export fp16 onnx models

.github/scripts/docker/Dockerfile

@@ -55,9 +55,9 @@ RUN pip install --no-cache-dir \
       "numpy<2.0" \
       onnxoptimizer \
       onnxsim \
-      onnx \
+      onnx==1.17.0 \
       onnxmltools \
-      onnxruntime \
+      onnxruntime==1.17.1 \
       piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html \
       pypinyin==0.50.0 \
       pytest \

.github/scripts/docker/generate_build_matrix.py

@@ -63,23 +63,24 @@ def get_torchaudio_version(torch_version):
 
 
 def get_matrix(min_torch_version, specified_torch_version, specified_python_version):
-    k2_version = "1.24.4.dev20241029"
+    k2_version = "1.24.4.dev20250630"
-    kaldifeat_version = "1.25.5.dev20241029"
+    kaldifeat_version = "1.25.5.dev20250630"
-    version = "20241218"
+    version = "20250630"
 
     # torchaudio 2.5.0 does not support python 3.13
-    python_version = ["3.8", "3.9", "3.10", "3.11", "3.12"]
+    python_version = ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13"]
     torch_version = []
     torch_version += ["1.13.0", "1.13.1"]
     torch_version += ["2.0.0", "2.0.1"]
-    #  torch_version += ["2.1.0", "2.1.1", "2.1.2"]
+    torch_version += ["2.1.0", "2.1.1", "2.1.2"]
-    #  torch_version += ["2.2.0", "2.2.1", "2.2.2"]
+    torch_version += ["2.2.0", "2.2.1", "2.2.2"]
     # Test only torch >= 2.3.0
     torch_version += ["2.3.0", "2.3.1"]
     torch_version += ["2.4.0"]
     torch_version += ["2.4.1"]
     torch_version += ["2.5.0"]
     torch_version += ["2.5.1"]
+    torch_version += ["2.6.0", "2.7.0", "2.7.1"]
 
     if specified_torch_version:
         torch_version = [specified_torch_version]
@@ -109,12 +110,8 @@ def get_matrix(min_torch_version, specified_torch_version, specified_python_vers
                 # torch>=2.5 requires python 3.10
                 continue
 
-            if t == "2.5.1":
+            k2_version_2 = k2_version
-                k2_version_2 = "1.24.4.dev20241122"
+            kaldifeat_version_2 = kaldifeat_version
-                kaldifeat_version_2 = "1.25.5.dev20241126"
-            else:
-                k2_version_2 = k2_version
-                kaldifeat_version_2 = kaldifeat_version
 
             matrix.append(
                 {

.github/scripts/multi_zh-hans/ASR/run.sh

@@ -6,6 +6,8 @@ git config --global user.name "k2-fsa"
 git config --global user.email "csukuangfj@gmail.com"
 git config --global lfs.allowincompletepush true
 
+python3 -m pip install onnxmltools==1.13.0 onnx==1.17.0 onnxruntime==1.17.1 sherpa-onnx
+
 log() {
   # This function is from espnet
   local fname=${BASH_SOURCE[1]##*/}
@@ -41,7 +43,8 @@ function run_2023_9_2() {
     --epoch 99 \
     --avg 1 \
     --exp-dir $repo/exp \
-    --causal False
+    --causal False \
+    --fp16 1
 
   ls -lh $repo/exp
 
@@ -57,6 +60,30 @@ function run_2023_9_2() {
     $repo/test_wavs/TEST_MEETING_T0000000219.wav \
     $repo/test_wavs/TEST_MEETING_T0000000351.wav
 
+  ./zipformer/onnx_pretrained.py \
+    --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.int8.onnx \
+    --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+    --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.int8.onnx \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    $repo/test_wavs/DEV_T0000000000.wav \
+    $repo/test_wavs/DEV_T0000000001.wav \
+    $repo/test_wavs/DEV_T0000000002.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000113.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000219.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000351.wav
+
+  ./zipformer/onnx_pretrained.py \
+    --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.fp16.onnx \
+    --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.fp16.onnx \
+    --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.fp16.onnx \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    $repo/test_wavs/DEV_T0000000000.wav \
+    $repo/test_wavs/DEV_T0000000001.wav \
+    $repo/test_wavs/DEV_T0000000002.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000113.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000219.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000351.wav
+
   rm -rf $repo
 }
 
@@ -91,7 +118,8 @@ function run_2023_11_05_streaming() {
     --use-averaged-model 0 \
     --chunk-size 16 \
     --left-context-frames 128 \
-    --use-ctc 1
+    --use-ctc 1 \
+    --fp16 1
 
   ls -lh $repo/exp/
 
@@ -109,36 +137,65 @@ function run_2023_11_05_streaming() {
   )
 
   for w in ${test_wavs[@]}; do
+    log "----fp32----"
+    ./zipformer/onnx_pretrained-streaming-ctc.py \
+      --model-filename $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.onnx \
+      --tokens $repo/data/lang_bpe_2000/tokens.txt \
+      $repo/test_wavs/$w
+
+    log "----int8----"
+
     ./zipformer/onnx_pretrained-streaming-ctc.py \
       --model-filename $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.int8.onnx \
       --tokens $repo/data/lang_bpe_2000/tokens.txt \
       $repo/test_wavs/$w
+
+    log "----fp16----"
+
+    ./zipformer/onnx_pretrained-streaming-ctc.py \
+      --model-filename $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.fp16.onnx \
+      --tokens $repo/data/lang_bpe_2000/tokens.txt \
+      $repo/test_wavs/$w
   done
 
