Add changes from master to decode.py, train.py

egs/librispeech/ASR/pruned_transducer_stateless2/decode.py

@@ -71,6 +71,7 @@ from beam_search import (
     beam_search,
     fast_beam_search,
     greedy_search,
+    greedy_search_batch,
     modified_beam_search,
 )
 from train import get_params, get_transducer_model
@@ -191,7 +192,7 @@ def get_parser():
     parser.add_argument(
         "--max-sym-per-frame",
         type=int,
-        default=3,
+        default=1,
         help="""Maximum number of symbols per frame.
         Used only when --decoding_method is greedy_search""",
     )
@@ -261,6 +262,24 @@ def decode_one_batch(
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
+        hyp_tokens = greedy_search_batch(
+            model=model,
+            encoder_out=encoder_out,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "modified_beam_search":
+        hyp_tokens = modified_beam_search(
+            model=model,
+            encoder_out=encoder_out,
+            beam=params.beam_size,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     else:
         batch_size = encoder_out.size(0)
 
@@ -280,12 +299,6 @@ def decode_one_batch(
                     encoder_out=encoder_out_i,
                     beam=params.beam_size,
                 )
-            elif params.decoding_method == "modified_beam_search":
-                hyp = modified_beam_search(
-                    model=model,
-                    encoder_out=encoder_out_i,
-                    beam=params.beam_size,
-                )
             else:
                 raise ValueError(
                     f"Unsupported decoding method: {params.decoding_method}"

egs/librispeech/ASR/pruned_transducer_stateless2/train.py

@@ -398,13 +398,17 @@ def load_checkpoint_if_available(
         "batch_idx_train",
         "best_train_loss",
         "best_valid_loss",
-        "cur_batch_idx",
     ]
     for k in keys:
         params[k] = saved_params[k]
 
+    if params.start_batch > 0:
+        if "cur_epoch" in saved_params:
             params["start_epoch"] = saved_params["cur_epoch"]
 
+        if "cur_batch_idx" in saved_params:
+            params["cur_batch_idx"] = saved_params["cur_batch_idx"]
+
     return saved_params
 
 
@@ -762,11 +766,20 @@ def run(rank, world_size, args):
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds
+        #
+        # Caution: There is a reason to select 20.0 here. Please see
+        # ../local/display_manifest_statistics.py
+        #
+        # You should use ../local/display_manifest_statistics.py to get
+        # an utterance duration distribution for your dataset to select
+        # the threshold
         return 1.0 <= c.duration <= 20.0
 
     train_cuts = train_cuts.filter(remove_short_and_long_utt)
 
-    if checkpoints and "sampler" in checkpoints:
+    if params.start_batch > 0 and checkpoints and "sampler" in checkpoints:
+        # We only load the sampler's state dict when it loads a checkpoint
+        # saved in the middle of an epoch
         sampler_state_dict = checkpoints["sampler"]
     else:
         sampler_state_dict = None