Add ONNX support for Zipformer and ConvEmformer (#884)

.github/scripts/run-librispeech-conv-emformer-transducer-stateless2-2022-12-05.sh

@@ -1,79 +0,0 @@
-#!/usr/bin/env bash
-#
-set -e
-
-log() {
-  # This function is from espnet
-  local fname=${BASH_SOURCE[1]##*/}
-  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
-}
-
-cd egs/librispeech/ASR
-
-repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05
-
-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 "exp/pretrained-epoch-30-avg-10-averaged.pt"
-git lfs pull --include "data/lang_bpe_500/bpe.model"
-cd exp
-ln -s pretrained-epoch-30-avg-10-averaged.pt epoch-99.pt
-popd
-
-log "Display test files"
-tree $repo/
-soxi $repo/test_wavs/*.wav
-ls -lh $repo/test_wavs/*.wav
-
-log  "Install ncnn and pnnx"
-
-# We are using a modified ncnn here. Will try to merge it to the official repo
-# of ncnn
-git clone https://github.com/csukuangfj/ncnn
-pushd ncnn
-git submodule init
-git submodule update python/pybind11
-python3 setup.py bdist_wheel
-ls -lh dist/
-pip install dist/*.whl
-cd tools/pnnx
-mkdir build
-cd build
-cmake -D Python3_EXECUTABLE=/opt/hostedtoolcache/Python/3.8.14/x64/bin/python3 ..
-make -j4 pnnx
-
-./src/pnnx || echo "pass"
-
-popd
-
-log "Test exporting to pnnx format"
-
-./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 \
-  \
-  --num-encoder-layers 12 \
-  --chunk-length 32 \
-  --cnn-module-kernel 31 \
-  --left-context-length 32 \
-  --right-context-length 8 \
-  --memory-size 32
-
-./ncnn/tools/pnnx/build/src/pnnx $repo/exp/encoder_jit_trace-pnnx.pt
-./ncnn/tools/pnnx/build/src/pnnx $repo/exp/decoder_jit_trace-pnnx.pt
-./ncnn/tools/pnnx/build/src/pnnx $repo/exp/joiner_jit_trace-pnnx.pt
-
-./conv_emformer_transducer_stateless2/streaming-ncnn-decode.py \
- --tokens $repo/data/lang_bpe_500/tokens.txt \
- --encoder-param-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.param \
- --encoder-bin-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.bin \
- --decoder-param-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.param \
- --decoder-bin-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.bin \
- --joiner-param-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.param \
- --joiner-bin-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.bin \
- $repo/test_wavs/1089-134686-0001.wav

.github/scripts/run-librispeech-lstm-transducer-stateless2-2022-09-03.sh

@@ -28,63 +28,6 @@ ln -s pretrained-iter-468000-avg-16.pt pretrained.pt
 ln -s pretrained-iter-468000-avg-16.pt epoch-99.pt
 popd
 
-log  "Install ncnn and pnnx"
-
-# We are using a modified ncnn here. Will try to merge it to the official repo
-# of ncnn
-git clone https://github.com/csukuangfj/ncnn
-pushd ncnn
-git submodule init
-git submodule update python/pybind11
-python3 setup.py bdist_wheel
-ls -lh dist/
-pip install dist/*.whl
-cd tools/pnnx
-mkdir build
-cd build
-cmake ..
-make -j4 pnnx
-
-./src/pnnx || echo "pass"
-
-popd
-
-log "Test exporting to pnnx format"
-
-./lstm_transducer_stateless2/export.py \
-  --exp-dir $repo/exp \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
-  --epoch 99 \
-  --avg 1 \
-  --use-averaged-model 0 \
-  --pnnx 1
-
-./ncnn/tools/pnnx/build/src/pnnx $repo/exp/encoder_jit_trace-pnnx.pt
-./ncnn/tools/pnnx/build/src/pnnx $repo/exp/decoder_jit_trace-pnnx.pt
-./ncnn/tools/pnnx/build/src/pnnx $repo/exp/joiner_jit_trace-pnnx.pt
-
-./lstm_transducer_stateless2/ncnn-decode.py \
- --bpe-model-filename $repo/data/lang_bpe_500/bpe.model \
- --encoder-param-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.param \
- --encoder-bin-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.bin \
- --decoder-param-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.param \
- --decoder-bin-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.bin \
- --joiner-param-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.param \
- --joiner-bin-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.bin \
- $repo/test_wavs/1089-134686-0001.wav
-
-./lstm_transducer_stateless2/streaming-ncnn-decode.py \
- --bpe-model-filename $repo/data/lang_bpe_500/bpe.model \
- --encoder-param-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.param \
- --encoder-bin-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.bin \
- --decoder-param-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.param \
- --decoder-bin-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.bin \
- --joiner-param-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.param \
- --joiner-bin-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.bin \
- $repo/test_wavs/1089-134686-0001.wav
-
-
-
 log "Test exporting with torch.jit.trace()"
 
 ./lstm_transducer_stateless2/export.py \
@@ -106,47 +49,6 @@ log "Decode with models exported by torch.jit.trace()"
   $repo/test_wavs/1221-135766-0001.wav \
   $repo/test_wavs/1221-135766-0002.wav
 
-log "Test exporting to ONNX"
-
-./lstm_transducer_stateless2/export.py \
-  --exp-dir $repo/exp \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
-  --epoch 99 \
-  --avg 1 \
-  --use-averaged-model 0 \
-  --onnx 1
-
-log "Decode with ONNX models "
-
-./lstm_transducer_stateless2/streaming-onnx-decode.py \
-  --bpe-model-filename $repo/data/lang_bpe_500/bpe.model \
-  --encoder-model-filename $repo//exp/encoder.onnx \
-  --decoder-model-filename $repo/exp/decoder.onnx \
-  --joiner-model-filename $repo/exp/joiner.onnx \
-  --joiner-encoder-proj-model-filename $repo/exp/joiner_encoder_proj.onnx \
-  --joiner-decoder-proj-model-filename $repo/exp/joiner_decoder_proj.onnx \
- $repo/test_wavs/1089-134686-0001.wav
-
-./lstm_transducer_stateless2/streaming-onnx-decode.py \
-  --bpe-model-filename $repo/data/lang_bpe_500/bpe.model \
-  --encoder-model-filename $repo//exp/encoder.onnx \
-  --decoder-model-filename $repo/exp/decoder.onnx \
-  --joiner-model-filename $repo/exp/joiner.onnx \
-  --joiner-encoder-proj-model-filename $repo/exp/joiner_encoder_proj.onnx \
-  --joiner-decoder-proj-model-filename $repo/exp/joiner_decoder_proj.onnx \
- $repo/test_wavs/1221-135766-0001.wav
-
-./lstm_transducer_stateless2/streaming-onnx-decode.py \
-  --bpe-model-filename $repo/data/lang_bpe_500/bpe.model \
-  --encoder-model-filename $repo//exp/encoder.onnx \
-  --decoder-model-filename $repo/exp/decoder.onnx \
-  --joiner-model-filename $repo/exp/joiner.onnx \
-  --joiner-encoder-proj-model-filename $repo/exp/joiner_encoder_proj.onnx \
-  --joiner-decoder-proj-model-filename $repo/exp/joiner_decoder_proj.onnx \
- $repo/test_wavs/1221-135766-0002.wav
-
-
-
 for sym in 1 2 3; do
   log "Greedy search with --max-sym-per-frame $sym"
 

.github/scripts/run-librispeech-pruned-transducer-stateless7-2022-11-11.sh

@@ -30,15 +30,6 @@ ln -s pretrained.pt epoch-99.pt
 ls -lh *.pt
 popd
 
-log "Test exporting to ONNX format"
-./pruned_transducer_stateless7/export.py \
-  --exp-dir $repo/exp \
-  --use-averaged-model false \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
-  --epoch 99 \
-  --avg 1 \
-  --onnx 1
-
 log "Export to torchscript model"
 ./pruned_transducer_stateless7/export.py \
   --exp-dir $repo/exp \
@@ -50,27 +41,6 @@ log "Export to torchscript model"
 
 ls -lh $repo/exp/*.pt
 
-log "Decode with ONNX models"
-
-./pruned_transducer_stateless7/onnx_check.py \
-  --jit-filename $repo/exp/cpu_jit.pt \
-  --onnx-encoder-filename $repo/exp/encoder.onnx \
-  --onnx-decoder-filename $repo/exp/decoder.onnx \
-  --onnx-joiner-filename $repo/exp/joiner.onnx \
-  --onnx-joiner-encoder-proj-filename $repo/exp/joiner_encoder_proj.onnx \
-  --onnx-joiner-decoder-proj-filename $repo/exp/joiner_decoder_proj.onnx
-
-./pruned_transducer_stateless7/onnx_pretrained.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
-  --encoder-model-filename $repo/exp/encoder.onnx \
-  --decoder-model-filename $repo/exp/decoder.onnx \
-  --joiner-model-filename $repo/exp/joiner.onnx \
-  --joiner-encoder-proj-model-filename $repo/exp/joiner_encoder_proj.onnx \
-  --joiner-decoder-proj-model-filename $repo/exp/joiner_decoder_proj.onnx \
-  $repo/test_wavs/1089-134686-0001.wav \
-  $repo/test_wavs/1221-135766-0001.wav \
-  $repo/test_wavs/1221-135766-0002.wav
-
 log "Decode with models exported by torch.jit.script()"
 
 ./pruned_transducer_stateless7/jit_pretrained.py \

.github/scripts/run-librispeech-pruned-transducer-stateless7-streaming-2022-12-29.sh

@@ -34,16 +34,6 @@ ln -s pretrained.pt epoch-99.pt
 ls -lh *.pt
 popd
 
-log "Test exporting to ONNX format"
-./pruned_transducer_stateless7_streaming/export.py \
-  --exp-dir $repo/exp \
-  --use-averaged-model false \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
-  --epoch 99 \
-  --avg 1 \
-  --fp16 \
-  --onnx 1
-
 log "Export to torchscript model"
 ./pruned_transducer_stateless7_streaming/export.py \
   --exp-dir $repo/exp \

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

@@ -0,0 +1,133 @@
+#!/usr/bin/env bash
+
+set -e
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+cd egs/librispeech/ASR
+
+log  "Install ncnn and pnnx"
+
+# We are using a modified ncnn here. Will try to merge it to the official repo
+# of ncnn
+git clone https://github.com/csukuangfj/ncnn
+pushd ncnn
+git submodule init
+git submodule update python/pybind11
+python3 setup.py bdist_wheel
+ls -lh dist/
+pip install dist/*.whl
+cd tools/pnnx
+mkdir build
+cd build
+
+echo "which python3"
+
+which python3
+#/opt/hostedtoolcache/Python/3.8.16/x64/bin/python3
+
+cmake -D Python3_EXECUTABLE=$(which python3) ..
+make -j4 pnnx
+
+./src/pnnx || echo "pass"
+
+popd
+
+log "=========================================================================="
+repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05
+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
+ln -s pretrained-epoch-30-avg-10-averaged.pt epoch-99.pt
+popd
+
+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 \
+  \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32
+
+./ncnn/tools/pnnx/build/src/pnnx $repo/exp/encoder_jit_trace-pnnx.pt
+./ncnn/tools/pnnx/build/src/pnnx $repo/exp/decoder_jit_trace-pnnx.pt
+./ncnn/tools/pnnx/build/src/pnnx $repo/exp/joiner_jit_trace-pnnx.pt
+
+python3 ./conv_emformer_transducer_stateless2/streaming-ncnn-decode.py \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  --encoder-param-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.param \
+  --encoder-bin-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.bin \
+  --decoder-param-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.param \
+  --decoder-bin-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.bin \
+  --joiner-param-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.param \
+  --joiner-bin-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.bin \
+  $repo/test_wavs/1089-134686-0001.wav
+
+rm -rf $repo
+log "--------------------------------------------------------------------------"
+
+log "=========================================================================="
+repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
+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
+ln -s pretrained-iter-468000-avg-16.pt epoch-99.pt
+popd
+
+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 \
+  --epoch 99 \
+  --avg 1 \
+  --use-averaged-model 0
+
+./ncnn/tools/pnnx/build/src/pnnx $repo/exp/encoder_jit_trace-pnnx.pt
+./ncnn/tools/pnnx/build/src/pnnx $repo/exp/decoder_jit_trace-pnnx.pt
+./ncnn/tools/pnnx/build/src/pnnx $repo/exp/joiner_jit_trace-pnnx.pt
+
+python3 ./lstm_transducer_stateless2/streaming-ncnn-decode.py \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  --encoder-param-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.param \
+  --encoder-bin-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.bin \
+  --decoder-param-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.param \
+  --decoder-bin-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.bin \
+  --joiner-param-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.param \
+  --joiner-bin-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.bin \
+  $repo/test_wavs/1089-134686-0001.wav
+
+python3 ./lstm_transducer_stateless2/ncnn-decode.py \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  --encoder-param-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.param \
+  --encoder-bin-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.bin \
+  --decoder-param-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.param \
+  --decoder-bin-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.bin \
+  --joiner-param-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.param \
+  --joiner-bin-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.bin \
+  $repo/test_wavs/1089-134686-0001.wav
+
+rm -rf $repo
+log "--------------------------------------------------------------------------"

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

@@ -10,6 +10,8 @@ log() {
 
 cd egs/librispeech/ASR
 
+
+
 log "=========================================================================="
 repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
 log "Downloading pre-trained model from $repo_url"
@@ -192,3 +194,158 @@ log "Run onnx_pretrained.py"
 
 rm -rf $repo
 log "--------------------------------------------------------------------------"
+
+log "=========================================================================="
+repo_url=
+
+rm -rf $repo
+log "--------------------------------------------------------------------------"
+repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11
+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
+ln -s pretrained.pt epoch-99.pt
+popd
+
+log "Export via torch.jit.script()"
+
+./pruned_transducer_stateless7/export.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp \
+  --feedforward-dims "1024,1024,2048,2048,1024" \
+  --jit 1
+
+log "Test exporting to ONNX format"
+
+./pruned_transducer_stateless7/export-onnx.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp \
+  --feedforward-dims "1024,1024,2048,2048,1024"
+
+ls -lh $repo/exp
+
+log "Run onnx_check.py"
+
+./pruned_transducer_stateless7/onnx_check.py \
+  --jit-filename $repo/exp/cpu_jit.pt \
+  --onnx-encoder-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
+  --onnx-decoder-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+  --onnx-joiner-filename $repo/exp/joiner-epoch-99-avg-1.onnx
+
+log "Run onnx_pretrained.py"
+
+./pruned_transducer_stateless7/onnx_pretrained.py \
+  --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
+  --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+  --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
+  --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
+
+log "=========================================================================="
+repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05
+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
+ln -s pretrained-epoch-30-avg-10-averaged.pt epoch-99.pt
+popd
+
+log "Test exporting to ONNX format"
+
+./conv_emformer_transducer_stateless2/export-onnx.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32
+
+log "Run onnx_pretrained.py"
+
+./conv_emformer_transducer_stateless2/onnx_pretrained.py \
+  --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
+  --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+  --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  $repo/test_wavs/1221-135766-0001.wav
+
+rm -rf $repo
+log "--------------------------------------------------------------------------"
+
+log "=========================================================================="
+repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
+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
+ln -s pretrained-iter-468000-avg-16.pt epoch-99.pt
+popd
+
+log "Export via torch.jit.trace()"
+
+./lstm_transducer_stateless2/export.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp/ \
+  --jit-trace 1
+
+log "Test exporting to ONNX format"
+
+./lstm_transducer_stateless2/export-onnx.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp
+
+ls -lh $repo/exp
+
+log "Run onnx_check.py"
+
+./lstm_transducer_stateless2/onnx_check.py \
+  --jit-encoder-filename $repo/exp/encoder_jit_trace.pt \
+  --jit-decoder-filename $repo/exp/decoder_jit_trace.pt \
+  --jit-joiner-filename $repo/exp/joiner_jit_trace.pt \
+  --onnx-encoder-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
+  --onnx-decoder-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+  --onnx-joiner-filename $repo/exp/joiner-epoch-99-avg-1.onnx
+
+log "Run onnx_pretrained.py"
+
+./lstm_transducer_stateless2/onnx_pretrained.py \
+  --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
+  --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+  --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  $repo/test_wavs/1221-135766-0001.wav
+
+rm -rf $repo
+log "--------------------------------------------------------------------------"

.github/workflows/run-librispeech-2022-11-11-stateless7.yml

@@ -39,7 +39,7 @@ concurrency:
 
 jobs:
   run_librispeech_2022_11_11_zipformer:
-    if: github.event.label.name == 'onnx' || github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
+    if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:

.github/workflows/run-librispeech-2022-12-29-stateless7-streaming.yml

@@ -39,7 +39,7 @@ concurrency:
 
 jobs:
   run_librispeech_2022_12_29_zipformer_streaming:
-    if: github.event.label.name == 'onnx' || github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event.label.name == 'streaming-zipformer' || github.event_name == 'push' || github.event_name == 'schedule'
+    if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event.label.name == 'streaming-zipformer' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:

.github/workflows/run-librispeech-lstm-transducer-stateless2-2022-09-03.yml

@@ -22,7 +22,7 @@ concurrency:
 
 jobs:
   run_librispeech_lstm_transducer_stateless2_2022_09_03:
-    if: github.event.label.name == 'ready' || github.event.label.name == 'LODR' || github.event.label.name == 'shallow-fusion' || github.event.label.name == 'ncnn' || github.event.label.name == 'onnx' || github.event_name == 'push' || github.event_name == 'schedule'
+    if: github.event.label.name == 'ready' || github.event.label.name == 'LODR' || github.event.label.name == 'shallow-fusion' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:

.github/workflows/test-ncnn-export.yml

@@ -1,4 +1,4 @@
-name: run-librispeech-conv-emformer-transducer-stateless2-2022-12-05
+name: test-ncnn-export
 
 on:
   push:
@@ -16,15 +16,18 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: test_ncnn_export-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
-  run_librispeech_conv_emformer_transducer_stateless2_2022_12_05:
+  test_ncnn_export:
     if: github.event.label.name == 'ready' || github.event.label.name == 'ncnn' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
         os: [ubuntu-latest]
         python-version: [3.8]
-
       fail-fast: false
 
     steps:
@@ -41,7 +44,7 @@ jobs:
 
       - name: Install Python dependencies
         run: |
-          grep -v '^#' ./requirements-ci.txt  | grep -v kaldifst | xargs -n 1 -L 1 pip install
+          grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
           pip install --no-binary protobuf protobuf
 
@@ -59,19 +62,14 @@ jobs:
         run: |
           .github/scripts/install-kaldifeat.sh
 
-      - name: Inference with pre-trained model
+      - name: Test ncnn export
         shell: bash
         env:
           GITHUB_EVENT_NAME: ${{ github.event_name }}
           GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
         run: |
-          mkdir -p egs/librispeech/ASR/data
-          ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank
-          ls -lh egs/librispeech/ASR/data/*
-
-          sudo apt-get -qq install git-lfs tree sox
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
 
-          .github/scripts/run-librispeech-conv-emformer-transducer-stateless2-2022-12-05.sh
+          .github/scripts/test-ncnn-export.sh

docs/source/model-export/export-onnx.rst

@@ -1,69 +1,78 @@
 Export to ONNX
 ==============
 
-In this section, we describe how to export models to ONNX.
+In this section, we describe how to export the following models to ONNX.
+
+In each recipe, there is a file called ``export-onnx.py``, which is used
+to export trained models to ONNX.
+
+There is also a file named ``onnx_pretrained.py``, which you can use
+the exported ONNX model in Python to decode sound files.
+
+Example
+=======
+
+In the following, we demonstrate how to export a streaming Zipformer pre-trained
+model from `<python3 ./python-api-examples/speech-recognition-from-microphone.py>`_
+to ONNX.
+
+Download the pre-trained model
+------------------------------
 
 .. hint::
 
-  Only non-streaming conformer transducer models are tested.
+   We assume you have installed `git-lfs`_.
-
-
-When to use it
---------------
-
-It you want to use an inference framework that supports ONNX
-to run the pretrained model.
-
-
-How to export
--------------
-
-We use
-`<https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless3>`_
-as an example in the following.
 
