Minor fix to maximum number of symbols per frame for RNN-T decoding. (#157)

* Minor fix to maximum number of symbols per frame RNN-T decoding.

* Minor fixes.

egs/librispeech/ASR/transducer_stateless/beam_search.py

@@ -22,13 +22,18 @@ import torch
 from model import Transducer
 
 
-def greedy_search(model: Transducer, encoder_out: torch.Tensor) -> List[int]:
+def greedy_search(
+    model: Transducer, encoder_out: torch.Tensor, max_sym_per_frame: int
+) -> List[int]:
     """
     Args:
       model:
         An instance of `Transducer`.
       encoder_out:
         A tensor of shape (N, T, C) from the encoder. Support only N==1 for now.
+      max_sym_per_frame:
+        Maximum number of symbols per frame. If it is set to 0, the WER
+        would be 100%.
     Returns:
       Return the decoded result.
     """
@@ -55,10 +60,6 @@ def greedy_search(model: Transducer, encoder_out: torch.Tensor) -> List[int]:
     # Maximum symbols per utterance.
     max_sym_per_utt = 1000
 
-    # If at frame t, it decodes more than this number of symbols,
-    # it will move to the next step t+1
-    max_sym_per_frame = 3
-
     # symbols per frame
     sym_per_frame = 0
 
@@ -66,6 +67,11 @@ def greedy_search(model: Transducer, encoder_out: torch.Tensor) -> List[int]:
     sym_per_utt = 0
 
     while t < T and sym_per_utt < max_sym_per_utt:
+        if sym_per_frame >= max_sym_per_frame:
+            sym_per_frame = 0
+            t += 1
+            continue
+
         # fmt: off
         current_encoder_out = encoder_out[:, t:t+1, :]
         # fmt: on
@@ -83,8 +89,7 @@ def greedy_search(model: Transducer, encoder_out: torch.Tensor) -> List[int]:
 
             sym_per_utt += 1
             sym_per_frame += 1
-
+        else:
-        if y == blank_id or sym_per_frame > max_sym_per_frame:
             sym_per_frame = 0
             t += 1
     hyp = hyp[context_size:]  # remove blanks

egs/librispeech/ASR/transducer_stateless/decode.py

@@ -114,6 +114,13 @@ def get_parser():
         help="Used only when --decoding-method is beam_search",
     )
 
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=3,
+        help="Maximum number of symbols per frame",
+    )
+
     return parser
 
 
@@ -237,7 +244,11 @@ def decode_one_batch(
         encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
         # fmt: on
         if params.decoding_method == "greedy_search":
-            hyp = greedy_search(model=model, encoder_out=encoder_out_i)
+            hyp = greedy_search(
+                model=model,
+                encoder_out=encoder_out_i,
+                max_sym_per_frame=params.max_sym_per_frame,
+            )
         elif params.decoding_method == "beam_search":
             hyp = beam_search(
                 model=model, encoder_out=encoder_out_i, beam=params.beam_size
@@ -381,6 +392,8 @@ def main():
     params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
     if params.decoding_method == "beam_search":
         params.suffix += f"-beam-{params.beam_size}"
+    else:
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
 
     setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
     logging.info("Decoding started")

egs/librispeech/ASR/transducer_stateless/pretrained.py

@@ -110,6 +110,15 @@ def get_parser():
         help="Used only when --method is beam_search",
     )
 
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=3,
+        help="""Maximum number of symbols per frame. Used only when
+        --method is greedy_search.
+        """,
+    )
+
     return parser
 
 
@@ -279,7 +288,11 @@ def main():
         encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
         # fmt: on
         if params.method == "greedy_search":
-            hyp = greedy_search(model=model, encoder_out=encoder_out_i)
+            hyp = greedy_search(
+                model=model,
+                encoder_out=encoder_out_i,
+                max_sym_per_frame=params.max_sym_per_frame,
+            )
         elif params.method == "beam_search":
             hyp = beam_search(
                 model=model, encoder_out=encoder_out_i, beam=params.beam_size