Merge branch 'dev_export_diff_acoustic_units' of https://github.com/JinZr/icefall into dev_export_diff_acoustic_units

.github/scripts/run-librispeech-conformer-ctc3-2022-11-28.sh

@@ -40,7 +40,7 @@ for m in ctc-decoding 1best; do
     --model-filename $repo/exp/jit_trace.pt \
     --words-file $repo/data/lang_bpe_500/words.txt \
     --HLG $repo/data/lang_bpe_500/HLG.pt \
-    --tokens $repo/data/lang_bpe_500/tokens.txt \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
     --G $repo/data/lm/G_4_gram.pt \
     --method $m \
     --sample-rate 16000 \

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

@@ -45,7 +45,6 @@ GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
 repo=$(basename $repo_url)
 
 pushd $repo
-git lfs pull --include "data/lang_bpe_500/bpe.model"
 git lfs pull --include "exp/pretrained-epoch-30-avg-10-averaged.pt"
 
 cd exp
@@ -56,11 +55,10 @@ log "Export via torch.jit.trace()"
 
 ./conv_emformer_transducer_stateless2/export-for-ncnn.py \
   --exp-dir $repo/exp \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
   --epoch 99 \
   --avg 1 \
   --use-averaged-model 0 \
-  \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --num-encoder-layers 12 \
   --chunk-length 32 \
   --cnn-module-kernel 31 \
@@ -91,7 +89,6 @@ GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
 repo=$(basename $repo_url)
 
 pushd $repo
-git lfs pull --include "data/lang_bpe_500/bpe.model"
 git lfs pull --include "exp/pretrained-iter-468000-avg-16.pt"
 
 cd exp
@@ -102,7 +99,7 @@ log "Export via torch.jit.trace()"
 
 ./lstm_transducer_stateless2/export-for-ncnn.py \
   --exp-dir $repo/exp \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --epoch 99 \
   --avg 1 \
   --use-averaged-model 0
@@ -140,7 +137,6 @@ GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
 repo=$(basename $repo_url)
 
 pushd $repo
-git lfs pull --include "data/lang_bpe_500/bpe.model"
 git lfs pull --include "exp/pretrained.pt"
 
 cd exp
@@ -148,7 +144,7 @@ ln -s pretrained.pt epoch-99.pt
 popd
 
 ./pruned_transducer_stateless7_streaming/export-for-ncnn.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --exp-dir $repo/exp \
   --use-averaged-model 0 \
   --epoch 99 \
@@ -199,7 +195,7 @@ ln -s pretrained.pt epoch-9999.pt
 popd
 
 ./pruned_transducer_stateless7_streaming/export-for-ncnn-zh.py \
-  --lang-dir $repo/data/lang_char_bpe \
+  --tokens $repo/data/lang_char_bpe/tokens.txt \
   --exp-dir $repo/exp \
   --use-averaged-model 0 \
   --epoch 9999 \

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

@@ -155,7 +155,7 @@ log "Export via torch.jit.trace()"
 log "Test exporting to ONNX format"
 
 ./pruned_transducer_stateless7_streaming/export-onnx.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --use-averaged-model 0 \
   --epoch 99 \
   --avg 1 \
@@ -204,7 +204,7 @@ popd
 log "Export via torch.jit.script()"
 
 ./pruned_transducer_stateless3/export.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --epoch 9999 \
   --avg 1 \
   --exp-dir $repo/exp/ \
@@ -213,7 +213,7 @@ log "Export via torch.jit.script()"
 log "Test exporting to ONNX format"
 
 ./pruned_transducer_stateless3/export-onnx.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --epoch 9999 \
   --avg 1 \
   --exp-dir $repo/exp/
@@ -258,7 +258,7 @@ popd
 log "Export via torch.jit.script()"
 
 ./pruned_transducer_stateless5/export.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --epoch 99 \
   --avg 1 \
   --use-averaged-model 0 \
@@ -274,7 +274,7 @@ log "Export via torch.jit.script()"
 log "Test exporting to ONNX format"
 
 ./pruned_transducer_stateless5/export-onnx.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --epoch 99 \
   --avg 1 \
   --use-averaged-model 0 \
@@ -384,7 +384,7 @@ popd
 log "Test exporting to ONNX format"
 
