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@@ -566,6 +566,7 @@ def inference_paraformer_vad_punc(
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hotword_list_or_file = kwargs['hotword']
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speech2vadsegment.vad_model.vad_opts.max_single_segment_time = kwargs.get("max_single_segment_time", 60000)
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+ batch_size_token_threshold_ms = kwargs.get("batch_size_token_threshold_ms", int(speech2vadsegment.vad_model.vad_opts.max_single_segment_time*0.67))
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batch_size_token = kwargs.get("batch_size_token", 6000)
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print("batch_size_token: ", batch_size_token)
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@@ -648,7 +649,7 @@ def inference_paraformer_vad_punc(
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beg_idx = 0
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for j, _ in enumerate(range(0, n)):
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batch_size_token_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
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- if j < n - 1 and (batch_size_token_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_token_ms and (sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < int(speech2vadsegment.vad_model.vad_opts.max_single_segment_time*0.67):
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+ if j < n - 1 and (batch_size_token_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_token_ms and (sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_token_threshold_ms:
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continue
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batch_size_token_ms_cum = 0
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end_idx = j + 1
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