update result

egs/aishell2/ASR/RESULTS.md

@@ -12,7 +12,10 @@ When training with context size equals to 1, the WERs are
 |------------------------------------|-------|----------|----------------------------------|
 |          greedy search             | 5.57  | 5.89     | --epoch 25, --avg 5, --max-duration 600  |
 | modified beam search (beam size 4) | 5.32  | 5.56     | --epoch 25, --avg 5, --max-duration 600  |
-| fast beam search (set as default)  | 5.5  |  5.78     | --epoch 25, --avg 5, --max-duration 600 |
+| fast beam search (set as default)  | 5.5   |  5.78    | --epoch 25, --avg 5, --max-duration 600 |
+| fast beam search nbest             | 5.46  |  5.74    | --epoch 25, --avg 5, --max-duration 600 |
+| fast beam search oracle            | 1.92  |  2.2     | --epoch 25, --avg 5, --max-duration 600 |
+| fast beam search nbest LG          | 5.59  |  5.93    | --epoch 25, --avg 5, --max-duration 600 |
 
 The training command for reproducing is given below:
 
@@ -37,11 +40,13 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
 The decoding command is:
 ```
-for method in greedy_search modified_beam_search fast_beam_search; do
+for method in greedy_search modified_beam_search \
+              fast_beam_search fast_beam_search_nbest \
+              fast_beam_search_nbest_oracle fast_beam_search_nbest_LG; do
   ./pruned_transducer_stateless5/decode.py \
     --epoch 25 \
     --avg 5 \
-    --exp-dir /result \
+    --exp-dir ./pruned_transducer_stateless5/exp \
     --max-duration 600 \
     --decoding-method $method \
     --max-sym-per-frame 1 \
@@ -51,7 +56,13 @@ for method in greedy_search modified_beam_search fast_beam_search; do
     --encoder-dim 384 \
     --decoder-dim 512 \
     --joiner-dim 512 \
-    --use-averaged-model True
+    --context-size 1 \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5 \
+    --use-averaged-model False
 done
 ```
 The tensorboard training log can be found at
@@ -66,6 +77,9 @@ When training with context size equals to 2, the WERs are
 |          greedy search             | 5.47  |  5.81    | --epoch 25, --avg 5, --max-duration 600  |
 | modified beam search (beam size 4) | 5.38  |  5.61    | --epoch 25, --avg 5, --max-duration 600  |
 | fast beam search (set as default)  | 5.36  |  5.61    | --epoch 25, --avg 5, --max-duration 600  |
+| fast beam search nbest             | 5.37  |  5.6     | --epoch 25, --avg 5, --max-duration 600 |
+| fast beam search oracle            | 2.04  |  2.2     | --epoch 25, --avg 5, --max-duration 600 |
+| fast beam search nbest LG          | 5.59  |  5.82     | --epoch 25, --avg 5, --max-duration 600 |
 
 The tensorboard training log can be found at
 https://tensorboard.dev/experiment/5AxJ8LHoSre8kDAuLp4L7Q/#scalars

egs/aishell2/ASR/prepare.sh

@@ -112,9 +112,9 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
   fi
 fi
 
+lang_char_dir=data/lang_char
 if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
   log "Stage 5: Prepare char based lang"
-  lang_char_dir=data/lang_char
   mkdir -p $lang_char_dir
 
   # Prepare text.
@@ -151,3 +151,31 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
     python3 ./local/prepare_char.py
   fi
 fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Prepare G"
+  # We assume you have install kaldilm, if not, please install
+  # it using: pip install kaldilm
+
+  if [ ! -f ${lang_char_dir}/3-gram.unpruned.arpa ]; then
+    ./shared/make_kn_lm.py \
+      -ngram-order 3 \
+      -text $lang_char_dir/text_words_segmentation \
+      -lm $lang_char_dir/3-gram.unpruned.arpa
+  fi
+
+  mkdir -p data/lm
+  if [ ! -f data/lm/G_3_gram.fst.txt ]; then
+    # It is used in building LG
+    python3 -m kaldilm \
+      --read-symbol-table="$lang_char_dir/words.txt" \
+      --disambig-symbol='#0' \
+      --max-order=3 \
+      $lang_char_dir/3-gram.unpruned.arpa > data/lm/G_3_gram.fst.txt
+  fi
+fi
+
+if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
+  log "Stage 7: Compile LG"
+  ./local/compile_lg.py --lang-dir $lang_char_dir
+fi

egs/aishell2/ASR/pruned_transducer_stateless5/decode.py

@@ -123,6 +123,7 @@ from beam_search import (
 )
 from train import add_model_arguments, get_params, get_transducer_model
 
