Use a modified ctc_topo.

egs/librispeech/asr/simple_v1/run.sh

@@ -31,23 +31,35 @@ fi
 
 if [ $stage -le 4 ]; then
   # Build G
-  python3 -m kaldilm \
-    --read-symbol-table="data/lang_nosp/words.txt" \
-    --disambig-symbol='#0' \
-    --max-order=1 \
-    data/local/lm/lm_tgmed.arpa >data/lang_nosp/G_uni.fst.txt
-
-  python3 -m kaldilm \
-    --read-symbol-table="data/lang_nosp/words.txt" \
-    --disambig-symbol='#0' \
-    --max-order=3 \
-    data/local/lm/lm_tgmed.arpa >data/lang_nosp/G.fst.txt
-
-  python3 -m kaldilm \
-    --read-symbol-table="data/lang_nosp/words.txt" \
-    --disambig-symbol='#0' \
-    --max-order=4 \
-    data/local/lm/lm_fglarge.arpa >data/lang_nosp/G_4_gram.fst.txt
+  if [ ! -f data/lang_nosp/G_uni.fst.txt ]; then
+    python3 -m kaldilm \
+      --read-symbol-table="data/lang_nosp/words.txt" \
+      --disambig-symbol='#0' \
+      --max-order=1 \
+      data/local/lm/lm_tgmed.arpa >data/lang_nosp/G_uni.fst.txt
+  else
+    echo "Skip generating data/lang_nosp/G_uni.fst.txt"
+  fi
+
+  if [ ! -f data/lang_nosp/G.fst.txt ]; then
+    python3 -m kaldilm \
+      --read-symbol-table="data/lang_nosp/words.txt" \
+      --disambig-symbol='#0' \
+      --max-order=3 \
+      data/local/lm/lm_tgmed.arpa >data/lang_nosp/G.fst.txt
+  else
+    echo "Skip generating data/lang_nosp/G.fst.txt"
+  fi
+
+  if [ ! -f data/lang_nosp/G_4_gram.fst.txt ]; then
+    python3 -m kaldilm \
+      --read-symbol-table="data/lang_nosp/words.txt" \
+      --disambig-symbol='#0' \
+      --max-order=4 \
+      data/local/lm/lm_fglarge.arpa >data/lang_nosp/G_4_gram.fst.txt
+  else
+    echo "Skip generating data/lang_nosp/G_4_gram.fst.txt"
+  fi
 
   echo ""
   echo "To load G:"

snowfall/training/ctc_graph.py

@@ -42,6 +42,38 @@ def build_ctc_topo(tokens: List[int]) -> k2.Fsa:
     return ans
 
 
+def build_ctc_topo2(phones: List[int]):
+    # See https://github.com/k2-fsa/k2/issues/746#issuecomment-856421616
+    assert 0 in phones, 'We assume 0 is the ID of the blank symbol'
+    phones = phones.copy()
+    phones.remove(0)
+
+    num_phones = len(phones)
+
+    start = 0
+    final = num_phones + 1
+
+    arcs = []
+    arcs.append([start, start, 0, 0, 0])
+    arcs.append([start, final, -1, -1, 0])
+    arcs.append([final])
+    for i, p in enumerate(phones):
+        i += 1
+        arcs.append([start, start, p, p, 0])
+
+        arcs.append([start, i, p, p, 0])
+        arcs.append([i, i, p, 0, 0])
+
+        arcs.append([i, start, p, 0, 0])
+
+    arcs = sorted(arcs, key=lambda arc: arc[0])
+    arcs = [[str(i) for i in arc] for arc in arcs]
+    arcs = [' '.join(arc) for arc in arcs]
+    arcs = '\n'.join(arcs)
+    ctc_topo = k2.Fsa.from_str(arcs, False)
+    return k2.arc_sort(ctc_topo)
+
+
 class CtcTrainingGraphCompiler(object):
 
     def __init__(self,