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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
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