 .. code-block:: bash
 
+
   cd egs/librispeech/ASR
-    epoch=14
-    avg=2
 
-    ./pruned_transducer_stateless3/export.py \
+  repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
-      --exp-dir ./pruned_transducer_stateless3/exp \
+  GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
-      --bpe-model data/lang_bpe_500/bpe.model \
+  repo=$(basename $repo_url)
-      --epoch $epoch \
-      --avg $avg \
-      --onnx 1
 
-It will generate the following files inside ``pruned_transducer_stateless3/exp``:
+  pushd $repo
+  git lfs pull --include "data/lang_bpe_500/bpe.model"
+  git lfs pull --include "exp/pretrained.pt"
+  cd exp
+  ln -s pretrained.pt epoch-99.pt
+  popd
 
-  - ``encoder.onnx``
+Export the model to ONNX
-  - ``decoder.onnx``
+------------------------
-  - ``joiner.onnx``
-  - ``joiner_encoder_proj.onnx``
-  - ``joiner_decoder_proj.onnx``
-
-You can use ``./pruned_transducer_stateless3/exp/onnx_pretrained.py`` to decode
-waves with the generated files:
 
 .. code-block:: bash
 
-  ./pruned_transducer_stateless3/onnx_pretrained.py \
+  ./pruned_transducer_stateless7_streaming/export-onnx.py \
-    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
-    --encoder-model-filename ./pruned_transducer_stateless3/exp/encoder.onnx \
+    --use-averaged-model 0 \
-    --decoder-model-filename ./pruned_transducer_stateless3/exp/decoder.onnx \
+    --epoch 99 \
-    --joiner-model-filename ./pruned_transducer_stateless3/exp/joiner.onnx \
+    --avg 1 \
-    --joiner-encoder-proj-model-filename ./pruned_transducer_stateless3/exp/joiner_encoder_proj.onnx \
+    --decode-chunk-len 32 \
-    --joiner-decoder-proj-model-filename ./pruned_transducer_stateless3/exp/joiner_decoder_proj.onnx \
+    --exp-dir $repo/exp/
-    /path/to/foo.wav \
-    /path/to/bar.wav \
-    /path/to/baz.wav
 
+.. warning::
 
-How to use the exported model
+   ``export-onnx.py`` from different recipes has different options.
------------------------------
 
-We also provide `<https://github.com/k2-fsa/sherpa-onnx>`_
+   In the above example, ``--decode-chunk-len`` is specific for the
-performing speech recognition using `onnxruntime <https://github.com/microsoft/onnxruntime>`_
+   streaming Zipformer. Other models won't have such an option.
-with exported models.
+
-It has been tested on Linux, macOS, and Windows.
+It will generate the following 3 files in ``$repo/exp``
+
+  - ``encoder-epoch-99-avg-1.onnx``
+  - ``decoder-epoch-99-avg-1.onnx``
+  - ``joiner-epoch-99-avg-1.onnx``
+
+Decode sound files with exported ONNX models
+--------------------------------------------
+
+.. code-block:: bash
+
+  ./pruned_transducer_stateless7_streaming/onnx_pretrained.py \
+    --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
+    --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+    --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
+    --tokens $repo/data/lang_bpe_500/tokens.txt \
+    $repo/test_wavs/1089-134686-0001.wav

docs/source/recipes/Streaming-ASR/librispeech/lstm_pruned_stateless_transducer.rst

@@ -580,12 +580,11 @@ for ``pnnx``:
   iter=468000
   avg=16
 
-  ./lstm_transducer_stateless2/export.py \
+  ./lstm_transducer_stateless2/export-for-ncnn.py \
     --exp-dir ./lstm_transducer_stateless2/exp \
     --bpe-model data/lang_bpe_500/bpe.model \
     --iter $iter \
-    --avg  $avg \
+    --avg  $avg
-    --pnnx 1
 
 It will generate 3 files:
 
@@ -615,7 +614,7 @@ To use the above generated files, run:
 .. code-block:: bash
 
   ./lstm_transducer_stateless2/ncnn-decode.py \
-   --bpe-model-filename ./data/lang_bpe_500/bpe.model \
+   --tokens ./data/lang_bpe_500/tokens.txt \
    --encoder-param-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.param \
    --encoder-bin-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.bin \
    --decoder-param-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.param \
@@ -627,7 +626,7 @@ To use the above generated files, run:
 .. code-block:: bash
 
   ./lstm_transducer_stateless2/streaming-ncnn-decode.py \
-   --bpe-model-filename ./data/lang_bpe_500/bpe.model \
+   --tokens ./data/lang_bpe_500/tokens.txt \
    --encoder-param-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.param \
    --encoder-bin-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-pnnx.ncnn.bin \
    --decoder-param-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-pnnx.ncnn.param \
@@ -657,6 +656,3 @@ by visiting the following links:
 
 You can find more usages of the pretrained models in
 `<https://k2-fsa.github.io/sherpa/python/streaming_asr/lstm/index.html>`_
-
-Export ConvEmformer transducer models for ncnn
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

egs/librispeech/ASR/conv_emformer_transducer_stateless2/emformer2.py

@@ -169,9 +169,11 @@ class ConvolutionModule(nn.Module):
         channels: int,
         kernel_size: int,
         bias: bool = True,
+        is_pnnx: bool = True,
     ) -> None:
         """Construct an ConvolutionModule object."""
         super().__init__()
+        self.is_pnnx = is_pnnx
         # kernerl_size should be an odd number for 'SAME' padding
         assert (kernel_size - 1) % 2 == 0, kernel_size
 
@@ -383,8 +385,10 @@ class ConvolutionModule(nn.Module):
             - output right_context of shape (R, B, D).
             - updated cache tensor of shape (B, D, cache_size).
         """
-        #  U, B, D = utterance.size()
+        if self.is_pnnx is False:
-        #  R, _, _ = right_context.size()
+            U, B, D = utterance.size()
+            R, _, _ = right_context.size()
+        else:
             U = self.chunk_length
             B = 1
             D = self.channels
@@ -448,8 +452,10 @@ class EmformerAttention(nn.Module):
         dropout: float = 0.0,
         tanh_on_mem: bool = False,
         negative_inf: float = -1e8,
+        is_pnnx: bool = True,
     ):
         super().__init__()
+        self.is_pnnx = is_pnnx
 
         if embed_dim % nhead != 0:
             raise ValueError(
@@ -539,10 +545,11 @@ class EmformerAttention(nn.Module):
         left_context_val: Optional[torch.Tensor] = None,
     ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """Underlying chunk-wise attention implementation."""
-        #  U, B, _ = utterance.size()
+        if self.is_pnnx is False:
-        #  R = right_context.size(0)
+            U, B, _ = utterance.size()
-        #  M = memory.size(0)
+            R = right_context.size(0)
-
+            M = memory.size(0)
+        else:
             U = self.chunk_length
             B = 1
             R = self.right_context_length
@@ -570,21 +577,29 @@ class EmformerAttention(nn.Module):
 
         # KV = key.size(0)
 
+        if self.is_pnnx is True:
             reshaped_query = query.view(Q, self.nhead, self.head_dim).permute(1, 0, 2)
             reshaped_key = key.view(M + R + U + L, self.nhead, self.head_dim).permute(
                 1, 0, 2
             )
-        reshaped_value = value.view(M + R + U + L, self.nhead, self.head_dim).permute(
+            reshaped_value = value.view(
-            1, 0, 2
+                M + R + U + L, self.nhead, self.head_dim
-        )
+            ).permute(1, 0, 2)
-
+        else:
-        #  reshaped_query, reshaped_key, reshaped_value = [
+            reshaped_query, reshaped_key, reshaped_value = [
-        #      tensor.contiguous().view(-1, B * self.nhead, self.head_dim).transpose(0, 1)
+                tensor.contiguous()
-        #      for tensor in [query, key, value]
+                .view(-1, B * self.nhead, self.head_dim)
-        #  ]  # (B * nhead, Q or KV, head_dim)
+                .transpose(0, 1)
+                for tensor in [query, key, value]
+            ]  # (B * nhead, Q or KV, head_dim)
+        if self.is_pnnx is True:
             attention_weights = torch.bmm(
                 reshaped_query * scaling, reshaped_key.permute(0, 2, 1)
             )  # (B * nhead, Q, KV)
+        else:
+            attention_weights = torch.bmm(
+                reshaped_query * scaling, reshaped_key.transpose(1, 2)
+            )  # (B * nhead, Q, KV)
 
         # compute attention probabilities
         if False:
@@ -597,10 +612,15 @@ class EmformerAttention(nn.Module):
         # compute attention outputs
         attention = torch.bmm(attention_probs, reshaped_value)
         assert attention.shape == (B * self.nhead, Q, self.head_dim)
+        if self.is_pnnx is True:
             attention = attention.permute(1, 0, 2).reshape(-1, self.embed_dim)
             # TODO(fangjun): ncnn does not support reshape(-1, 1, self.embed_dim)
             # We have to change InnerProduct in ncnn to ignore the extra dim below
             attention = attention.unsqueeze(1)
+        else:
+            attention = (
+                attention.transpose(0, 1).contiguous().view(Q, B, self.embed_dim)
+            )
 
         # apply output projection
         output_right_context_utterance = self.out_proj(attention)
@@ -733,11 +753,12 @@ class EmformerAttention(nn.Module):
             - attention value of left context and utterance, which would be
               cached for next computation, with shape (L + U, B, D).
         """
-        #  U = utterance.size(0)
+        if self.is_pnnx is False:
-        #  R = right_context.size(0)
+            U = utterance.size(0)
-        #  L = left_context_key.size(0)
+            R = right_context.size(0)
-        #  M = memory.size(0)
+            L = left_context_key.size(0)
-
+            M = memory.size(0)
+        else:
             U = self.chunk_length
             R = self.right_context_length
             L = self.left_context_length
@@ -811,6 +832,7 @@ class EmformerEncoderLayer(nn.Module):
         memory_size: int = 0,
         tanh_on_mem: bool = False,
         negative_inf: float = -1e8,
+        is_pnnx: bool = True,
     ):
         super().__init__()
 
@@ -824,6 +846,7 @@ class EmformerEncoderLayer(nn.Module):
             dropout=dropout,
             tanh_on_mem=tanh_on_mem,
             negative_inf=negative_inf,
+            is_pnnx=is_pnnx,
         )
         self.summary_op = nn.AvgPool1d(
             kernel_size=chunk_length, stride=chunk_length, ceil_mode=True
@@ -850,6 +873,7 @@ class EmformerEncoderLayer(nn.Module):
             right_context_length,
             d_model,
             cnn_module_kernel,
+            is_pnnx=is_pnnx,
         )
 
         self.norm_final = BasicNorm(d_model)
@@ -1204,6 +1228,7 @@ class EmformerEncoder(nn.Module):
         memory_size: int = 0,
         tanh_on_mem: bool = False,
         negative_inf: float = -1e8,
+        is_pnnx: bool = True,
     ):
         super().__init__()
 
@@ -1229,6 +1254,7 @@ class EmformerEncoder(nn.Module):
                     memory_size=memory_size,
                     tanh_on_mem=tanh_on_mem,
                     negative_inf=negative_inf,
+                    is_pnnx=is_pnnx,
                 )
                 for layer_idx in range(num_encoder_layers)
             ]
@@ -1561,6 +1587,20 @@ class Emformer(EncoderInterface):
         self.encoder_embed = Conv2dSubsampling(num_features, d_model, is_pnnx=is_pnnx)
         self.is_pnnx = is_pnnx
 
+        self.num_encoder_layers = num_encoder_layers
+        self.memory_size = memory_size
+        self.d_model = d_model
+        self.cnn_module_kernel = cnn_module_kernel
+        self.left_context_length = left_context_length // subsampling_factor
+        self.right_context_length = right_context_length
+        self.subsampling_factor = subsampling_factor
+
+        assert subsampling_factor == 4, subsampling_factor
+        pad_length = right_context_length + 2 * 4 + 3
+
+        # before subsampling
+        self.T = self.chunk_length + pad_length
+
         self.encoder = EmformerEncoder(
             chunk_length=chunk_length // subsampling_factor,
             d_model=d_model,
@@ -1575,6 +1615,7 @@ class Emformer(EncoderInterface):
             memory_size=memory_size,
             tanh_on_mem=tanh_on_mem,
             negative_inf=negative_inf,
+            is_pnnx=is_pnnx,
         )
 
     def _forward(
@@ -1691,7 +1732,7 @@ class Conv2dSubsampling(nn.Module):
         layer1_channels: int = 8,
         layer2_channels: int = 32,
         layer3_channels: int = 128,
-        is_pnnx: bool = False,
+        is_pnnx: bool = True,
     ) -> None:
         """
         Args:
@@ -1767,7 +1808,7 @@ class Conv2dSubsampling(nn.Module):
         x = x.unsqueeze(1)  # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W)
         x = self.conv(x)
 
-        if torch.jit.is_tracing() and self.is_pnnx:
+        if torch.jit.is_tracing() and self.is_pnnx is True:
             x = x.permute(0, 2, 1, 3).reshape(1, -1, self.conv_out_dim)
             x = self.out(x)
         else:

egs/librispeech/ASR/conv_emformer_transducer_stateless2/export-for-ncnn.py

@@ -1,6 +1,10 @@
 #!/usr/bin/env python3
 
 """
+Please see
+https://k2-fsa.github.io/icefall/model-export/export-ncnn.html
+for more details about how to use this file.
+
 Usage:
 ./conv_emformer_transducer_stateless2/export-for-ncnn.py \
   --exp-dir ./conv_emformer_transducer_stateless2/exp \
@@ -44,7 +48,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import str2bool
+from icefall.utils import setup_logger, str2bool
 
 
 def get_parser():
@@ -96,14 +100,6 @@ def get_parser():
         help="Path to the BPE model",
     )
 
-    parser.add_argument(
-        "--jit",
-        type=str2bool,
-        default=False,
-        help="""True to save a model after applying torch.jit.script.
-        """,
-    )
-
     parser.add_argument(
         "--context-size",
         type=int,
@@ -217,6 +213,8 @@ def main():
 
     device = torch.device("cpu")
 
+    setup_logger(f"{params.exp_dir}/log-export/log-export-ncnn")
+
     logging.info(f"device: {device}")
 
     sp = spm.SentencePieceProcessor()
@@ -330,5 +328,4 @@ def main():
 if __name__ == "__main__":
     formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
 
