gigaspeech shallow fussion

egs/gigaspeech/ASR/zipformer/ctc_decode.py

@@ -127,6 +127,10 @@ from gigaspeech_scoring import asr_text_post_processing
 from lhotse import set_caching_enabled
 from train_cr_aed import add_model_arguments, get_model, get_params
 
+from icefall.context_graph import ContextGraph, ContextState
+from icefall.ngram_lm import NgramLm, NgramLmStateCost
+from icefall.lm_wrapper import LmScorer
+
 from icefall.checkpoint import (
     average_checkpoints,
     average_checkpoints_with_averaged_model,
@@ -137,6 +141,7 @@ from icefall.decode import (
     ctc_greedy_search,
     ctc_prefix_beam_search,
     ctc_prefix_beam_search_attention_decoder_rescoring,
+    ctc_prefix_beam_search_shallow_fussion,
     get_lattice,
     nbest_decoding,
     nbest_oracle,
@@ -286,6 +291,23 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--lm-type",
+        type=str,
+        default="rnn",
+        help="Type of NN lm",
+        choices=["rnn", "transformer"],
+    )
+
+    parser.add_argument(
+        "--lm-scale",
+        type=float,
+        default=0.3,
+        help="""The scale of the neural network LM
+        Used only when `--use-shallow-fusion` is set to True.
+        """,
+    )
+
     parser.add_argument(
         "--hlg-scale",
         type=float,
@@ -320,8 +342,9 @@ def get_decoding_params() -> AttributeDict:
     params = AttributeDict(
         {
             "frame_shift_ms": 10,
-            "search_beam": 20,
+            "search_beam": 20,  # for k2 fsa composition
-            "output_beam": 8,
+            "output_beam": 8,  # for k2 fsa composition
+            "beam": 4,  # for prefix-beam-search
             "min_active_states": 30,
             "max_active_states": 10000,
             "use_double_scores": True,
@@ -350,6 +373,7 @@ def decode_one_batch(
     batch: dict,
     word_table: k2.SymbolTable,
     G: Optional[k2.Fsa] = None,
+    LM: Optional[LmScorer] = None,
 ) -> Dict[str, List[List[str]]]:
     """Decode one batch and return the result in a dict. The dict has the
     following format:
@@ -453,6 +477,20 @@ def decode_one_batch(
             ans[a_scale_str] = hyps
         return ans
 
+    if params.decoding_method == "ctc-prefix-beam-search-shallow-fussion":
+        token_ids = ctc_prefix_beam_search_shallow_fussion(
+            ctc_output=ctc_output,
+            encoder_out_lens=encoder_out_lens,
+            LM=LM,
+        )
+        # hyps is a list of str, e.g., ['xxx yyy zzz', ...]
+        hyps = bpe_model.decode(token_ids)
+
+        # hyps is a list of list of str, e.g., [['xxx', 'yyy', 'zzz'], ... ]
+        hyps = [s.split() for s in hyps]
+        key = "prefix-beam-search-shallow-fussion"
+        return {key: hyps}
+
     supervision_segments = torch.stack(
         (
             supervisions["sequence_idx"],
@@ -626,6 +664,7 @@ def decode_dataset(
     bpe_model: Optional[spm.SentencePieceProcessor],
     word_table: k2.SymbolTable,
     G: Optional[k2.Fsa] = None,
+    LM: Optional[LmScorer] = None,
 ) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
     """Decode dataset.
 
@@ -676,6 +715,7 @@ def decode_dataset(
             batch=batch,
             word_table=word_table,
             G=G,
+            LM=LM,
         )
 
         for name, hyps in hyps_dict.items():
@@ -779,6 +819,7 @@ def main():
         "ctc-greedy-search",
         "prefix-beam-search",
         "ctc-prefix-beam-search-attention-decoder-rescoring",
+        "ctc-prefix-beam-search-shallow-fussion",
         "ctc-decoding",
         "1best",
         "nbest",
@@ -805,6 +846,11 @@ def main():
         params.suffix += f"_chunk-{params.chunk_size}"
         params.suffix += f"_left-context-{params.left_context_frames}"
 
+    if "prefix-beam-search" in params.decoding_method:
+        params.suffix += f"_beam-{params.beam}"
+        if params.decoding_method == "ctc-prefix-beam-search-shallow-fussion":
+            params.suffix += f"_lm-scale-{params.lm_scale}"
+
     if params.use_averaged_model:
         params.suffix += "_use-averaged-model"
 
@@ -834,6 +880,7 @@ def main():
         "ctc-decoding",
         "prefix-beam-search",
         "ctc-prefix-beam-search-attention-decoder-rescoring",
+        "ctc-prefix-beam-search-shallow-fussion",
         "attention-decoder-rescoring-no-ngram",
     ]:
         HLG = None
@@ -912,6 +959,19 @@ def main():
     else:
         G = None
 
+    # only load the neural network LM if required
+    if params.decoding_method == "ctc-prefix-beam-search-shallow-fussion":
+        LM = LmScorer(
+            lm_type=params.lm_type,
+            params=params,
+            device=device,
+            lm_scale=params.lm_scale,
+        )
+        LM.to(device)
+        LM.eval()
+    else:
+        LM = None
+
     logging.info("About to create model")
     model = get_model(params)