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vocos/helpers.py

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+import matplotlib
+import numpy as np
+import torch
+from matplotlib import pyplot as plt
+from pytorch_lightning import Callback
+
+matplotlib.use("Agg")
+
+
+def save_figure_to_numpy(fig: plt.Figure) -> np.ndarray:
+    """
+    Save a matplotlib figure to a numpy array.
+
+    Args:
+        fig (Figure): Matplotlib figure object.
+
+    Returns:
+        ndarray: Numpy array representing the figure.
+    """
+    data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="")
+    data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+    return data
+
+
+def plot_spectrogram_to_numpy(spectrogram: np.ndarray) -> np.ndarray:
+    """
+    Plot a spectrogram and convert it to a numpy array.
+
+    Args:
+        spectrogram (ndarray): Spectrogram data.
+
+    Returns:
+        ndarray: Numpy array representing the plotted spectrogram.
+    """
+    spectrogram = spectrogram.astype(np.float32)
+    fig, ax = plt.subplots(figsize=(12, 3))
+    im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none")
+    plt.colorbar(im, ax=ax)
+    plt.xlabel("Frames")
+    plt.ylabel("Channels")
+    plt.tight_layout()
+
+    fig.canvas.draw()
+    data = save_figure_to_numpy(fig)
+    plt.close()
+    return data
+
+
+class GradNormCallback(Callback):
+    """
+    Callback to log the gradient norm.
+    """
+
+    def on_after_backward(self, trainer, model):
+        model.log("grad_norm", gradient_norm(model))
+
+
+def gradient_norm(model: torch.nn.Module, norm_type: float = 2.0) -> torch.Tensor:
+    """
+    Compute the gradient norm.
+
+    Args:
+        model (Module): PyTorch model.
+        norm_type (float, optional): Type of the norm. Defaults to 2.0.
+
+    Returns:
+        Tensor: Gradient norm.
+    """
+    grads = [p.grad for p in model.parameters() if p.grad is not None]
+    total_norm = torch.norm(torch.stack([torch.norm(g.detach(), norm_type) for g in grads]), norm_type)
+    return total_norm