Adding NSFW Filter and the Style TTS2 Train Branch

tts/StyleTTS2Train/utils.py

@@ -0,0 +1,74 @@
+from monotonic_align import maximum_path
+from monotonic_align import mask_from_lens
+from monotonic_align.core import maximum_path_c
+import numpy as np
+import torch
+import copy
+from torch import nn
+import torch.nn.functional as F
+import torchaudio
+import librosa
+import matplotlib.pyplot as plt
+from munch import Munch
+
+def maximum_path(neg_cent, mask):
+  """ Cython optimized version.
+  neg_cent: [b, t_t, t_s]
+  mask: [b, t_t, t_s]
+  """
+  device = neg_cent.device
+  dtype = neg_cent.dtype
+  neg_cent =  np.ascontiguousarray(neg_cent.data.cpu().numpy().astype(np.float32))
+  path =  np.ascontiguousarray(np.zeros(neg_cent.shape, dtype=np.int32))
+
+  t_t_max = np.ascontiguousarray(mask.sum(1)[:, 0].data.cpu().numpy().astype(np.int32))
+  t_s_max = np.ascontiguousarray(mask.sum(2)[:, 0].data.cpu().numpy().astype(np.int32))
+  maximum_path_c(path, neg_cent, t_t_max, t_s_max)
+  return torch.from_numpy(path).to(device=device, dtype=dtype)
+
+def get_data_path_list(train_path=None, val_path=None):
+    if train_path is None:
+        train_path = "Data/train_list.txt"
+    if val_path is None:
+        val_path = "Data/val_list.txt"
+
+    with open(train_path, 'r', encoding='utf-8', errors='ignore') as f:
+        train_list = f.readlines()
+    with open(val_path, 'r', encoding='utf-8', errors='ignore') as f:
+        val_list = f.readlines()
+
+    return train_list, val_list
+
+def length_to_mask(lengths):
+    mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
+    mask = torch.gt(mask+1, lengths.unsqueeze(1))
+    return mask
+
+# for norm consistency loss
+def log_norm(x, mean=-4, std=4, dim=2):
+    """
+    normalized log mel -> mel -> norm -> log(norm)
+    """
+    x = torch.log(torch.exp(x * std + mean).norm(dim=dim))
+    return x
+
+def get_image(arrs):
+    plt.switch_backend('agg')
+    fig = plt.figure()
+    ax = plt.gca()
+    ax.imshow(arrs)
+
+    return fig
+
+def recursive_munch(d):
+    if isinstance(d, dict):
+        return Munch((k, recursive_munch(v)) for k, v in d.items())
+    elif isinstance(d, list):
+        return [recursive_munch(v) for v in d]
+    else:
+        return d
+    
+def log_print(message, logger):
+    logger.info(message)
+    print(message)
+