Fix #2815: AutoModel中，vad影响punc。

[danielalanbates]

Fixes #2815

Summary

This PR addresses: AutoModel中，vad影响punc。

Changes

funasr/auto/auto_model.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

Testing

Please review the changes carefully. The fix was verified against the existing test suite.

---

This PR was created with the assistance of Claude Sonnet 4.6 by Anthropic | effort: low. Happy to make any adjustments!

[gemini-code-assist]

Summary of Changes

Hello @danielalanbates, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

此拉取请求旨在解决 AutoModel 中标点处理的一个缺陷，即当语音活动检测（VAD）模型未激活时，标点推理步骤被意外跳过。通过确保在没有 VAD 的情况下，主推理结果会经过标点模型的处理，从而保证了输出文本的完整性和准确性。

Highlights

• 修复标点处理逻辑: 当 vad_model 为 None 时，确保 generate 方法在主推理完成后正确调用 punc_model 进行标点处理。
• 增强配置更新: 在应用标点模型之前，使用 deep_update 更新 punc_kwargs，以确保配置的灵活性。
• 支持原始文本返回: 如果 return_raw_text 配置为 True，则在标点处理前保留原始文本，并将其存储在 raw_text 字段中。

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Changelog

• funasr/auto/auto_model.py
  • 在 generate 方法中，当 vad_model 为 None 时，添加了对 punc_model 的显式调用。
  • 更新了 punc_kwargs 并处理了 return_raw_text 选项。

Activity

• 此拉取请求由 danielalanbates 创建，旨在解决问题 AutoModel中，vad影响punc。 #2815。
• 此拉取请求是在 Anthropic 的 Claude Sonnet 4.6 协助下创建的。

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[gemini-code-assist]

Code Review

The pull request correctly addresses the issue where punctuation was not applied when VAD was disabled in AutoModel.generate. I have suggested an optimization to batch the punctuation inference calls, which will improve performance when processing multiple inputs, and noted a redundant string copy.

[gemini-code-assist]

Applying punctuation sequentially in a loop is inefficient when multiple results are present (e.g., during batch inference). The inference method supports list inputs, so you can batch all texts together for a single punctuation model call. Additionally, copy.copy() is redundant for strings as they are immutable in Python.

Suggested change

	deep_update(self.punc_kwargs, cfg)
	for result in results:
	punc_res = self.inference(
	result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg
	)
	if cfg.get("return_raw_text", self.kwargs.get("return_raw_text", False)):
	result["raw_text"] = copy.copy(result["text"])
	result["text"] = punc_res[0]["text"]
	deep_update(self.punc_kwargs, cfg)
	punc_results = self.inference(
	[r["text"] for r in results], model=self.punc_model, kwargs=self.punc_kwargs, **cfg
	)
	for result, punc_res in zip(results, punc_results):
	if cfg.get("return_raw_text", self.kwargs.get("return_raw_text", False)):
	result["raw_text"] = result["text"]
	result["text"] = punc_res["text"]