a trained model

egs/librispeech/ASR/streaming_conformer_ctc/README.md

@@ -0,0 +1,90 @@
+## Train and Decode
+Commands of data preparation/train/decode steps are almost the same with 
+../conformer_ctc experiment except some options.
+
+Please read the code and understand following new added options before running this experiment:
+
+  For data preparation:
+  
+    Nothing new.
+
+  For streaming_conformer_ctc/train.py:
+  
+    --dynamic-chunk-training
+    --short-chunk-proportion
+
+  For streaming_conformer_ctc/streaming_decode.py:
+  
+    --chunk-size
+    --tailing-num-frames
+    --simulate-streaming
+
+## Performence and a trained model.
+
+The latest results with this streaming code is shown in following table:
+
+chunk size | wer on test-clean | wer on test-other
+-- | -- | --
+full | 3.53 | 8.52
+40(1.96s) | 3.78 | 9.38
+32(1.28s) | 3.82 | 9.44
+24(0.96s) | 3.95 | 9.76
+16(0.64s) | 4.06 | 9.98
+8(0.32s) | 4.30 | 10.55
+4(0.16s) | 5.88 | 12.01
+
+
+A trained model is also provided.
+By run
+```
+git clone https://huggingface.co/GuoLiyong/streaming_conformer
+
+# You may want to manually check md5sum values of downloaded files
+# 8e633bc1de37f5ae57a2694ceee32a93  trained_streaming_conformer.pt
+# 4c0aeefe26c784ec64873cc9b95420f1  L.pt
+# d1f91d81005fb8ce4d65953a4a984ee7  Linv.pt
+# e1c1902feb7b9fc69cd8d26e663c2608  bpe.model
+# 8617e67159b0ff9118baa54f04db24cc  tokens.txt
+# 72b075ab5e851005cd854e666c82c3bb  words.txt
+```
+
+If there is any different md5sum values, please run
+```
+cd streaming_models
+git lfs pull
+```
+And check md5sum values again.
+
+Finally, following files will be downloaded:
+<pre>
+streaming_models/  
+|-- lang_bpe  
+|   |-- L.pt  
+|   |-- Linv.pt  
+|   |-- bpe.model
+|   |-- tokens.txt
+|   `-- words.txt
+`-- trained_streaming_conformer.pt
+</pre>
+
+
+And run commands you will get the same results of previous table:
+```
+trained_models=/path/to/downloaded/streaming_models/
+for chunk_size in 4 8 16 24 36 40 -1; do
+    ./streaming_conformer_ctc/streaming_decode.py \
+      --chunk-size=${chunk_size} \
+      --trained-dir=${trained_models}
+done
+```
+Results of following command is indentical to previous one,
+but model consumes features chunk_by_chunk, i.e. a streaming way.
+```
+trained_models=/path/to/downloaded/streaming_models/
+for chunk_size in 4 8 16 24 36 40 -1; do
+    ./streaming_conformer_ctc/streaming_decode.py \
+      --simulate-streaming=True \
+      --chunk-size=${chunk_size} \
+      --trained-dir=${trained_models}
+done
+```

egs/librispeech/ASR/streaming_conformer_ctc/streaming_decode.py

@@ -121,6 +121,13 @@ def get_parser():
         help="The experiment dir",
     )
 
+    parser.add_argument(
+        "--trained-dir",
+        type=Path,
+        default=None,
+        help="The experiment dir",
+    )
+
     parser.add_argument(
         "--lang-dir",
         type=Path,
@@ -144,7 +151,6 @@ def get_params() -> AttributeDict:
         {
             "exp_dir": Path("conformer_ctc/exp"),
             "lang_dir": Path("data/lang_bpe"),
-            "lm_dir": Path("data/lm"),
             # parameters for conformer
             "causal": True,
             "subsampling_factor": 4,
@@ -410,6 +416,12 @@ def main():
     logging.info("Decoding started")
     logging.info(params)
 
+    if params.trained_dir is not None:
+        params.lang_dir = Path(params.trained_dir) / "lang_bpe"
+        # used naming result files
+        params.epoch = "trained_model"
+        params.avg = 1
+
     lexicon = Lexicon(params.lang_dir)
     max_token_id = max(lexicon.tokens)
     num_classes = max_token_id + 1  # +1 for the blank
@@ -441,7 +453,10 @@ def main():
         causal=params.causal,
     )
 
-    if params.avg == 1 and params.avg_models is not None:
+    if params.trained_dir is not None:
+        model_name = f"{params.trained_dir}/trained_streaming_conformer.pt"
+        load_checkpoint(model_name, model)
+    elif params.avg == 1 and params.avg_models is not None:
         load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
     else:
         filenames = []

egs/librispeech/ASR/streaming_conformer_ctc/train.py

@@ -127,7 +127,7 @@ def get_parser():
     parser.add_argument(
         "--dynamic-chunk-training",
         type=str2bool,
-        default=False,
+        default=True,
         help="Whether to use dynamic right context during training.",
     )