-    logging.basicConfig(format=formatter, level=logging.INFO)
     main()

egs/librispeech/ASR/conv_emformer_transducer_stateless2/export-onnx.py

@@ -0,0 +1,644 @@
+#!/usr/bin/env python3
+#
+# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)
+
+"""
+This script exports a transducer model from PyTorch to ONNX.
+
+We use the pre-trained model from
+https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05
+as an example to show how to use this file.
+
+1. Download the pre-trained model
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05
+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
+ln -s pretrained-epoch-30-avg-10-averaged.pt epoch-99.pt
+popd
+
+2. Export the model to ONNX
+
+./conv_emformer_transducer_stateless2/export-onnx.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32
+
+It will generate the following 3 files inside $repo/exp:
+
+  - encoder-epoch-99-avg-1.onnx
+  - decoder-epoch-99-avg-1.onnx
+  - joiner-epoch-99-avg-1.onnx
+
+See ./onnx_pretrained.py for how to
+use the exported ONNX models.
+"""
+
+import argparse
+import logging
+from pathlib import Path
+from typing import Dict, Tuple
+
+import onnx
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from decoder import Decoder
+from scaling_converter import convert_scaled_to_non_scaled
+from train2 import add_model_arguments, get_params, get_transducer_model
+from emformer import Emformer
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import setup_logger, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="""It specifies the checkpoint to use for averaging.
+        Note: Epoch counts from 0.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=True,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless5/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, str]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    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)
+
+
+class OnnxEncoder(nn.Module):
+    """A wrapper for Emformer and the encoder_proj from the joiner"""
+
+    def __init__(self, encoder: Emformer, encoder_proj: nn.Linear):
+        """
+        Args:
+          encoder:
+            A Emformer encoder.
+          encoder_proj:
+            The projection layer for encoder from the joiner.
+        """
+        super().__init__()
+        self.encoder = encoder
+        self.encoder_proj = encoder_proj
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Please see the help information of Emformer.forward
+
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C)
+          x_lens:
+            A 1-D tensor of shape (N,). Its dtype is torch.int64
+        Returns:
+          Return a tuple containing:
+            - encoder_out, A 3-D tensor of shape (N, T', joiner_dim)
+            - encoder_out_lens, A 1-D tensor of shape (N,)
+        """
+        encoder_out, encoder_out_lens = self.encoder(x, x_lens)
+
+        encoder_out = self.encoder_proj(encoder_out)
+        # Now encoder_out is of shape (N, T, joiner_dim)
+
+        return encoder_out, encoder_out_lens
+
+
+class OnnxDecoder(nn.Module):
+    """A wrapper for Decoder and the decoder_proj from the joiner"""
+
+    def __init__(self, decoder: Decoder, decoder_proj: nn.Linear):
+        super().__init__()
+        self.decoder = decoder
+        self.decoder_proj = decoder_proj
+
+    def forward(self, y: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+          y:
+            A 2-D tensor of shape (N, context_size).
+        Returns
+          Return a 2-D tensor of shape (N, joiner_dim)
+        """
+        need_pad = False
+        decoder_output = self.decoder(y, need_pad=need_pad)
+        decoder_output = decoder_output.squeeze(1)
+        output = self.decoder_proj(decoder_output)
+
+        return output
+
+
+class OnnxJoiner(nn.Module):
+    """A wrapper for the joiner"""
+
+    def __init__(self, output_linear: nn.Linear):
+        super().__init__()
+        self.output_linear = output_linear
+
+    def forward(
+        self,
+        encoder_out: torch.Tensor,
+        decoder_out: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+          encoder_out:
+            A 2-D tensor of shape (N, joiner_dim)
+          decoder_out:
+            A 2-D tensor of shape (N, joiner_dim)
+        Returns:
+          Return a 2-D tensor of shape (N, vocab_size)
+        """
+        logit = encoder_out + decoder_out
+        logit = self.output_linear(torch.tanh(logit))
+        return logit
+
+
+def export_encoder_model_onnx(
+    encoder_model: OnnxEncoder,
+    encoder_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the given encoder model to ONNX format.
+    The exported model has the following inputs:
+
+        - x, a tensor of shape (N, T, C); dtype is torch.float32
+        - a list of states (each layers has 4 states)
+
+
+    and it has two outputs:
+
+        - encoder_out, a tensor of shape (N, T', joiner_dim)
+        - a list of states (each layers has 4 states)
+
+    Args:
+      encoder_model:
+        The input encoder model
+      encoder_filename:
+        The filename to save the exported ONNX model.
+      opset_version:
+        The opset version to use.
+    """
+    num_encoder_layers = encoder_model.encoder.num_encoder_layers
+    memory_size = encoder_model.encoder.memory_size
+    cnn_module_kernel = encoder_model.encoder.cnn_module_kernel
+    chunk_length = encoder_model.encoder.chunk_length
+    right_context_length = encoder_model.encoder.right_context_length
+    encoder_dim = encoder_model.encoder.d_model
+    left_context_length = encoder_model.encoder.left_context_length
+
+    T = encoder_model.encoder.T
+
+    logging.info(f"num_encoder_layers={num_encoder_layers}")
+    logging.info(f"memory_size={memory_size}")
+    logging.info(f"cnn_module_kernel={cnn_module_kernel}")
+    logging.info(f"chunk_length={chunk_length}")
+    logging.info(f"right_context_length={right_context_length}")
+    logging.info(f"encoder_dim={encoder_dim}")
+    logging.info(f"left_context_length={left_context_length} (after subsampling)")
+    logging.info(f"T={T}")
+
+    meta_data = {
+        "model_type": "conv-emformer",
+        "version": "1",
+        "model_author": "k2-fsa",
+        "decode_chunk_len": str(chunk_length),  # 32
+        "T": str(T),  # 32
+        "num_encoder_layers": str(num_encoder_layers),
+        "memory_size": str(memory_size),
+        "cnn_module_kernel": str(cnn_module_kernel),
+        "right_context_length": str(right_context_length),
+        "left_context_length": str(left_context_length),
+        "encoder_dim": str(encoder_dim),
+    }
+    logging.info(f"meta_data: {meta_data}")
+
+    x = torch.zeros(1, T, 80, dtype=torch.float32)
+    states = encoder_model.encoder.init_states()
+
+    # Each layer has 4 states
+    assert len(states) == num_encoder_layers * 4, (len(states), num_encoder_layers)
+    # layer 0:
+    #  state0: (memory_size, 1, encoder_dim)
+    #  state1: (left_context_length, 1, encoder_dim)
+    #  state2: (left_context_length, 1, encoder_dim)
+    #  state3: (1, encoder_dim, cnn_module_kernel-1)
+
+    inputs = {}
+    input_names = ["x"]
+
+    outputs = {}
+    output_names = ["encoder_out"]
+
+    def build_inputs_outputs(s, name):
+        assert len(s) == 4, len(s)
+        logging.info(f"{name}_0.shape: {s[0].shape}")
+        input_names.append(f"{name}_0")
+        inputs[f"{name}_0"] = {1: "N"}
+        output_names.append(f"new_{name}_0")
+
+        logging.info(f"{name}_1.shape: {s[1].shape}")
+        input_names.append(f"{name}_1")
+        inputs[f"{name}_1"] = {1: "N"}
+        output_names.append(f"new_{name}_1")
+
+        logging.info(f"{name}_2.shape: {s[2].shape}")
+        input_names.append(f"{name}_2")
+        inputs[f"{name}_2"] = {1: "N"}
+        output_names.append(f"new_{name}_2")
+
+        logging.info(f"{name}_3.shape: {s[3].shape}")
+        input_names.append(f"{name}_3")
+        inputs[f"{name}_3"] = {0: "N"}
+        output_names.append(f"new_{name}_3")
+
+    for i in range(num_encoder_layers):
+        base_name = f"layer{i}"
+        s = states[i * 4 : (i + 1) * 4]
+        build_inputs_outputs(s, base_name)
+
+    torch.onnx.export(
+        encoder_model,
+        (x, states),
+        encoder_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=input_names,
+        output_names=output_names,
+        dynamic_axes={
+            "x": {0: "N"},
+            "encoder_out": {0: "N"},
+            **inputs,
+            **outputs,
+        },
+    )
+
+    add_meta_data(filename=encoder_filename, meta_data=meta_data)
+
+
+def export_decoder_model_onnx(
+    decoder_model: OnnxDecoder,
+    decoder_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the decoder model to ONNX format.
+
+    The exported model has one input:
+
+        - y: a torch.int64 tensor of shape (N, decoder_model.context_size)
+
+    and has one output:
+
+        - decoder_out: a torch.float32 tensor of shape (N, joiner_dim)
+
+    Args:
+      decoder_model:
+        The decoder model to be exported.
+      decoder_filename:
+        Filename to save the exported ONNX model.
+      opset_version:
+        The opset version to use.
+    """
+    context_size = decoder_model.decoder.context_size
+    vocab_size = decoder_model.decoder.vocab_size
+
+    y = torch.zeros(10, context_size, dtype=torch.int64)
+    torch.onnx.export(
+        decoder_model,
+        y,
+        decoder_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=["y"],
+        output_names=["decoder_out"],
+        dynamic_axes={
+            "y": {0: "N"},
+            "decoder_out": {0: "N"},
+        },
+    )
+
+    meta_data = {
+        "context_size": str(context_size),
+        "vocab_size": str(vocab_size),
+    }
+    add_meta_data(filename=decoder_filename, meta_data=meta_data)
+
+
+def export_joiner_model_onnx(
+    joiner_model: nn.Module,
+    joiner_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the joiner model to ONNX format.
+    The exported joiner model has two inputs:
+
+        - encoder_out: a tensor of shape (N, joiner_dim)
+        - decoder_out: a tensor of shape (N, joiner_dim)
+
+    and produces one output:
+
+        - logit: a tensor of shape (N, vocab_size)
+    """
+    joiner_dim = joiner_model.output_linear.weight.shape[1]
+    logging.info(f"joiner dim: {joiner_dim}")
+
+    projected_encoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
+    projected_decoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
+
+    torch.onnx.export(
+        joiner_model,
+        (projected_encoder_out, projected_decoder_out),
+        joiner_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=[
+            "encoder_out",
+            "decoder_out",
+        ],
+        output_names=["logit"],
+        dynamic_axes={
+            "encoder_out": {0: "N"},
+            "decoder_out": {0: "N"},
+            "logit": {0: "N"},
+        },
+    )
+    meta_data = {
+        "joiner_dim": str(joiner_dim),
+    }
+    add_meta_data(filename=joiner_filename, meta_data=meta_data)
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+    params.is_pnnx = False
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    setup_logger(f"{params.exp_dir}/log-export/log-export-onnx")
+
+    logging.info(f"device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    model.to(device)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg + 1
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0, params.avg
+            start = params.epoch - params.avg
+            assert start >= 1, start
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to("cpu")
+    model.eval()
+
+    convert_scaled_to_non_scaled(model, inplace=True)
+
+    encoder = OnnxEncoder(
+        encoder=model.encoder,
+        encoder_proj=model.joiner.encoder_proj,
+    )
+
+    decoder = OnnxDecoder(
+        decoder=model.decoder,
+        decoder_proj=model.joiner.decoder_proj,
+    )
+
+    joiner = OnnxJoiner(output_linear=model.joiner.output_linear)
+
+    encoder_num_param = sum([p.numel() for p in encoder.parameters()])
+    decoder_num_param = sum([p.numel() for p in decoder.parameters()])
+    joiner_num_param = sum([p.numel() for p in joiner.parameters()])
+    total_num_param = encoder_num_param + decoder_num_param + joiner_num_param
+    logging.info(f"encoder parameters: {encoder_num_param}")
+    logging.info(f"decoder parameters: {decoder_num_param}")
+    logging.info(f"joiner parameters: {joiner_num_param}")
+    logging.info(f"total parameters: {total_num_param}")
+
+    if params.iter > 0:
+        suffix = f"iter-{params.iter}"
+    else:
+        suffix = f"epoch-{params.epoch}"
+
+    suffix += f"-avg-{params.avg}"
+
+    opset_version = 13
+
+    logging.info("Exporting encoder")
+    encoder_filename = params.exp_dir / f"encoder-{suffix}.onnx"
+    export_encoder_model_onnx(
+        encoder,
+        encoder_filename,
+        opset_version=opset_version,
+    )
+    logging.info(f"Exported encoder to {encoder_filename}")
+
+    logging.info("Exporting decoder")
+    decoder_filename = params.exp_dir / f"decoder-{suffix}.onnx"
+    export_decoder_model_onnx(
+        decoder,
+        decoder_filename,
+        opset_version=opset_version,
+    )
+    logging.info(f"Exported decoder to {decoder_filename}")
+
+    logging.info("Exporting joiner")
+    joiner_filename = params.exp_dir / f"joiner-{suffix}.onnx"
+    export_joiner_model_onnx(
+        joiner,
+        joiner_filename,
+        opset_version=opset_version,
+    )
+    logging.info(f"Exported joiner to {joiner_filename}")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    main()

egs/librispeech/ASR/conv_emformer_transducer_stateless2/export.py

@@ -64,6 +64,7 @@ from pathlib import Path
 
 import sentencepiece as spm
 import torch
+from scaling_converter import convert_scaled_to_non_scaled
 from train import add_model_arguments, get_params, get_transducer_model
 
 from icefall.checkpoint import (
@@ -258,6 +259,7 @@ def main():
     model.eval()
 
     if params.jit:
+        convert_scaled_to_non_scaled(model, inplace=True)
         # We won't use the forward() method of the model in C++, so just ignore
         # it here.
         # Otherwise, one of its arguments is a ragged tensor and is not

egs/librispeech/ASR/conv_emformer_transducer_stateless2/onnx_pretrained.py

@@ -0,0 +1,456 @@
+#!/usr/bin/env python3
+# Copyright      2023  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+"""
+This script loads ONNX models exported by ./export-onnx.py
+and uses them to decode waves.
+
+We use the pre-trained model from
+https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05
+as an example to show how to use this file.
+
+1. Download the pre-trained model
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-conv-emformer-transducer-stateless2-2022-07-05
+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
+ln -s pretrained-epoch-30-avg-10-averaged.pt epoch-99.pt
+popd
+
+2. Export the model to ONNX
+
+./conv_emformer_transducer_stateless2/export-onnx.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32
+
+It will generate the following 3 files inside $repo/exp:
+
+  - encoder-epoch-99-avg-1.onnx
+  - decoder-epoch-99-avg-1.onnx
+  - joiner-epoch-99-avg-1.onnx
+
+3. Run this file with the exported ONNX models
+
+./conv_emformer_transducer_stateless2/onnx_pretrained.py \
+  --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
+  --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+  --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  $repo/test_wavs/1089-134686-0001.wav
+
+Note: Even though this script only supports decoding a single file,
+the exported ONNX models do support batch processing.
+"""
+
+import argparse
+import logging
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import numpy as np
+import onnxruntime as ort
+import torch
+import torchaudio
+from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--encoder-model-filename",
+        type=str,
+        required=True,
+        help="Path to the encoder onnx model. ",
+    )
+
+    parser.add_argument(
+        "--decoder-model-filename",
+        type=str,
+        required=True,
+        help="Path to the decoder onnx model. ",
+    )
+
+    parser.add_argument(
+        "--joiner-model-filename",
+        type=str,
+        required=True,
+        help="Path to the joiner onnx model. ",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        help="""Path to tokens.txt.""",
+    )
+
+    parser.add_argument(
+        "sound_file",
+        type=str,
+        help="The input sound file to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    return parser
+
+
+class OnnxModel:
+    def __init__(
+        self,
+        encoder_model_filename: str,
+        decoder_model_filename: str,
+        joiner_model_filename: str,
+    ):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 1
+
+        self.session_opts = session_opts
+
+        self.init_encoder(encoder_model_filename)
+        self.init_decoder(decoder_model_filename)
+        self.init_joiner(joiner_model_filename)
+
+    def init_encoder(self, encoder_model_filename: str):
+        self.encoder = ort.InferenceSession(
+            encoder_model_filename,
+            sess_options=self.session_opts,
+        )
+        self.init_encoder_states()
+
+    def init_encoder_states(self, batch_size: int = 1):
+        encoder_meta = self.encoder.get_modelmeta().custom_metadata_map
+
+        model_type = encoder_meta["model_type"]
+        assert model_type == "conv-emformer", model_type
+
+        decode_chunk_len = int(encoder_meta["decode_chunk_len"])
+        T = int(encoder_meta["T"])
+
+        num_encoder_layers = int(encoder_meta["num_encoder_layers"])
+        memory_size = int(encoder_meta["memory_size"])
+        cnn_module_kernel = int(encoder_meta["cnn_module_kernel"])
+        right_context_length = int(encoder_meta["right_context_length"])
+        left_context_length = int(encoder_meta["left_context_length"])
+        encoder_dim = int(encoder_meta["encoder_dim"])
+
+        logging.info(f"decode_chunk_len: {decode_chunk_len}")
+        logging.info(f"T: {T}")
+        logging.info(f"num_encoder_layers: {num_encoder_layers}")
+        logging.info(f"memory_size: {memory_size}")
+        logging.info(f"cnn_module_kernel: {cnn_module_kernel}")
+        logging.info(f"left_context_length: {left_context_length} (after subsampling)")
+        logging.info(f"right_context_length: {right_context_length}")
+        logging.info(f"encoder_dim: {encoder_dim}")
+
+        N = batch_size
+
+        states = []
+        for i in range(num_encoder_layers):
+            s0 = torch.zeros(memory_size, N, encoder_dim)
+            s1 = torch.zeros(left_context_length, N, encoder_dim)
+            s2 = torch.zeros(left_context_length, N, encoder_dim)
+            s3 = torch.zeros(N, encoder_dim, cnn_module_kernel - 1)
+            states.extend([s0, s1, s2, s3])
+
+        self.states = states
+
+        self.segment = T
+        self.offset = decode_chunk_len
+        self.num_encoder_layers = num_encoder_layers
+
+    def init_decoder(self, decoder_model_filename: str):
+        self.decoder = ort.InferenceSession(
+            decoder_model_filename,
+            sess_options=self.session_opts,
+        )
+
+        decoder_meta = self.decoder.get_modelmeta().custom_metadata_map
+        self.context_size = int(decoder_meta["context_size"])
+        self.vocab_size = int(decoder_meta["vocab_size"])
+
+        logging.info(f"context_size: {self.context_size}")
+        logging.info(f"vocab_size: {self.vocab_size}")
+
+    def init_joiner(self, joiner_model_filename: str):
+        self.joiner = ort.InferenceSession(
+            joiner_model_filename,
+            sess_options=self.session_opts,
+        )
+
+        joiner_meta = self.joiner.get_modelmeta().custom_metadata_map
+        self.joiner_dim = int(joiner_meta["joiner_dim"])
+
+        logging.info(f"joiner_dim: {self.joiner_dim}")
+
+    def _build_encoder_input_output(
+        self,
+        x: torch.Tensor,
+    ) -> Tuple[Dict[str, np.ndarray], List[str]]:
+        encoder_input = {"x": x.numpy()}
+        encoder_output = ["encoder_out"]
+
+        def build_inputs_outputs(states: List[torch.Tensor], name: str):
+            for i in range(4):
+                if isinstance(states[i], torch.Tensor):
+                    encoder_input[f"{name}_{i}"] = states[i].numpy()
+                else:
+                    encoder_input[f"{name}_{i}"] = states[i]
+
+                encoder_output.append(f"new_{name}_{i}")
+
+        for i in range(self.num_encoder_layers):
+            base_name = f"layer{i}"
+            s = self.states[i * 4 : (i + 1) * 4]
+            build_inputs_outputs(s, base_name)
+
+        return encoder_input, encoder_output
+
+    def _update_states(self, states: List[np.ndarray]):
+        self.states = states
+
+    def run_encoder(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C)
+        Returns:
+          Return a 3-D tensor of shape (N, T', joiner_dim) where
+          T' is usually equal to ((T-7)//2+1)//2
+        """
+        encoder_input, encoder_output_names = self._build_encoder_input_output(x)
+        out = self.encoder.run(encoder_output_names, encoder_input)
+
+        self._update_states(out[1:])
+
+        return torch.from_numpy(out[0])
+
+    def run_decoder(self, decoder_input: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+          decoder_input:
+            A 2-D tensor of shape (N, context_size)
+        Returns:
+          Return a 2-D tensor of shape (N, joiner_dim)
+        """
+        out = self.decoder.run(
+            [self.decoder.get_outputs()[0].name],
+            {self.decoder.get_inputs()[0].name: decoder_input.numpy()},
+        )[0]
+
+        return torch.from_numpy(out)
+
+    def run_joiner(
+        self, encoder_out: torch.Tensor, decoder_out: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Args:
+          encoder_out:
+            A 2-D tensor of shape (N, joiner_dim)
+          decoder_out:
+            A 2-D tensor of shape (N, joiner_dim)
+        Returns:
+          Return a 2-D tensor of shape (N, vocab_size)
+        """
+        out = self.joiner.run(
+            [self.joiner.get_outputs()[0].name],
+            {
+                self.joiner.get_inputs()[0].name: encoder_out.numpy(),
+                self.joiner.get_inputs()[1].name: decoder_out.numpy(),
+            },
+        )[0]
+
+        return torch.from_numpy(out)
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert (
+            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].contiguous())
+    return ans
+
+
+def create_streaming_feature_extractor() -> OnlineFeature:
+    """Create a CPU streaming feature extractor.
+
+    At present, we assume it returns a fbank feature extractor with
+    fixed options. In the future, we will support passing in the options
+    from outside.
+
+    Returns:
+      Return a CPU streaming feature extractor.
+    """
+    opts = FbankOptions()
+    opts.device = "cpu"
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = 16000
+    opts.mel_opts.num_bins = 80
+    return OnlineFbank(opts)
+
+
+def greedy_search(
+    model: OnnxModel,
+    encoder_out: torch.Tensor,
+    context_size: int,
+    decoder_out: Optional[torch.Tensor] = None,
+    hyp: Optional[List[int]] = None,
+) -> List[int]:
+    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
+    Args:
+      model:
+        The transducer model.
+      encoder_out:
+        A 3-D tensor of shape (1, T, joiner_dim)
+      context_size:
+        The context size of the decoder model.
+      decoder_out:
+        Optional. Decoder output of the previous chunk.
+      hyp:
+        Decoding results for previous chunks.
+    Returns:
+      Return the decoded results so far.
+    """
+
+    blank_id = 0
+
+    if decoder_out is None:
+        assert hyp is None, hyp
+        hyp = [blank_id] * context_size
+        decoder_input = torch.tensor([hyp], dtype=torch.int64)
+        decoder_out = model.run_decoder(decoder_input)
+    else:
+        assert hyp is not None, hyp
+
+    encoder_out = encoder_out.squeeze(0)
+    T = encoder_out.size(0)
+    for t in range(T):
+        cur_encoder_out = encoder_out[t : t + 1]
+        joiner_out = model.run_joiner(cur_encoder_out, decoder_out).squeeze(0)
+        y = joiner_out.argmax(dim=0).item()
+        if y != blank_id:
+            hyp.append(y)
+            decoder_input = hyp[-context_size:]
+            decoder_input = torch.tensor([decoder_input], dtype=torch.int64)
+            decoder_out = model.run_decoder(decoder_input)
+
+    return hyp, decoder_out
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+    logging.info(vars(args))
+
+    model = OnnxModel(
+        encoder_model_filename=args.encoder_model_filename,
+        decoder_model_filename=args.decoder_model_filename,
+        joiner_model_filename=args.joiner_model_filename,
+    )
+
+    sample_rate = 16000
+
+    logging.info("Constructing Fbank computer")
+    online_fbank = create_streaming_feature_extractor()
+
+    logging.info(f"Reading sound files: {args.sound_file}")
+    waves = read_sound_files(
+        filenames=[args.sound_file],
+        expected_sample_rate=sample_rate,
+    )[0]
+
+    tail_padding = torch.zeros(int(0.3 * sample_rate), dtype=torch.float32)
+    wave_samples = torch.cat([waves, tail_padding])
+
+    num_processed_frames = 0
+    segment = model.segment
+    offset = model.offset
+
+    context_size = model.context_size
+    hyp = None
+    decoder_out = None
+
+    chunk = int(1 * sample_rate)  # 1 second
+    start = 0
+    while start < wave_samples.numel():
+        end = min(start + chunk, wave_samples.numel())
+        samples = wave_samples[start:end]
+        start += chunk
+
+        online_fbank.accept_waveform(
+            sampling_rate=sample_rate,
+            waveform=samples,
+        )
+
+        while online_fbank.num_frames_ready - num_processed_frames >= segment:
+            frames = []
+            for i in range(segment):
+                frames.append(online_fbank.get_frame(num_processed_frames + i))
+            num_processed_frames += offset
+            frames = torch.cat(frames, dim=0)
+            frames = frames.unsqueeze(0)
+            encoder_out = model.run_encoder(frames)
+            hyp, decoder_out = greedy_search(
+                model,
+                encoder_out,
+                context_size,
+                decoder_out,
+                hyp,
+            )
+
+    symbol_table = k2.SymbolTable.from_file(args.tokens)
+
+    text = ""
+    for i in hyp[context_size:]:
+        text += symbol_table[i]
+    text = text.replace("▁", " ").strip()
+
+    logging.info(args.sound_file)
+    logging.info(text)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/librispeech/ASR/conv_emformer_transducer_stateless2/train2.py

