| | 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 |
| |
|