   log "Upload onnx CTC models to huggingface"
-  url=https://huggingface.co/k2-fsa/sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13
+  name=(
-  GIT_LFS_SKIP_SMUDGE=1 git clone $url
+    sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13
-  dst=$(basename $url)
+    sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-int8-2023-12-13
-  cp -v $repo/exp/ctc*.onnx $dst
+    sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-fp16-2023-12-13
-  cp -v $repo/data/lang_bpe_2000/tokens.txt $dst
+    )
-  cp -v $repo/data/lang_bpe_2000/bpe.model $dst
+  for n in ${name[@]}; do
-  mkdir -p $dst/test_wavs
+      url=https://huggingface.co/k2-fsa/$n
-  cp -v $repo/test_wavs/*.wav $dst/test_wavs
+      GIT_LFS_SKIP_SMUDGE=1 git clone $url
-  cd $dst
+      dst=$(basename $url)
-  git lfs track "*.onnx" "bpe.model"
+      if [[ $n == sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13 ]]; then
-  ls -lh
+        cp -v $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.onnx $dst
-  file bpe.model
+      elif [[ $n == sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-int8-2023-12-13 ]]; then
-  git status
+        cp -v $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.int8.onnx $dst
-  git add .
+      elif [[ $n == sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-fp16-2023-12-13 ]]; then
-  git commit -m "upload model" && git push https://k2-fsa:${HF_TOKEN}@huggingface.co/k2-fsa/$dst main || true
+        cp -v $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.fp16.onnx $dst
+      fi
 
-  log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      cp -v $repo/data/lang_bpe_2000/tokens.txt $dst
-  rm -rf .git
+      cp -v $repo/data/lang_bpe_2000/bpe.model $dst
-  rm -fv .gitattributes
+      mkdir -p $dst/test_wavs
-  cd ..
+      cp -v $repo/test_wavs/*.wav $dst/test_wavs
-  tar cjfv $dst.tar.bz2 $dst
+      cd $dst
-  ls -lh *.tar.bz2
+      git lfs track "*.onnx" "bpe.model" "*.wav"
-  mv -v $dst.tar.bz2 ../../../
+      ls -lh
+      file bpe.model
+      git status
+      git add .
+      git commit -m "upload model" && git push https://k2-fsa:${HF_TOKEN}@huggingface.co/k2-fsa/$dst main || true
+
+      log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      rm -rf .git
+      rm -fv .gitattributes
+      cd ..
+      tar cjfv $dst.tar.bz2 $dst
+      ls -lh *.tar.bz2
+      mv -v $dst.tar.bz2 ../../../
+  done
 
   log "----------------------------------------"
   log "Export streaming ONNX transducer models "
@@ -153,7 +210,8 @@ function run_2023_11_05_streaming() {
     --use-averaged-model 0 \
     --chunk-size 16 \
     --left-context-frames 128 \
-    --use-ctc 0
+    --use-ctc 0 \
+    --fp16 1
 
   ls -lh $repo/exp
 
@@ -176,6 +234,59 @@ function run_2023_11_05_streaming() {
     --joiner-model-filename $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.int8.onnx \
     --tokens $repo/data/lang_bpe_2000/tokens.txt \
     $repo/test_wavs/DEV_T0000000000.wav
+
+  log "test fp16"
+  ./zipformer/onnx_pretrained-streaming.py \
+    --encoder-model-filename $repo/exp/encoder-epoch-20-avg-1-chunk-16-left-128.fp16.onnx \
+    --decoder-model-filename $repo/exp/decoder-epoch-20-avg-1-chunk-16-left-128.fp16.onnx \
+    --joiner-model-filename $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.fp16.onnx \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    $repo/test_wavs/DEV_T0000000000.wav
+
+  name=(
+    sherpa-onnx-streaming-zipformer-multi-zh-hans-2023-12-13
+    sherpa-onnx-streaming-zipformer-multi-zh-hans-int8-2023-12-13
+    sherpa-onnx-streaming-zipformer-multi-zh-hans-fp16-2023-12-13
+  )
+
+  for n in ${name[@]}; do
+      url=https://huggingface.co/csukuangfj/$n
+      GIT_LFS_SKIP_SMUDGE=1 git clone $url
+      dst=$(basename $url)
+      if [[ $n == sherpa-onnx-streaming-zipformer-multi-zh-hans-2023-12-13 ]]; then
+        cp -v $repo/exp/encoder-epoch-20-avg-1-chunk-16-left-128.onnx $dst
+        cp -v $repo/exp/decoder-epoch-20-avg-1-chunk-16-left-128.onnx $dst
+        cp -v $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-multi-zh-hans-int8-2023-12-13 ]]; then
+        cp -v $repo/exp/encoder-epoch-20-avg-1-chunk-16-left-128.int8.onnx $dst
+        cp -v $repo/exp/decoder-epoch-20-avg-1-chunk-16-left-128.onnx $dst
+        cp -v $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.int8.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-multi-zh-hans-fp16-2023-12-13 ]]; then
+        cp -v $repo/exp/encoder-epoch-20-avg-1-chunk-16-left-128.fp16.onnx $dst
+        cp -v $repo/exp/decoder-epoch-20-avg-1-chunk-16-left-128.fp16.onnx $dst
+        cp -v $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.fp16.onnx $dst
+      fi
+
+      cp -v $repo/data/lang_bpe_2000/tokens.txt $dst
+      cp -v $repo/data/lang_bpe_2000/bpe.model $dst
+      mkdir -p $dst/test_wavs
+      cp -v $repo/test_wavs/*.wav $dst/test_wavs
+      cd $dst
+      git lfs track "*.onnx" "bpe.model" "*.wav"
+      ls -lh
+      file bpe.model
+      git status
+      git add .
+      git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$dst main || true
+
+      log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      rm -rf .git
+      rm -fv .gitattributes
+      cd ..
+      tar cjfv $dst.tar.bz2 $dst
+      ls -lh *.tar.bz2
+      mv -v $dst.tar.bz2 ../../../
+  done
 }
 
 function run_2023_12_12_streaming() {
@@ -192,7 +303,7 @@ function run_2023_12_12_streaming() {
   mkdir -p $dst/test_wavs
   cp -v $repo/test_wavs/*.wav $dst/test_wavs
   cd $dst
-  git lfs track "*.onnx" bpe.model
+  git lfs track "*.onnx" bpe.model "*.wav"
   git add .
   git commit -m "upload model" && git push https://k2-fsa:${HF_TOKEN}@huggingface.co/k2-fsa/$dst main || true
 
@@ -205,6 +316,441 @@ function run_2023_12_12_streaming() {
   mv -v $dst.tar.bz2 ../../../
 }
 
-run_2023_9_2
+function run_yuekai_large() {
+  repo_url=https://csukuangfj:${HF_TOKEN}@huggingface.co/yuekai/icefall-asr-multi-zh-hans-zipformer-large
+  log "Downloading pre-trained model from $repo_url"
+  GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+  repo=$(basename $repo_url)
+  pushd $repo
+  git lfs pull --include pretrained.pt
+  mv pretrained.pt epoch-99.pt
+  curl -SL -O https://huggingface.co/pingzxy/icefall-asr-multi-zh-hans-zipformer-large-onnx/resolve/main/tokens.txt
+  popd
+
+  log "----------------------------------------"
+  log "Export streaming ONNX CTC models "
+  log "----------------------------------------"
+  ./zipformer/export-onnx-streaming-ctc.py \
+    --exp-dir $repo/ \
+    --tokens $repo/tokens.txt \
+    --causal 1 \
+    --avg 1 \
+    --epoch 99 \
+    --use-averaged-model 0 \
+    --chunk-size 16 \
+    --left-context-frames 128 \
+    --use-ctc 1 \
+    \
+    --num-encoder-layers 2,2,4,5,4,2 \
+    --feedforward-dim 768,1024,1536,2048,1536,768 \
+    --encoder-dim 256,384,512,768,512,256 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+    \
+    --fp16 1 \
+    --use-whisper-features 1
+
+
+  ls -lh $repo/
+  pushd $repo
+
+cat >README.md <<EOF
+# Introduction
+
+This model is converted
+from
+https://huggingface.co/yuekai/icefall-asr-multi-zh-hans-zipformer-large
+
+The training code can be found at
+https://github.com/k2-fsa/icefall/blob/master/egs/multi_zh-hans/ASR/RESULTS.md#multi-chinese-datasets-char-based-training-results-streaming-on-zipformer-large-model
+EOF
+
+  mv -v ctc-epoch-99-avg-1-chunk-16-left-128.fp16.onnx model.fp16.onnx
+  mv -v ctc-epoch-99-avg-1-chunk-16-left-128.int8.onnx model.int8.onnx
+  mv -v ctc-epoch-99-avg-1-chunk-16-left-128.onnx model.onnx
+
+  ls -lh *.onnx
+
+  mkdir test_wavs
+  cd test_wavs
+  curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-small-ctc-zh-int8-2025-04-01/resolve/main/test_wavs/0.wav
+  curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-small-ctc-zh-int8-2025-04-01/resolve/main/test_wavs/1.wav
+  curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-small-ctc-zh-int8-2025-04-01/resolve/main/test_wavs/8k.wav
+  popd
+
+  for w in 0.wav 1.wav 8k.wav; do
+    log "---fp32---"
+    sherpa-onnx \
+      --zipformer2-ctc-model=$repo/model.onnx \
+      --tokens=$repo/tokens.txt \
+      $repo/test_wavs/$w
+
+    log "---int8---"
+
+    sherpa-onnx \
+      --zipformer2-ctc-model=$repo/model.int8.onnx \
+      --tokens=$repo/tokens.txt \
+      $repo/test_wavs/$w
+
+    log "---fp16---"
+
+    sherpa-onnx \
+      --zipformer2-ctc-model=$repo/model.fp16.onnx \
+      --tokens=$repo/tokens.txt \
+      $repo/test_wavs/$w
+  done
+
+  name=(
+    sherpa-onnx-streaming-zipformer-ctc-zh-2025-06-30
+    sherpa-onnx-streaming-zipformer-ctc-zh-int8-2025-06-30
+    sherpa-onnx-streaming-zipformer-ctc-zh-fp16-2025-06-30
+  )
+  for n in ${name[@]}; do
+      url=https://huggingface.co/csukuangfj/$n
+      GIT_LFS_SKIP_SMUDGE=1 git clone $url
+      dst=$(basename $url)
+      if [[ $n == sherpa-onnx-streaming-zipformer-ctc-zh-2025-06-30 ]]; then
+        cp -v $repo/model.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-ctc-zh-int8-2025-06-30 ]]; then
+        cp -v $repo/model.int8.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-ctc-zh-fp16-2025-06-30 ]]; then
+        cp -v $repo/model.fp16.onnx $dst
+      fi
+
+      cp -v $repo/tokens.txt $dst
+      cp -v $repo/README.md $dst
+      mkdir -p $dst/test_wavs
+      cp -v $repo/test_wavs/*.wav $dst/test_wavs
+      cd $dst
+      git lfs track "*.onnx" "*.wav"
+      ls -lh
+      git status
+      git add .
+      git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$dst main || true
+
+      log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      rm -rf .git
+      rm -fv .gitattributes
+      cd ..
+      tar cjfv $dst.tar.bz2 $dst
+      ls -lh *.tar.bz2
+      mv -v $dst.tar.bz2 ../../../
+  done
+
+  rm $repo/*.onnx
+
+  log "----------------------------------------"
+  log "Export streaming ONNX transducer models "
+  log "----------------------------------------"
+
+  ./zipformer/export-onnx-streaming.py \
+    --exp-dir $repo \
+    --tokens $repo/tokens.txt \
+    --causal 1 \
+    --avg 1 \
+    --epoch 99 \
+    --use-averaged-model 0 \
+    --chunk-size 16 \
+    --left-context-frames 128 \
+    --use-ctc 0 \
+    \
+    --num-encoder-layers 2,2,4,5,4,2 \
+    --feedforward-dim 768,1024,1536,2048,1536,768 \
+    --encoder-dim 256,384,512,768,512,256 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+    \
