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60 lines
1.6 KiB
Markdown
60 lines
1.6 KiB
Markdown
# How to run batch predictions
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This guide shows practical command patterns for directory-based and file-list
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prediction runs.
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Use it after you already know which input mode you want and need concrete command templates for a repeatable batch run.
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## Predict from a directory
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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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```
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Use this when BatDetect2 should discover the audio files for you.
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## Predict from a file list
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```bash
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batdetect2 predict file_list \
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path/to/model.ckpt \
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path/to/audio_files.txt \
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path/to/outputs
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```
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Use this when another part of your workflow already produced the exact recording list to process.
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## Predict from a dataset config
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```bash
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batdetect2 predict dataset \
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path/to/model.ckpt \
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path/to/annotation_set.json \
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path/to/outputs
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```
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Use this when your project already has a `soundevent` annotation set and you want to extract unique recording paths from it.
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## Useful options
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- `--batch-size` to control throughput.
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- `--workers` to set data-loading parallelism.
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- `--format` to select output format.
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- `--inference-config` to control clipping and loader behavior.
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- `--outputs-config` to control serialization and output transforms.
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- `--detection-threshold` to override the detection threshold for a run.
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## Practical workflow
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For large runs:
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1. test the command on a small reviewed subset,
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2. lock the config files and command shape,
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3. write outputs to a dedicated directory per run,
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4. record the checkpoint, config paths, and thresholds used.
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For complete option details, see {doc}`../reference/cli/predict`.
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