📊 Evaluation (Voice Activity Detection - VAD)

This page describes how to evaluate a trained Voice Activity Detection (VAD) model using the soundkit CLI. You can run inference on custom WAV files and generate voice activity predictions.

---

🔧 Run evaluate Mode

soundkit -t vad -m evaluate -c configs/vad/vad.yaml

---

🧾 Evaluation Parameters

Parameter	Description
epoch_loaded	Model checkpoint to load for evaluation. Use best, latest, or a specific integer
data.dir	Path to the folder containing WAV files for evaluation
data.files	List of WAV filenames (relative to data.dir) to evaluate
result_folder	Directory to save prediction results, plots, and related outputs

Example:

evaluate:
  epoch_loaded: best
  data:
    dir: ./wavs/vad/test_wavs
    files: [rpc_audio_raw.wav, speech.wav, i_like_steak.wav, keyboard_steak.wav, steak_hairdryer.wav]
  result_folder: ./soundkit/tasks/vad/test_results/crnn100_lookahead0

---

📈 Output

Running the evaluation step will generate:

• Prediction results showing voice activity regions
• Visualization of audio signals and detected speech segments

📌 Be sure the vad.yaml configuration aligns with the model settings used during training and export.

---