batdetect2/docs/source/reference/api.md

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(config)
  • build a full stack from a BatDetect2Config object.

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.
• 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