zipformer wenetspeech

.github/scripts/run-librispeech-streaming-zipformer-2023-05-18.sh

@@ -23,6 +23,7 @@ ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp
 git lfs pull --include "data/lang_bpe_500/bpe.model"
+git lfs pull --include "data/lang_bpe_500/tokens.txt"
 git lfs pull --include "exp/jit_script_chunk_16_left_128.pt"
 git lfs pull --include "exp/pretrained.pt"
 ln -s pretrained.pt epoch-99.pt
@@ -33,7 +34,7 @@ log "Export to torchscript model"
 ./zipformer/export.py \
   --exp-dir $repo/exp \
   --use-averaged-model false \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --causal 1 \
   --chunk-size 16 \
   --left-context-frames 128 \
@@ -46,7 +47,7 @@ ls -lh $repo/exp/*.pt
 log "Decode with models exported by torch.jit.script()"
 
 ./zipformer/jit_pretrained_streaming.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --nn-model-filename $repo/exp/jit_script_chunk_16_left_128.pt \
   $repo/test_wavs/1089-134686-0001.wav
 
@@ -60,7 +61,7 @@ for method in greedy_search modified_beam_search fast_beam_search; do
     --method $method \
     --beam-size 4 \
     --checkpoint $repo/exp/pretrained.pt \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav

.github/scripts/run-librispeech-zipformer-2023-05-18.sh

@@ -23,6 +23,7 @@ ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp
 git lfs pull --include "data/lang_bpe_500/bpe.model"
+git lfs pull --include "data/lang_bpe_500/tokens.txt"
 git lfs pull --include "exp/jit_script.pt"
 git lfs pull --include "exp/pretrained.pt"
 ln -s pretrained.pt epoch-99.pt
@@ -33,7 +34,7 @@ log "Export to torchscript model"
 ./zipformer/export.py \
   --exp-dir $repo/exp \
   --use-averaged-model false \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --epoch 99 \
   --avg 1 \
   --jit 1
@@ -43,7 +44,7 @@ ls -lh $repo/exp/*.pt
 log "Decode with models exported by torch.jit.script()"
 
 ./zipformer/jit_pretrained.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --nn-model-filename $repo/exp/jit_script.pt \
   $repo/test_wavs/1089-134686-0001.wav \
   $repo/test_wavs/1221-135766-0001.wav \
@@ -56,7 +57,7 @@ for method in greedy_search modified_beam_search fast_beam_search; do
     --method $method \
     --beam-size 4 \
     --checkpoint $repo/exp/pretrained.pt \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     $repo/test_wavs/1089-134686-0001.wav \
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav

.github/scripts/run-librispeech-zipformer-ctc-2023-06-14.sh

@@ -23,6 +23,7 @@ ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp
 git lfs pull --include "data/lang_bpe_500/bpe.model"
+git lfs pull --include "data/lang_bpe_500/tokens.txt"
 git lfs pull --include "data/lang_bpe_500/HLG.pt"
 git lfs pull --include "data/lang_bpe_500/L.pt"
 git lfs pull --include "data/lang_bpe_500/LG.pt"
@@ -40,7 +41,7 @@ log "Export to torchscript model"
   --use-transducer 1 \
   --use-ctc 1 \
   --use-averaged-model false \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --epoch 99 \
   --avg 1 \
   --jit 1
@@ -51,7 +52,7 @@ log "Decode with models exported by torch.jit.script()"
 
 for method in ctc-decoding 1best; do
   ./zipformer/jit_pretrained_ctc.py \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     --model-filename $repo/exp/jit_script.pt \
     --HLG $repo/data/lang_bpe_500/HLG.pt \
     --words-file $repo/data/lang_bpe_500/words.txt  \
@@ -71,8 +72,7 @@ for method in ctc-decoding 1best; do
     --use-ctc 1 \
     --method $method \
     --checkpoint $repo/exp/pretrained.pt \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
-    --words-file $repo/data/lang_bpe_500/words.txt  \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
     --HLG $repo/data/lang_bpe_500/HLG.pt \
     --G $repo/data/lm/G_4_gram.pt \
     --words-file $repo/data/lang_bpe_500/words.txt  \

