Merge branch 'master' of github.com:marcoyang1998/icefall into add_lstm_transducer

egs/librispeech/ASR/lstm_transducer_stateless3/export-for-ncnn.py

@@ -0,0 +1 @@
+../lstm_transducer_stateless2/export-for-ncnn.py

egs/librispeech/ASR/lstm_transducer_stateless3/lstm.py

@@ -121,6 +121,8 @@ class RNN(EncoderInterface):
         Period of auxiliary layers used for random combiner during training.
         If set to 0, will not use the random combiner (Default).
         You can set a positive integer to use the random combiner, e.g., 3.
+      is_pnnx:
+        True to make this class exportable via PNNX.
     """
 
     def __init__(
@@ -135,6 +137,7 @@ class RNN(EncoderInterface):
         dropout: float = 0.1,
         layer_dropout: float = 0.075,
         aux_layer_period: int = 0,
+        is_pnnx: bool = False,
     ) -> None:
         super(RNN, self).__init__()
 
@@ -148,7 +151,13 @@ class RNN(EncoderInterface):
         # That is, it does two things simultaneously:
         #   (1) subsampling: T -> T//subsampling_factor
         #   (2) embedding: num_features -> d_model
-        self.encoder_embed = Conv2dSubsampling(num_features, d_model)
+        self.encoder_embed = Conv2dSubsampling(
+            num_features,
+            d_model,
+            is_pnnx=is_pnnx,
+        )
+
+        self.is_pnnx = is_pnnx
 
         self.num_encoder_layers = num_encoder_layers
         self.d_model = d_model
@@ -216,7 +225,13 @@ class RNN(EncoderInterface):
         # lengths = ((x_lens - 3) // 2 - 1) // 2 # issue an warning
         #
         # Note: rounding_mode in torch.div() is available only in torch >= 1.8.0
-        lengths = (((x_lens - 3) >> 1) - 1) >> 1
+        if not self.is_pnnx:
+            lengths = (((x_lens - 3) >> 1) - 1) >> 1
+        else:
+            lengths1 = torch.floor((x_lens - 3) / 2)
+            lengths = torch.floor((lengths1 - 1) / 2)
+            lengths = lengths.to(x_lens)
+
         if not torch.jit.is_tracing():
             assert x.size(0) == lengths.max().item()
 
@@ -377,7 +392,7 @@ class RNNEncoderLayer(nn.Module):
                 # for cell state
                 assert states[1].shape == (1, src.size(1), self.rnn_hidden_size)
             src_lstm, new_states = self.lstm(src, states)
-        src = src + self.dropout(src_lstm)
+        src = self.dropout(src_lstm) + src
 
         # feed forward module
         src = src + self.dropout(self.feed_forward(src))
@@ -523,6 +538,7 @@ class Conv2dSubsampling(nn.Module):
         layer1_channels: int = 8,
         layer2_channels: int = 32,
         layer3_channels: int = 128,
+        is_pnnx: bool = False,
     ) -> None:
         """
         Args:
@@ -535,6 +551,9 @@ class Conv2dSubsampling(nn.Module):
             Number of channels in layer1
           layer1_channels:
             Number of channels in layer2
+          is_pnnx:
+            True if we are converting the model to PNNX format.
+            False otherwise.
         """
         assert in_channels >= 9
         super().__init__()
@@ -577,6 +596,10 @@ class Conv2dSubsampling(nn.Module):
             channel_dim=-1, min_positive=0.45, max_positive=0.55
         )
 
+        # ncnn supports only batch size == 1
+        self.is_pnnx = is_pnnx
+        self.conv_out_dim = self.out.weight.shape[1]
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Subsample x.
 
@@ -590,9 +613,15 @@ class Conv2dSubsampling(nn.Module):
         # On entry, x is (N, T, idim)
         x = x.unsqueeze(1)  # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W)
         x = self.conv(x)
-        # Now x is of shape (N, odim, ((T-3)//2-1)//2, ((idim-3)//2-1)//2)
+
-        b, c, t, f = x.size()
+        if torch.jit.is_tracing() and self.is_pnnx:
-        x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
+            x = x.permute(0, 2, 1, 3).reshape(1, -1, self.conv_out_dim)
+            x = self.out(x)
+        else:
+            # Now x is of shape (N, odim, ((T-3)//2-1)//2, ((idim-3)//2-1)//2)
+            b, c, t, f = x.size()
+            x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
+
         # Now x is of shape (N, ((T-3)//2-1))//2, odim)
         x = self.out_norm(x)
         x = self.out_balancer(x)

egs/librispeech/ASR/lstm_transducer_stateless3/train.py

@@ -102,7 +102,28 @@ def add_model_arguments(parser: argparse.ArgumentParser):
         "--encoder-dim",
         type=int,
         default=512,
-        help="Encoder output dimesion.",
+        help="Encoder output dimension.",
+    )
+
+    parser.add_argument(
+        "--decoder-dim",
+        type=int,
+        default=512,
+        help="Decoder output dimension.",
+    )
+
+    parser.add_argument(
+        "--joiner-dim",
+        type=int,
+        default=512,
+        help="Joiner output dimension.",
+    )
+
+    parser.add_argument(
+        "--dim-feedforward",
+        type=int,
+        default=2048,
+        help="Dimension of feed forward.",
     )
 
     parser.add_argument(
@@ -395,14 +416,10 @@ def get_params() -> AttributeDict:
             # parameters for conformer
             "feature_dim": 80,
             "subsampling_factor": 4,
-            "dim_feedforward": 2048,
-            # parameters for decoder
-            "decoder_dim": 512,
-            # parameters for joiner
-            "joiner_dim": 512,
             # parameters for Noam
             "model_warm_step": 3000,  # arg given to model, not for lrate
             "env_info": get_env_info(),
+            "is_pnnx": False,
         }
     )
 
@@ -419,6 +436,7 @@ def get_encoder_model(params: AttributeDict) -> nn.Module:
         dim_feedforward=params.dim_feedforward,
         num_encoder_layers=params.num_encoder_layers,
         aux_layer_period=params.aux_layer_period,
+        is_pnnx=params.is_pnnx,
     )
     return encoder
 

egs/tal_csasr/ASR/prepare.sh

@@ -12,9 +12,12 @@ stop_stage=100
 # directories and files. If not, they will be downloaded
 # by this script automatically.
 #
-#  - $dl_dir/tal_csasr
+#  - $dl_dir/TALCS_corpus
 #      You can find three directories:train_set, dev_set, and test_set.
 #      You can get it from https://ai.100tal.com/dataset
+#     - dev_set
+#     - test_set
+#     - train_set
 #
 #  - $dl_dir/musan
 #      This directory contains the following directories downloaded from
@@ -44,7 +47,9 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
   log "Stage 0: Download data"
   # Before you run this script, you must get the TAL_CSASR dataset
   # from https://ai.100tal.com/dataset
-  mv $dl_dir/TALCS_corpus $dl_dir/tal_csasr
+  if [ ! -d $dl_dir/tal_csasr/TALCS_corpus ]; then
+    mv $dl_dir/TALCS_corpus $dl_dir/tal_csasr
+  fi
 
   # If you have pre-downloaded it to /path/to/TALCS_corpus,
   # you can create a symlink
@@ -116,7 +121,7 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
   fi
 
   # Prepare text.
-  # Note: in Linux, you can install jq with the  following command:
+  # Note: in Linux, you can install jq with the following command:
   # 1. wget -O jq https://github.com/stedolan/jq/releases/download/jq-1.6/jq-linux64
   # 2. chmod +x ./jq
   # 3. cp jq /usr/bin