+    --fp16 1 \
+    --use-whisper-features 1
+
+  ls -lh $repo
+  pushd $repo
+  for m in encoder decoder joiner; do
+    mv -v $m-epoch-99-avg-1-chunk-16-left-128.onnx $m.onnx
+    mv -v $m-epoch-99-avg-1-chunk-16-left-128.fp16.onnx $m.fp16.onnx
+    mv -v $m-epoch-99-avg-1-chunk-16-left-128.int8.onnx $m.int8.onnx
+  done
+  ls -lh *.onnx
+  popd
+
+  for w in 0.wav 1.wav 8k.wav; do
+    log "---fp32---"
+      sherpa-onnx \
+        --encoder=$repo/encoder.onnx \
+        --decoder=$repo/decoder.onnx \
+        --joiner=$repo/joiner.onnx \
+        --tokens=$repo/tokens.txt \
+        $repo/test_wavs/$w
+
+    log "---int8---"
+
+      sherpa-onnx \
+        --encoder=$repo/encoder.int8.onnx \
+        --decoder=$repo/decoder.onnx \
+        --joiner=$repo/joiner.int8.onnx \
+        --tokens=$repo/tokens.txt \
+        $repo/test_wavs/$w
+
+    log "---fp16---"
+
+      sherpa-onnx \
+        --encoder=$repo/encoder.fp16.onnx \
+        --decoder=$repo/decoder.fp16.onnx \
+        --joiner=$repo/joiner.fp16.onnx \
+        --tokens=$repo/tokens.txt \
+        $repo/test_wavs/$w
+  done
+
+  name=(
+    sherpa-onnx-streaming-zipformer-zh-2025-06-30
+    sherpa-onnx-streaming-zipformer-zh-int8-2025-06-30
+    sherpa-onnx-streaming-zipformer-zh-fp16-2025-06-30
+  )
+  for n in ${name[@]}; do
+      url=https://huggingface.co/csukuangfj/$n
+      GIT_LFS_SKIP_SMUDGE=1 git clone $url
+      dst=$(basename $url)
+      if [[ $n == sherpa-onnx-streaming-zipformer-zh-2025-06-30 ]]; then
+        cp -v $repo/encoder.onnx $dst
+        cp -v $repo/decoder.onnx $dst
+        cp -v $repo/joiner.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-zh-int8-2025-06-30 ]]; then
+        cp -v $repo/encoder.int8.onnx $dst
+        cp -v $repo/decoder.onnx $dst
+        cp -v $repo/joiner.int8.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-zh-fp16-2025-06-30 ]]; then
+        cp -v $repo/encoder.fp16.onnx $dst
+        cp -v $repo/decoder.fp16.onnx $dst
+        cp -v $repo/joiner.fp16.onnx $dst
+      fi
+
+      cp -v $repo/tokens.txt $dst
+      cp -v $repo/README.md $dst
+      mkdir -p $dst/test_wavs
+      cp -v $repo/test_wavs/*.wav $dst/test_wavs
+      cd $dst
+      git lfs track "*.onnx" "*.wav"
+      ls -lh
+      git status
+      git add .
+      git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$dst main || true
+
+      log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      rm -rf .git
+      rm -fv .gitattributes
+      cd ..
+      tar cjfv $dst.tar.bz2 $dst
+      ls -lh *.tar.bz2
+      mv -v $dst.tar.bz2 ../../../
+  done
+}
+
+function run_yuekai_xl() {
+  repo_url=https://csukuangfj:${HF_TOKEN}@huggingface.co/yuekai/icefall-asr-multi-zh-hans-zipformer-xl
+  log "Downloading pre-trained model from $repo_url"
+  GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+  repo=$(basename $repo_url)
+
+  pushd $repo
+  git lfs pull --include pretrained.pt
+  git lfs pull --include data/lang_bpe_2000/bpe.model
+  mv pretrained.pt epoch-99.pt
+  ls -lh *.pt
+  popd
+
+  log "----------------------------------------"
+  log "Export streaming ONNX CTC models "
+  log "----------------------------------------"
+  ./zipformer/export-onnx-streaming-ctc.py \
+    --exp-dir $repo/ \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    --causal 1 \
+    --avg 1 \
+    --epoch 99 \
+    --use-averaged-model 0 \
+    --chunk-size 16 \
+    --left-context-frames 128 \
+    --use-ctc 1 \
+    \
+    --num-encoder-layers 2,3,5,6,5,3 \
+    --feedforward-dim 1536,2048,3072,4096,3072,1536 \
+    --encoder-dim 512,768,1024,1536,1024,512 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+    --decoder-dim 768 --joiner-dim 768 \
+    --value-head-dim 18 \
+    --query-head-dim 48 \
+    --num-heads 4,4,4,8,4,4 \
+    \
+    --fp16 1 \
+    --use-whisper-features 1 \
+    --use-external-data 1
+
+  mv -v ctc-epoch-99-avg-1-chunk-16-left-128.int8.onnx model.int8.onnx
+  mv -v ctc-epoch-99-avg-1-chunk-16-left-128.fp16.onnx model.fp16.onnx
+
+  ls -lh *.onnx
+
+  mkdir test_wavs
+  pushd test_wavs
+  curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-small-ctc-zh-int8-2025-04-01/resolve/main/test_wavs/0.wav
+  curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-small-ctc-zh-int8-2025-04-01/resolve/main/test_wavs/1.wav
+  curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-small-ctc-zh-int8-2025-04-01/resolve/main/test_wavs/8k.wav
+  popd
+
+  for w in 0.wav 1.wav 8k.wav; do
+    log "---int8---"
+
+    sherpa-onnx \
+      --zipformer2-ctc-model=./model.int8.onnx \
+      --tokens=$repo/data/lang_bpe_2000/tokens.txt \
+      test_wavs/$w
+