.github/scripts/test-ncnn-export.sh

@@ -195,14 +195,14 @@ git lfs pull --include "data/lang_char_bpe/Linv.pt"
 git lfs pull --include "exp/pretrained.pt"
 
 cd exp
-ln -s pretrained.pt epoch-99.pt
+ln -s pretrained.pt epoch-9999.pt
 popd
 
 ./pruned_transducer_stateless7_streaming/export-for-ncnn-zh.py \
   --lang-dir $repo/data/lang_char_bpe \
   --exp-dir $repo/exp \
   --use-averaged-model 0 \
-  --epoch 99 \
+  --epoch 9999 \
   --avg 1 \
   --decode-chunk-len 32 \
   --num-encoder-layers "2,4,3,2,4" \

egs/aishell/ASR/pruned_transducer_stateless7_bbpe/decode.py

@@ -397,7 +397,6 @@ def decode_one_batch(
             beam=params.beam,
             max_contexts=params.max_contexts,
             max_states=params.max_states,
-            subtract_ilme=True,
             ilme_scale=params.ilme_scale,
         )
         for hyp in hyp_tokens:

egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py

@@ -22,6 +22,7 @@
 import k2
 import sentencepiece as spm
 import torch
+from torch import nn
 
 from icefall import ContextGraph, ContextState, NgramLm, NgramLmStateCost
 from icefall.decode import Nbest, one_best_decoding
@@ -35,7 +36,6 @@
     get_texts,
     get_texts_with_timestamp,
 )
-from torch import nn
 
 
 def fast_beam_search_one_best(
@@ -47,8 +47,8 @@ def fast_beam_search_one_best(
     max_states: int,
     max_contexts: int,
     temperature: float = 1.0,
-    subtract_ilme: bool = False,
-    ilme_scale: float = 0.1,
+    ilme_scale: float = 0.0,
+    blank_penalty: float = 0.0,
     return_timestamps: bool = False,
 ) -> Union[List[List[int]], DecodingResults]:
     """It limits the maximum number of symbols per frame to 1.
@@ -90,8 +90,8 @@ def fast_beam_search_one_best(
         max_states=max_states,
         max_contexts=max_contexts,
         temperature=temperature,
-        subtract_ilme=subtract_ilme,
         ilme_scale=ilme_scale,
+        blank_penalty=blank_penalty,
     )
 
     best_path = one_best_decoding(lattice)
@@ -114,6 +114,8 @@ def fast_beam_search_nbest_LG(
     nbest_scale: float = 0.5,
     use_double_scores: bool = True,
     temperature: float = 1.0,
+    blank_penalty: float = 0.0,
+    ilme_scale: float = 0.0,
     return_timestamps: bool = False,
 ) -> Union[List[List[int]], DecodingResults]:
     """It limits the maximum number of symbols per frame to 1.
@@ -168,6 +170,8 @@ def fast_beam_search_nbest_LG(
         max_states=max_states,
         max_contexts=max_contexts,
         temperature=temperature,
+        blank_penalty=blank_penalty,
+        ilme_scale=ilme_scale,
     )
 
     nbest = Nbest.from_lattice(
@@ -240,6 +244,7 @@ def fast_beam_search_nbest(
     nbest_scale: float = 0.5,
     use_double_scores: bool = True,
     temperature: float = 1.0,
+    blank_penalty: float = 0.0,
     return_timestamps: bool = False,
 ) -> Union[List[List[int]], DecodingResults]:
     """It limits the maximum number of symbols per frame to 1.
@@ -293,6 +298,7 @@ def fast_beam_search_nbest(
         beam=beam,
         max_states=max_states,
         max_contexts=max_contexts,
+        blank_penalty=blank_penalty,
         temperature=temperature,
     )
 
@@ -331,6 +337,7 @@ def fast_beam_search_nbest_oracle(
     use_double_scores: bool = True,
     nbest_scale: float = 0.5,
     temperature: float = 1.0,
+    blank_penalty: float = 0.0,
     return_timestamps: bool = False,
 ) -> Union[List[List[int]], DecodingResults]:
     """It limits the maximum number of symbols per frame to 1.
@@ -389,6 +396,7 @@ def fast_beam_search_nbest_oracle(
         max_states=max_states,
         max_contexts=max_contexts,
         temperature=temperature,
+        blank_penalty=blank_penalty,
     )
 
     nbest = Nbest.from_lattice(
@@ -432,8 +440,8 @@ def fast_beam_search(
     max_states: int,
     max_contexts: int,
     temperature: float = 1.0,
-    subtract_ilme: bool = False,
-    ilme_scale: float = 0.1,
+    ilme_scale: float = 0.0,
+    blank_penalty: float = 0.0,
 ) -> k2.Fsa:
     """It limits the maximum number of symbols per frame to 1.
 