@@ -425,6 +425,7 @@ def get_params() -> AttributeDict:
             "joiner_dim": 512,
             # parameters for Noam
             "model_warm_step": 3000,  # arg given to model, not for lrate
+            "is_pnnx": True,
             "env_info": get_env_info(),
         }
     )
@@ -446,6 +447,7 @@ def get_encoder_model(params: AttributeDict) -> nn.Module:
         left_context_length=params.left_context_length,
         right_context_length=params.right_context_length,
         memory_size=params.memory_size,
+        is_pnnx=params.is_pnnx,
     )
     return encoder
 

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

@@ -0,0 +1 @@
+../lstm_transducer_stateless2/export-onnx.py

egs/librispeech/ASR/lstm_transducer_stateless/onnx_check.py

@@ -0,0 +1 @@
+../lstm_transducer_stateless2/onnx_check.py

egs/librispeech/ASR/lstm_transducer_stateless/onnx_pretrained.py

@@ -0,0 +1 @@
+../lstm_transducer_stateless2/onnx_pretrained.py

egs/librispeech/ASR/lstm_transducer_stateless2/export-for-ncnn.py

@@ -0,0 +1,337 @@
+#!/usr/bin/env python3
+
+"""
+Please see
+https://k2-fsa.github.io/icefall/model-export/export-ncnn.html
+for more details about how to use this file.
+
+We use the pre-trained model from
+https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
+as an example to show how to use this file.
+
+1. Download the pre-trained model
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
+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
+ln -s pretrained-iter-468000-avg-16.pt epoch-99.pt
+popd
+
+2. 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 \
+  --epoch 99 \
+  --avg 1 \
+  --use-averaged-model 0 \
+
+cd ./lstm_transducer_stateless2/exp
+pnnx encoder_jit_trace-pnnx.pt
+pnnx decoder_jit_trace-pnnx.pt
+pnnx joiner_jit_trace-pnnx.pt
+
+See ./streaming-ncnn-decode.py
+and
+https://github.com/k2-fsa/sherpa-ncnn
+for usage.
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import sentencepiece as spm
+import torch
+from scaling_converter import convert_scaled_to_non_scaled
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import setup_logger, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="""It specifies the checkpoint to use for averaging.
+        Note: Epoch counts from 0.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless2/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=True,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def export_encoder_model_jit_trace(
+    encoder_model: torch.nn.Module,
+    encoder_filename: str,
+) -> None:
+    """Export the given encoder model with torch.jit.trace()
+
+    Note: The warmup argument is fixed to 1.
+
+    Args:
+      encoder_model:
+        The input encoder model
+      encoder_filename:
+        The filename to save the exported model.
+    """
+    x = torch.zeros(1, 100, 80, dtype=torch.float32)
+    x_lens = torch.tensor([100], dtype=torch.int64)
+    states = encoder_model.get_init_states()
+
+    traced_model = torch.jit.trace(encoder_model, (x, x_lens, states))
+    traced_model.save(encoder_filename)
+    logging.info(f"Saved to {encoder_filename}")
+
+
+def export_decoder_model_jit_trace(
+    decoder_model: torch.nn.Module,
+    decoder_filename: str,
+) -> None:
+    """Export the given decoder model with torch.jit.trace()
+
+    Note: The argument need_pad is fixed to False.
+
+    Args:
+      decoder_model:
+        The input decoder model
+      decoder_filename:
+        The filename to save the exported model.
+    """
+    y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64)
+    need_pad = torch.tensor([False])
+
+    traced_model = torch.jit.trace(decoder_model, (y, need_pad))
+    traced_model.save(decoder_filename)
+    logging.info(f"Saved to {decoder_filename}")
+
+
+def export_joiner_model_jit_trace(
+    joiner_model: torch.nn.Module,
+    joiner_filename: str,
+) -> None:
+    """Export the given joiner model with torch.jit.trace()
+
+    Note: The argument project_input is fixed to True. A user should not
+    project the encoder_out/decoder_out by himself/herself. The exported joiner
+    will do that for the user.
+
+    Args:
+      joiner_model:
+        The input joiner model
+      joiner_filename:
+        The filename to save the exported model.
+
+    """
+    encoder_out_dim = joiner_model.encoder_proj.weight.shape[1]
+    decoder_out_dim = joiner_model.decoder_proj.weight.shape[1]
+    encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32)
+    decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32)
+
+    traced_model = torch.jit.trace(joiner_model, (encoder_out, decoder_out))
+    traced_model.save(joiner_filename)
+    logging.info(f"Saved to {joiner_filename}")
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+
+    setup_logger(f"{params.exp_dir}/log-export/log-export-ncnn")
+
+    logging.info(f"device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    params.is_pnnx = True
+
+    logging.info("About to create model")
+    model = get_transducer_model(params, enable_giga=False)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg + 1
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0, params.avg
+            start = params.epoch - params.avg
+            assert start >= 1, start
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to("cpu")
+    model.eval()
+
+    convert_scaled_to_non_scaled(model, inplace=True)
+    logging.info("Using torch.jit.trace()")
+
+    logging.info("Exporting encoder")
+    encoder_filename = params.exp_dir / "encoder_jit_trace-pnnx.pt"
+    export_encoder_model_jit_trace(model.encoder, encoder_filename)
+
+    logging.info("Exporting decoder")
+    decoder_filename = params.exp_dir / "decoder_jit_trace-pnnx.pt"
+    export_decoder_model_jit_trace(model.decoder, decoder_filename)
+
+    logging.info("Exporting joiner")
+    joiner_filename = params.exp_dir / "joiner_jit_trace-pnnx.pt"
+    export_joiner_model_jit_trace(model.joiner, joiner_filename)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    main()

egs/librispeech/ASR/lstm_transducer_stateless2/export-onnx.py

@@ -0,0 +1,593 @@
+#!/usr/bin/env python3
+#
+# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)
+
+"""
+This script exports a transducer model from PyTorch to ONNX.
+
+We use the pre-trained model from
+https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
+as an example to show how to use this file.
+
+1. Download the pre-trained model
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
+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
+ln -s pretrained-iter-468000-avg-16.pt epoch-99.pt
+popd
+
+2. Export the model to ONNX
+
+./lstm_transducer_stateless2/export-onnx.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp
+
+It will generate the following 3 files inside $repo/exp:
+
+  - encoder-epoch-99-avg-1.onnx
+  - decoder-epoch-99-avg-1.onnx
+  - joiner-epoch-99-avg-1.onnx
+
+See ./onnx_pretrained.py and ./onnx_check.py for how to
+use the exported ONNX models.
+"""
+
+import argparse
+import logging
+from pathlib import Path
+from typing import Dict, Optional, Tuple
+
+import onnx
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from decoder import Decoder
+from lstm import RNN
+from scaling_converter import convert_scaled_to_non_scaled
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import setup_logger, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="""It specifies the checkpoint to use for averaging.
+        Note: Epoch counts from 0.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=True,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless5/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, str]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    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)
+
+
+class OnnxEncoder(nn.Module):
+    """A wrapper for RNN and the encoder_proj from the joiner"""
+
+    def __init__(self, encoder: RNN, encoder_proj: nn.Linear):
+        """
+        Args:
+          encoder:
+            An RNN encoder.
+          encoder_proj:
+            The projection layer for encoder from the joiner.
+        """
+        super().__init__()
+        self.encoder = encoder
+        self.encoder_proj = encoder_proj
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        states: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Please see the help information of RNN.forward
+
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C)
+          states:
+            A tuple of 2 tensors (optional). It is for streaming inference.
+            states[0] is the hidden states of all layers,
+              with shape of (num_layers, N, d_model);
+            states[1] is the cell states of all layers,
+              with shape of (num_layers, N, rnn_hidden_size).
+        Returns:
+          Return a tuple containing:
+            - encoder_out, A 3-D tensor of shape (N, T', joiner_dim)
+            - updated states, whose shape is the same as the input states.
+        """
+        N = x.size(0)
+        T = x.size(1)
+        x_lens = torch.tensor([T] * N, dtype=torch.int64, device=x.device)
+        encoder_out, _, next_states = self.encoder(x, x_lens, states)
+
+        encoder_out = self.encoder_proj(encoder_out)
+        # Now encoder_out is of shape (N, T, joiner_dim)
+
+        return encoder_out, next_states
+
+
+class OnnxDecoder(nn.Module):
+    """A wrapper for Decoder and the decoder_proj from the joiner"""
+
+    def __init__(self, decoder: Decoder, decoder_proj: nn.Linear):
+        super().__init__()
+        self.decoder = decoder
+        self.decoder_proj = decoder_proj
+
+    def forward(self, y: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+          y:
+            A 2-D tensor of shape (N, context_size).
+        Returns
+          Return a 2-D tensor of shape (N, joiner_dim)
+        """
+        need_pad = False
+        decoder_output = self.decoder(y, need_pad=need_pad)
+        decoder_output = decoder_output.squeeze(1)
+        output = self.decoder_proj(decoder_output)
+
+        return output
+
+
+class OnnxJoiner(nn.Module):
+    """A wrapper for the joiner"""
+
+    def __init__(self, output_linear: nn.Linear):
+        super().__init__()
+        self.output_linear = output_linear
+
+    def forward(
+        self,
+        encoder_out: torch.Tensor,
+        decoder_out: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+          encoder_out:
+            A 2-D tensor of shape (N, joiner_dim)
+          decoder_out:
+            A 2-D tensor of shape (N, joiner_dim)
+        Returns:
+          Return a 2-D tensor of shape (N, vocab_size)
+        """
+        logit = encoder_out + decoder_out
+        logit = self.output_linear(torch.tanh(logit))
+        return logit
+
+
+def export_encoder_model_onnx(
+    encoder_model: OnnxEncoder,
+    encoder_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the given encoder model to ONNX format.
+    The exported model has the following inputs:
+
+        - x, a tensor of shape (N, T, C); dtype is torch.float32
+        - state0, a tensor of shape (num_encoder_layers, batch_size, d_model)
+        - state1, a tensor of shape (num_encoder_layers, batch_size, rnn_hidden_size)
+
+    and it has 3 outputs:
+
+        - encoder_out, a tensor of shape (N, T', joiner_dim)
+        - new_state0, a tensor of shape (num_encoder_layers, batch_size, d_model)
+        - new_state1, a tensor of shape (num_encoder_layers, batch_size, rnn_hidden_size)
+
+    Args:
+      encoder_model:
+        The input encoder model
+      encoder_filename:
+        The filename to save the exported ONNX model.
+      opset_version:
+        The opset version to use.
+    """
+    num_encoder_layers = encoder_model.encoder.num_encoder_layers
+    d_model = encoder_model.encoder.d_model
+    rnn_hidden_size = encoder_model.encoder.rnn_hidden_size
+
+    decode_chunk_len = 4
+    T = 9
+
+    x = torch.zeros(1, T, 80, dtype=torch.float32)
+    states = encoder_model.encoder.get_init_states()
+    # state0: (num_encoder_layers, batch_size, d_model)
+    # state1: (num_encoder_layers, batch_size, rnn_hidden_size)
+
+    torch.onnx.export(
+        encoder_model,
+        (x, states),
+        encoder_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=["x", "state0", "state1"],
+        output_names=["encoder_out", "new_state0", "new_state1"],
+        dynamic_axes={
+            "x": {0: "N"},
+            "state0": {1: "N"},
+            "state1": {1: "N"},
+            "encoder_out": {0: "N"},
+            "new_state0": {1: "N"},
+            "new_state1": {1: "N"},
+        },
+    )
+
+    meta_data = {
+        "model_type": "lstm",
+        "version": "1",
+        "model_author": "k2-fsa",
+        "decode_chunk_len": str(decode_chunk_len),  # 32
+        "T": str(T),  # 39
+        "num_encoder_layers": str(num_encoder_layers),
+        "d_model": str(d_model),
+        "rnn_hidden_size": str(rnn_hidden_size),
+    }
+    logging.info(f"meta_data: {meta_data}")
+
+    add_meta_data(filename=encoder_filename, meta_data=meta_data)
+
+
+def export_decoder_model_onnx(
+    decoder_model: OnnxDecoder,
+    decoder_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the decoder model to ONNX format.
+
+    The exported model has one input:
+
+        - y: a torch.int64 tensor of shape (N, decoder_model.context_size)
+
+    and has one output:
+
+        - decoder_out: a torch.float32 tensor of shape (N, joiner_dim)
+
+    Args:
+      decoder_model:
+        The decoder model to be exported.
+      decoder_filename:
+        Filename to save the exported ONNX model.
+      opset_version:
+        The opset version to use.
+    """
+    context_size = decoder_model.decoder.context_size
+    vocab_size = decoder_model.decoder.vocab_size
+
+    y = torch.zeros(10, context_size, dtype=torch.int64)
+    torch.onnx.export(
+        decoder_model,
+        y,
+        decoder_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=["y"],
+        output_names=["decoder_out"],
+        dynamic_axes={
+            "y": {0: "N"},
+            "decoder_out": {0: "N"},
+        },
+    )
+
+    meta_data = {
+        "context_size": str(context_size),
+        "vocab_size": str(vocab_size),
+    }
+    add_meta_data(filename=decoder_filename, meta_data=meta_data)
+
+
+def export_joiner_model_onnx(
+    joiner_model: nn.Module,
+    joiner_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the joiner model to ONNX format.
+    The exported joiner model has two inputs:
+
+        - encoder_out: a tensor of shape (N, joiner_dim)
+        - decoder_out: a tensor of shape (N, joiner_dim)
+
+    and produces one output:
+
+        - logit: a tensor of shape (N, vocab_size)
+    """
+    joiner_dim = joiner_model.output_linear.weight.shape[1]
+    logging.info(f"joiner dim: {joiner_dim}")
+
+    projected_encoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
+    projected_decoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
+
+    torch.onnx.export(
+        joiner_model,
+        (projected_encoder_out, projected_decoder_out),
+        joiner_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=[
+            "encoder_out",
+            "decoder_out",
+        ],
+        output_names=["logit"],
+        dynamic_axes={
+            "encoder_out": {0: "N"},
+            "decoder_out": {0: "N"},
+            "logit": {0: "N"},
+        },
+    )
+    meta_data = {
+        "joiner_dim": str(joiner_dim),
+    }
+    add_meta_data(filename=joiner_filename, meta_data=meta_data)
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    setup_logger(f"{params.exp_dir}/log-export/log-export-onnx")
+
+    logging.info(f"device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params, enable_giga=False)
+
+    model.to(device)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg + 1
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0, params.avg
+            start = params.epoch - params.avg
+            assert start >= 1, start
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to("cpu")
+    model.eval()
+
+    convert_scaled_to_non_scaled(model, inplace=True, is_onnx=True)
+
+    encoder = OnnxEncoder(
+        encoder=model.encoder,
+        encoder_proj=model.joiner.encoder_proj,
+    )
+
+    decoder = OnnxDecoder(
+        decoder=model.decoder,
+        decoder_proj=model.joiner.decoder_proj,
+    )
+
+    joiner = OnnxJoiner(output_linear=model.joiner.output_linear)
+
+    encoder_num_param = sum([p.numel() for p in encoder.parameters()])
+    decoder_num_param = sum([p.numel() for p in decoder.parameters()])
+    joiner_num_param = sum([p.numel() for p in joiner.parameters()])
+    total_num_param = encoder_num_param + decoder_num_param + joiner_num_param
+    logging.info(f"encoder parameters: {encoder_num_param}")
+    logging.info(f"decoder parameters: {decoder_num_param}")
+    logging.info(f"joiner parameters: {joiner_num_param}")
+    logging.info(f"total parameters: {total_num_param}")
+
+    if params.iter > 0:
+        suffix = f"iter-{params.iter}"
+    else:
+        suffix = f"epoch-{params.epoch}"
+
+    suffix += f"-avg-{params.avg}"
+
+    opset_version = 13
+
+    logging.info("Exporting encoder")
+    encoder_filename = params.exp_dir / f"encoder-{suffix}.onnx"
+    export_encoder_model_onnx(
+        encoder,
+        encoder_filename,
+        opset_version=opset_version,
+    )
+    logging.info(f"Exported encoder to {encoder_filename}")
+
+    logging.info("Exporting decoder")
+    decoder_filename = params.exp_dir / f"decoder-{suffix}.onnx"
+    export_decoder_model_onnx(
+        decoder,
+        decoder_filename,
+        opset_version=opset_version,
+    )
+    logging.info(f"Exported decoder to {decoder_filename}")
+
+    logging.info("Exporting joiner")
+    joiner_filename = params.exp_dir / f"joiner-{suffix}.onnx"
+    export_joiner_model_onnx(
+        joiner,
+        joiner_filename,
+        opset_version=opset_version,
+    )
+    logging.info(f"Exported joiner to {joiner_filename}")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    main()