+    log "---fp16---"
+
+    sherpa-onnx \
+      --zipformer2-ctc-model=./model.fp16.onnx \
+      --tokens=$repo/data/lang_bpe_2000/tokens.txt \
+      test_wavs/$w
+  done
+
+  pushd $repo
+cat >README.md <<EOF
+# Introduction
+
+This model is converted
+from
+https://huggingface.co/yuekai/icefall-asr-multi-zh-hans-zipformer-xl
+
+The training code can be found at
+https://github.com/k2-fsa/icefall/blob/master/egs/multi_zh-hans/ASR/RESULTS.md#multi-chinese-datasets-char-based-training-results-streaming-on-zipformer-xl-model
+EOF
+  popd
+
+  name=(
+    sherpa-onnx-streaming-zipformer-ctc-zh-xlarge-int8-2025-06-30
+    sherpa-onnx-streaming-zipformer-ctc-zh-xlarge-fp16-2025-06-30
+  )
+
+  for n in ${name[@]}; do
+      url=https://huggingface.co/csukuangfj/$n
+      GIT_LFS_SKIP_SMUDGE=1 git clone $url
+      dst=$(basename $url)
+      if [[ $n == sherpa-onnx-streaming-zipformer-ctc-zh-xlarge-fp16-2025-06-30 ]]; then
+        cp -v model.fp16.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-ctc-zh-xlarge-int8-2025-06-30 ]]; then
+        cp -v model.int8.onnx $dst
+      fi
+
+      cp -v $repo/data/lang_bpe_2000/tokens.txt $dst
+      cp -v $repo/data/lang_bpe_2000/bpe.model $dst
+      cp -v $repo/README.md $dst
+      mkdir -p $dst/test_wavs
+      cp -v ./test_wavs/*.wav $dst/test_wavs
+      cd $dst
+      git lfs track "*.onnx" "*.wav" "bpe.model"
+      ls -lh
+      git status
+      git add .
+      git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$dst main || true
+
+      log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      rm -rf .git
+      rm -fv .gitattributes
+      cd ..
+
+      ls -lh $dst
+      tar cjfv $dst.tar.bz2 $dst
+      ls -lh *.tar.bz2
+      mv -v $dst.tar.bz2 ../../../
+  done
+
+  rm -fv *.onnx *.weights
+
+  log "----------------------------------------"
+  log "Export streaming ONNX transducer models "
+  log "----------------------------------------"
+
+  ./zipformer/export-onnx-streaming.py \
+    --exp-dir $repo/ \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    --causal 1 \
+    --avg 1 \
+    --epoch 99 \
+    --use-averaged-model 0 \
+    --chunk-size 16 \
+    --left-context-frames 128 \
+    --use-ctc 0 \
+    \
+    --num-encoder-layers 2,3,5,6,5,3 \
+    --feedforward-dim 1536,2048,3072,4096,3072,1536 \
+    --encoder-dim 512,768,1024,1536,1024,512 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+    --decoder-dim 768 --joiner-dim 768 \
+    --value-head-dim 18 \
+    --query-head-dim 48 \
+    --num-heads 4,4,4,8,4,4 \
+    \
+    --fp16 1 \
+    --use-whisper-features 1 \
+    --use-external-data 1
+
+    ls -lh *.onnx
+    ls -lh *.weights
+
+    mv encoder-epoch-99-avg-1-chunk-16-left-128.fp16.onnx encoder.fp16.onnx
+    mv encoder-epoch-99-avg-1-chunk-16-left-128.int8.onnx encoder.int8.onnx
+
+    mv $repo/decoder-epoch-99-avg-1-chunk-16-left-128.onnx decoder.onnx
+    mv $repo/decoder-epoch-99-avg-1-chunk-16-left-128.fp16.onnx decoder.fp16.onnx
+
+    mv $repo/joiner-epoch-99-avg-1-chunk-16-left-128.int8.onnx joiner.int8.onnx
+    mv $repo/joiner-epoch-99-avg-1-chunk-16-left-128.fp16.onnx joiner.fp16.onnx
+
+  name=(
+    sherpa-onnx-streaming-zipformer-zh-xlarge-int8-2025-06-30
+    sherpa-onnx-streaming-zipformer-zh-xlarge-fp16-2025-06-30
+  )
+
+  for n in ${name[@]}; do
+      url=https://huggingface.co/csukuangfj/$n
+      GIT_LFS_SKIP_SMUDGE=1 git clone $url
+      dst=$(basename $url)
+      if [[ $n == sherpa-onnx-streaming-zipformer-zh-xlarge-fp16-2025-06-30 ]]; then
+        cp -v encoder.fp16.onnx $dst
+        cp -v decoder.fp16.onnx $dst
+        cp -v joiner.fp16.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-zh-xlarge-int8-2025-06-30 ]]; then
+        cp -v encoder.int8.onnx $dst
+        cp -v decoder.onnx $dst
+        cp -v joiner.int8.onnx $dst
+      fi
+
+      cp -v $repo/data/lang_bpe_2000/tokens.txt $dst
+      cp -v $repo/data/lang_bpe_2000/bpe.model $dst
+      cp -v $repo/README.md $dst
+      mkdir -p $dst/test_wavs
+      cp -v ./test_wavs/*.wav $dst/test_wavs
+      cd $dst
+      git lfs track "*.onnx" "*.wav" "bpe.model"
+      ls -lh
+      git status
+      git add .
+      git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$dst main || true
+
+      log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      rm -rf .git
+      rm -fv .gitattributes
+      cd ..
+
+      ls -lh $dst
+      tar cjfv $dst.tar.bz2 $dst
+      ls -lh *.tar.bz2
+      mv -v $dst.tar.bz2 ../../../
+  done
+
+  rm -fv *.onnx *.weights
+}
+
+# run_yuekai_large
+# run_yuekai_xl
+# run_2023_9_2
 run_2023_11_05_streaming
-run_2023_12_12_streaming
+# run_2023_12_12_streaming

