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| from typing import Any | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import torch | |
| def validate_numpy_array(value: Any): | |
| r""" | |
| 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 | |
| """ | |
| if isinstance(value, np.ndarray): | |
| pass | |
| elif isinstance(value, list): | |
| value = np.array(value) | |
| elif torch.is_tensor(value): | |
| value = value.cpu().numpy() | |
| else: | |
| raise TypeError("Value must be a numpy array, a torch tensor or a list") | |
| return value | |
| def get_spec_from_most_probable_state(log_alpha_scaled, means, decoder=None): | |
| """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. | |
| """ | |
| max_state_numbers = torch.max(log_alpha_scaled, dim=1)[1] | |
| max_len = means.shape[0] | |
| n_mel_channels = means.shape[2] | |
| max_state_numbers = max_state_numbers.unsqueeze(1).unsqueeze(1).expand(max_len, 1, n_mel_channels) | |
| means = torch.gather(means, 1, max_state_numbers).squeeze(1).to(log_alpha_scaled.dtype) | |
| if decoder is not None: | |
| mel = ( | |
| decoder(means.T.unsqueeze(0), torch.tensor([means.shape[0]], device=means.device), reverse=True)[0] | |
| .squeeze(0) | |
| .T | |
| ) | |
| else: | |
| mel = means | |
| return mel | |
| def plot_transition_probabilities_to_numpy(states, transition_probabilities, output_fig=False): | |
| """Generates trainsition probabilities plot for the states and the probability of transition. | |
| Args: | |
| states (torch.IntTensor): the states | |
| transition_probabilities (torch.FloatTensor): the transition probabilities | |
| """ | |
| states = validate_numpy_array(states) | |
| transition_probabilities = validate_numpy_array(transition_probabilities) | |
| fig, ax = plt.subplots(figsize=(30, 3)) | |
| ax.plot(transition_probabilities, "o") | |
| ax.set_title("Transition probability of state") | |
| ax.set_xlabel("hidden state") | |
| ax.set_ylabel("probability") | |
| ax.set_xticks([i for i in range(len(transition_probabilities))]) # pylint: disable=unnecessary-comprehension | |
| ax.set_xticklabels([int(x) for x in states], rotation=90) | |
| plt.tight_layout() | |
| if not output_fig: | |
| plt.close() | |
| return fig | |