update code

paddlespeech/s2t/models/wav2vec2/modules/modeling_wav2vec2.py

@@ -129,7 +129,7 @@ def _compute_mask_indices(
                      [sequence_length for _ in range(batch_size)])
 
     # SpecAugment mask to fill
-    spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=bool)
+    spec_aug_mask = np.zeros((batch_size, sequence_length), dtype=np.bool_)
     spec_aug_mask_idxs = []
 
     max_num_masked_span = compute_num_masked_span(sequence_length)
@@ -207,9 +207,9 @@ def _sample_negative_indices(features_shape: Tuple,
     sampled_negative_indices = np.zeros(
         shape=(batch_size, sequence_length, num_negatives), dtype=np.int32)
 
-    mask_time_indices = (mask_time_indices.astype(bool)
+    mask_time_indices = (mask_time_indices.astype(np.bool_)
                          if mask_time_indices is not None else
-                         np.ones(features_shape, dtype=bool))
+                         np.ones(features_shape, dtype=np.bool_))
 
     for batch_idx in range(batch_size):
         high = mask_time_indices[batch_idx].sum() - 1

paddlespeech/s2t/models/wav2vec2/modules/wav2vec2_model.py

@@ -1476,7 +1476,7 @@ def compute_mask_indices(
                 lens = np.fromiter(
                     (e - s if e - s >= length + min_space else 0
                      for s, e in parts),
-                    int, )
+                    np.int_, )
                 l_sum = np.sum(lens)
                 if l_sum == 0:
                     break

paddlespeech/s2t/models/wavlm/wavlm_paddle.py

@@ -116,7 +116,7 @@ def compute_mask_indices(
                 lens = np.fromiter(
                     (e - s if e - s >= length + min_space else 0
                      for s, e in parts),
-                    int, )
+                    np.int_, )
                 l_sum = np.sum(lens)
                 if l_sum == 0:
                     break

paddlespeech/t2s/datasets/get_feats.py

@@ -138,7 +138,7 @@ class Pitch():
                       input: np.ndarray,
                       use_continuous_f0: bool=True,
                       use_log_f0: bool=True) -> np.ndarray:
-        input = input.astype(float)
+        input = input.astype(np.float_)
         frame_period = 1000 * self.hop_length / self.sr
         f0, timeaxis = pyworld.dio(
             input,

paddlespeech/t2s/utils/internals.py

@@ -36,7 +36,7 @@ def convert_dtype_to_np_dtype_(dtype):
     elif dtype is core.VarDesc.VarType.FP16:
         return np.float16
     elif dtype is core.VarDesc.VarType.BOOL:
-        return bool
+        return np.bool_
     elif dtype is core.VarDesc.VarType.INT32:
         return np.int32
     elif dtype is core.VarDesc.VarType.INT64: