using bw rnn in ds2

README.md

@@ -18,7 +18,7 @@
 All tested under:  
 * Ubuntu 16.04
 * python>=3.7
-* paddlepaddle>=2.1.2
+* paddlepaddle>=2.2.0rc
 
 Please see [install](doc/src/install.md).
 

README_cn.md

@@ -20,7 +20,7 @@
 
 * Ubuntu 16.04
 * python>=3.7
-* paddlepaddle>=2.1.2
+* paddlepaddle>=2.2.0rc
 
 参看 [安装](doc/src/install.md)。
 

deepspeech/models/ds2/rnn.py

@@ -29,13 +29,13 @@ __all__ = ['RNNStack']
 
 class RNNCell(nn.RNNCellBase):
     r"""
-    Elman RNN (SimpleRNN) cell. Given the inputs and previous states, it 
+    Elman RNN (SimpleRNN) cell. Given the inputs and previous states, it
     computes the outputs and updates states.
     The formula used is as follows:
     .. math::
         h_{t} & = act(x_{t} + b_{ih} + W_{hh}h_{t-1} + b_{hh})
         y_{t} & = h_{t}
-    
+
     where :math:`act` is for :attr:`activation`.
     """
 
@@ -92,7 +92,7 @@ class RNNCell(nn.RNNCellBase):
 
 class GRUCell(nn.RNNCellBase):
     r"""
-    Gated Recurrent Unit (GRU) RNN cell. Given the inputs and previous states, 
+    Gated Recurrent Unit (GRU) RNN cell. Given the inputs and previous states,
     it computes the outputs and updates states.
     The formula for GRU used is as follows:
     ..  math::
@@ -101,8 +101,8 @@ class GRUCell(nn.RNNCellBase):
         \widetilde{h}_{t} & = \tanh(W_{ic}x_{t} + b_{ic} + r_{t} * (W_{hc}h_{t-1} + b_{hc}))
         h_{t} & = z_{t} * h_{t-1} + (1 - z_{t}) * \widetilde{h}_{t}
         y_{t} & = h_{t}
-    
+
-    where :math:`\sigma` is the sigmoid fucntion, and * is the elemetwise 
+    where :math:`\sigma` is the sigmoid fucntion, and * is the elemetwise
     multiplication operator.
     """
 
@@ -202,7 +202,7 @@ class BiRNNWithBN(nn.Layer):
         self.fw_rnn = nn.RNN(
             self.fw_cell, is_reverse=False, time_major=False)  #[B, T, D]
         self.bw_rnn = nn.RNN(
-            self.fw_cell, is_reverse=True, time_major=False)  #[B, T, D]
+            self.bw_cell, is_reverse=True, time_major=False)  #[B, T, D]
 
     def forward(self, x: paddle.Tensor, x_len: paddle.Tensor):
         # x, shape [B, T, D]
@@ -246,7 +246,7 @@ class BiGRUWithBN(nn.Layer):
         self.fw_rnn = nn.RNN(
             self.fw_cell, is_reverse=False, time_major=False)  #[B, T, D]
         self.bw_rnn = nn.RNN(
-            self.fw_cell, is_reverse=True, time_major=False)  #[B, T, D]
+            self.bw_cell, is_reverse=True, time_major=False)  #[B, T, D]
 
     def forward(self, x, x_len):
         # x, shape [B, T, D]

deepspeech/modules/ctc.py

@@ -22,6 +22,13 @@ from deepspeech.utils.log import Log
 
 logger = Log(__name__).getlog()
 
+try:
+    from deepspeech.decoders.swig_wrapper import ctc_beam_search_decoder_batch  # noqa: F401
+    from deepspeech.decoders.swig_wrapper import ctc_greedy_decoder  # noqa: F401
+    from deepspeech.decoders.swig_wrapper import Scorer  # noqa: F401
+except Exception as e:
+    logger.info("ctcdecoder not installed!")
+
 __all__ = ['CTCDecoder']
 
 
@@ -216,9 +223,6 @@ class CTCDecoder(nn.Layer):
 
     def init_decode(self, beam_alpha, beam_beta, lang_model_path, vocab_list,
                     decoding_method):
-        from deepspeech.decoders.swig_wrapper import ctc_beam_search_decoder_batch  # noqa: F401
-        from deepspeech.decoders.swig_wrapper import ctc_greedy_decoder  # noqa: F401
-        from deepspeech.decoders.swig_wrapper import Scorer  # noqa: F401
 
         if decoding_method == "ctc_beam_search":
             self._init_ext_scorer(beam_alpha, beam_beta, lang_model_path,

examples/aishell/s0/README.md

@@ -10,8 +10,11 @@
 
 | Model | Params | Release | Config | Test set | Loss | CER |  
 | --- | --- | --- | --- | --- | --- | --- |  
-| DeepSpeech2 | 58.4M | 2.2.0 | conf/deepspeech2.yaml + spec aug | test | 5.71956205368042 | 0.064287 |  
+| DeepSpeech2 | 58.4M | 2.2.0 | conf/deepspeech2.yaml + spec aug | test | 6.016139030456543 | 0.066549 |  
+| --- | --- | --- | --- | --- | --- | --- |  
+| DeepSpeech2 | 58.4M | 7181e427 | conf/deepspeech2.yaml + spec aug | test | 5.71956205368042 | 0.064287 |  
 | DeepSpeech2 | 58.4M | 2.1.0 | conf/deepspeech2.yaml + spec aug | test | 7.483316898345947 | 0.077860 |  
 | DeepSpeech2 | 58.4M | 2.1.0 | conf/deepspeech2.yaml | test | 7.299022197723389 | 0.078671 |
 | DeepSpeech2 | 58.4M | 2.0.0 | conf/deepspeech2.yaml | test | - | 0.078977 |  
+| --- | --- | --- | --- | --- | --- | --- |  
 | DeepSpeech2 | 58.4M | 1.8.5 | - | test | - | 0.080447 |  

utils/avg.sh

@@ -5,8 +5,8 @@ if [ $# != 3 ]; then
     exit -1
 fi
 
-ckpt_dir=${1}
+avg_mode=${1} # best,latest
-avg_mode=${2} # best,latest
+ckpt_dir=${2}
 average_num=${3}
 decode_checkpoint=${ckpt_dir}/avg_${average_num}.pdparams