 ./conv_emformer_transducer_stateless2/export-onnx.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --use-averaged-model 0 \
   --epoch 99 \
   --avg 1 \
@@ -424,7 +424,7 @@ popd
 log "Export via torch.jit.trace()"
 
 ./lstm_transducer_stateless2/export.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --use-averaged-model 0 \
   --epoch 99 \
   --avg 1 \
@@ -434,7 +434,7 @@ log "Export via torch.jit.trace()"
 log "Test exporting to ONNX format"
 
 ./lstm_transducer_stateless2/export-onnx.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --use-averaged-model 0 \
   --epoch 99 \
   --avg 1 \

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export.py

@@ -139,8 +139,8 @@ import argparse
 import logging
 from pathlib import Path
 
+import k2
 import onnxruntime
-import sentencepiece as spm
 import torch
 import torch.nn as nn
 from onnx_model_wrapper import OnnxStreamingEncoder, TritonOnnxDecoder, TritonOnnxJoiner
@@ -154,7 +154,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import str2bool
+from icefall.utils import num_tokens, str2bool
 
 
 def get_parser():
@@ -211,10 +211,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
+        default="data/lang_bpe_500/tokens.txt",
-        help="Path to the BPE model",
+        help="Path to the tokens.txt",
     )
 
     parser.add_argument(
@@ -675,12 +675,14 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
+    # Load tokens.txt here
-    sp.load(params.bpe_model)
+    token_table = k2.SymbolTable.from_file(params.tokens)
 
+    # Load id of the <blk> token and the vocab size
     # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
+    params.blank_id = token_table["<blk>"]
-    params.vocab_size = sp.get_piece_size()
+    params.unk_id = token_table["<unk>"]
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for <blk>
 
     logging.info(params)
 

egs/librispeech/ASR/zipformer/jit_pretrained_ctc.py

@@ -410,10 +410,20 @@ def main():
         raise ValueError(f"Unsupported decoding method: {params.method}")
 
     s = "\n"
-    for filename, hyp in zip(params.sound_files, hyps):
+    if params.method == "ctc-decoding":
-        words = " ".join(hyp)
+        for filename, hyp in zip(params.sound_files, hyps):
-        words = words.replace("▁", " ").strip()
+            words = "".join(hyp)
-        s += f"{filename}:\n{words}\n\n"
+            words = words.replace("▁", " ").strip()
+            s += f"{filename}:\n{words}\n\n"
+    elif params.method in [
+        "1best",
+        "nbest-rescoring",
+        "whole-lattice-rescoring",
+    ]:
+        for filename, hyp in zip(params.sound_files, hyps):
+            words = " ".join(hyp)
+            words = words.replace("▁", " ").strip()
+            s += f"{filename}:\n{words}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")

egs/librispeech/ASR/zipformer/pretrained_ctc.py

@@ -274,7 +274,7 @@ def main():
     params.update(vars(args))
 
     token_table = k2.SymbolTable.from_file(params.tokens)
-    params.vocab_size = num_tokens(token_table)
+    params.vocab_size = num_tokens(token_table) + 1  # +1 for blank
     params.blank_id = token_table["<blk>"]
     assert params.blank_id == 0
 
@@ -429,10 +429,20 @@ def main():
         raise ValueError(f"Unsupported decoding method: {params.method}")
 
     s = "\n"
-    for filename, hyp in zip(params.sound_files, hyps):
+    if params.method == "ctc-decoding":
-        words = " ".join(hyp)
+        for filename, hyp in zip(params.sound_files, hyps):
-        words = words.replace("▁", " ").strip()
+            words = "".join(hyp)
-        s += f"{filename}:\n{words}\n\n"
+            words = words.replace("▁", " ").strip()
+            s += f"{filename}:\n{words}\n\n"
+    elif params.method in [
+        "1best",
+        "nbest-rescoring",
+        "whole-lattice-rescoring",
+    ]:
+        for filename, hyp in zip(params.sound_files, hyps):
+            words = " ".join(hyp)
+            words = words.replace("▁", " ").strip()
+            s += f"{filename}:\n{words}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")