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Refactor: run "hatch run style:fmt"

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tsukumi
2024-03-12 18:27:08 +00:00
parent e8a76e547b
commit 07d246b98b
3 changed files with 24 additions and 13 deletions
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14
pyproject.toml
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@@ -75,18 +75,18 @@ cov = [
[tool.hatch.envs.style] [tool.hatch.envs.style]
detached = true detached = true
dependencies = [ dependencies = [
"black", "black",
"isort", "isort",
] ]
[tool.hatch.envs.style.scripts] [tool.hatch.envs.style.scripts]
check = [ check = [
"black --check --diff .", "black --check --diff .",
"isort --check-only --diff --profile black --gitignore --lai 2 .", "isort --check-only --diff --profile black --gitignore --lai 2 .",
] ]
fmt = [ fmt = [
"black .", "black .",
"isort --profile black --gitignore --lai 2 .", "isort --profile black --gitignore --lai 2 .",
"check", "check",
] ]
[[tool.hatch.envs.test.matrix]] [[tool.hatch.envs.test.matrix]]

20
style_bert_vits2/nlp/bert_models.py
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@@ -80,10 +80,14 @@ def load_model(
if language == Languages.EN: if language == Languages.EN:
model = cast( model = cast(
DebertaV2Model, DebertaV2Model,
DebertaV2Model.from_pretrained(pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision), DebertaV2Model.from_pretrained(
pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision
),
) )
else: else:
model = AutoModelForMaskedLM.from_pretrained(pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision) model = AutoModelForMaskedLM.from_pretrained(
pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision
)
__loaded_models[language] = model __loaded_models[language] = model
logger.info( logger.info(
f"Loaded the {language} BERT model from {pretrained_model_name_or_path}" f"Loaded the {language} BERT model from {pretrained_model_name_or_path}"
@@ -135,9 +139,17 @@ def load_tokenizer(
# BERT トークナイザーをロードし、辞書に格納して返す # BERT トークナイザーをロードし、辞書に格納して返す
## 英語のみ DebertaV2Tokenizer でロードする必要がある ## 英語のみ DebertaV2Tokenizer でロードする必要がある
if language == Languages.EN: if language == Languages.EN:
tokenizer = DebertaV2Tokenizer.from_pretrained(pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision) tokenizer = DebertaV2Tokenizer.from_pretrained(
pretrained_model_name_or_path,
cache_dir=cache_dir,
revision=revision,
)
else: else:
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, cache_dir=cache_dir, revision=revision) tokenizer = AutoTokenizer.from_pretrained(
pretrained_model_name_or_path,
cache_dir=cache_dir,
revision=revision,
)
__loaded_tokenizers[language] = tokenizer __loaded_tokenizers[language] = tokenizer
logger.info( logger.info(
f"Loaded the {language} BERT tokenizer from {pretrained_model_name_or_path}" f"Loaded the {language} BERT tokenizer from {pretrained_model_name_or_path}"

3
style_bert_vits2/nlp/chinese/g2p.py
View File

@@ -10,8 +10,7 @@ from style_bert_vits2.nlp.symbols import PUNCTUATIONS
with open(Path(__file__).parent / "opencpop-strict.txt", "r", encoding="utf-8") as f: with open(Path(__file__).parent / "opencpop-strict.txt", "r", encoding="utf-8") as f:
__PINYIN_TO_SYMBOL_MAP = { __PINYIN_TO_SYMBOL_MAP = {
line.split("\t")[0]: line.strip().split("\t")[1] line.split("\t")[0]: line.strip().split("\t")[1] for line in f.readlines()
for line in f.readlines()
} }