egs/librispeech/ASR/lstm_transducer_stateless2/export.py

@@ -74,29 +74,6 @@ with the following commands:
     git lfs install
     git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
     # You will find the pre-trained models in icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03/exp
-
-(3) Export to ONNX format
-
-./lstm_transducer_stateless2/export.py \
-  --exp-dir ./lstm_transducer_stateless2/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
-  --epoch 20 \
-  --avg 10 \
-  --onnx 1
-
-It will generate the following files in the given `exp_dir`.
-
-    - encoder.onnx
-    - decoder.onnx
-    - joiner.onnx
-    - joiner_encoder_proj.onnx
-    - joiner_decoder_proj.onnx
-
-Please see ./streaming-onnx-decode.py for usage of the generated files
-
-Check
-https://github.com/k2-fsa/sherpa-onnx
-for how to use the exported models outside of icefall.
 """
 
 import argparse
@@ -192,35 +169,6 @@ def get_parser():
         """,
     )
 
-    parser.add_argument(
-        "--pnnx",
-        type=str2bool,
-        default=False,
-        help="""True to save a model after applying torch.jit.trace for later
-        converting to PNNX. It will generate 3 files:
-         - encoder_jit_trace-pnnx.pt
-         - decoder_jit_trace-pnnx.pt
-         - joiner_jit_trace-pnnx.pt
-        """,
-    )
-
-    parser.add_argument(
-        "--onnx",
-        type=str2bool,
-        default=False,
-        help="""If True, --jit and --pnnx are ignored and it exports the model
-        to onnx format. It will generate the following files:
-
-            - encoder.onnx
-            - decoder.onnx
-            - joiner.onnx
-            - joiner_encoder_proj.onnx
-            - joiner_decoder_proj.onnx
-
-        Refer to ./onnx_check.py and ./onnx_pretrained.py for how to use them.
-        """,
-    )
-
     parser.add_argument(
         "--context-size",
         type=int,
@@ -305,209 +253,6 @@ def export_joiner_model_jit_trace(
     logging.info(f"Saved to {joiner_filename}")
 
 
-def export_encoder_model_onnx(
-    encoder_model: nn.Module,
-    encoder_filename: str,
-    opset_version: int = 11,
-) -> None:
-    """Export the given encoder model to ONNX format.
-    The exported model has 3 inputs:
-
-        - x, a tensor of shape (N, T, C); dtype is torch.float32
-        - x_lens, a tensor of shape (N,); dtype is torch.int64
-        - states: a tuple containing:
-            - h0: a tensor of shape (num_layers, N, proj_size)
-            - c0: a tensor of shape (num_layers, N, hidden_size)
-
-    and it has 3 outputs:
-
-        - encoder_out, a tensor of shape (N, T, C)
-        - encoder_out_lens, a tensor of shape (N,)
-        - states: a tuple containing:
-            - next_h0: a tensor of shape (num_layers, N, proj_size)
-            - next_c0: a tensor of shape (num_layers, N, hidden_size)
-
-    Note: The warmup argument is fixed to 1.
-
-    Args:
-      encoder_model:
-        The input encoder model
-      encoder_filename:
-        The filename to save the exported ONNX model.
-      opset_version:
-        The opset version to use.
-    """
-    N = 1
-    x = torch.zeros(N, 9, 80, dtype=torch.float32)
-    x_lens = torch.tensor([9], dtype=torch.int64)
-    h = torch.rand(encoder_model.num_encoder_layers, N, encoder_model.d_model)
-    c = torch.rand(encoder_model.num_encoder_layers, N, encoder_model.rnn_hidden_size)
-
-    warmup = 1.0
-    torch.onnx.export(
-        encoder_model,  # use torch.jit.trace() internally
-        (x, x_lens, (h, c), warmup),
-        encoder_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=["x", "x_lens", "h", "c", "warmup"],
-        output_names=["encoder_out", "encoder_out_lens", "next_h", "next_c"],
-        dynamic_axes={
-            "x": {0: "N", 1: "T"},
-            "x_lens": {0: "N"},
-            "h": {1: "N"},
-            "c": {1: "N"},
-            "encoder_out": {0: "N", 1: "T"},
-            "encoder_out_lens": {0: "N"},
-            "next_h": {1: "N"},
-            "next_c": {1: "N"},
-        },
-    )
-    logging.info(f"Saved to {encoder_filename}")
-
-
-def export_decoder_model_onnx(
-    decoder_model: nn.Module,
-    decoder_filename: str,
-    opset_version: int = 11,
-) -> None:
-    """Export the decoder model to ONNX format.
-
-    The exported model has one input:
-
-        - y: a torch.int64 tensor of shape (N, decoder_model.context_size)
-
-    and has one output:
-
-        - decoder_out: a torch.float32 tensor of shape (N, 1, C)
-
-    Note: The argument need_pad is fixed to False.
-
-    Args:
-      decoder_model:
-        The decoder model to be exported.
-      decoder_filename:
-        Filename to save the exported ONNX model.
-      opset_version:
-        The opset version to use.
-    """
-    y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64)
-    need_pad = False  # Always False, so we can use torch.jit.trace() here
-    # Note(fangjun): torch.jit.trace() is more efficient than torch.jit.script()
-    # in this case
-    torch.onnx.export(
-        decoder_model,
-        (y, need_pad),
-        decoder_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=["y", "need_pad"],
-        output_names=["decoder_out"],
-        dynamic_axes={
-            "y": {0: "N"},
-            "decoder_out": {0: "N"},
-        },
-    )
-    logging.info(f"Saved to {decoder_filename}")
-
-
-def export_joiner_model_onnx(
-    joiner_model: nn.Module,
-    joiner_filename: str,
-    opset_version: int = 11,
-) -> None:
-    """Export the joiner model to ONNX format.
-    The exported joiner model has two inputs:
-
-        - projected_encoder_out: a tensor of shape (N, joiner_dim)
-        - projected_decoder_out: a tensor of shape (N, joiner_dim)
-
-    and produces one output:
-
-        - logit: a tensor of shape (N, vocab_size)
-
-    The exported encoder_proj model has one input:
-
-        - encoder_out: a tensor of shape (N, encoder_out_dim)
-
-    and produces one output:
-
-        - projected_encoder_out: a tensor of shape (N, joiner_dim)
-
-    The exported decoder_proj model has one input:
-
-        - decoder_out: a tensor of shape (N, decoder_out_dim)
-
-    and produces one output:
-
-        - projected_decoder_out: a tensor of shape (N, joiner_dim)
-    """
-    encoder_proj_filename = str(joiner_filename).replace(".onnx", "_encoder_proj.onnx")
-
-    decoder_proj_filename = str(joiner_filename).replace(".onnx", "_decoder_proj.onnx")
-
-    encoder_out_dim = joiner_model.encoder_proj.weight.shape[1]
-    decoder_out_dim = joiner_model.decoder_proj.weight.shape[1]
-    joiner_dim = joiner_model.decoder_proj.weight.shape[0]
-
-    projected_encoder_out = torch.rand(1, joiner_dim, dtype=torch.float32)
-    projected_decoder_out = torch.rand(1, joiner_dim, dtype=torch.float32)
-
-    project_input = False
-    # Note: It uses torch.jit.trace() internally
-    torch.onnx.export(
-        joiner_model,
-        (projected_encoder_out, projected_decoder_out, project_input),
-        joiner_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=[
-            "projected_encoder_out",
-            "projected_decoder_out",
-            "project_input",
-        ],
-        output_names=["logit"],
-        dynamic_axes={
-            "projected_encoder_out": {0: "N"},
-            "projected_decoder_out": {0: "N"},
-            "logit": {0: "N"},
-        },
-    )
-    logging.info(f"Saved to {joiner_filename}")
-
-    encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32)
-    torch.onnx.export(
-        joiner_model.encoder_proj,
-        encoder_out,
-        encoder_proj_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=["encoder_out"],
-        output_names=["projected_encoder_out"],
-        dynamic_axes={
-            "encoder_out": {0: "N"},
-            "projected_encoder_out": {0: "N"},
-        },
-    )
-    logging.info(f"Saved to {encoder_proj_filename}")
-
-    decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32)
-    torch.onnx.export(
-        joiner_model.decoder_proj,
-        decoder_out,
-        decoder_proj_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=["decoder_out"],
-        output_names=["projected_decoder_out"],
-        dynamic_axes={
-            "decoder_out": {0: "N"},
-            "projected_decoder_out": {0: "N"},
-        },
-    )
-    logging.info(f"Saved to {decoder_proj_filename}")
-
-
 @torch.no_grad()
 def main():
     args = get_parser().parse_args()
@@ -531,10 +276,6 @@ def main():
 
     logging.info(params)
 
-    if params.pnnx:
-        params.is_pnnx = params.pnnx
-        logging.info("For PNNX")
-
     logging.info("About to create model")
     model = get_transducer_model(params, enable_giga=False)
 
@@ -629,44 +370,7 @@ def main():
     model.to("cpu")
     model.eval()
 
-    if params.onnx:
+    if params.jit_trace is True:
-        logging.info("Export model to ONNX format")
-        convert_scaled_to_non_scaled(model, inplace=True, is_onnx=True)
-
-        opset_version = 11
-        encoder_filename = params.exp_dir / "encoder.onnx"
-        export_encoder_model_onnx(
-            model.encoder,
-            encoder_filename,
-            opset_version=opset_version,
-        )
-
-        decoder_filename = params.exp_dir / "decoder.onnx"
-        export_decoder_model_onnx(
-            model.decoder,
-            decoder_filename,
-            opset_version=opset_version,
-        )
-
-        joiner_filename = params.exp_dir / "joiner.onnx"
-        export_joiner_model_onnx(
-            model.joiner,
-            joiner_filename,
-            opset_version=opset_version,
-        )
-
-    elif params.pnnx:
-        convert_scaled_to_non_scaled(model, inplace=True)
-        logging.info("Using torch.jit.trace()")
-        encoder_filename = params.exp_dir / "encoder_jit_trace-pnnx.pt"
-        export_encoder_model_jit_trace(model.encoder, encoder_filename)
-
-        decoder_filename = params.exp_dir / "decoder_jit_trace-pnnx.pt"
-        export_decoder_model_jit_trace(model.decoder, decoder_filename)
-
-        joiner_filename = params.exp_dir / "joiner_jit_trace-pnnx.pt"
-        export_joiner_model_jit_trace(model.joiner, joiner_filename)
-    elif params.jit_trace is True:
         convert_scaled_to_non_scaled(model, inplace=True)
         logging.info("Using torch.jit.trace()")
         encoder_filename = params.exp_dir / "encoder_jit_trace.pt"

egs/librispeech/ASR/lstm_transducer_stateless2/ncnn-decode.py

@@ -19,7 +19,7 @@
 """
 Usage:
   ./lstm_transducer_stateless2/ncnn-decode.py \
-   --bpe-model-filename ./data/lang_bpe_500/bpe.model \
+   --tokens ./data/lang_bpe_500/tokens.txt \
    --encoder-param-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-iter-468000-avg-16-pnnx.ncnn.param \
    --encoder-bin-filename ./lstm_transducer_stateless2/exp/encoder_jit_trace-iter-468000-avg-16-pnnx.ncnn.bin \
    --decoder-param-filename ./lstm_transducer_stateless2/exp/decoder_jit_trace-iter-468000-avg-16-pnnx.ncnn.param \
@@ -27,15 +27,19 @@ Usage:
    --joiner-param-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-iter-468000-avg-16-pnnx.ncnn.param \
    --joiner-bin-filename ./lstm_transducer_stateless2/exp/joiner_jit_trace-iter-468000-avg-16-pnnx.ncnn.bin \
    ./test_wavs/1089-134686-0001.wav
+
+Please see
+https://k2-fsa.github.io/icefall/model-export/export-ncnn.html
+for details.
 """
 
 import argparse
 import logging
 from typing import List
 
+import k2
 import kaldifeat
 import ncnn
-import sentencepiece as spm
 import torch
 import torchaudio
 
@@ -44,9 +48,9 @@ def get_args():
     parser = argparse.ArgumentParser()
 
     parser.add_argument(
-        "--bpe-model-filename",
+        "--tokens",
         type=str,
-        help="Path to bpe.model",
+        help="Path to tokens.txt",
     )
 
     parser.add_argument(
@@ -240,9 +244,6 @@ def main():
 
     model = Model(args)
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(args.bpe_model_filename)
-
     sound_file = args.sound_filename
 
     sample_rate = 16000
@@ -280,8 +281,16 @@ def main():
 
     encoder_out, encoder_out_lens, hx, cx = model.run_encoder(features, states)
     hyp = greedy_search(model, encoder_out)
+
+    symbol_table = k2.SymbolTable.from_file(args.tokens)
+
+    text = ""
+    for i in hyp:
+        text += symbol_table[i]
+    text = text.replace("▁", " ").strip()
+
     logging.info(sound_file)
-    logging.info(sp.decode(hyp))
+    logging.info(text)
 
 
 if __name__ == "__main__":

