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Refactor: Format Code with Ruff and Update Deprecated G2PW Link (#2255)

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* ruff check --fix

* ruff format --line-length 120 --target-version py39

* Change the link for G2PW Model

* update pytorch version and colab
This commit is contained in:
XXXXRT666
2025-04-07 09:42:47 +01:00
committed by GitHub
parent 9da7e17efe
commit 53cac93589
132 changed files with 8185 additions and 6648 deletions
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48
GPT_SoVITS/text/tone_sandhi.py
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@@ -510,12 +510,7 @@ class ToneSandhi:
# e.g. 走了, 看着, 去过
elif len(word) == 1 and word in "了着过" and pos in {"ul", "uz", "ug"}:
finals[-1] = finals[-1][:-1] + "5"
elif (
len(word) > 1
and word[-1] in "们子"
and pos in {"r", "n"}
and word not in self.must_not_neural_tone_words
):
elif len(word) > 1 and word[-1] in "们子" and pos in {"r", "n"} and word not in self.must_not_neural_tone_words:
finals[-1] = finals[-1][:-1] + "5"
# e.g. 桌上, 地下, 家里
elif len(word) > 1 and word[-1] in "上下里" and pos in {"s", "l", "f"}:
@@ -525,25 +520,18 @@ class ToneSandhi:
finals[-1] = finals[-1][:-1] + "5"
# 个做量词
elif (
ge_idx >= 1
and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是")
ge_idx >= 1 and (word[ge_idx - 1].isnumeric() or word[ge_idx - 1] in "几有两半多各整每做是")
) or word == "个":
finals[ge_idx] = finals[ge_idx][:-1] + "5"
else:
if (
word in self.must_neural_tone_words
or word[-2:] in self.must_neural_tone_words
):
if word in self.must_neural_tone_words or word[-2:] in self.must_neural_tone_words:
finals[-1] = finals[-1][:-1] + "5"
word_list = self._split_word(word)
finals_list = [finals[: len(word_list[0])], finals[len(word_list[0]) :]]
for i, word in enumerate(word_list):
# conventional neural in Chinese
if (
word in self.must_neural_tone_words
or word[-2:] in self.must_neural_tone_words
):
if word in self.must_neural_tone_words or word[-2:] in self.must_neural_tone_words:
finals_list[i][-1] = finals_list[i][-1][:-1] + "5"
finals = sum(finals_list, [])
return finals
@@ -561,9 +549,7 @@ class ToneSandhi:
def _yi_sandhi(self, word: str, finals: List[str]) -> List[str]:
# "一" in number sequences, e.g. 一零零, 二一零
if word.find("一") != -1 and all(
[item.isnumeric() for item in word if item != "一"]
):
if word.find("一") != -1 and all([item.isnumeric() for item in word if item != "一"]):
return finals
# "一" between reduplication words shold be yi5, e.g. 看一看
elif len(word) == 3 and word[1] == "一" and word[0] == word[-1]:
@@ -697,13 +683,10 @@ class ToneSandhi:
return new_seg
# the first and the second words are all_tone_three
def _merge_continuous_three_tones(
self, seg: List[Tuple[str, str]]
) -> List[Tuple[str, str]]:
def _merge_continuous_three_tones(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
new_seg = []
sub_finals_list = [
lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
for (word, pos) in seg
lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) for (word, pos) in seg
]
assert len(sub_finals_list) == len(seg)
merge_last = [False] * len(seg)
@@ -715,10 +698,7 @@ class ToneSandhi:
and not merge_last[i - 1]
):
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
if (
not self._is_reduplication(seg[i - 1][0])
and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
):
if not self._is_reduplication(seg[i - 1][0]) and len(seg[i - 1][0]) + len(seg[i][0]) <= 3:
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
merge_last[i] = True
else:
@@ -732,13 +712,10 @@ class ToneSandhi:
return len(word) == 2 and word[0] == word[1]
# the last char of first word and the first char of second word is tone_three
def _merge_continuous_three_tones_2(
self, seg: List[Tuple[str, str]]
) -> List[Tuple[str, str]]:
def _merge_continuous_three_tones_2(self, seg: List[Tuple[str, str]]) -> List[Tuple[str, str]]:
new_seg = []
sub_finals_list = [
lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3)
for (word, pos) in seg
lazy_pinyin(word, neutral_tone_with_five=True, style=Style.FINALS_TONE3) for (word, pos) in seg
]
assert len(sub_finals_list) == len(seg)
merge_last = [False] * len(seg)
@@ -750,10 +727,7 @@ class ToneSandhi:
and not merge_last[i - 1]
):
# if the last word is reduplication, not merge, because reduplication need to be _neural_sandhi
if (
not self._is_reduplication(seg[i - 1][0])
and len(seg[i - 1][0]) + len(seg[i][0]) <= 3
):
if not self._is_reduplication(seg[i - 1][0]) and len(seg[i - 1][0]) + len(seg[i][0]) <= 3:
new_seg[-1][0] = new_seg[-1][0] + seg[i][0]
merge_last[i] = True
else: