def predict(input_file): model = HMMPredictor(feature_type = "crp", model_path = os.path.join(MODEL_DIR, "uniformcrp"), lda = None, window_size = 4410, variance_filter = 0.16, min_frames = 4, plot_variance = False, frame_split = None, group_filter = 4, max_count_filter = True) predictions = model.run(input_file) if predictions is not None and len(predictions): return chord.decode(int(predictions[0])); else: return ""
# same as model = default_crp() model = HMMPredictor(feature_type = "crp", model_path = os.path.join(MODEL_DIR, "uniformcrp"), lda = None, window_size = 4410, variance_filter = 0.16, min_frames = 4, plot_variance = False, frame_split = None, group_filter = 4, max_count_filter = True) predictions = model.run(input_file) if predictions is not None and len(predictions): print chord.decode(int(predictions[0])); else: print "" # parameters can be changed here and parts of the model rerun. # # update number of frames to group: # # predictions.frame_split = 7 # predictions.process_features() # predictions.predict() # # update group filter: # # predictions.group_filter = 5 # predictions.predict()