minor updates

egs/librispeech/ASR/conv_emformer_transducer_stateless/emformer.py

@@ -646,12 +646,7 @@ class EmformerAttention(nn.Module):
             - output of right context and utterance, with shape (R + U, B, D).
             - memory output, with shape (M, B, D), where M = S - 1 or M = 0.
         """
-        (
-            output_right_context_utterance,
-            output_memory,
-            _,
-            _,
-        ) = self._forward_impl(
+        (output_right_context_utterance, output_memory, _, _,) = self._forward_impl(
             utterance,
             right_context,
             summary,
@@ -1115,11 +1110,7 @@ class EmformerEncoderLayer(nn.Module):
         src = src + self.dropout(self.feed_forward_macaron(src))
 
         # emformer attention module
-        (
-            src_att,
-            output_memory,
-            attn_cache,
-        ) = self._apply_attention_module_infer(
+        (src_att, output_memory, attn_cache,) = self._apply_attention_module_infer(
             src, R, memory, attn_cache, padding_mask=padding_mask
         )
         src = src + self.dropout(src_att)

egs/librispeech/ASR/conv_emformer_transducer_stateless2/emformer.py

@@ -927,11 +927,7 @@ class EmformerEncoderLayer(nn.Module):
             ]
         else:
             memory = torch.empty(0).to(dtype=utterance.dtype, device=utterance.device)
-        (
-            output_right_context_utterance,
-            next_key,
-            next_val,
-        ) = self.attention.infer(
+        (output_right_context_utterance, next_key, next_val,) = self.attention.infer(
             utterance=utterance,
             right_context=right_context,
             memory=pre_memory,

egs/librispeech/ASR/conv_emformer_transducer_stateless2/emformer2.py

@@ -1502,11 +1502,7 @@ class EmformerEncoder(nn.Module):
             end = start + 4
             cache = states[start:end]
 
-            (
-                output,
-                right_context,
-                output_cache,
-            ) = layer.infer(
+            (output, right_context, output_cache,) = layer.infer(
                 output,
                 right_context,
                 padding_mask=None,

egs/librispeech/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,

requirements-ci.txt

@@ -20,6 +20,7 @@ kaldialign==0.7.1
 sentencepiece==0.1.96
 tensorboard==2.8.0
 typeguard==2.13.3
+black==22.3.0
 multi_quantization
 
 onnx

requirements.txt

@@ -5,3 +5,4 @@ sentencepiece>=0.1.96
 tensorboard
 typeguard
 dill
+black==22.3.0