pass