+from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
 from icefall.checkpoint import (
     average_checkpoints,
     average_checkpoints_with_averaged_model,
@@ -306,6 +307,7 @@ def decode_one_batch(
     params: AttributeDict,
     model: nn.Module,
     lexicon: Lexicon,
+    graph_compiler: CharCtcTrainingGraphCompiler,
     batch: dict,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[List[str]]]:
@@ -376,7 +378,8 @@ def decode_one_batch(
             nbest_scale=params.nbest_scale,
         )
         for hyp in hyp_tokens:
-            hyps.append([lexicon.word_table[i] for i in hyp])
+            sentence = "".join([lexicon.word_table[i] for i in hyp])
+            hyps.append(list(sentence))
     elif params.decoding_method == "fast_beam_search_nbest":
         hyp_tokens = fast_beam_search_nbest(
             model=model,
@@ -401,7 +404,7 @@ def decode_one_batch(
             max_contexts=params.max_contexts,
             max_states=params.max_states,
             num_paths=params.num_paths,
-            ref_texts=supervisions["text"],
+            ref_texts=graph_compiler.texts_to_ids(supervisions["text"]),
             nbest_scale=params.nbest_scale,
         )
         for i in range(encoder_out.size(0)):
@@ -473,6 +476,7 @@ def decode_dataset(
     params: AttributeDict,
     model: nn.Module,
     lexicon: Lexicon,
+    graph_compiler: CharCtcTrainingGraphCompiler,
     decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
@@ -515,6 +519,7 @@ def decode_dataset(
             params=params,
             model=model,
             lexicon=lexicon,
+            graph_compiler=graph_compiler,
             decoding_graph=decoding_graph,
             batch=batch,
         )
@@ -642,6 +647,11 @@ def main():
     params.unk_id = lexicon.token_table["<unk>"]
     params.vocab_size = max(lexicon.tokens) + 1
 
+    graph_compiler = CharCtcTrainingGraphCompiler(
+        lexicon=lexicon,
+        device=device,
+    )
+
     logging.info(params)
 
     logging.info("About to create model")
@@ -728,7 +738,18 @@ def main():
     model.eval()
 
     if "fast_beam_search" in params.decoding_method:
-        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+        if params.decoding_method == "fast_beam_search_nbest_LG":
+            lexicon = Lexicon(params.lang_dir)
+            lg_filename = params.lang_dir / "LG.pt"
+            logging.info(f"Loading {lg_filename}")
+            decoding_graph = k2.Fsa.from_dict(
+                torch.load(lg_filename, map_location=device)
+            )
+            decoding_graph.scores *= params.ngram_lm_scale
+        else:
+            decoding_graph = k2.trivial_graph(
+                params.vocab_size - 1, device=device
+            )
     else:
         decoding_graph = None
 
@@ -753,6 +774,7 @@ def main():
             params=params,
             model=model,
             lexicon=lexicon,
+            graph_compiler=graph_compiler,
             decoding_graph=decoding_graph,
         )
 

egs/wenetspeech/ASR/local/prepare_words.py

@@ -75,6 +75,16 @@ def main():
 
     logging.info("Starting writing the words.txt")
     f_out = open(output_file, "w", encoding="utf-8")
+
+    # LG decoding needs below symbols.
+    id1, id2, id3 = (
+        str(len(new_lines)),
+        str(len(new_lines) + 1),
+        str(len(new_lines) + 2),
+    )
+    add_words = ["#0 " + id1, "<s> " + id2, "</s> " + id3]
+    new_lines.extend(add_words)
+
     for line in new_lines:
         f_out.write(line)
         f_out.write("\n")