egs/librispeech/ASR/lstm_transducer_stateless2/onnx_check.py

@@ -0,0 +1,261 @@
+#!/usr/bin/env python3
+#
+# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)
+
+"""
+This script checks that exported ONNX models produce the same output
+with the given torchscript model for the same input.
+
+We use the pre-trained model from
+https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
+as an example to show how to use this file.
+
+1. Download the pre-trained model
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
+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
+ln -s pretrained-iter-468000-avg-16.pt epoch-99.pt
+popd
+
+2. Export the model via torch.jit.trace()
+
+./lstm_transducer_stateless2/export.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp/ \
+  --jit-trace 1
+
+It will generate the following 3 files inside $repo/exp
+
+  - encoder_jit_trace.pt
+  - decoder_jit_trace.pt
+  - joiner_jit_trace.pt
+
+3. Export the model to ONNX
+
+./lstm_transducer_stateless2/export-onnx.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp
+
+
+It will generate the following 3 files inside $repo/exp:
+
+  - encoder-epoch-99-avg-1.onnx
+  - decoder-epoch-99-avg-1.onnx
+  - joiner-epoch-99-avg-1.onnx
+
+4. Run this file
+
+./lstm_transducer_stateless2/onnx_check.py \
+  --jit-encoder-filename $repo/exp/encoder_jit_trace.pt \
+  --jit-decoder-filename $repo/exp/decoder_jit_trace.pt \
+  --jit-joiner-filename $repo/exp/joiner_jit_trace.pt \
+  --onnx-encoder-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
+  --onnx-decoder-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+  --onnx-joiner-filename $repo/exp/joiner-epoch-99-avg-1.onnx
+
+"""
+
+import argparse
+import logging
+
+from onnx_pretrained import OnnxModel
+
+from icefall import is_module_available
+
+import torch
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--jit-encoder-filename",
+        required=True,
+        type=str,
+        help="Path to the torchscript encoder model",
+    )
+
+    parser.add_argument(
+        "--jit-decoder-filename",
+        required=True,
+        type=str,
+        help="Path to the torchscript decoder model",
+    )
+
+    parser.add_argument(
+        "--jit-joiner-filename",
+        required=True,
+        type=str,
+        help="Path to the torchscript joiner model",
+    )
+
+    parser.add_argument(
+        "--onnx-encoder-filename",
+        required=True,
+        type=str,
+        help="Path to the ONNX encoder model",
+    )
+
+    parser.add_argument(
+        "--onnx-decoder-filename",
+        required=True,
+        type=str,
+        help="Path to the ONNX decoder model",
+    )
+
+    parser.add_argument(
+        "--onnx-joiner-filename",
+        required=True,
+        type=str,
+        help="Path to the ONNX joiner model",
+    )
+
+    return parser
+
+
+def test_encoder(
+    torch_encoder_model: torch.jit.ScriptModule,
+    torch_encoder_proj_model: torch.jit.ScriptModule,
+    onnx_model: OnnxModel,
+):
+    N = torch.randint(1, 100, size=(1,)).item()
+    T = onnx_model.segment
+    C = 80
+    x_lens = torch.tensor([T] * N)
+    torch_states = torch_encoder_model.get_init_states(N)
+
+    onnx_model.init_encoder_states(N)
+
+    for i in range(5):
+        logging.info(f"test_encoder: iter {i}")
+        x = torch.rand(N, T, C)
+        torch_encoder_out, _, torch_states = torch_encoder_model(
+            x, x_lens, torch_states
+        )
+        torch_encoder_out = torch_encoder_proj_model(torch_encoder_out)
+
+        onnx_encoder_out = onnx_model.run_encoder(x)
+
+        assert torch.allclose(torch_encoder_out, onnx_encoder_out, atol=1e-4), (
+            (torch_encoder_out - onnx_encoder_out).abs().max()
+        )
+
+
+def test_decoder(
+    torch_decoder_model: torch.jit.ScriptModule,
+    torch_decoder_proj_model: torch.jit.ScriptModule,
+    onnx_model: OnnxModel,
+):
+    context_size = onnx_model.context_size
+    vocab_size = onnx_model.vocab_size
+    for i in range(10):
+        N = torch.randint(1, 100, size=(1,)).item()
+        logging.info(f"test_decoder: iter {i}, N={N}")
+        x = torch.randint(
+            low=1,
+            high=vocab_size,
+            size=(N, context_size),
+            dtype=torch.int64,
+        )
+        torch_decoder_out = torch_decoder_model(x, need_pad=torch.tensor([False]))
+        torch_decoder_out = torch_decoder_proj_model(torch_decoder_out)
+        torch_decoder_out = torch_decoder_out.squeeze(1)
+
+        onnx_decoder_out = onnx_model.run_decoder(x)
+        assert torch.allclose(torch_decoder_out, onnx_decoder_out, atol=1e-4), (
+            (torch_decoder_out - onnx_decoder_out).abs().max()
+        )
+
+
+def test_joiner(
+    torch_joiner_model: torch.jit.ScriptModule,
+    onnx_model: OnnxModel,
+):
+    encoder_dim = torch_joiner_model.encoder_proj.weight.shape[1]
+    decoder_dim = torch_joiner_model.decoder_proj.weight.shape[1]
+    for i in range(10):
+        N = torch.randint(1, 100, size=(1,)).item()
+        logging.info(f"test_joiner: iter {i}, N={N}")
+        encoder_out = torch.rand(N, encoder_dim)
+        decoder_out = torch.rand(N, decoder_dim)
+
+        projected_encoder_out = torch_joiner_model.encoder_proj(encoder_out)
+        projected_decoder_out = torch_joiner_model.decoder_proj(decoder_out)
+
+        torch_joiner_out = torch_joiner_model(encoder_out, decoder_out)
+        onnx_joiner_out = onnx_model.run_joiner(
+            projected_encoder_out, projected_decoder_out
+        )
+
+        assert torch.allclose(torch_joiner_out, onnx_joiner_out, atol=1e-4), (
+            (torch_joiner_out - onnx_joiner_out).abs().max()
+        )
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    logging.info(vars(args))
+
+    torch_encoder_model = torch.jit.load(args.jit_encoder_filename)
+    torch_decoder_model = torch.jit.load(args.jit_decoder_filename)
+    torch_joiner_model = torch.jit.load(args.jit_joiner_filename)
+
+    onnx_model = OnnxModel(
+        encoder_model_filename=args.onnx_encoder_filename,
+        decoder_model_filename=args.onnx_decoder_filename,
+        joiner_model_filename=args.onnx_joiner_filename,
+    )
+
+    logging.info("Test encoder")
+    # When exporting the model to onnx, we have already put the encoder_proj
+    # inside the encoder.
+    test_encoder(torch_encoder_model, torch_joiner_model.encoder_proj, onnx_model)
+
+    logging.info("Test decoder")
+    # When exporting the model to onnx, we have already put the decoder_proj
+    # inside the decoder.
+    test_decoder(torch_decoder_model, torch_joiner_model.decoder_proj, onnx_model)
+
+    logging.info("Test joiner")
+    test_joiner(torch_joiner_model, onnx_model)
+
+    logging.info("Finished checking ONNX models")
+
+
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+# See https://github.com/pytorch/pytorch/issues/38342
+# and https://github.com/pytorch/pytorch/issues/33354
+#
+# If we don't do this, the delay increases whenever there is
+# a new request that changes the actual batch size.
+# If you use `py-spy dump --pid <server-pid> --native`, you will
+# see a lot of time is spent in re-compiling the torch script model.
+torch._C._jit_set_profiling_executor(False)
+torch._C._jit_set_profiling_mode(False)
+torch._C._set_graph_executor_optimize(False)
+if __name__ == "__main__":
+    torch.manual_seed(20230207)
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/librispeech/ASR/lstm_transducer_stateless2/onnx_pretrained.py

@@ -0,0 +1,428 @@
+#!/usr/bin/env python3
+# Copyright      2023  Xiaomi Corp.        (authors: Fangjun Kuang)
+
+"""
+This script loads ONNX models exported by ./export-onnx.py
+and uses them to decode waves.
+
+We use the pre-trained model from
+https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
+as an example to show how to use this file.
+
+1. Download the pre-trained model
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03
+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
+ln -s exp/pretrained-iter-468000-avg-16.pt epoch-99.pt
+popd
+
+2. Export the model to ONNX
+
+./lstm_transducer_stateless2/export-onnx.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp
+
+It will generate the following 3 files inside $repo/exp:
+
+  - encoder-epoch-99-avg-1.onnx
+  - decoder-epoch-99-avg-1.onnx
+  - joiner-epoch-99-avg-1.onnx
+
+3. Run this file with the exported ONNX models
+
+./lstm_transducer_stateless2/onnx_pretrained.py \
+  --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
+  --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+  --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  $repo/test_wavs/1221-135766-0001.wav
+
+Note: Even though this script only supports decoding a single file,
+the exported ONNX models do support batch processing.
+"""
+
+import argparse
+import logging
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import numpy as np
+import onnxruntime as ort
+import torch
+import torchaudio
+from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--encoder-model-filename",
+        type=str,
+        required=True,
+        help="Path to the encoder onnx model. ",
+    )
+
+    parser.add_argument(
+        "--decoder-model-filename",
+        type=str,
+        required=True,
+        help="Path to the decoder onnx model. ",
+    )
+
+    parser.add_argument(
+        "--joiner-model-filename",
+        type=str,
+        required=True,
+        help="Path to the joiner onnx model. ",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        help="""Path to tokens.txt.""",
+    )
+
+    parser.add_argument(
+        "sound_file",
+        type=str,
+        help="The input sound file to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    return parser
+
+
+class OnnxModel:
+    def __init__(
+        self,
+        encoder_model_filename: str,
+        decoder_model_filename: str,
+        joiner_model_filename: str,
+    ):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 1
+
+        self.session_opts = session_opts
+
+        self.init_encoder(encoder_model_filename)
+        self.init_decoder(decoder_model_filename)
+        self.init_joiner(joiner_model_filename)
+
+    def init_encoder(self, encoder_model_filename: str):
+        self.encoder = ort.InferenceSession(
+            encoder_model_filename,
+            sess_options=self.session_opts,
+        )
+        self.init_encoder_states()
+
+    def init_encoder_states(self, batch_size: int = 1):
+        encoder_meta = self.encoder.get_modelmeta().custom_metadata_map
+
+        model_type = encoder_meta["model_type"]
+        assert model_type == "lstm", model_type
+
+        decode_chunk_len = int(encoder_meta["decode_chunk_len"])
+        T = int(encoder_meta["T"])
+
+        num_encoder_layers = int(encoder_meta["num_encoder_layers"])
+        d_model = int(encoder_meta["d_model"])
+        rnn_hidden_size = int(encoder_meta["rnn_hidden_size"])
+
+        logging.info(f"decode_chunk_len: {decode_chunk_len}")
+        logging.info(f"T: {T}")
+        logging.info(f"num_encoder_layers: {num_encoder_layers}")
+        logging.info(f"d_model: {d_model}")
+        logging.info(f"rnn_hidden_size: {rnn_hidden_size}")
+
+        N = batch_size
+
+        s0 = torch.zeros(num_encoder_layers, N, d_model)
+        s1 = torch.zeros(num_encoder_layers, N, rnn_hidden_size)
+        states = [s0.numpy(), s1.numpy()]
+
+        self.states = states
+
+        self.segment = T
+        self.offset = decode_chunk_len
+
+    def init_decoder(self, decoder_model_filename: str):
+        self.decoder = ort.InferenceSession(
+            decoder_model_filename,
+            sess_options=self.session_opts,
+        )
+
+        decoder_meta = self.decoder.get_modelmeta().custom_metadata_map
+        self.context_size = int(decoder_meta["context_size"])
+        self.vocab_size = int(decoder_meta["vocab_size"])
+
+        logging.info(f"context_size: {self.context_size}")
+        logging.info(f"vocab_size: {self.vocab_size}")
+
+    def init_joiner(self, joiner_model_filename: str):
+        self.joiner = ort.InferenceSession(
+            joiner_model_filename,
+            sess_options=self.session_opts,
+        )
+
+        joiner_meta = self.joiner.get_modelmeta().custom_metadata_map
+        self.joiner_dim = int(joiner_meta["joiner_dim"])
+
+        logging.info(f"joiner_dim: {self.joiner_dim}")
+
+    def _build_encoder_input_output(
+        self,
+        x: torch.Tensor,
+    ) -> Tuple[Dict[str, np.ndarray], List[str]]:
+        encoder_input = {
+            "x": x.numpy(),
+            "state0": self.states[0],
+            "state1": self.states[1],
+        }
+        encoder_output = ["encoder_out", "new_state0", "new_state1"]
+
+        return encoder_input, encoder_output
+
+    def _update_states(self, states: List[np.ndarray]):
+        self.states = states
+
+    def run_encoder(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C)
+        Returns:
+          Return a 3-D tensor of shape (N, T', joiner_dim) where
+          T' is usually equal to ((T-3)//2-1)//2
+        """
+        encoder_input, encoder_output_names = self._build_encoder_input_output(x)
+        out = self.encoder.run(encoder_output_names, encoder_input)
+
+        self._update_states(out[1:])
+
+        return torch.from_numpy(out[0])
+
+    def run_decoder(self, decoder_input: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+          decoder_input:
+            A 2-D tensor of shape (N, context_size)
+        Returns:
+          Return a 2-D tensor of shape (N, joiner_dim)
+        """
+        out = self.decoder.run(
+            [self.decoder.get_outputs()[0].name],
+            {self.decoder.get_inputs()[0].name: decoder_input.numpy()},
+        )[0]
+
+        return torch.from_numpy(out)
+
+    def run_joiner(
+        self, encoder_out: torch.Tensor, decoder_out: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Args:
+          encoder_out:
+            A 2-D tensor of shape (N, joiner_dim)
+          decoder_out:
+            A 2-D tensor of shape (N, joiner_dim)
+        Returns:
+          Return a 2-D tensor of shape (N, vocab_size)
+        """
+        out = self.joiner.run(
+            [self.joiner.get_outputs()[0].name],
+            {
+                self.joiner.get_inputs()[0].name: encoder_out.numpy(),
+                self.joiner.get_inputs()[1].name: decoder_out.numpy(),
+            },
+        )[0]
+
+        return torch.from_numpy(out)
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert (
+            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].contiguous())
+    return ans
+
+
+def create_streaming_feature_extractor() -> OnlineFeature:
+    """Create a CPU streaming feature extractor.
+
+    At present, we assume it returns a fbank feature extractor with
+    fixed options. In the future, we will support passing in the options
+    from outside.
+
+    Returns:
+      Return a CPU streaming feature extractor.
+    """
+    opts = FbankOptions()
+    opts.device = "cpu"
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = 16000
+    opts.mel_opts.num_bins = 80
+    return OnlineFbank(opts)
+
+
+def greedy_search(
+    model: OnnxModel,
+    encoder_out: torch.Tensor,
+    context_size: int,
+    decoder_out: Optional[torch.Tensor] = None,
+    hyp: Optional[List[int]] = None,
+) -> List[int]:
+    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
+    Args:
+      model:
+        The transducer model.
+      encoder_out:
+        A 3-D tensor of shape (1, T, joiner_dim)
+      context_size:
+        The context size of the decoder model.
+      decoder_out:
+        Optional. Decoder output of the previous chunk.
+      hyp:
+        Decoding results for previous chunks.
+    Returns:
+      Return the decoded results so far.
+    """
+
+    blank_id = 0
+
+    if decoder_out is None:
+        assert hyp is None, hyp
+        hyp = [blank_id] * context_size
+        decoder_input = torch.tensor([hyp], dtype=torch.int64)
+        decoder_out = model.run_decoder(decoder_input)
+    else:
+        assert hyp is not None, hyp
+
+    encoder_out = encoder_out.squeeze(0)
+    T = encoder_out.size(0)
+    for t in range(T):
+        cur_encoder_out = encoder_out[t : t + 1]
+        joiner_out = model.run_joiner(cur_encoder_out, decoder_out).squeeze(0)
+        y = joiner_out.argmax(dim=0).item()
+        if y != blank_id:
+            hyp.append(y)
+            decoder_input = hyp[-context_size:]
+            decoder_input = torch.tensor([decoder_input], dtype=torch.int64)
+            decoder_out = model.run_decoder(decoder_input)
+
+    return hyp, decoder_out
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+    logging.info(vars(args))
+
+    model = OnnxModel(
+        encoder_model_filename=args.encoder_model_filename,
+        decoder_model_filename=args.decoder_model_filename,
+        joiner_model_filename=args.joiner_model_filename,
+    )
+
+    sample_rate = 16000
+
+    logging.info("Constructing Fbank computer")
+    online_fbank = create_streaming_feature_extractor()
+
+    logging.info(f"Reading sound files: {args.sound_file}")
+    waves = read_sound_files(
+        filenames=[args.sound_file],
+        expected_sample_rate=sample_rate,
+    )[0]
+
+    tail_padding = torch.zeros(int(0.3 * sample_rate), dtype=torch.float32)
+    wave_samples = torch.cat([waves, tail_padding])
+
+    num_processed_frames = 0
+    segment = model.segment
+    offset = model.offset
+
+    context_size = model.context_size
+    hyp = None
+    decoder_out = None
+
+    chunk = int(1 * sample_rate)  # 1 second
+    start = 0
+    while start < wave_samples.numel():
+        end = min(start + chunk, wave_samples.numel())
+        samples = wave_samples[start:end]
+        start += chunk
+
+        online_fbank.accept_waveform(
+            sampling_rate=sample_rate,
+            waveform=samples,
+        )
+
+        while online_fbank.num_frames_ready - num_processed_frames >= segment:
+            frames = []
+            for i in range(segment):
+                frames.append(online_fbank.get_frame(num_processed_frames + i))
+            num_processed_frames += offset
+            frames = torch.cat(frames, dim=0)
+            frames = frames.unsqueeze(0)
+            encoder_out = model.run_encoder(frames)
+            hyp, decoder_out = greedy_search(
+                model,
+                encoder_out,
+                context_size,
+                decoder_out,
+                hyp,
+            )
+
+    symbol_table = k2.SymbolTable.from_file(args.tokens)
+
+    text = ""
+    for i in hyp[context_size:]:
+        text += symbol_table[i]
+    text = text.replace("▁", " ").strip()
+
+    logging.info(args.sound_file)
+    logging.info(text)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/librispeech/ASR/lstm_transducer_stateless2/streaming-ncnn-decode.py

@@ -16,13 +16,18 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""
+Please see
+https://k2-fsa.github.io/icefall/model-export/export-ncnn.html
+for usage
+"""
 
 import argparse
 import logging
 from typing import List, Optional
 
+import k2
 import ncnn
-import sentencepiece as spm
 import torch
 import torchaudio
 from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
@@ -32,9 +37,9 @@ def get_args():
     parser = argparse.ArgumentParser()
 
     parser.add_argument(
-        "--bpe-model-filename",
+        "--tokens",
         type=str,
-        help="Path to bpe.model",
+        help="Path to tokens.txt",
     )
 
     parser.add_argument(
@@ -251,9 +256,6 @@ def main():
 
     model = Model(args)
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(args.bpe_model_filename)
-
     sound_file = args.sound_filename
 
     sample_rate = 16000
@@ -329,10 +331,16 @@ def main():
             model, encoder_out.squeeze(0), decoder_out, hyp
         )
 
