Minor fixes

egs/aishell/ASR/pruned_transducer_stateless/README.md

@@ -1,21 +0,0 @@
-## Introduction
-
-The decoder, i.e., the prediction network, is from
-https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419
-(Rnn-Transducer with Stateless Prediction Network)
-
-You can use the following command to start the training:
-
-```bash
-cd egs/aishell/ASR
-
-export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
-
-./transducer_stateless/train.py \
-  --world-size 8 \
-  --num-epochs 30 \
-  --start-epoch 0 \
-  --exp-dir transducer_stateless/exp \
-  --max-duration 250 \
-  --lr-factor 2.5
-```

egs/aishell/ASR/pruned_transducer_stateless/beam_search.py

@@ -128,7 +128,6 @@ class HypothesisList(object):
     def data(self):
         return self._data
 
-    #  def add(self, ys: List[int], log_prob: float):
     def add(self, hyp: Hypothesis):
         """Add a Hypothesis to `self`.
 
@@ -266,7 +265,7 @@ def beam_search(
 
     while t < T and sym_per_utt < max_sym_per_utt:
         # fmt: off
-        current_encoder_out = encoder_out[:, t:t+1, :]
+        current_encoder_out = encoder_out[:, t:t+1, :].unsqueeze(2)
         # fmt: on
         A = B
         B = HypothesisList()
@@ -294,7 +293,9 @@ def beam_search(
 
             cached_key += f"-t-{t}"
             if cached_key not in joint_cache:
-                logits = model.joiner(current_encoder_out, decoder_out)
+                logits = model.joiner(
+                    current_encoder_out, decoder_out.unsqueeze(1)
+                )
 
                 # TODO(fangjun): Scale the blank posterior
 

egs/aishell/ASR/pruned_transducer_stateless/decode.py

@@ -127,11 +127,11 @@ def get_params() -> AttributeDict:
         {
             # parameters for conformer
             "feature_dim": 80,
-            "embedding_dim": 256,
+            "embedding_dim": 512,
             "subsampling_factor": 4,
-            "attention_dim": 256,
+            "attention_dim": 512,
             "nhead": 4,
-            "dim_feedforward": 1024,
+            "dim_feedforward": 2048,
             "num_encoder_layers": 12,
             "vgg_frontend": False,
             "env_info": get_env_info(),

egs/aishell/ASR/pruned_transducer_stateless/export.py

@@ -121,11 +121,11 @@ def get_params() -> AttributeDict:
         {
             # parameters for conformer
             "feature_dim": 80,
-            "embedding_dim": 256,
+            "embedding_dim": 512,
             "subsampling_factor": 4,
-            "attention_dim": 256,
+            "attention_dim": 512,
             "nhead": 4,
-            "dim_feedforward": 1024,
+            "dim_feedforward": 2048,
             "num_encoder_layers": 12,
             "vgg_frontend": False,
             "env_info": get_env_info(),

egs/aishell/ASR/pruned_transducer_stateless/test_decoder.py

@@ -1,58 +0,0 @@
-#!/usr/bin/env python3
-# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-To run this file, do:
-
-    cd icefall/egs/aishell/ASR
-    python ./transducer_stateless/test_decoder.py
-"""
-
-import torch
-from decoder import Decoder
-
-
-def test_decoder():
-    vocab_size = 3
-    blank_id = 0
-    embedding_dim = 128
-    context_size = 4
-
-    decoder = Decoder(
-        vocab_size=vocab_size,
-        embedding_dim=embedding_dim,
-        blank_id=blank_id,
-        context_size=context_size,
-    )
-    N = 100
-    U = 20
-    x = torch.randint(low=0, high=vocab_size, size=(N, U))
-    y = decoder(x)
-    assert y.shape == (N, U, vocab_size)
-
-    # for inference
-    x = torch.randint(low=0, high=vocab_size, size=(N, context_size))
-    y = decoder(x, need_pad=False)
-    assert y.shape == (N, 1, vocab_size)
-
-
-def main():
-    test_decoder()
-
-
-if __name__ == "__main__":
-    main()