fix typos

egs/librispeech/ASR/zipformer/jit_pretrained.py

@@ -97,7 +97,7 @@ def read_sound_files(
             sample_rate == expected_sample_rate
         ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
         # We use only the first channel
-        ans.append(wave[0])
+        ans.append(wave[0].contiguous())
     return ans
 
 

egs/librispeech/ASR/zipformer/jit_pretrained_ctc.py

@@ -159,11 +159,11 @@ def get_parser():
         (2) nbest-rescoring. Extract n paths from the decoding lattice,
             rescore them with an LM, the path with
             the highest score is the decoding result.
-            We call it HLG decoding + n-gram LM rescoring.
+            We call it HLG decoding + nbest n-gram LM rescoring.
         (3) whole-lattice-rescoring - Use an LM to rescore the
             decoding lattice and then use 1best to decode the
             rescored lattice.
-            We call it HLG decoding + n-gram LM rescoring.
+            We call it HLG decoding + whole-lattice n-gram LM rescoring.
         """,
     )
 
@@ -210,15 +210,6 @@ def get_parser():
         """,
     )
 
-    parser.add_argument(
-        "--num-classes",
-        type=int,
-        default=500,
-        help="""
-        Vocab size in the BPE model.
-        """,
-    )
-
     parser.add_argument(
         "--sample-rate",
         type=int,
@@ -258,7 +249,7 @@ def read_sound_files(
             sample_rate == expected_sample_rate
         ), f"Expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
         # We use only the first channel
-        ans.append(wave[0])
+        ans.append(wave[0].contiguous())
     return ans
 
 
@@ -272,6 +263,11 @@ def main():
     params.update(get_decoding_params())
     params.update(vars(args))
 
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    params.vocab_size = sp.get_piece_size()
+
     logging.info(f"{params}")
 
     device = torch.device("cpu")
@@ -321,9 +317,7 @@ def main():
 
     if params.method == "ctc-decoding":
         logging.info("Use CTC decoding")
-        bpe_model = spm.SentencePieceProcessor()
+        max_token_id = params.vocab_size - 1
-        bpe_model.load(params.bpe_model)
-        max_token_id = params.num_classes - 1
 
         H = k2.ctc_topo(
             max_token=max_token_id,
@@ -346,7 +340,7 @@ def main():
             lattice=lattice, use_double_scores=params.use_double_scores
         )
         token_ids = get_texts(best_path)
-        hyps = bpe_model.decode(token_ids)
+        hyps = sp.decode(token_ids)
         hyps = [s.split() for s in hyps]
     elif params.method in [
         "1best",

egs/librispeech/ASR/zipformer/model.py

@@ -75,7 +75,7 @@ class AsrModel(nn.Module):
 
         assert (
             use_transducer or use_ctc
-        ), f"At least one of them should be True, but gotten use_transducer={use_transducer}, use_ctc={use_ctc}"
+        ), f"At least one of them should be True, but got use_transducer={use_transducer}, use_ctc={use_ctc}"
 
         assert isinstance(encoder, EncoderInterface), type(encoder)
 
@@ -98,6 +98,9 @@ class AsrModel(nn.Module):
             self.simple_lm_proj = ScaledLinear(
                 decoder_dim, vocab_size, initial_scale=0.25
             )
+        else:
+            assert decoder is None
+            assert joiner is None
 
         self.use_ctc = use_ctc
         if use_ctc:
@@ -135,7 +138,7 @@ class AsrModel(nn.Module):
         encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask)
 
         encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
-        assert torch.all(encoder_out_lens > 0)
+        assert torch.all(encoder_out_lens > 0), (x_lens, encoder_out_lens)
 
         return encoder_out, encoder_out_lens
 
@@ -342,10 +345,7 @@ class AsrModel(nn.Module):
 
         if self.use_ctc:
             # Compute CTC loss
-            targets = [t for tokens in y.tolist() for t in tokens]
+            targets = y.values
-            # of shape (sum(y_lens),)
-            targets = torch.tensor(targets, device=x.device, dtype=torch.int64)
-
             ctc_loss = self.forward_ctc(
                 encoder_out=encoder_out,
                 encoder_out_lens=encoder_out_lens,

egs/librispeech/ASR/zipformer/pretrained.py

@@ -245,7 +245,7 @@ def read_sound_files(
             sample_rate == expected_sample_rate
         ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
         # We use only the first channel
-        ans.append(wave[0])
+        ans.append(wave[0].contiguous())
     return ans
 
 

egs/librispeech/ASR/zipformer/pretrained_ctc.py

@@ -167,11 +167,11 @@ def get_parser():
         (2) nbest-rescoring. Extract n paths from the decoding lattice,
             rescore them with an LM, the path with
             the highest score is the decoding result.
-            We call it HLG decoding + n-gram LM rescoring.
+            We call it HLG decoding + nbest n-gram LM rescoring.
         (3) whole-lattice-rescoring - Use an LM to rescore the
             decoding lattice and then use 1best to decode the
             rescored lattice.
-            We call it HLG decoding + n-gram LM rescoring.
+            We call it HLG decoding + whole-lattice n-gram LM rescoring.
         """,
     )
 
@@ -218,15 +218,6 @@ def get_parser():
         """,
     )
 
-    parser.add_argument(
-        "--num-classes",
-        type=int,
-        default=500,
-        help="""
-        Vocab size in the BPE model.
-        """,
-    )
-
     parser.add_argument(
         "--sample-rate",
         type=int,
@@ -268,7 +259,7 @@ def read_sound_files(
             f"expected sample rate: {expected_sample_rate}. " f"Given: {sample_rate}"
         )
         # We use only the first channel
-        ans.append(wave[0])
+        ans.append(wave[0].contiguous())
     return ans
 
 
@@ -281,7 +272,11 @@ def main():
     # add decoding params
     params.update(get_decoding_params())
     params.update(vars(args))
-    params.vocab_size = params.num_classes
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    params.vocab_size = sp.get_piece_size()
     params.blank_id = 0
 
     logging.info(f"{params}")
@@ -340,9 +335,7 @@ def main():
 
     if params.method == "ctc-decoding":
         logging.info("Use CTC decoding")
-        bpe_model = spm.SentencePieceProcessor()
+        max_token_id = params.vocab_size - 1
-        bpe_model.load(params.bpe_model)
-        max_token_id = params.num_classes - 1
 
         H = k2.ctc_topo(
             max_token=max_token_id,
@@ -365,7 +358,7 @@ def main():
             lattice=lattice, use_double_scores=params.use_double_scores
         )
         token_ids = get_texts(best_path)
-        hyps = bpe_model.decode(token_ids)
+        hyps = sp.decode(token_ids)
         hyps = [s.split() for s in hyps]
     elif params.method in [
         "1best",

egs/librispeech/ASR/zipformer/train.py

@@ -607,7 +607,7 @@ def get_model(params: AttributeDict) -> nn.Module:
     assert (
         params.use_transducer or params.use_ctc
     ), (f"At least one of them should be True, "
-        f"but gotten params.use_transducer={params.use_transducer}, "
+        f"but got params.use_transducer={params.use_transducer}, "
         f"params.use_ctc={params.use_ctc}")
 
     encoder_embed = get_encoder_embed(params)