.github/workflows/multi-zh-hans.yml

@@ -1,4 +1,4 @@
-name: run-multi-zh-hans
+name: multi-zh-hans
 
 on:
   push:
@@ -8,7 +8,7 @@ on:
   workflow_dispatch:
 
 concurrency:
-  group: run-multi-zh-hans-${{ github.ref }}
+  group: multi-zh-hans-${{ github.ref }}
   cancel-in-progress: true
 
 permissions:
@@ -29,8 +29,8 @@ jobs:
         id: set-matrix
         run: |
           # outputting for debugging purposes
-          python ./.github/scripts/docker/generate_build_matrix.py --torch-version "2.0.0" --python-version "3.10"
+          python ./.github/scripts/docker/generate_build_matrix.py --torch-version "2.7.0" --python-version "3.11"
-          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --torch-version "2.0.0" --python-version "3.10")
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --torch-version "2.7.0" --python-version "3.11")
           echo "::set-output name=matrix::${MATRIX}"
   multi-zh-hans:
     needs: generate_build_matrix
@@ -75,20 +75,6 @@ jobs:
         run: |
           ls -lh *.tar.bz2
 
-      - name: export-model
-        if: false
-        shell: bash
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
-        run: |
-          sudo apt-get -qq install git-lfs tree
-          export PYTHONPATH=$PWD:$PYTHONPATH
-          export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
-          export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
-
-          .github/scripts/multi-zh-hans.sh
-          ls -lh
-
       - name: upload model to https://github.com/k2-fsa/sherpa-onnx
         uses: svenstaro/upload-release-action@v2
         with:

egs/librispeech/ASR/zipformer/export-onnx-ctc.py

@@ -153,6 +153,13 @@ def get_parser():
         help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
     )
 
+    parser.add_argument(
+        "--fp16",
+        type=str2bool,
+        default=False,
+        help="Whether to export models in fp16",
+    )
+
     add_model_arguments(parser)
 
     return parser
@@ -176,6 +183,15 @@ def add_meta_data(filename: str, meta_data: Dict[str, str]):
     onnx.save(model, filename)
 
 
+def export_onnx_fp16(onnx_fp32_path, onnx_fp16_path):
+    import onnxmltools
+    from onnxmltools.utils.float16_converter import convert_float_to_float16
+
+    onnx_fp32_model = onnxmltools.utils.load_model(onnx_fp32_path)
+    onnx_fp16_model = convert_float_to_float16(onnx_fp32_model, keep_io_types=True)
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
+
+
 class OnnxModel(nn.Module):
     """A wrapper for encoder_embed, Zipformer, and ctc_output layer"""
 
@@ -407,7 +423,7 @@ def main():
     opset_version = 13
 
     logging.info("Exporting ctc model")
-    filename = params.exp_dir / f"model.onnx"
+    filename = params.exp_dir / "model.onnx"
     export_ctc_model_onnx(
         model,
         filename,
@@ -420,7 +436,7 @@ def main():
 
     logging.info("Generate int8 quantization models")
 
-    filename_int8 = params.exp_dir / f"model.int8.onnx"
+    filename_int8 = params.exp_dir / "model.int8.onnx"
     quantize_dynamic(
         model_input=filename,
         model_output=filename_int8,
@@ -428,6 +444,10 @@ def main():
         weight_type=QuantType.QInt8,
     )
 
+    if params.fp16:
+        filename_fp16 = params.exp_dir / "model.fp16.onnx"
+        export_onnx_fp16(filename, filename_fp16)
+
 
 if __name__ == "__main__":
     formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

egs/librispeech/ASR/zipformer/export-onnx-streaming-ctc.py

@@ -143,12 +143,28 @@ def get_parser():
         help="True to use whisper features. Must match the one used in training",
     )
 
+    parser.add_argument(
+        "--fp16",
+        type=str2bool,
+        default=False,
+        help="Whether to export models in fp16",
+    )
+
+    parser.add_argument(
+        "--use-external-data",
+        type=str2bool,
+        default=False,
+        help="Set it to true for model file size > 2GB",
+    )
+
     add_model_arguments(parser)
 
     return parser
 
 
-def add_meta_data(filename: str, meta_data: Dict[str, str]):
+def add_meta_data(
+    filename: str, meta_data: Dict[str, str], use_external_data: bool = False
+):
     """Add meta data to an ONNX model. It is changed in-place.
 
