| | |
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| | | batch_size_token=5000, batch_size_token_threshold_s=40, max_single_segment_time=6000) |
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| | | print(rec_result) |
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| | | ``` |
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| | | |
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| | | Where, |
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| | | - `batch_size_token` refs to dynamic batch_size and the total tokens of batch is `batch_size_token`, 1 token = 60 ms. |
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| | | - `batch_size_token_threshold_s`: The batch_size is set to 1, when the audio duration exceeds the threshold value of `batch_size_token_threshold_s`, specified in `s`. |
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| | | - `max_single_segment_time`: The maximum length for audio segmentation in VAD, specified in `ms`. |
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| | | |
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| | | Suggestion: When encountering OOM (Out of Memory) issues with long audio inputs, as the GPU memory usage increases with the square of the audio duration, there are three possible scenarios: |
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| | | |
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| | | a) In the initial inference stage, GPU memory usage primarily depends on `batch_size_token`. Reducing this value appropriately can help reduce memory usage. |
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