Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.
| Endpoint Name | Data Format | Nature |
|---|---|---|
| speech | system/array_1d_floats/1 | Input |
| features | system/array_2d_floats/1 | Output |
xxxxxxxxxx# You may import any python packages that will be available in the environment you will run this algorithm in# Environments can change based on the experiment's settingsimport sidekitimport numpyclass Algorithm: # initialise fields to store cross-input data (e.g. machines, aggregations, etc.) def __init__(self): self.fe = sidekit.FeaturesExtractor( audio_filename_structure="", feature_filename_structure=None, sampling_frequency=16000, lower_frequency=133.3333, higher_frequency=6855.4976, filter_bank="log", filter_bank_size=40, window_size=0.025, shift=0.01, ceps_number=13, pre_emphasis=0.97, keep_all_features=True, vad="percentil", save_param=["energy", "cep", "vad"], ) self.fs = sidekit.FeaturesServer( feature_filename_structure="", dataset_list=("cep"), delta=False, double_delta=False, keep_all_features=True, ) # this will be called each time the sync'd input has more data available to be processed def process(self, inputs, dataloader, outputs): speech = inputs["speech"].data.value label, energy, cep, _ = self.fe.extract_from_signal(speech, 16000) # Concatenate energy and cep feat = numpy.hstack([energy[:, None], cep]) # Add dynamic features feat = self.fs._delta_and_2delta(feat) # Smooth the labels label = sidekit.frontend.vad.label_fusion(label) outputs["features"].write({"value": feat}) # always return True, it signals BEAT to continue processing return TrueThe code for this algorithm in Python
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This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.