merge tts and t2s into NeuralSeq

NeuralSeq/utils/plot.py

@@ -0,0 +1,56 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+LINE_COLORS = ['w', 'r', 'y', 'cyan', 'm', 'b', 'lime']
+
+
+def spec_to_figure(spec, vmin=None, vmax=None):
+    if isinstance(spec, torch.Tensor):
+        spec = spec.cpu().numpy()
+    fig = plt.figure(figsize=(12, 6))
+    plt.pcolor(spec.T, vmin=vmin, vmax=vmax)
+    return fig
+
+
+def spec_f0_to_figure(spec, f0s, figsize=None):
+    max_y = spec.shape[1]
+    if isinstance(spec, torch.Tensor):
+        spec = spec.detach().cpu().numpy()
+        f0s = {k: f0.detach().cpu().numpy() for k, f0 in f0s.items()}
+    f0s = {k: f0 / 10 for k, f0 in f0s.items()}
+    fig = plt.figure(figsize=(12, 6) if figsize is None else figsize)
+    plt.pcolor(spec.T)
+    for i, (k, f0) in enumerate(f0s.items()):
+        plt.plot(f0.clip(0, max_y), label=k, c=LINE_COLORS[i], linewidth=1, alpha=0.8)
+    plt.legend()
+    return fig
+
+
+def dur_to_figure(dur_gt, dur_pred, txt):
+    dur_gt = dur_gt.long().cpu().numpy()
+    dur_pred = dur_pred.long().cpu().numpy()
+    dur_gt = np.cumsum(dur_gt)
+    dur_pred = np.cumsum(dur_pred)
+    fig = plt.figure(figsize=(12, 6))
+    for i in range(len(dur_gt)):
+        shift = (i % 8) + 1
+        plt.text(dur_gt[i], shift, txt[i])
+        plt.text(dur_pred[i], 10 + shift, txt[i])
+        plt.vlines(dur_gt[i], 0, 10, colors='b')  # blue is gt
+        plt.vlines(dur_pred[i], 10, 20, colors='r')  # red is pred
+    return fig
+
+
+def f0_to_figure(f0_gt, f0_cwt=None, f0_pred=None):
+    fig = plt.figure()
+    f0_gt = f0_gt.cpu().numpy()
+    plt.plot(f0_gt, color='r', label='gt')
+    if f0_cwt is not None:
+        f0_cwt = f0_cwt.cpu().numpy()
+        plt.plot(f0_cwt, color='b', label='cwt')
+    if f0_pred is not None:
+        f0_pred = f0_pred.cpu().numpy()
+        plt.plot(f0_pred, color='green', label='pred')
+    plt.legend()
+    return fig