+    symbol_table = k2.SymbolTable.from_file(args.tokens)
+
     context_size = 2
+    text = ""
+    for i in hyp[context_size:]:
+        text += symbol_table[i]
+    text = text.replace("▁", " ").strip()
 
     logging.info(sound_file)
-    logging.info(sp.decode(hyp[context_size:]))
+    logging.info(text)
 
 
 if __name__ == "__main__":

egs/librispeech/ASR/lstm_transducer_stateless3/export-onnx.py

@@ -0,0 +1 @@
+../lstm_transducer_stateless2/export-onnx.py

egs/librispeech/ASR/lstm_transducer_stateless3/onnx_check.py

@@ -0,0 +1 @@
+../lstm_transducer_stateless2/onnx_check.py

egs/librispeech/ASR/lstm_transducer_stateless3/onnx_pretrained.py

@@ -0,0 +1 @@
+../lstm_transducer_stateless2/onnx_pretrained.py

egs/librispeech/ASR/pruned_transducer_stateless3/onnx_check.py

@@ -125,13 +125,13 @@ def test_encoder(
     onnx_model: OnnxModel,
 ):
     C = 80
-    for i in range(10):
+    for i in range(3):
         N = torch.randint(low=1, high=20, size=(1,)).item()
-        T = torch.randint(low=50, high=100, size=(1,)).item()
+        T = torch.randint(low=30, high=50, size=(1,)).item()
         logging.info(f"test_encoder: iter {i}, N={N}, T={T}")
 
         x = torch.rand(N, T, C)
-        x_lens = torch.randint(low=10, high=T + 1, size=(N,))
+        x_lens = torch.randint(low=30, high=T + 1, size=(N,))
         x_lens[0] = T
 
         torch_encoder_out, torch_encoder_out_lens = torch_model.encoder(x, x_lens)

egs/librispeech/ASR/pruned_transducer_stateless7/export-onnx.py

@@ -0,0 +1,560 @@
+#!/usr/bin/env python3
+#
+# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)
+
+"""
+This script exports a transducer model from PyTorch to ONNX.
+
+We use the pre-trained model from
+https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11
+as an example to show how to use this file.
+
+1. Download the pre-trained model
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11
+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-9.pt"
+
+cd exp
+ln -s pretrained-epoch-30-avg-9.pt epoch-99.pt
+popd
+
+2. Export the model to ONNX
+
+./pruned_transducer_stateless7/export-onnx.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp \
+  --feedforward-dims "1024,1024,2048,2048,1024"
+
+It will generate the following 3 files inside $repo/exp:
+
+  - encoder-epoch-99-avg-1.onnx
+  - decoder-epoch-99-avg-1.onnx
+  - joiner-epoch-99-avg-1.onnx
+
+See ./onnx_pretrained.py and ./onnx_check.py for how to
+use the exported ONNX models.
+"""
+
+import argparse
+import logging
+from pathlib import Path
+from typing import Dict, Tuple
+
+import onnx
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from decoder import Decoder
+from scaling_converter import convert_scaled_to_non_scaled
+from train import add_model_arguments, get_params, get_transducer_model
+from zipformer import Zipformer
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import setup_logger, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="""It specifies the checkpoint to use for averaging.
+        Note: Epoch counts from 0.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=True,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless5/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def add_meta_data(filename: str, meta_data: Dict[str, str]):
+    """Add meta data to an ONNX model. It is changed in-place.
+
+    Args:
+      filename:
+        Filename of the ONNX model to be changed.
+      meta_data:
+        Key-value pairs.
+    """
+    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)
+
+
+class OnnxEncoder(nn.Module):
+    """A wrapper for Zipformer and the encoder_proj from the joiner"""
+
+    def __init__(self, encoder: Zipformer, encoder_proj: nn.Linear):
+        """
+        Args:
+          encoder:
+            A Zipformer encoder.
+          encoder_proj:
+            The projection layer for encoder from the joiner.
+        """
+        super().__init__()
+        self.encoder = encoder
+        self.encoder_proj = encoder_proj
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Please see the help information of Zipformer.forward
+
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C)
+          x_lens:
+            A 1-D tensor of shape (N,). Its dtype is torch.int64
+        Returns:
+          Return a tuple containing:
+            - encoder_out, A 3-D tensor of shape (N, T', joiner_dim)
+            - encoder_out_lens, A 1-D tensor of shape (N,)
+        """
+        encoder_out, encoder_out_lens = self.encoder(x, x_lens)
+
+        encoder_out = self.encoder_proj(encoder_out)
+        # Now encoder_out is of shape (N, T, joiner_dim)
+
+        return encoder_out, encoder_out_lens
+
+
+class OnnxDecoder(nn.Module):
+    """A wrapper for Decoder and the decoder_proj from the joiner"""
+
+    def __init__(self, decoder: Decoder, decoder_proj: nn.Linear):
+        super().__init__()
+        self.decoder = decoder
+        self.decoder_proj = decoder_proj
+
+    def forward(self, y: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+          y:
+            A 2-D tensor of shape (N, context_size).
+        Returns
+          Return a 2-D tensor of shape (N, joiner_dim)
+        """
+        need_pad = False
+        decoder_output = self.decoder(y, need_pad=need_pad)
+        decoder_output = decoder_output.squeeze(1)
+        output = self.decoder_proj(decoder_output)
+
+        return output
+
+
+class OnnxJoiner(nn.Module):
+    """A wrapper for the joiner"""
+
+    def __init__(self, output_linear: nn.Linear):
+        super().__init__()
+        self.output_linear = output_linear
+
+    def forward(
+        self,
+        encoder_out: torch.Tensor,
+        decoder_out: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+          encoder_out:
+            A 2-D tensor of shape (N, joiner_dim)
+          decoder_out:
+            A 2-D tensor of shape (N, joiner_dim)
+        Returns:
+          Return a 2-D tensor of shape (N, vocab_size)
+        """
+        logit = encoder_out + decoder_out
+        logit = self.output_linear(torch.tanh(logit))
+        return logit
+
+
+def export_encoder_model_onnx(
+    encoder_model: OnnxEncoder,
+    encoder_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the given encoder model to ONNX format.
+    The exported model has two inputs:
+
+        - x, a tensor of shape (N, T, C); dtype is torch.float32
+        - x_lens, a tensor of shape (N,); dtype is torch.int64
+
+    and it has two outputs:
+
+        - encoder_out, a tensor of shape (N, T', joiner_dim)
+        - encoder_out_lens, a tensor of shape (N,)
+
+    Args:
+      encoder_model:
+        The input encoder model
+      encoder_filename:
+        The filename to save the exported ONNX model.
+      opset_version:
+        The opset version to use.
+    """
+    x = torch.zeros(1, 100, 80, dtype=torch.float32)
+    x_lens = torch.tensor([100], dtype=torch.int64)
+
+    torch.onnx.export(
+        encoder_model,
+        (x, x_lens),
+        encoder_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=["x", "x_lens"],
+        output_names=["encoder_out", "encoder_out_lens"],
+        dynamic_axes={
+            "x": {0: "N", 1: "T"},
+            "x_lens": {0: "N"},
+            "encoder_out": {0: "N", 1: "T"},
+            "encoder_out_lens": {0: "N"},
+        },
+    )
+
+
+def export_decoder_model_onnx(
+    decoder_model: OnnxDecoder,
+    decoder_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the decoder model to ONNX format.
+
+    The exported model has one input:
+
+        - y: a torch.int64 tensor of shape (N, decoder_model.context_size)
+
+    and has one output:
+
+        - decoder_out: a torch.float32 tensor of shape (N, joiner_dim)
+
+    Args:
+      decoder_model:
+        The decoder model to be exported.
+      decoder_filename:
+        Filename to save the exported ONNX model.
+      opset_version:
+        The opset version to use.
+    """
+    context_size = decoder_model.decoder.context_size
+    vocab_size = decoder_model.decoder.vocab_size
+
+    y = torch.zeros(10, context_size, dtype=torch.int64)
+    torch.onnx.export(
+        decoder_model,
+        y,
+        decoder_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=["y"],
+        output_names=["decoder_out"],
+        dynamic_axes={
+            "y": {0: "N"},
+            "decoder_out": {0: "N"},
+        },
+    )
+
+    meta_data = {
+        "context_size": str(context_size),
+        "vocab_size": str(vocab_size),
+    }
+    add_meta_data(filename=decoder_filename, meta_data=meta_data)
+
+
+def export_joiner_model_onnx(
+    joiner_model: nn.Module,
+    joiner_filename: str,
+    opset_version: int = 11,
+) -> None:
+    """Export the joiner model to ONNX format.
+    The exported joiner model has two inputs:
+
+        - encoder_out: a tensor of shape (N, joiner_dim)
+        - decoder_out: a tensor of shape (N, joiner_dim)
+
+    and produces one output:
+
+        - logit: a tensor of shape (N, vocab_size)
+    """
+    joiner_dim = joiner_model.output_linear.weight.shape[1]
+    logging.info(f"joiner dim: {joiner_dim}")
+
+    projected_encoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
+    projected_decoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
+
+    torch.onnx.export(
+        joiner_model,
+        (projected_encoder_out, projected_decoder_out),
+        joiner_filename,
+        verbose=False,
+        opset_version=opset_version,
+        input_names=[
+            "encoder_out",
+            "decoder_out",
+        ],
+        output_names=["logit"],
+        dynamic_axes={
+            "encoder_out": {0: "N"},
+            "decoder_out": {0: "N"},
+            "logit": {0: "N"},
+        },
+    )
+    meta_data = {
+        "joiner_dim": str(joiner_dim),
+    }
+    add_meta_data(filename=joiner_filename, meta_data=meta_data)
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    setup_logger(f"{params.exp_dir}/log-export/log-export-onnx")
+
+    logging.info(f"device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    model.to(device)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg + 1
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0, params.avg
+            start = params.epoch - params.avg
+            assert start >= 1, start
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to("cpu")
+    model.eval()
+
+    convert_scaled_to_non_scaled(model, inplace=True)
+
+    encoder = OnnxEncoder(
+        encoder=model.encoder,
+        encoder_proj=model.joiner.encoder_proj,
+    )
+
+    decoder = OnnxDecoder(
+        decoder=model.decoder,
+        decoder_proj=model.joiner.decoder_proj,
+    )
+
+    joiner = OnnxJoiner(output_linear=model.joiner.output_linear)
+
+    encoder_num_param = sum([p.numel() for p in encoder.parameters()])
+    decoder_num_param = sum([p.numel() for p in decoder.parameters()])
+    joiner_num_param = sum([p.numel() for p in joiner.parameters()])
+    total_num_param = encoder_num_param + decoder_num_param + joiner_num_param
+    logging.info(f"encoder parameters: {encoder_num_param}")
+    logging.info(f"decoder parameters: {decoder_num_param}")
+    logging.info(f"joiner parameters: {joiner_num_param}")
+    logging.info(f"total parameters: {total_num_param}")
+
+    if params.iter > 0:
+        suffix = f"iter-{params.iter}"
+    else:
+        suffix = f"epoch-{params.epoch}"
+
+    suffix += f"-avg-{params.avg}"
+
+    opset_version = 13
+
+    logging.info("Exporting encoder")
+    encoder_filename = params.exp_dir / f"encoder-{suffix}.onnx"
+    export_encoder_model_onnx(
+        encoder,
+        encoder_filename,
+        opset_version=opset_version,
+    )
+    logging.info(f"Exported encoder to {encoder_filename}")
+
+    logging.info("Exporting decoder")
+    decoder_filename = params.exp_dir / f"decoder-{suffix}.onnx"
+    export_decoder_model_onnx(
+        decoder,
+        decoder_filename,
+        opset_version=opset_version,
+    )
+    logging.info(f"Exported decoder to {decoder_filename}")
+
+    logging.info("Exporting joiner")
+    joiner_filename = params.exp_dir / f"joiner-{suffix}.onnx"
+    export_joiner_model_onnx(
+        joiner,
+        joiner_filename,
+        opset_version=opset_version,
+    )
+    logging.info(f"Exported joiner to {joiner_filename}")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    main()

egs/librispeech/ASR/pruned_transducer_stateless7/export.py

@@ -41,31 +41,7 @@ Check
 https://github.com/k2-fsa/sherpa
 for how to use the exported models outside of icefall.
 
-(2) Export to ONNX format
+(2) Export `model.state_dict()`
-
-./pruned_transducer_stateless7/export.py \
-  --exp-dir ./pruned_transducer_stateless7/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
-  --epoch 20 \
-  --avg 10 \
-  --onnx 1
-
-It will generate the following files in the given `exp_dir`.
-Check `onnx_check.py` for how to use them.
-
-    - encoder.onnx
-    - decoder.onnx
-    - joiner.onnx
-    - joiner_encoder_proj.onnx
-    - joiner_decoder_proj.onnx
-
-Please see ./onnx_pretrained.py for usage of the generated files
-
-Check
-https://github.com/k2-fsa/sherpa-onnx
-for how to use the exported models outside of icefall.
-
-(3) Export `model.state_dict()`
 
 ./pruned_transducer_stateless7/export.py \
   --exp-dir ./pruned_transducer_stateless7/exp \
@@ -196,23 +172,6 @@ def get_parser():
         """,
     )
 
-    parser.add_argument(
-        "--onnx",
-        type=str2bool,
-        default=False,
-        help="""If True, --jit is ignored and it exports the model
-        to onnx format. It will generate the following files:
-
-            - encoder.onnx
-            - decoder.onnx
-            - joiner.onnx
-            - joiner_encoder_proj.onnx
-            - joiner_decoder_proj.onnx
-
-        Refer to ./onnx_check.py and ./onnx_pretrained.py for how to use them.
-        """,
-    )
-
     parser.add_argument(
         "--context-size",
         type=int,
@@ -225,204 +184,6 @@ def get_parser():
     return parser
 
 
-def export_encoder_model_onnx(
-    encoder_model: nn.Module,
-    encoder_filename: str,
-    opset_version: int = 11,
-) -> None:
-    """Export the given encoder model to ONNX format.
-    The exported model has two inputs:
-
-        - x, a tensor of shape (N, T, C); dtype is torch.float32
-        - x_lens, a tensor of shape (N,); dtype is torch.int64
-
-    and it has two outputs:
-
-        - encoder_out, a tensor of shape (N, T, C)
-        - encoder_out_lens, a tensor of shape (N,)
-
-    Note: The warmup argument is fixed to 1.
-
-    Args:
-      encoder_model:
-        The input encoder model
-      encoder_filename:
-        The filename to save the exported ONNX model.
-      opset_version:
-        The opset version to use.
-    """
-    x = torch.zeros(1, 101, 80, dtype=torch.float32)
-    x_lens = torch.tensor([101], dtype=torch.int64)
-
-    #  encoder_model = torch.jit.script(encoder_model)
-    # It throws the following error for the above statement
-    #
-    # RuntimeError: Exporting the operator __is_ to ONNX opset version
-    # 11 is not supported. Please feel free to request support or
-    # submit a pull request on PyTorch GitHub.
-    #
-    # I cannot find which statement causes the above error.
-    # torch.onnx.export() will use torch.jit.trace() internally, which
-    # works well for the current reworked model
-    torch.onnx.export(
-        encoder_model,
-        (x, x_lens),
-        encoder_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=["x", "x_lens"],
-        output_names=["encoder_out", "encoder_out_lens"],
-        dynamic_axes={
-            "x": {0: "N", 1: "T"},
-            "x_lens": {0: "N"},
-            "encoder_out": {0: "N", 1: "T"},
-            "encoder_out_lens": {0: "N"},
-        },
-    )
-    logging.info(f"Saved to {encoder_filename}")
-
-
-def export_decoder_model_onnx(
-    decoder_model: nn.Module,
-    decoder_filename: str,
-    opset_version: int = 11,
-) -> None:
-    """Export the decoder model to ONNX format.
-
-    The exported model has one input:
-
-        - y: a torch.int64 tensor of shape (N, decoder_model.context_size)
-
-    and has one output:
-
-        - decoder_out: a torch.float32 tensor of shape (N, 1, C)
-
-    Note: The argument need_pad is fixed to False.
-
-    Args:
-      decoder_model:
-        The decoder model to be exported.
-      decoder_filename:
-        Filename to save the exported ONNX model.
-      opset_version:
-        The opset version to use.
-    """
-    y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64)
-    need_pad = False  # Always False, so we can use torch.jit.trace() here
-    # Note(fangjun): torch.jit.trace() is more efficient than torch.jit.script()
-    # in this case
-    torch.onnx.export(
-        decoder_model,
-        (y, need_pad),
-        decoder_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=["y", "need_pad"],
-        output_names=["decoder_out"],
-        dynamic_axes={
-            "y": {0: "N"},
-            "decoder_out": {0: "N"},
-        },
-    )
-    logging.info(f"Saved to {decoder_filename}")
-
-
-def export_joiner_model_onnx(
-    joiner_model: nn.Module,
-    joiner_filename: str,
-    opset_version: int = 11,
-) -> None:
-    """Export the joiner model to ONNX format.
-    The exported joiner model has two inputs:
-
-        - projected_encoder_out: a tensor of shape (N, joiner_dim)
-        - projected_decoder_out: a tensor of shape (N, joiner_dim)
-
-    and produces one output:
-
-        - logit: a tensor of shape (N, vocab_size)
-
-    The exported encoder_proj model has one input:
-
-        - encoder_out: a tensor of shape (N, encoder_out_dim)
-
-    and produces one output:
-
-        - projected_encoder_out: a tensor of shape (N, joiner_dim)
-
-    The exported decoder_proj model has one input:
-
-        - decoder_out: a tensor of shape (N, decoder_out_dim)
-
-    and produces one output:
-
-        - projected_decoder_out: a tensor of shape (N, joiner_dim)
-    """
-    encoder_proj_filename = str(joiner_filename).replace(".onnx", "_encoder_proj.onnx")
-    decoder_proj_filename = str(joiner_filename).replace(".onnx", "_decoder_proj.onnx")
-
-    encoder_out_dim = joiner_model.encoder_proj.weight.shape[1]
-    decoder_out_dim = joiner_model.decoder_proj.weight.shape[1]
-    joiner_dim = joiner_model.decoder_proj.weight.shape[0]
-
-    projected_encoder_out = torch.rand(1, 1, 1, joiner_dim, dtype=torch.float32)
-    projected_decoder_out = torch.rand(1, 1, 1, joiner_dim, dtype=torch.float32)
-
-    project_input = False
-    # Note: It uses torch.jit.trace() internally
-    torch.onnx.export(
-        joiner_model,
-        (projected_encoder_out, projected_decoder_out, project_input),
-        joiner_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=[
-            "encoder_out",
-            "decoder_out",
-            "project_input",
-        ],
-        output_names=["logit"],
-        dynamic_axes={
-            "encoder_out": {0: "N"},
-            "decoder_out": {0: "N"},
-            "logit": {0: "N"},
-        },
-    )
-    logging.info(f"Saved to {joiner_filename}")
-
-    encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32)
-    torch.onnx.export(
-        joiner_model.encoder_proj,
-        encoder_out,
-        encoder_proj_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=["encoder_out"],
-        output_names=["projected_encoder_out"],
-        dynamic_axes={
-            "encoder_out": {0: "N"},
-            "projected_encoder_out": {0: "N"},
-        },
-    )
-    logging.info(f"Saved to {encoder_proj_filename}")
-
-    decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32)
-    torch.onnx.export(
-        joiner_model.decoder_proj,
-        decoder_out,
-        decoder_proj_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=["decoder_out"],
-        output_names=["projected_decoder_out"],
-        dynamic_axes={
-            "decoder_out": {0: "N"},
-            "projected_decoder_out": {0: "N"},
-        },
-    )
-    logging.info(f"Saved to {decoder_proj_filename}")
-
-
 @torch.no_grad()
 def main():
     args = get_parser().parse_args()
@@ -531,31 +292,7 @@ def main():
     model.to("cpu")
     model.eval()
 
-    if params.onnx is True:
+    if params.jit is True:
-        convert_scaled_to_non_scaled(model, inplace=True)
-        opset_version = 13
-        logging.info("Exporting to onnx format")
-        encoder_filename = params.exp_dir / "encoder.onnx"
-        export_encoder_model_onnx(
-            model.encoder,
-            encoder_filename,
-            opset_version=opset_version,
-        )
-
-        decoder_filename = params.exp_dir / "decoder.onnx"
-        export_decoder_model_onnx(
-            model.decoder,
-            decoder_filename,
-            opset_version=opset_version,
-        )
-
-        joiner_filename = params.exp_dir / "joiner.onnx"
-        export_joiner_model_onnx(
-            model.joiner,
-            joiner_filename,
-            opset_version=opset_version,
-        )
-    elif params.jit is True:
         convert_scaled_to_non_scaled(model, inplace=True)
         # We won't use the forward() method of the model in C++, so just ignore
         # it here.

