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64 lines
1.6 KiB
Markdown
64 lines
1.6 KiB
Markdown
# How to tune inference clipping
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Use this guide when long recordings need to be split into smaller clips during inference.
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## What clipping controls
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`InferenceConfig.clipping` controls how recordings are split before batching.
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Key fields are:
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- `duration`: clip duration in seconds,
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- `overlap`: overlap between adjacent clips,
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- `max_empty`: how much empty padding is allowed,
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- `discard_empty`: whether empty clips are dropped.
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## Start from the defaults
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Use the built-in clipping behavior first unless you already know you need something else.
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Only tune clipping when:
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- recordings are much longer than your normal working set,
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- you are seeing edge effects around calls,
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- you need tighter control over throughput or padding behavior.
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## Override clipping with an inference config
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Create an inference config file and pass it to `predict` or `evaluate`.
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Example:
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```yaml
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clipping:
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enabled: true
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duration: 0.5
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overlap: 0.1
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max_empty: 0.0
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discard_empty: true
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loader:
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batch_size: 8
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```
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Run with:
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```bash
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batdetect2 predict directory \
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path/to/model.ckpt \
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path/to/audio_dir \
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path/to/outputs \
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--inference-config path/to/inference.yaml
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```
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## Validate clipping changes on a small reviewed subset
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Changing clipping changes what the model sees per batch and can change how events near clip boundaries behave.
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Check a reviewed subset before applying clipping changes to a full project.
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## Related pages
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- Inference config reference: {doc}`../reference/inference-config`
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- Run batch predictions: {doc}`run-batch-predictions`
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- Understanding the pipeline: {doc}`../explanation/pipeline-overview`
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