Add pruned RNN-T multi-dataset setup for aishell

egs/aishell/ASR/README.md

@@ -14,6 +14,7 @@ The following table lists the differences among them.
 | `transducer_stateless`             | Conformer | Embedding + Conv1d | with `k2.rnnt_loss`                                                               |
 | `transducer_stateless_modified`    | Conformer | Embedding + Conv1d | with modified transducer from `optimized_transducer`                     |
 | `transducer_stateless_modified-2`  | Conformer | Embedding + Conv1d | with modified transducer from `optimized_transducer` + extra data      |
+| `pruned_transducer_stateless3`     | Reworked Conformer | Embedding + Conv1d | Multi dataset: Pruned RNN-T + aishell + aidatatang_200zh|
 
 The decoder in `transducer_stateless` is modified from the paper
 [Rnn-Transducer with Stateless Prediction Network](https://ieeexplore.ieee.org/document/9054419/).

egs/aishell/ASR/pruned_transducer_stateless3/test_model.py

@@ -33,7 +33,7 @@ def test_model():
     params.blank_id = 0
     params.context_size = 2
     params.unk_id = 2
-    params.num_encoder_layers = 36
+    params.num_encoder_layers = 24
     params.dim_feedforward = 1024
     params.nhead = 8
     params.encoder_dim = 256

egs/aishell/ASR/pruned_transducer_stateless3/train.py

@@ -101,14 +101,14 @@ def add_model_arguments(parser: argparse.ArgumentParser):
     parser.add_argument(
         "--num-encoder-layers",
         type=int,
-        default=24,
+        default=36,
         help="Number of conformer encoder layers..",
     )
 
     parser.add_argument(
         "--dim-feedforward",
         type=int,
-        default=1536,
+        default=1024,
         help="Feedforward dimension of the conformer encoder layer.",
     )
 
@@ -122,7 +122,7 @@ def add_model_arguments(parser: argparse.ArgumentParser):
     parser.add_argument(
         "--encoder-dim",
         type=int,
-        default=384,
+        default=256,
         help="Attention dimension in the conformer encoder layer.",
     )