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egs/librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_H.py

@@ -8,13 +8,13 @@ on CPU using OpenFST and decoders from kaldi.
 Usage:
 
     ./conformer_ctc/jit_pretrained_decode_with_H.py \
-      --nn-model ./cpu_jit.pt \
+      --nn-model ./conformer_ctc/exp/cpu_jit.pt \
       --H ./data/lang_bpe_500/H.fst \
       --tokens ./data/lang_bpe_500/tokens.txt \
       ./download/LibriSpeech/test-clean/1089/134686/1089-134686-0002.flac \
       ./download/LibriSpeech/test-clean/1221/135766/1221-135766-0001.flac
 
-Note that to generate ./tdnn/exp/cpu_jit.pt,
+Note that to generate ./conformer_ctc/exp/cpu_jit.pt,
 you can use ./export.py --jit 1
 """
 
@@ -42,7 +42,7 @@ def get_parser():
         type=str,
         required=True,
         help="""Path to the torchscript model.
-        You can use ./tdnn/export.py --jit 1
+        You can use ./conformer_ctc/export.py --jit 1
         to obtain it
         """,
     )
@@ -111,8 +111,22 @@ def decode(
     H: kaldifst,
     id2token: Dict[int, str],
 ) -> List[str]:
+    """
+    Args:
+      filename:
+        Path to the filename for decoding. Used for debugging.
+      nnet_output:
+        A 2-D float32 tensor of shape (num_frames, vocab_size). It
+        contains output from log_softmax.
+      H:
+        The H graph.
+      id2token:
+        A map mapping token ID to token string.
+    Returns:
+      Return a list of decoded tokens.
+    """
     logging.info(f"{filename}, {nnet_output.shape}")
-    decodable = DecodableCtc(nnet_output)
+    decodable = DecodableCtc(nnet_output.cpu())
 
     decoder_opts = FasterDecoderOptions(max_active=3000)
     decoder = FasterDecoder(H, decoder_opts)
@@ -120,7 +134,7 @@ def decode(
 
     if not decoder.reached_final():
         print(f"failed to decode {filename}")
-        return ""
+        return [""]
 
     ok, best_path = decoder.get_best_path()
 
@@ -132,7 +146,7 @@ def decode(
     ) = kaldifst.get_linear_symbol_sequence(best_path)
     if not ok:
         print(f"failed to get linear symbol sequence for {filename}")
-        return ""
+        return [""]
 
     # tokens are incremented during graph construction
     # so they need to be decremented

egs/librispeech/ASR/conformer_ctc/jit_pretrained_decode_with_HL.py

@@ -8,13 +8,13 @@ on CPU using OpenFST and decoders from kaldi.
 Usage:
 
     ./conformer_ctc/jit_pretrained_decode_with_H.py \
-      --nn-model ./cpu_jit.pt \
+      --nn-model ./conformer_ctc/exp/cpu_jit.pt \
       --HL ./data/lang_bpe_500/HL.fst \
       --words ./data/lang_bpe_500/words.txt \
       ./download/LibriSpeech/test-clean/1089/134686/1089-134686-0002.flac \
       ./download/LibriSpeech/test-clean/1221/135766/1221-135766-0001.flac
 
-Note that to generate ./tdnn/exp/cpu_jit.pt,
+Note that to generate ./conformer_ctc/exp/cpu_jit.pt,
 you can use ./export.py --jit 1
 """
 
@@ -42,7 +42,7 @@ def get_parser():
         type=str,
         required=True,
         help="""Path to the torchscript model.
-        You can use ./tdnn/export.py --jit 1
+        You can use ./conformer_ctc/export.py --jit 1
         to obtain it
         """,
     )
@@ -111,8 +111,22 @@ def decode(
     HL: kaldifst,
     id2word: Dict[int, str],
 ) -> List[str]:
+    """
+    Args:
+      filename:
+        Path to the filename for decoding. Used for debugging.
+      nnet_output:
+        A 2-D float32 tensor of shape (num_frames, vocab_size). It
+        contains output from log_softmax.
+      HL:
+        The HL graph.
+      word2token:
+        A map mapping token ID to word string.
+    Returns:
+      Return a list of decoded words.
+    """
     logging.info(f"{filename}, {nnet_output.shape}")
-    decodable = DecodableCtc(nnet_output)
+    decodable = DecodableCtc(nnet_output.cpu())
 
     decoder_opts = FasterDecoderOptions(max_active=3000)
     decoder = FasterDecoder(HL, decoder_opts)
@@ -120,7 +134,7 @@ def decode(
 
     if not decoder.reached_final():
         print(f"failed to decode {filename}")
-        return ""
+        return [""]
 
     ok, best_path = decoder.get_best_path()
 
@@ -132,7 +146,7 @@ def decode(
     ) = kaldifst.get_linear_symbol_sequence(best_path)
     if not ok:
         print(f"failed to get linear symbol sequence for {filename}")
-        return ""
+        return [""]
 
     # are shifted by 1 during graph construction
     hyps = [id2word[i] for i in osymbols_out]

egs/yesno/ASR/tdnn/jit_pretrained_decode_with_H.py

@@ -118,7 +118,7 @@ def decode(
 
     if not decoder.reached_final():
         print(f"failed to decode {filename}")
-        return ""
+        return [""]
 
     ok, best_path = decoder.get_best_path()
 
@@ -130,7 +130,7 @@ def decode(
     ) = kaldifst.get_linear_symbol_sequence(best_path)
     if not ok:
         print(f"failed to get linear symbol sequence for {filename}")
-        return ""
+        return [""]
 
     # are shifted by 1 during graph construction
     hyps = [id2token[i - 1] for i in osymbols_out if id2token[i - 1] != "SIL"]

egs/yesno/ASR/tdnn/jit_pretrained_decode_with_HL.py

@@ -118,7 +118,7 @@ def decode(
 
     if not decoder.reached_final():
         print(f"failed to decode {filename}")
-        return ""
+        return [""]
 
     ok, best_path = decoder.get_best_path()
 
@@ -130,7 +130,7 @@ def decode(
     ) = kaldifst.get_linear_symbol_sequence(best_path)
     if not ok:
         print(f"failed to get linear symbol sequence for {filename}")
-        return ""
+        return [""]
 
     hyps = [id2word[i] for i in osymbols_out if id2word[i] != "<SIL>"]