o i @sddlZddlmZddlZddlZddlZddlZddlZddlm Z ddl m Z ddl m Z mZ  dded ejd ed ed ef d dZddZdejdejfddZdededed efddZdS)N)tqdm)LogisticRegression) make_pipeline)FunctionTransformerStandardScaler?reference_clip oww_model model_name thresholdNcKstt}t|D]X}t|tkrtjj |\}} n|} |dkr,| t j ddd} d} td| j d| | D]'} |j| | | | fi|} | ||kr`|j|j|} ||| q9q t||dkrz||t d|j|dft ||S)a8 Processes input audio files (16-bit, 16-khz single-channel WAV files) and gets the openWakeWord audio features that produce a prediction from the specified model greater than the threshold value. Args: reference_clip (str): The target audio file to get features from oww_model (openwakeword.Model): The openWakeWord model object used to get predictions model_name (str): The name of the model to get predictions from (should correspond to a python dictionary key in the oww_model.models attribute) threshold (float): The minimum score from the model required to capture the associated features N (int): How many times to run feature extraction for a given clip, adding some slight variation in the starting position each time to ensure that the features are not identical Returns: ndarray: A numpy array of shape N x M x L, where N is the number of examples, M is the number of frames in the window, and L is the audio feature/embedding dimension. riN`) collections defaultdictlistrangetypestrscipyioZwavfilereadnprandomrandintshapeZpredictZ preprocessorZ get_featuresZ model_inputsappendlenemptyvstack)r r r r r kwargsZ positive_data_srZdatZ step_sizeiZ predictionsfeaturesr&\/home/kim/smarthome/.venv/lib/python3.10/site-packages/openwakeword/custom_verifier_model.pyget_reference_clip_featuress,    r(cCsdd|DS)NcSsg|]}|qSr&)flatten.0r$r&r&r' Zsz$flatten_features..r&)xr&r&r'flatten_featuresYsr.r%labelscCs0tdddd}tttt|}||||S)a Train a logistic regression binary classifier model on the provided features and labels Args: features (ndarray): A N x M numpy array, where N is the number of examples and M is the number of features labels (ndarray): A 1D numpy array where each value corresponds to the label of the Nth example in the `features` argument Returns: The trained scikit-learn logistic regression model rigMbP?)Z random_stateZmax_iterC)rrrr.rfit)r%r/ZclfZpipeliner&r&r'train_verifier_model]s r2positive_reference_clipsnegative_reference_clips output_pathc stjr!tjddgi|tjdtjjdntjdi|t fddt |ddD}|j ddkrEt dt fd dt |d dD}t d tt ||ftd g|j ddg|j d}t d t|t|ddS)a Trains a voice-specific custom verifier model on examples of wake word/phrase speech and other speech from a single user. Args: positive_reference_clips (str): The path to a directory containing single-channel 16khz, 16-bit WAV files of the target wake word/phrase. negative_reference_clips (str): The path to a directory containing single-channel 16khz, 16-bit WAV files of miscellaneous speech not containing the target wake word/phrase. output_path (str): The location to save the trained verifier model (as a scikit-learn .joblib file) model_name (str): The name or path of the trained openWakeWord model that the verifier model will be based on. If only a name, it must be one of the pre-trained models included in the openWakeWord release. kwargs: Any other keyword arguments to pass to the openWakeWord model initialization Returns: None Zwakeword_modelsrcsg|] }t|ddqS))r r(r*r Zowwr&r'r,sz)train_custom_verifier..z#Processing positive reference clips)ZdesczThe positive features were created! Make sure that the positive reference clips contain the appropriate audio for the desired modelc sg|] }t|dddqS)gr)r r r8r*r9r&r'r,sz#Processing negative reference clipsz%Training and saving verifier model...rzDone!wbNr&)ospathexists openwakewordModelsplitextsplitseprr rr ValueErrorprintr2arraypickledumpopen)r3r4r5r r!Zpositive_featuresZnegative_featuresZlr_modelr&r9r'train_custom_verifierrs6       $rI)rr)r;rrr>numpyrrrFZsklearn.linear_modelrZsklearn.pipelinerZsklearn.preprocessingrrrr?floatintr(r.Zndarrayr2rIr&r&r&r'sD    ;