     Args:
@@ -157,13 +173,46 @@ def add_meta_data(filename: str, meta_data: Dict[str, str]):
       meta_data:
         Key-value pairs.
     """
+    filename = str(filename)
+
     model = onnx.load(filename)
     for key, value in meta_data.items():
         meta = model.metadata_props.add()
         meta.key = key
         meta.value = value
 
-    onnx.save(model, filename)
+    if use_external_data:
+        # For models file size > 2GB
+        external_filename = Path(filename).stem
+
+        onnx.save(
+            model,
+            filename,
+            save_as_external_data=True,
+            all_tensors_to_one_file=True,
+            location=external_filename + ".weights",
+        )
+    else:
+        onnx.save(model, filename)
+
+
+def export_onnx_fp16(onnx_fp32_path, onnx_fp16_path):
+    import onnxmltools
+    from onnxmltools.utils.float16_converter import convert_float_to_float16
+
+    onnx_fp32_model = onnxmltools.utils.load_model(onnx_fp32_path)
+    onnx_fp16_model = convert_float_to_float16(onnx_fp32_model, keep_io_types=True)
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
+
+
+def export_onnx_fp16_large_2gb(onnx_fp32_path, onnx_fp16_path):
+    import onnxmltools
+    from onnxmltools.utils.float16_converter import convert_float_to_float16_model_path
+
+    onnx_fp16_model = convert_float_to_float16_model_path(
+        onnx_fp32_path, keep_io_types=True
+    )
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
 
 
 class OnnxModel(nn.Module):
@@ -278,6 +327,7 @@ def export_streaming_ctc_model_onnx(
     encoder_filename: str,
     opset_version: int = 11,
     use_whisper_features: bool = False,
+    use_external_data: bool = False,
 ) -> None:
     model.encoder.__class__.forward = model.encoder.__class__.streaming_forward
 
@@ -423,7 +473,11 @@ def export_streaming_ctc_model_onnx(
         },
     )
 
-    add_meta_data(filename=encoder_filename, meta_data=meta_data)
+    add_meta_data(
+        filename=encoder_filename,
+        meta_data=meta_data,
+        use_external_data=use_external_data,
+    )
 
 
 @torch.no_grad()
@@ -554,12 +608,18 @@ def main():
     opset_version = 13
 
     logging.info("Exporting model")
-    model_filename = params.exp_dir / f"ctc-{suffix}.onnx"
+
+    if params.use_external_data:
+        model_filename = f"ctc-{suffix}.onnx"
+    else:
+        model_filename = params.exp_dir / f"ctc-{suffix}.onnx"
+
     export_streaming_ctc_model_onnx(
         model,
-        model_filename,
+        str(model_filename),
         opset_version=opset_version,
         use_whisper_features=params.use_whisper_features,
+        use_external_data=params.use_external_data,
     )
     logging.info(f"Exported model to {model_filename}")
 
@@ -568,7 +628,11 @@ def main():
 
     logging.info("Generate int8 quantization models")
 
-    model_filename_int8 = params.exp_dir / f"ctc-{suffix}.int8.onnx"
+    if params.use_external_data:
+        model_filename_int8 = f"ctc-{suffix}.int8.onnx"
+    else:
+        model_filename_int8 = params.exp_dir / f"ctc-{suffix}.int8.onnx"
+
     quantize_dynamic(
         model_input=model_filename,
         model_output=model_filename_int8,
@@ -576,6 +640,14 @@ def main():
         weight_type=QuantType.QInt8,
     )
 
+    if params.fp16:
+        if params.use_external_data:
+            model_filename_fp16 = f"ctc-{suffix}.fp16.onnx"
+            export_onnx_fp16_large_2gb(model_filename, model_filename_fp16)
+        else:
+            model_filename_fp16 = params.exp_dir / f"ctc-{suffix}.fp16.onnx"
+            export_onnx_fp16(model_filename, model_filename_fp16)
+
 
 if __name__ == "__main__":
     formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"

egs/librispeech/ASR/zipformer/export-onnx-streaming.py

@@ -169,12 +169,40 @@ def get_parser():
         help="True to use whisper features. Must match the one used in training",
     )
 
+    parser.add_argument(
+        "--use-external-data",
+        type=str2bool,
+        default=False,
+        help="Set it to true for model file size > 2GB",
+    )
+
     add_model_arguments(parser)
 
     return parser
 
 
-def add_meta_data(filename: str, meta_data: Dict[str, str]):
+def export_onnx_fp16(onnx_fp32_path, onnx_fp16_path):
+    import onnxmltools
+    from onnxmltools.utils.float16_converter import convert_float_to_float16
+
+    onnx_fp32_model = onnxmltools.utils.load_model(onnx_fp32_path)
+    onnx_fp16_model = convert_float_to_float16(onnx_fp32_model, keep_io_types=True)
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
+
+
+def export_onnx_fp16_large_2gb(onnx_fp32_path, onnx_fp16_path):
+    import onnxmltools
+    from onnxmltools.utils.float16_converter import convert_float_to_float16_model_path
+
+    onnx_fp16_model = convert_float_to_float16_model_path(
+        onnx_fp32_path, keep_io_types=True
+    )
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
+
+
+def add_meta_data(
+    filename: str, meta_data: Dict[str, str], use_external_data: bool = False
+):
     """Add meta data to an ONNX model. It is changed in-place.
 
     Args:
@@ -189,7 +217,19 @@ def add_meta_data(filename: str, meta_data: Dict[str, str]):
         meta.key = key
         meta.value = value
 
-    onnx.save(model, filename)
+    if use_external_data:
+        # For models file size > 2GB
+        external_filename = Path(filename).stem
+
+        onnx.save(
+            model,
+            filename,
+            save_as_external_data=True,
+            all_tensors_to_one_file=True,
+            location=external_filename + ".weights",
+        )
+    else:
+        onnx.save(model, filename)
 
 
 class OnnxEncoder(nn.Module):
@@ -350,6 +390,7 @@ def export_encoder_model_onnx(
     opset_version: int = 11,
     feature_dim: int = 80,
     use_whisper_features: bool = False,
+    use_external_data: bool = False,
 ) -> None:
     encoder_model.encoder.__class__.forward = (
         encoder_model.encoder.__class__.streaming_forward
@@ -496,7 +537,11 @@ def export_encoder_model_onnx(
         },
     )
 
