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BatDetect2API reference
BatDetect2API is the main entry point for the current Python workflow.
It wraps model loading, inference, evaluation, output formatting, and training-related entry points behind one object.
Defined in batdetect2.api_v2.
Create an API instance
BatDetect2API.from_checkpoint(path, ...)- load a trained checkpoint and optional config overrides.
BatDetect2API.from_config(model_config=..., targets_config=..., ...)- build a full stack from separate config objects.
Inference methods
process_file(audio_file, ...)- run inference for one recording.
process_files(audio_files, ...)- run batch inference across a sequence of file paths.
process_directory(audio_dir, ...)- run inference across the audio files found in one directory.
process_clips(clips, ...)- run inference on an explicit sequence of clip objects.
process_audio(audio, ...)- run inference starting from a waveform array.
process_spectrogram(spec, ...)- run inference starting from a spectrogram tensor.
Prediction inspection helpers
get_top_class_name(detection)- return the highest-scoring class name for one detection.
get_class_scores(detection, include_top_class=True, sort_descending=True)- return ranked
(class_name, score)pairs.
- return ranked
get_detection_features(detection)- return the per-detection feature vector.
Audio loading helpers
load_audio(path)load_recording(recording)load_clip(clip)generate_spectrogram(audio)
Output persistence helpers
save_predictions(predictions, path, audio_dir=None, format=None, config=None)load_predictions(path, format=None, config=None)
Use these when you want to save programmatic predictions without going through the CLI.
Training and evaluation entry points
train(...)finetune(...)evaluate(...)evaluate_predictions(...)
Related pages
- Python tutorial:
{doc}
../tutorials/integrate-with-a-python-pipeline - Outputs config reference:
{doc}
outputs-config - Output formats reference:
{doc}
output-formats