fixed formatting issues

egs/multi_zh-hans/ASR/local/prepare_char.py

@@ -240,4 +240,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-

egs/multi_zh-hans/ASR/zipformer/decoder.py

@@ -61,10 +61,15 @@ class Decoder(nn.Module):
         )
         # the balancers are to avoid any drift in the magnitude of the
         # embeddings, which would interact badly with parameter averaging.
-        self.balancer = Balancer(decoder_dim, channel_dim=-1,
+        self.balancer = Balancer(
-                                 min_positive=0.0, max_positive=1.0,
+            decoder_dim,
-                                 min_abs=0.5, max_abs=1.0,
+            channel_dim=-1,
-                                 prob=0.05)
+            min_positive=0.0,
+            max_positive=1.0,
+            min_abs=0.5,
+            max_abs=1.0,
+            prob=0.05,
+        )
 
         self.blank_id = blank_id
 
@@ -81,10 +86,15 @@ class Decoder(nn.Module):
                 groups=decoder_dim // 4,  # group size == 4
                 bias=False,
             )
-            self.balancer2 = Balancer(decoder_dim, channel_dim=-1,
+            self.balancer2 = Balancer(
-                                      min_positive=0.0, max_positive=1.0,
+                decoder_dim,
-                                      min_abs=0.5, max_abs=1.0,
+                channel_dim=-1,
-                                      prob=0.05)
+                min_positive=0.0,
+                max_positive=1.0,
+                min_abs=0.5,
+                max_abs=1.0,
+                prob=0.05,
+            )
 
     def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
         """
@@ -107,9 +117,7 @@ class Decoder(nn.Module):
         if self.context_size > 1:
             embedding_out = embedding_out.permute(0, 2, 1)
             if need_pad is True:
-                embedding_out = F.pad(
+                embedding_out = F.pad(embedding_out, pad=(self.context_size - 1, 0))
-                    embedding_out, pad=(self.context_size - 1, 0)
-                )
             else:
                 # During inference time, there is no need to do extra padding
                 # as we only need one output

egs/multi_zh-hans/ASR/zipformer/joiner.py

@@ -52,12 +52,13 @@ class Joiner(nn.Module):
         Returns:
           Return a tensor of shape (N, T, s_range, C).
         """
-        assert encoder_out.ndim == decoder_out.ndim, (encoder_out.shape, decoder_out.shape)
+        assert encoder_out.ndim == decoder_out.ndim, (
+            encoder_out.shape,
+            decoder_out.shape,
+        )
 
         if project_input:
-            logit = self.encoder_proj(encoder_out) + self.decoder_proj(
+            logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out)
-                decoder_out
-            )
         else:
             logit = encoder_out + decoder_out
 

egs/multi_zh-hans/ASR/zipformer/optim.py

@@ -667,8 +667,7 @@ class ScaledAdam(BatchedOptimizer):
         # We have to look at the trained model for parameters at or around the
         # param_max_rms, because sometimes they can indicate a problem with the
         # topology or settings.
-        scale_step = torch.minimum(scale_step,
+        scale_step = torch.minimum(scale_step, (param_max_rms - param_rms) / param_rms)
-                                   (param_max_rms - param_rms) / param_rms)
 
         delta = state["delta"]
         # the factor of (1-beta1) relates to momentum.
@@ -879,7 +878,8 @@ class Eden(LRScheduler):
         warmup_factor = (
             1.0
             if self.batch >= self.warmup_batches
-            else self.warmup_start + (1.0 - self.warmup_start) * (self.batch / self.warmup_batches)
+            else self.warmup_start
+            + (1.0 - self.warmup_start) * (self.batch / self.warmup_batches)
             # else 0.5 + 0.5 * (self.batch / self.warmup_batches)
         )
 

egs/multi_zh-hans/ASR/zipformer/profile.py

@@ -100,17 +100,13 @@ class Model(nn.Module):
         self.encoder_embed = encoder_embed
         self.encoder_proj = encoder_proj
 
-    def forward(
+    def forward(self, feature: Tensor, feature_lens: Tensor) -> Tuple[Tensor, Tensor]:
-        self, feature: Tensor, feature_lens: Tensor
-    ) -> Tuple[Tensor, Tensor]:
         x, x_lens = self.encoder_embed(feature, feature_lens)
 
         src_key_padding_mask = make_pad_mask(x_lens)
         x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
 
-        encoder_out, encoder_out_lens = self.encoder(
+        encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask)
-            x, x_lens, src_key_padding_mask
-        )
 
         encoder_out = encoder_out.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
         logits = self.encoder_proj(encoder_out)
@@ -168,9 +164,7 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = (
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-    )
     logging.basicConfig(format=formatter, level=logging.INFO)
 
     main()

egs/multi_zh-hans/ASR/zipformer/streaming_decode.py

@@ -374,11 +374,7 @@ def streaming_forward(
     Returns encoder outputs, output lengths, and updated states.
     """
     cached_embed_left_pad = states[-2]
-    (
+    (x, x_lens, new_cached_embed_left_pad,) = model.encoder_embed.streaming_forward(
-        x,
-        x_lens,
-        new_cached_embed_left_pad,
-    ) = model.encoder_embed.streaming_forward(
         x=features,
         x_lens=feature_lens,
         cached_left_pad=cached_embed_left_pad,

egs/multi_zh-hans/ASR/zipformer/subsampling.py

@@ -107,9 +107,7 @@ class ConvNeXt(nn.Module):
         if layerdrop_rate != 0.0:
             batch_size = x.shape[0]
             mask = (
-                torch.rand(
+                torch.rand((batch_size, 1, 1, 1), dtype=x.dtype, device=x.device)
-                    (batch_size, 1, 1, 1), dtype=x.dtype, device=x.device
-                )
                 > layerdrop_rate
             )
         else:
@@ -275,9 +273,7 @@ class Conv2dSubsampling(nn.Module):
         # many copies of this extra gradient term.
         self.out_whiten = Whiten(
             num_groups=1,
-            whitening_limit=ScheduledFloat(
+            whitening_limit=ScheduledFloat((0.0, 4.0), (20000.0, 8.0), default=4.0),
-                (0.0, 4.0), (20000.0, 8.0), default=4.0
-            ),
             prob=(0.025, 0.25),
             grad_scale=0.02,
         )
@@ -400,8 +396,8 @@ class Conv2dSubsampling(nn.Module):
         left_pad = self.convnext.padding[0]
         freq = self.out_width
         channels = self.layer3_channels
-        cached_embed_left_pad = torch.zeros(
+        cached_embed_left_pad = torch.zeros(batch_size, channels, left_pad, freq).to(
-            batch_size, channels, left_pad, freq
+            device
-        ).to(device)
+        )
 
         return cached_embed_left_pad