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Enhance G2PW model input handling by introducing polyphonic context character support and updating the data preparation method to return additional query IDs. This improves the processing of polyphonic characters in sentences.

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baicai-1145 2026-03-08 03:01:20 +08:00
parent 800acd45ff
commit b250e62402
1 changed files with 28 additions and 9 deletions
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37
GPT_SoVITS/text/g2pw/onnx_api.py
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@ -187,6 +187,8 @@ class _G2PWBaseOnnxConverter:
"y",
"on",
}
# 聚焦到多音字附近上下文，默认左右各16字；设为0表示关闭裁剪（整句）。
self.polyphonic_context_chars = max(0, int(os.getenv("g2pw_polyphonic_context_chars", "16")))
def _convert_bopomofo_to_pinyin(self, bopomofo: str) -> str:
tone = bopomofo[-1]
@ -209,7 +211,7 @@ class _G2PWBaseOnnxConverter:
translated_sentences.append(translated_sent)
sentences = translated_sentences
texts, query_ids, sent_ids, partial_results = self._prepare_data(sentences=sentences)
texts, model_query_ids, result_query_ids, sent_ids, partial_results = self._prepare_data(sentences=sentences)
if len(texts) == 0:
return partial_results
@ -219,7 +221,7 @@ class _G2PWBaseOnnxConverter:
char2phonemes=self.char2phonemes,
chars=self.chars,
texts=texts,
query_ids=query_ids,
query_ids=model_query_ids,
use_mask=self.config.use_mask,
window_size=None,
char2id=self.char2id,
@ -238,22 +240,23 @@ class _G2PWBaseOnnxConverter:
preds = [pred.split(" ")[1] for pred in preds]
results = partial_results
for sent_id, query_id, pred in zip(sent_ids, query_ids, preds):
for sent_id, query_id, pred in zip(sent_ids, result_query_ids, preds):
results[sent_id][query_id] = self.style_convert_func(pred)
return results
def _prepare_data(self, sentences: List[str]) -> Tuple[List[str], List[int], List[int], List[List[str]]]:
texts, query_ids, sent_ids, partial_results = [], [], [], []
def _prepare_data(
self, sentences: List[str]
) -> Tuple[List[str], List[int], List[int], List[int], List[List[str]]]:
texts, model_query_ids, result_query_ids, sent_ids, partial_results = [], [], [], [], []
for sent_id, sent in enumerate(sentences):
sent_s = tranditional_to_simplified(sent)
pypinyin_result = pinyin(sent_s, neutral_tone_with_five=True, style=Style.TONE3)
partial_result = [None] * len(sent)
polyphonic_indices: List[int] = []
for i, char in enumerate(sent):
if char in self.polyphonic_chars_new:
texts.append(sent)
query_ids.append(i)
sent_ids.append(sent_id)
polyphonic_indices.append(i)
elif char in self.monophonic_chars_dict:
partial_result[i] = self.style_convert_func(self.monophonic_chars_dict[char])
elif char in self.char_bopomofo_dict:
@ -261,8 +264,24 @@ class _G2PWBaseOnnxConverter:
else:
partial_result[i] = pypinyin_result[i][0]
if polyphonic_indices:
if self.polyphonic_context_chars > 0:
left = max(0, polyphonic_indices[0] - self.polyphonic_context_chars)
right = min(len(sent), polyphonic_indices[-1] + self.polyphonic_context_chars + 1)
sent_for_predict = sent[left:right]
query_offset = left
else:
sent_for_predict = sent
query_offset = 0
for index in polyphonic_indices:
texts.append(sent_for_predict)
model_query_ids.append(index - query_offset)
result_query_ids.append(index)
sent_ids.append(sent_id)
partial_results.append(partial_result)
return texts, query_ids, sent_ids, partial_results
return texts, model_query_ids, result_query_ids, sent_ids, partial_results
def _predict(self, model_input: Dict[str, Any]) -> Tuple[List[str], List[float]]:
raise NotImplementedError