o jt @sNddlmZddlmZddlZddlZdefddZd ddZ d d d Z dS) )AnyNvaluecCsLt|tjr |St|trt|}|St|r"|}|St d)a Validates the input and makes sure it returns a numpy array (i.e on CPU) Args: value (Any): the input value Raises: TypeError: if the value is not a numpy array or torch tensor Returns: np.ndarray: numpy array of the value z5Value must be a numpy array, a torch tensor or a list) isinstancenpndarraylistarraytorch is_tensorcpunumpy TypeError)rrY/home/kuhnn/.local/lib/python3.10/site-packages/TTS/tts/layers/overflow/plotting_utils.pyvalidate_numpy_arrays   rcCstj|ddd}|jd}|jd}|dd|d|}t|d|d|j}|durO||j dtj |jdg|j dddddj }|S|}|S) a|Get the most probable state means from the log_alpha_scaled. Args: log_alpha_scaled (torch.Tensor): Log alpha scaled values. - Shape: :math:`(T, N)` means (torch.Tensor): Means of the states. - Shape: :math:`(N, T, D_out)` decoder (torch.nn.Module): Decoder module to decode the latent to melspectrogram. Defaults to None. )dimrN)deviceT)reverse) r maxshape unsqueezeexpandgathersqueezetodtypeTtensorr)log_alpha_scaledmeansdecodermax_state_numbersmax_lenn_mel_channelsmelrrr!get_spec_from_most_probable_state!s  ,r'FcCst|}t|}tjdd\}}||d|d|d|d|ddtt |D|j d d|Dd d t |sIt |S) zGenerates trainsition probabilities plot for the states and the probability of transition. Args: states (torch.IntTensor): the states transition_probabilities (torch.FloatTensor): the transition probabilities ))figsizeozTransition probability of statez hidden state probabilitycSsg|]}|qSrr).0irrr Jsz:plot_transition_probabilities_to_numpy..cSsg|]}t|qSr)int)r-xrrrr/KsZ)rotation) rpltsubplotsplot set_title set_xlabel set_ylabel set_xticksrangelenset_xticklabels tight_layoutclose)statestransition_probabilities output_figfigaxrrr&plot_transition_probabilities_to_numpy;s    rE)N)F) typingrmatplotlib.pyplotpyplotr4r rr rr'rErrrrs