@@ -503,8 +511,13 @@ def fast_beam_search(
             project_input=False,
         )
         logits = logits.squeeze(1).squeeze(1)
+
+        if blank_penalty != 0:
+            logits[:, 0] -= blank_penalty
+
         log_probs = (logits / temperature).log_softmax(dim=-1)
-        if subtract_ilme:
+
+        if ilme_scale != 0:
             ilme_logits = model.joiner(
                 torch.zeros_like(
                     current_encoder_out, device=current_encoder_out.device
@@ -513,8 +526,11 @@ def fast_beam_search(
                 project_input=False,
             )
             ilme_logits = ilme_logits.squeeze(1).squeeze(1)
+            if blank_penalty != 0:
+                ilme_logits[:, 0] -= blank_penalty
             ilme_log_probs = (ilme_logits / temperature).log_softmax(dim=-1)
             log_probs -= ilme_scale * ilme_log_probs
+
         decoding_streams.advance(log_probs)
     decoding_streams.terminate_and_flush_to_streams()
     lattice = decoding_streams.format_output(encoder_out_lens.tolist())
@@ -526,6 +542,7 @@ def greedy_search(
     model: nn.Module,
     encoder_out: torch.Tensor,
     max_sym_per_frame: int,
+    blank_penalty: float = 0.0,
     return_timestamps: bool = False,
 ) -> Union[List[int], DecodingResults]:
     """Greedy search for a single utterance.
@@ -595,6 +612,9 @@ def greedy_search(
         )
         # logits is (1, 1, 1, vocab_size)
 
+        if blank_penalty != 0:
+            logits[:, :, :, 0] -= blank_penalty
+
         y = logits.argmax().item()
         if y not in (blank_id, unk_id):
             hyp.append(y)
@@ -626,6 +646,7 @@ def greedy_search_batch(
     model: nn.Module,
     encoder_out: torch.Tensor,
     encoder_out_lens: torch.Tensor,
+    blank_penalty: float = 0,
     return_timestamps: bool = False,
 ) -> Union[List[List[int]], DecodingResults]:
     """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
@@ -703,6 +724,10 @@ def greedy_search_batch(
 
         logits = logits.squeeze(1).squeeze(1)  # (batch_size, vocab_size)
         assert logits.ndim == 2, logits.shape
+
+        if blank_penalty != 0:
+            logits[:, 0] -= blank_penalty
+
         y = logits.argmax(dim=1).tolist()
         emitted = False
         for i, v in enumerate(y):
@@ -923,6 +948,7 @@ def modified_beam_search(
     context_graph: Optional[ContextGraph] = None,
     beam: int = 4,
     temperature: float = 1.0,
+    blank_penalty: float = 0.0,
     return_timestamps: bool = False,
 ) -> Union[List[List[int]], DecodingResults]:
     """Beam search in batch mode with --max-sym-per-frame=1 being hardcoded.
@@ -1028,6 +1054,9 @@ def modified_beam_search(
 
         logits = logits.squeeze(1).squeeze(1)  # (num_hyps, vocab_size)
 
+        if blank_penalty != 0:
+            logits[:, 0] -= blank_penalty
+
         log_probs = (logits / temperature).log_softmax(dim=-1)  # (num_hyps, vocab_size)
 
         log_probs.add_(ys_log_probs)
@@ -1662,6 +1691,7 @@ def beam_search(
     encoder_out: torch.Tensor,
     beam: int = 4,
     temperature: float = 1.0,
+    blank_penalty: float = 0.0,
     return_timestamps: bool = False,
 ) -> Union[List[int], DecodingResults]:
     """
@@ -1758,6 +1788,9 @@ def beam_search(
                     project_input=False,
                 )
 
+                if blank_penalty != 0:
+                    logits[:, :, :, 0] -= blank_penalty
+
                 # TODO(fangjun): Scale the blank posterior
                 log_prob = (logits / temperature).log_softmax(dim=-1)
                 # log_prob is (1, 1, 1, vocab_size)