add pretrained model to HF

egs/spgispeech/ASR/RESULTS.md

@@ -40,17 +40,43 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
   --use-fp16 True
 ```
 
-The tensorboard training log can be found at
-<https://tensorboard.dev/experiment/ExSoBmrPRx6liMTGLu0Tgw/#scalars>
-
 The decoding command is:
 ```
-## fast beam search
+# greedy search
-./pruned_transducer_stateless/decode.py \
+./pruned_transducer_stateless2/decode.py \
-  --avg-last-n 10 \
+        --avg-last-n 10 \
-  --exp-dir pruned_transducer_stateless/exp \
+        --exp-dir ./pruned_transducer_stateless2/exp \
-  --max-duration 500 \
+        --max-duration 100 \
-  --beam-size 4 \
+        --decoding-method greedy_search
-  --max-contexts 4 \
+
-  --max-states 8
+# beam search
+./pruned_transducer_stateless2/decode.py \
+        --avg-last-n 10 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --max-duration 100 \
+        --decoding-method beam_search \
+        --beam-size 4
+
+# modified beam search
+./pruned_transducer_stateless2/decode.py \
+        --avg-last-n 10 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --max-duration 100 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+
+# fast beam search
+./pruned_transducer_stateless2/decode.py \
+        --avg-last-n 10 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --max-duration 1500 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
 ```
+
+Pretrained model is available at <https://huggingface.co/desh2608/icefall-asr-spgispeech-pruned-transducer-stateless2>
+
+The tensorboard training log can be found at
+<https://tensorboard.dev/experiment/ExSoBmrPRx6liMTGLu0Tgw/#scalars>

egs/spgispeech/ASR/pruned_transducer_stateless2/export.py

@@ -23,8 +23,7 @@ Usage:
 ./pruned_transducer_stateless2/export.py \
   --exp-dir ./pruned_transducer_stateless2/exp \
   --bpe-model data/lang_bpe_500/bpe.model \
-  --epoch 20 \
+  --avg-last-n 10
-  --avg 10
 
 It will generate a file exp_dir/pretrained.pt
 
@@ -34,7 +33,7 @@ you can do:
     cd /path/to/exp_dir
     ln -s pretrained.pt epoch-9999.pt
 
-    cd /path/to/egs/librispeech/ASR
+    cd /path/to/egs/spgispeech/ASR
     ./pruned_transducer_stateless2/decode.py \
         --exp-dir ./pruned_transducer_stateless2/exp \
         --epoch 9999 \
@@ -51,7 +50,11 @@ import sentencepiece as spm
 import torch
 from train import get_params, get_transducer_model
 
-from icefall.checkpoint import average_checkpoints, load_checkpoint
+from icefall.checkpoint import (
+    average_checkpoints,
+    find_checkpoints,
+    load_checkpoint,
+)
 from icefall.utils import str2bool
 
 
@@ -77,6 +80,17 @@ def get_parser():
         "'--epoch'. ",
     )
 
+    parser.add_argument(
+        "--avg-last-n",
+        type=int,
+        default=0,
+        help="""If positive, --epoch and --avg are ignored and it
+        will use the last n checkpoints exp_dir/checkpoint-xxx.pt
+        where xxx is the number of processed batches while
+        saving that checkpoint.
+        """,
+    )
+
     parser.add_argument(
         "--exp-dir",
         type=str,
@@ -105,8 +119,7 @@ def get_parser():
         "--context-size",
         type=int,
         default=2,
-        help="The context size in the decoder. 1 means bigram; "
+        help="The context size in the decoder. 1 means bigram; " "2 means tri-gram",
-        "2 means tri-gram",
     )
 
     return parser
@@ -141,7 +154,12 @@ def main():
 
     model.to(device)
 
-    if params.avg == 1:
+    if params.avg_last_n > 0:
+        filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n]
+        logging.info(f"averaging {filenames}")
+        model.to(device)
+        model.load_state_dict(average_checkpoints(filenames, device=device))
+    elif params.avg == 1:
         load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
     else:
         start = params.epoch - params.avg + 1
@@ -174,9 +192,7 @@ def main():
 
 
 if __name__ == "__main__":
-    formatter = (
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-    )
 
     logging.basicConfig(format=formatter, level=logging.INFO)
     main()