| 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))]) |
| ax.set_xticklabels([int(x) for x in states], rotation=90) |
| plt.tight_layout() |
| if not output_fig: |
| plt.close() |
| return fig |
|
|
