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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))]) |
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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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