80 lines
2.6 KiB
Python
80 lines
2.6 KiB
Python
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from typing import Any
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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def validate_numpy_array(value: Any):
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r"""
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Validates the input and makes sure it returns a numpy array (i.e on CPU)
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Args:
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value (Any): the input value
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Raises:
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TypeError: if the value is not a numpy array or torch tensor
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Returns:
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np.ndarray: numpy array of the value
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"""
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if isinstance(value, np.ndarray):
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pass
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elif isinstance(value, list):
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value = np.array(value)
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elif torch.is_tensor(value):
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value = value.cpu().numpy()
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else:
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raise TypeError("Value must be a numpy array, a torch tensor or a list")
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return value
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def get_spec_from_most_probable_state(log_alpha_scaled, means, decoder=None):
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"""Get the most probable state means from the log_alpha_scaled.
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Args:
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log_alpha_scaled (torch.Tensor): Log alpha scaled values.
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- Shape: :math:`(T, N)`
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means (torch.Tensor): Means of the states.
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- Shape: :math:`(N, T, D_out)`
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decoder (torch.nn.Module): Decoder module to decode the latent to melspectrogram. Defaults to None.
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"""
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max_state_numbers = torch.max(log_alpha_scaled, dim=1)[1]
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max_len = means.shape[0]
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n_mel_channels = means.shape[2]
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max_state_numbers = max_state_numbers.unsqueeze(1).unsqueeze(1).expand(max_len, 1, n_mel_channels)
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means = torch.gather(means, 1, max_state_numbers).squeeze(1).to(log_alpha_scaled.dtype)
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if decoder is not None:
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mel = (
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decoder(means.T.unsqueeze(0), torch.tensor([means.shape[0]], device=means.device), reverse=True)[0]
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.squeeze(0)
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.T
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)
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else:
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mel = means
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return mel
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def plot_transition_probabilities_to_numpy(states, transition_probabilities, output_fig=False):
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"""Generates trainsition probabilities plot for the states and the probability of transition.
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Args:
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states (torch.IntTensor): the states
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transition_probabilities (torch.FloatTensor): the transition probabilities
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"""
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states = validate_numpy_array(states)
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transition_probabilities = validate_numpy_array(transition_probabilities)
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fig, ax = plt.subplots(figsize=(30, 3))
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ax.plot(transition_probabilities, "o")
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ax.set_title("Transition probability of state")
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ax.set_xlabel("hidden state")
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ax.set_ylabel("probability")
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ax.set_xticks([i for i in range(len(transition_probabilities))]) # pylint: disable=unnecessary-comprehension
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ax.set_xticklabels([int(x) for x in states], rotation=90)
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plt.tight_layout()
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if not output_fig:
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plt.close()
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return fig
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