fix vits with CSMSC (#3920)

paddlespeech/t2s/models/vits/generator.py

@@ -577,8 +577,9 @@ class VITSGenerator(nn.Layer):
             # decoder
             z_p = m_p + paddle.randn(
                 paddle.shape(m_p)) * paddle.exp(logs_p) * noise_scale
-            z = self.flow(z_p, y_mask, g=g, inverse=True)
-            wav = self.decoder((z * y_mask)[:, :, :max_len], g=g)
+            z = self.flow(z_p, y_mask.astype(z_p.dtype), g=g, inverse=True)
+            wav = self.decoder(
+                (z * y_mask.astype(z.dtype))[:, :, :max_len], g=g)
 
         return wav.squeeze(1), attn.squeeze(1), dur.squeeze(1)
 
@@ -695,4 +696,5 @@ class VITSGenerator(nn.Layer):
         path = paddle.cast(path, dtype='float32')
         pad_tmp = self.pad1d(path)[:, :-1]
         path = path - pad_tmp
-        return path.unsqueeze(1).transpose([0, 1, 3, 2]) * mask
+        return path.unsqueeze(1).transpose(
+            [0, 1, 3, 2]) * mask.astype(path.dtype)

paddlespeech/t2s/models/vits/posterior_encoder.py

@@ -129,6 +129,7 @@ class PosteriorEncoder(nn.Layer):
 
         """
         x_mask = make_non_pad_mask(x_lengths).unsqueeze(1)
+        x_mask = x_mask.astype(x.dtype)
         x = self.input_conv(x) * x_mask
         x = self.encoder(x, x_mask, g=g)
         stats = self.proj(x) * x_mask

paddlespeech/t2s/models/vits/text_encoder.py

@@ -155,6 +155,7 @@ class TextEncoder(nn.Layer):
         """
         x = self.emb(x) * math.sqrt(self.attention_dim)
         x_mask = make_non_pad_mask(x_lengths).unsqueeze(1)
+        x_mask = x_mask.astype(x.dtype)
         # encoder assume the channel last (B, T_text, attention_dim)
         # but mask shape shoud be (B, 1, T_text)
         x, _ = self.encoder(x, x_mask)

paddlespeech/t2s/modules/nets_utils.py

@@ -181,6 +181,10 @@ def make_pad_mask(lengths, xs=None, length_dim=-1):
     if length_dim == 0:
         raise ValueError("length_dim cannot be 0: {}".format(length_dim))
 
+    # check if ilens is 0-dim tensor, if so, add a dimension
+    if lengths.ndim == 0:
+        lengths = lengths.unsqueeze(0)
+
     bs = paddle.shape(lengths)
     if xs is None:
         maxlen = paddle.cast(lengths.max(), dtype=bs.dtype)
@@ -348,7 +352,9 @@ def get_random_segments(
     """
     b, c, t = paddle.shape(x)
     max_start_idx = x_lengths - segment_size
-    start_idxs = paddle.cast(paddle.rand([b]) * max_start_idx, 'int64')
+    rand_number = paddle.rand([b])
+    start_idxs = paddle.cast(rand_number *
+                             max_start_idx.astype(rand_number.dtype), 'int64')
     segments = get_segments(x, start_idxs, segment_size)
 
     return segments, start_idxs