-    add_meta_data(filename=encoder_filename, meta_data=meta_data)
+    add_meta_data(
+        filename=encoder_filename,
+        meta_data=meta_data,
+        use_external_data=use_external_data,
+    )
 
 
 def export_decoder_model_onnx(
@@ -739,13 +784,17 @@ def main():
     opset_version = 13
 
     logging.info("Exporting encoder")
-    encoder_filename = params.exp_dir / f"encoder-{suffix}.onnx"
+    if params.use_external_data:
+        encoder_filename = f"encoder-{suffix}.onnx"
+    else:
+        encoder_filename = params.exp_dir / f"encoder-{suffix}.onnx"
     export_encoder_model_onnx(
         encoder,
-        encoder_filename,
+        str(encoder_filename),
         opset_version=opset_version,
         feature_dim=params.feature_dim,
         use_whisper_features=params.use_whisper_features,
+        use_external_data=params.use_external_data,
     )
     logging.info(f"Exported encoder to {encoder_filename}")
 
@@ -768,31 +817,31 @@ def main():
     logging.info(f"Exported joiner to {joiner_filename}")
 
     if params.fp16:
-        from onnxconverter_common import float16
-
         logging.info("Generate fp16 models")
 
-        encoder = onnx.load(encoder_filename)
+        if params.use_external_data:
-        encoder_fp16 = float16.convert_float_to_float16(encoder, keep_io_types=True)
+            encoder_filename_fp16 = f"encoder-{suffix}.fp16.onnx"
-        encoder_filename_fp16 = params.exp_dir / f"encoder-{suffix}.fp16.onnx"
+            export_onnx_fp16_large_2gb(encoder_filename, encoder_filename_fp16)
-        onnx.save(encoder_fp16, encoder_filename_fp16)
+        else:
+            encoder_filename_fp16 = params.exp_dir / f"encoder-{suffix}.fp16.onnx"
+            export_onnx_fp16(encoder_filename, encoder_filename_fp16)
 
-        decoder = onnx.load(decoder_filename)
-        decoder_fp16 = float16.convert_float_to_float16(decoder, keep_io_types=True)
         decoder_filename_fp16 = params.exp_dir / f"decoder-{suffix}.fp16.onnx"
-        onnx.save(decoder_fp16, decoder_filename_fp16)
+        export_onnx_fp16(decoder_filename, decoder_filename_fp16)
 
-        joiner = onnx.load(joiner_filename)
-        joiner_fp16 = float16.convert_float_to_float16(joiner, keep_io_types=True)
         joiner_filename_fp16 = params.exp_dir / f"joiner-{suffix}.fp16.onnx"
-        onnx.save(joiner_fp16, joiner_filename_fp16)
+        export_onnx_fp16(joiner_filename, joiner_filename_fp16)
 
     # Generate int8 quantization models
     # See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection
 
     logging.info("Generate int8 quantization models")
 
-    encoder_filename_int8 = params.exp_dir / f"encoder-{suffix}.int8.onnx"
+    if params.use_external_data:
+        encoder_filename_int8 = f"encoder-{suffix}.int8.onnx"
+    else:
+        encoder_filename_int8 = params.exp_dir / f"encoder-{suffix}.int8.onnx"
+
     quantize_dynamic(
         model_input=encoder_filename,
         model_output=encoder_filename_int8,

egs/librispeech/ASR/zipformer/export-onnx.py

@@ -70,7 +70,6 @@ import onnx
 import torch
 import torch.nn as nn
 from decoder import Decoder
-from onnxconverter_common import float16
 from onnxruntime.quantization import QuantType, quantize_dynamic
 from scaling_converter import convert_scaled_to_non_scaled
 from train import add_model_arguments, get_model, get_params
@@ -182,6 +181,15 @@ def add_meta_data(filename: str, meta_data: Dict[str, str]):
     onnx.save(model, filename)
 
 
+def export_onnx_fp16(onnx_fp32_path, onnx_fp16_path):
+    import onnxmltools
+    from onnxmltools.utils.float16_converter import convert_float_to_float16
+
+    onnx_fp32_model = onnxmltools.utils.load_model(onnx_fp32_path)
+    onnx_fp16_model = convert_float_to_float16(onnx_fp32_model, keep_io_types=True)
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
+
+
 class OnnxEncoder(nn.Module):
     """A wrapper for Zipformer and the encoder_proj from the joiner"""
 
@@ -595,20 +603,14 @@ def main():
     if params.fp16:
         logging.info("Generate fp16 models")
 
-        encoder = onnx.load(encoder_filename)
-        encoder_fp16 = float16.convert_float_to_float16(encoder, keep_io_types=True)
         encoder_filename_fp16 = params.exp_dir / f"encoder-{suffix}.fp16.onnx"
-        onnx.save(encoder_fp16, encoder_filename_fp16)
+        export_onnx_fp16(encoder_filename, encoder_filename_fp16)
 
-        decoder = onnx.load(decoder_filename)
-        decoder_fp16 = float16.convert_float_to_float16(decoder, keep_io_types=True)
         decoder_filename_fp16 = params.exp_dir / f"decoder-{suffix}.fp16.onnx"
-        onnx.save(decoder_fp16, decoder_filename_fp16)
+        export_onnx_fp16(decoder_filename, decoder_filename_fp16)
 
-        joiner = onnx.load(joiner_filename)
-        joiner_fp16 = float16.convert_float_to_float16(joiner, keep_io_types=True)
         joiner_filename_fp16 = params.exp_dir / f"joiner-{suffix}.fp16.onnx"
-        onnx.save(joiner_fp16, joiner_filename_fp16)
+        export_onnx_fp16(joiner_filename, joiner_filename_fp16)
 
     # Generate int8 quantization models
     # See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection