fix for mix_lang

paddlespeech/t2s/exps/ernie_sat/train.py

@@ -154,6 +154,7 @@ def train_sp(args, config):
         dataloader=train_dataloader,
         text_masking=config["model"]["text_masking"],
         odim=odim,
+        vocab_size=vocab_size,
         output_dir=output_dir)
 
     trainer = Trainer(updater, (config.max_epoch, 'epoch'), output_dir)
@@ -163,6 +164,7 @@ def train_sp(args, config):
         dataloader=dev_dataloader,
         text_masking=config["model"]["text_masking"],
         odim=odim,
+        vocab_size=vocab_size,
         output_dir=output_dir, )
 
     if dist.get_rank() == 0:

paddlespeech/t2s/models/ernie_sat/ernie_sat_updater.py

@@ -40,11 +40,13 @@ class ErnieSATUpdater(StandardUpdater):
                  init_state=None,
                  text_masking: bool=False,
                  odim: int=80,
+                 vocab_size: int=100,
                  output_dir: Path=None):
         super().__init__(model, optimizer, dataloader, init_state=None)
         self.scheduler = scheduler
 
-        self.criterion = MLMLoss(text_masking=text_masking, odim=odim)
+        self.criterion = MLMLoss(
+            text_masking=text_masking, odim=odim, vocab_size=vocab_size)
 
         log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
         self.filehandler = logging.FileHandler(str(log_file))
@@ -104,6 +106,7 @@ class ErnieSATEvaluator(StandardEvaluator):
                  dataloader: DataLoader,
                  text_masking: bool=False,
                  odim: int=80,
+                 vocab_size: int=100,
                  output_dir: Path=None):
         super().__init__(model, dataloader)
 
@@ -113,7 +116,8 @@ class ErnieSATEvaluator(StandardEvaluator):
         self.logger = logger
         self.msg = ""
 
-        self.criterion = MLMLoss(text_masking=text_masking, odim=odim)
+        self.criterion = MLMLoss(
+            text_masking=text_masking, odim=odim, vocab_size=vocab_size)
 
     def evaluate_core(self, batch):
         self.msg = "Evaluate: "

paddlespeech/t2s/modules/losses.py

@@ -1013,6 +1013,7 @@ class KLDivergenceLoss(nn.Layer):
 class MLMLoss(nn.Layer):
     def __init__(self,
                  odim: int,
+                 vocab_size: int=0,
                  lsm_weight: float=0.1,
                  ignore_id: int=-1,
                  text_masking: bool=False):
@@ -1025,6 +1026,7 @@ class MLMLoss(nn.Layer):
             self.l1_loss_func = nn.L1Loss(reduction='none')
         self.text_masking = text_masking
         self.odim = odim
+        self.vocab_size = vocab_size
 
     def forward(
             self,
@@ -1059,10 +1061,12 @@ class MLMLoss(nn.Layer):
             assert text is not None
             assert text_outs is not None
             assert text_masked_pos is not None
-            text_mlm_loss = paddle.sum((self.text_mlm_loss(
-                paddle.reshape(text_outs, (-1, self.vocab_size)),
-                paddle.reshape(text, (-1))) * paddle.reshape(
-                    text_masked_pos,
-                    (-1)))) / paddle.sum((text_masked_pos) + 1e-10)
+            text_outs = paddle.reshape(text_outs, [-1, self.vocab_size])
+            text = paddle.reshape(text, [-1])
+            text_mlm_loss = self.text_mlm_loss(text_outs, text)
+            text_masked_pos_reshape = paddle.reshape(text_masked_pos, [-1])
+            text_mlm_loss = paddle.sum(
+                text_mlm_loss *
+                text_masked_pos_reshape) / paddle.sum((text_masked_pos) + 1e-10)
 
         return mlm_loss, text_mlm_loss

paddlespeech/t2s/modules/nets_utils.py

@@ -464,14 +464,15 @@ def phones_text_masking(xs_pad: paddle.Tensor,
                     set(range(length)) - set(masked_phn_idxs[0].tolist()))
                 np.random.shuffle(unmasked_phn_idxs)
                 masked_text_idxs = unmasked_phn_idxs[:text_mask_num_lower]
-                text_masked_pos[idx][masked_text_idxs] = 1
+                text_masked_pos[idx, masked_text_idxs] = 1
                 masked_start = align_start[idx][masked_phn_idxs].tolist()
                 masked_end = align_end[idx][masked_phn_idxs].tolist()
                 for s, e in zip(masked_start, masked_end):
                     masked_pos[idx, s:e] = 1
-    non_eos_mask = paddle.reshape(src_mask, paddle.shape(xs_pad)[:2])
+    non_eos_mask = paddle.reshape(src_mask, shape=paddle.shape(xs_pad)[:2])
     masked_pos = masked_pos * non_eos_mask
-    non_eos_text_mask = paddle.reshape(text_mask, paddle.shape(xs_pad)[:2])
+    non_eos_text_mask = paddle.reshape(
+        text_mask, shape=paddle.shape(text_pad)[:2])
     text_masked_pos = text_masked_pos * non_eos_text_mask
     masked_pos = paddle.cast(masked_pos, 'bool')
     text_masked_pos = paddle.cast(text_masked_pos, 'bool')