Add more doc.

egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py

@@ -93,7 +93,7 @@ class Decoder(nn.Module):
           Return a tensor of shape (N, U, decoder_dim).
         """
         if isinstance(need_pad, torch.Tensor):
-            # This if for torch.jit.trace(), which cannot handle the case
+            # This is for torch.jit.trace(), which cannot handle the case
             # when the input argument is not a tensor.
             need_pad = bool(need_pad)
 

egs/librispeech/ASR/pruned_transducer_stateless3/export.py

@@ -19,6 +19,7 @@
 # This script converts several saved checkpoints
 # to a single one using model averaging.
 """
+
 Usage:
 
 (1) Export to torchscript model using torch.jit.script()
@@ -36,7 +37,7 @@ load it by `torch.jit.load("cpu_jit.pt")`.
 Note `cpu` in the name `cpu_jit.pt` means the parameters when loaded into Python
 are on CPU. You can use `to("cuda")` to move them to a CUDA device.
 
-It will also generates 3 other files: `encoder_jit_script.pt`,
+It will also generate 3 other files: `encoder_jit_script.pt`,
 `decoder_jit_script.pt`, and `joiner_jit_script.pt`.
 
 (2) Export to torchscript model using torch.jit.trace()
@@ -96,6 +97,18 @@ you can do:
         --bpe-model data/lang_bpe_500/bpe.model
 
 Check ./pretrained.py for its usage.
+
+Note: If you don't want to train a model from scratch, we have
+provided one for you. You can get it at
+
+https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
+
+with the following commands:
+
+    sudo apt-get install git-lfs
+    git lfs install
+    git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
+    # You will find the pre-trained model in icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13/exp
 """
 
 import argparse
@@ -170,6 +183,13 @@ def get_parser():
         type=str2bool,
         default=False,
         help="""True to save a model after applying torch.jit.script.
+        It will generate 4 files:
+         - encoder_jit_script.pt
+         - decoder_jit_script.pt
+         - joiner_jit_script.pt
+         - cpu_jit.pt (which combines the above 3 files)
+
+        Check ./jit_pretrained.py for how to use them.
         """,
     )
 
@@ -178,6 +198,12 @@ def get_parser():
         type=str2bool,
         default=False,
         help="""True to save a model after applying torch.jit.trace.
+        It will generate 3 files:
+         - encoder_jit_trace.pt
+         - decoder_jit_trace.pt
+         - joiner_jit_trace.pt
+
+        Check ./jit_pretrained.py for how to use them.
         """,
     )
 
@@ -191,6 +217,8 @@ def get_parser():
             - encoder.onnx
             - decoder.onnx
             - joiner.onnx
+
+        Check ./onnx_check.py and ./onnx_pretrained.py for how to use them.
         """,
     )