egs/librispeech/ASR/pruned_transducer_stateless7/onnx_check.py

@@ -1,286 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright 2022 Xiaomi Corporation (Author: Fangjun Kuang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-This script checks that exported onnx models produce the same output
-with the given torchscript model for the same input.
-"""
-
-import argparse
-import logging
-
-import onnxruntime as ort
-import torch
-
-from icefall import is_module_available
-
-if not is_module_available("onnxruntime"):
-    raise ValueError("Please 'pip install onnxruntime' first.")
-
-
-ort.set_default_logger_severity(3)
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--jit-filename",
-        required=True,
-        type=str,
-        help="Path to the torchscript model",
-    )
-
-    parser.add_argument(
-        "--onnx-encoder-filename",
-        required=True,
-        type=str,
-        help="Path to the onnx encoder model",
-    )
-
-    parser.add_argument(
-        "--onnx-decoder-filename",
-        required=True,
-        type=str,
-        help="Path to the onnx decoder model",
-    )
-
-    parser.add_argument(
-        "--onnx-joiner-filename",
-        required=True,
-        type=str,
-        help="Path to the onnx joiner model",
-    )
-
-    parser.add_argument(
-        "--onnx-joiner-encoder-proj-filename",
-        required=True,
-        type=str,
-        help="Path to the onnx joiner encoder projection model",
-    )
-
-    parser.add_argument(
-        "--onnx-joiner-decoder-proj-filename",
-        required=True,
-        type=str,
-        help="Path to the onnx joiner decoder projection model",
-    )
-
-    return parser
-
-
-def test_encoder(
-    model: torch.jit.ScriptModule,
-    encoder_session: ort.InferenceSession,
-):
-    inputs = encoder_session.get_inputs()
-    outputs = encoder_session.get_outputs()
-    input_names = [n.name for n in inputs]
-    output_names = [n.name for n in outputs]
-
-    assert inputs[0].shape == ["N", "T", 80]
-    assert inputs[1].shape == ["N"]
-
-    for N in [1, 5]:
-        for T in [12, 50]:
-            print("N, T", N, T)
-            x = torch.rand(N, T, 80, dtype=torch.float32)
-            x_lens = torch.randint(low=10, high=T + 1, size=(N,))
-            x_lens[0] = T
-
-            encoder_inputs = {
-                input_names[0]: x.numpy(),
-                input_names[1]: x_lens.numpy(),
-            }
-
-            torch_encoder_out, torch_encoder_out_lens = model.encoder(x, x_lens)
-
-            encoder_out, encoder_out_lens = encoder_session.run(
-                output_names,
-                encoder_inputs,
-            )
-
-            torch_encoder_out, torch_encoder_out_lens = model.encoder(x, x_lens)
-
-            encoder_out = torch.from_numpy(encoder_out)
-            assert torch.allclose(encoder_out, torch_encoder_out, atol=1e-05), (
-                (encoder_out - torch_encoder_out).abs().max(),
-                encoder_out.shape,
-                torch_encoder_out.shape,
-            )
-
-
-def test_decoder(
-    model: torch.jit.ScriptModule,
-    decoder_session: ort.InferenceSession,
-):
-    inputs = decoder_session.get_inputs()
-    outputs = decoder_session.get_outputs()
-    input_names = [n.name for n in inputs]
-    output_names = [n.name for n in outputs]
-
-    assert inputs[0].shape == ["N", 2]
-    for N in [1, 5, 10]:
-        y = torch.randint(low=1, high=500, size=(10, 2))
-
-        decoder_inputs = {input_names[0]: y.numpy()}
-        decoder_out = decoder_session.run(
-            output_names,
-            decoder_inputs,
-        )[0]
-        decoder_out = torch.from_numpy(decoder_out)
-
-        torch_decoder_out = model.decoder(y, need_pad=False)
-        assert torch.allclose(decoder_out, torch_decoder_out, atol=1e-5), (
-            (decoder_out - torch_decoder_out).abs().max()
-        )
-
-
-def test_joiner(
-    model: torch.jit.ScriptModule,
-    joiner_session: ort.InferenceSession,
-    joiner_encoder_proj_session: ort.InferenceSession,
-    joiner_decoder_proj_session: ort.InferenceSession,
-):
-    joiner_inputs = joiner_session.get_inputs()
-    joiner_outputs = joiner_session.get_outputs()
-    joiner_input_names = [n.name for n in joiner_inputs]
-    joiner_output_names = [n.name for n in joiner_outputs]
-
-    assert joiner_inputs[0].shape == ["N", 1, 1, 512]
-    assert joiner_inputs[1].shape == ["N", 1, 1, 512]
-
-    joiner_encoder_proj_inputs = joiner_encoder_proj_session.get_inputs()
-    encoder_proj_input_name = joiner_encoder_proj_inputs[0].name
-
-    assert joiner_encoder_proj_inputs[0].shape == ["N", 384]
-
-    joiner_encoder_proj_outputs = joiner_encoder_proj_session.get_outputs()
-    encoder_proj_output_name = joiner_encoder_proj_outputs[0].name
-
-    joiner_decoder_proj_inputs = joiner_decoder_proj_session.get_inputs()
-    decoder_proj_input_name = joiner_decoder_proj_inputs[0].name
-
-    assert joiner_decoder_proj_inputs[0].shape == ["N", 512]
-
-    joiner_decoder_proj_outputs = joiner_decoder_proj_session.get_outputs()
-    decoder_proj_output_name = joiner_decoder_proj_outputs[0].name
-
-    for N in [1, 5, 10]:
-        encoder_out = torch.rand(N, 384)
-        decoder_out = torch.rand(N, 512)
-
-        projected_encoder_out = torch.rand(N, 1, 1, 512)
-        projected_decoder_out = torch.rand(N, 1, 1, 512)
-
-        joiner_inputs = {
-            joiner_input_names[0]: projected_encoder_out.numpy(),
-            joiner_input_names[1]: projected_decoder_out.numpy(),
-        }
-        joiner_out = joiner_session.run(joiner_output_names, joiner_inputs)[0]
-        joiner_out = torch.from_numpy(joiner_out)
-
-        torch_joiner_out = model.joiner(
-            projected_encoder_out,
-            projected_decoder_out,
-            project_input=False,
-        )
-        assert torch.allclose(joiner_out, torch_joiner_out, atol=1e-5), (
-            (joiner_out - torch_joiner_out).abs().max()
-        )
-
-        # Now test encoder_proj
-        joiner_encoder_proj_inputs = {encoder_proj_input_name: encoder_out.numpy()}
-        joiner_encoder_proj_out = joiner_encoder_proj_session.run(
-            [encoder_proj_output_name], joiner_encoder_proj_inputs
-        )[0]
-        joiner_encoder_proj_out = torch.from_numpy(joiner_encoder_proj_out)
-
-        torch_joiner_encoder_proj_out = model.joiner.encoder_proj(encoder_out)
-        assert torch.allclose(
-            joiner_encoder_proj_out, torch_joiner_encoder_proj_out, atol=1e-5
-        ), ((joiner_encoder_proj_out - torch_joiner_encoder_proj_out).abs().max())
-
-        # Now test decoder_proj
-        joiner_decoder_proj_inputs = {decoder_proj_input_name: decoder_out.numpy()}
-        joiner_decoder_proj_out = joiner_decoder_proj_session.run(
-            [decoder_proj_output_name], joiner_decoder_proj_inputs
-        )[0]
-        joiner_decoder_proj_out = torch.from_numpy(joiner_decoder_proj_out)
-
-        torch_joiner_decoder_proj_out = model.joiner.decoder_proj(decoder_out)
-        assert torch.allclose(
-            joiner_decoder_proj_out, torch_joiner_decoder_proj_out, atol=1e-5
-        ), ((joiner_decoder_proj_out - torch_joiner_decoder_proj_out).abs().max())
-
-
-@torch.no_grad()
-def main():
-    args = get_parser().parse_args()
-    logging.info(vars(args))
-
-    model = torch.jit.load(args.jit_filename)
-
-    options = ort.SessionOptions()
-    options.inter_op_num_threads = 1
-    options.intra_op_num_threads = 1
-
-    logging.info("Test encoder")
-    encoder_session = ort.InferenceSession(
-        args.onnx_encoder_filename,
-        sess_options=options,
-    )
-    test_encoder(model, encoder_session)
-
-    logging.info("Test decoder")
-    decoder_session = ort.InferenceSession(
-        args.onnx_decoder_filename,
-        sess_options=options,
-    )
-    test_decoder(model, decoder_session)
-
-    logging.info("Test joiner")
-    joiner_session = ort.InferenceSession(
-        args.onnx_joiner_filename,
-        sess_options=options,
-    )
-    joiner_encoder_proj_session = ort.InferenceSession(
-        args.onnx_joiner_encoder_proj_filename,
-        sess_options=options,
-    )
-    joiner_decoder_proj_session = ort.InferenceSession(
-        args.onnx_joiner_decoder_proj_filename,
-        sess_options=options,
-    )
-    test_joiner(
-        model,
-        joiner_session,
-        joiner_encoder_proj_session,
-        joiner_decoder_proj_session,
-    )
-    logging.info("Finished checking ONNX models")
-
-
-if __name__ == "__main__":
-    torch.manual_seed(20220727)
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()

egs/librispeech/ASR/pruned_transducer_stateless7/onnx_check.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless5/onnx_check.py

egs/librispeech/ASR/pruned_transducer_stateless7/onnx_pretrained.py

@@ -1,388 +0,0 @@
-#!/usr/bin/env python3
-# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-This script loads ONNX models and uses them to decode waves.
-You can use the following command to get the exported models:
-
-./pruned_transducer_stateless7/export.py \
-  --exp-dir ./pruned_transducer_stateless7/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
-  --epoch 20 \
-  --avg 10 \
-  --onnx 1
-
-Usage of this script:
-
-./pruned_transducer_stateless7/onnx_pretrained.py \
-  --encoder-model-filename ./pruned_transducer_stateless7/exp/encoder.onnx \
-  --decoder-model-filename ./pruned_transducer_stateless7/exp/decoder.onnx \
-  --joiner-model-filename ./pruned_transducer_stateless7/exp/joiner.onnx \
-  --joiner-encoder-proj-model-filename ./pruned_transducer_stateless7/exp/joiner_encoder_proj.onnx \
-  --joiner-decoder-proj-model-filename ./pruned_transducer_stateless7/exp/joiner_decoder_proj.onnx \
-  --bpe-model ./data/lang_bpe_500/bpe.model \
-  /path/to/foo.wav \
-  /path/to/bar.wav
-"""
-
-import argparse
-import logging
-import math
-from typing import List
-
-import kaldifeat
-import numpy as np
-import onnxruntime as ort
-import sentencepiece as spm
-import torch
-import torchaudio
-from torch.nn.utils.rnn import pad_sequence
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--encoder-model-filename",
-        type=str,
-        required=True,
-        help="Path to the encoder onnx model. ",
-    )
-
-    parser.add_argument(
-        "--decoder-model-filename",
-        type=str,
-        required=True,
-        help="Path to the decoder onnx model. ",
-    )
-
-    parser.add_argument(
-        "--joiner-model-filename",
-        type=str,
-        required=True,
-        help="Path to the joiner onnx model. ",
-    )
-
-    parser.add_argument(
-        "--joiner-encoder-proj-model-filename",
-        type=str,
-        required=True,
-        help="Path to the joiner encoder_proj onnx model. ",
-    )
-
-    parser.add_argument(
-        "--joiner-decoder-proj-model-filename",
-        type=str,
-        required=True,
-        help="Path to the joiner decoder_proj onnx model. ",
-    )
-
-    parser.add_argument(
-        "--bpe-model",
-        type=str,
-        help="""Path to bpe.model.""",
-    )
-
-    parser.add_argument(
-        "sound_files",
-        type=str,
-        nargs="+",
-        help="The input sound file(s) to transcribe. "
-        "Supported formats are those supported by torchaudio.load(). "
-        "For example, wav and flac are supported. "
-        "The sample rate has to be 16kHz.",
-    )
-
-    parser.add_argument(
-        "--sample-rate",
-        type=int,
-        default=16000,
-        help="The sample rate of the input sound file",
-    )
-
-    parser.add_argument(
-        "--context-size",
-        type=int,
-        default=2,
-        help="Context size of the decoder model",
-    )
-
-    return parser
-
-
-def read_sound_files(
-    filenames: List[str], expected_sample_rate: float
-) -> List[torch.Tensor]:
-    """Read a list of sound files into a list 1-D float32 torch tensors.
-    Args:
-      filenames:
-        A list of sound filenames.
-      expected_sample_rate:
-        The expected sample rate of the sound files.
-    Returns:
-      Return a list of 1-D float32 torch tensors.
-    """
-    ans = []
-    for f in filenames:
-        wave, sample_rate = torchaudio.load(f)
-        assert (
-            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])
-    return ans
-
-
-def greedy_search(
-    decoder: ort.InferenceSession,
-    joiner: ort.InferenceSession,
-    joiner_encoder_proj: ort.InferenceSession,
-    joiner_decoder_proj: ort.InferenceSession,
-    encoder_out: np.ndarray,
-    encoder_out_lens: np.ndarray,
-    context_size: int,
-) -> List[List[int]]:
-    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
-    Args:
-      decoder:
-        The decoder model.
-      joiner:
-        The joiner model.
-      joiner_encoder_proj:
-        The joiner encoder projection model.
-      joiner_decoder_proj:
-        The joiner decoder projection model.
-      encoder_out:
-        A 3-D tensor of shape (N, T, C)
-      encoder_out_lens:
-        A 1-D tensor of shape (N,).
-      context_size:
-        The context size of the decoder model.
-    Returns:
-      Return the decoded results for each utterance.
-    """
-    encoder_out = torch.from_numpy(encoder_out)
-    encoder_out_lens = torch.from_numpy(encoder_out_lens)
-    assert encoder_out.ndim == 3
-    assert encoder_out.size(0) >= 1, encoder_out.size(0)
-
-    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
-        input=encoder_out,
-        lengths=encoder_out_lens.cpu(),
-        batch_first=True,
-        enforce_sorted=False,
-    )
-
-    projected_encoder_out = joiner_encoder_proj.run(
-        [joiner_encoder_proj.get_outputs()[0].name],
-        {joiner_encoder_proj.get_inputs()[0].name: packed_encoder_out.data.numpy()},
-    )[0]
-
-    blank_id = 0  # hard-code to 0
-
-    batch_size_list = packed_encoder_out.batch_sizes.tolist()
-    N = encoder_out.size(0)
-
-    assert torch.all(encoder_out_lens > 0), encoder_out_lens
-    assert N == batch_size_list[0], (N, batch_size_list)
-
-    hyps = [[blank_id] * context_size for _ in range(N)]
-
-    decoder_input_nodes = decoder.get_inputs()
-    decoder_output_nodes = decoder.get_outputs()
-
-    joiner_input_nodes = joiner.get_inputs()
-    joiner_output_nodes = joiner.get_outputs()
-
-    decoder_input = torch.tensor(
-        hyps,
-        dtype=torch.int64,
-    )  # (N, context_size)
-
-    decoder_out = decoder.run(
-        [decoder_output_nodes[0].name],
-        {
-            decoder_input_nodes[0].name: decoder_input.numpy(),
-        },
-    )[0].squeeze(1)
-    projected_decoder_out = joiner_decoder_proj.run(
-        [joiner_decoder_proj.get_outputs()[0].name],
-        {joiner_decoder_proj.get_inputs()[0].name: decoder_out},
-    )[0]
-
-    projected_decoder_out = torch.from_numpy(projected_decoder_out)
-
-    offset = 0
-    for batch_size in batch_size_list:
-        start = offset
-        end = offset + batch_size
-        current_encoder_out = projected_encoder_out[start:end]
-        # current_encoder_out's shape: (batch_size, encoder_out_dim)
-        offset = end
-
-        projected_decoder_out = projected_decoder_out[:batch_size]
-
-        logits = joiner.run(
-            [joiner_output_nodes[0].name],
-            {
-                joiner_input_nodes[0].name: np.expand_dims(
-                    np.expand_dims(current_encoder_out, axis=1), axis=1
-                ),
-                joiner_input_nodes[1]
-                .name: projected_decoder_out.unsqueeze(1)
-                .unsqueeze(1)
-                .numpy(),
-            },
-        )[0]
-        logits = torch.from_numpy(logits).squeeze(1).squeeze(1)
-        # logits'shape (batch_size, vocab_size)
-
-        assert logits.ndim == 2, logits.shape
-        y = logits.argmax(dim=1).tolist()
-        emitted = False
-        for i, v in enumerate(y):
-            if v != blank_id:
-                hyps[i].append(v)
-                emitted = True
-        if emitted:
-            # update decoder output
-            decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
-            decoder_input = torch.tensor(
-                decoder_input,
-                dtype=torch.int64,
-            )
-            decoder_out = decoder.run(
-                [decoder_output_nodes[0].name],
-                {
-                    decoder_input_nodes[0].name: decoder_input.numpy(),
-                },
-            )[0].squeeze(1)
-            projected_decoder_out = joiner_decoder_proj.run(
-                [joiner_decoder_proj.get_outputs()[0].name],
-                {joiner_decoder_proj.get_inputs()[0].name: decoder_out},
-            )[0]
-            projected_decoder_out = torch.from_numpy(projected_decoder_out)
-
-    sorted_ans = [h[context_size:] for h in hyps]
-    ans = []
-    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
-    for i in range(N):
-        ans.append(sorted_ans[unsorted_indices[i]])
-
-    return ans
-
-
-@torch.no_grad()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-    logging.info(vars(args))
-
-    session_opts = ort.SessionOptions()
-    session_opts.inter_op_num_threads = 1
-    session_opts.intra_op_num_threads = 1
-
-    encoder = ort.InferenceSession(
-        args.encoder_model_filename,
-        sess_options=session_opts,
-    )
-
-    decoder = ort.InferenceSession(
-        args.decoder_model_filename,
-        sess_options=session_opts,
-    )
-
-    joiner = ort.InferenceSession(
-        args.joiner_model_filename,
-        sess_options=session_opts,
-    )
-
-    joiner_encoder_proj = ort.InferenceSession(
-        args.joiner_encoder_proj_model_filename,
-        sess_options=session_opts,
-    )
-
-    joiner_decoder_proj = ort.InferenceSession(
-        args.joiner_decoder_proj_model_filename,
-        sess_options=session_opts,
-    )
-
-    sp = spm.SentencePieceProcessor()
-    sp.load(args.bpe_model)
-
-    logging.info("Constructing Fbank computer")
-    opts = kaldifeat.FbankOptions()
-    opts.device = "cpu"
-    opts.frame_opts.dither = 0
-    opts.frame_opts.snip_edges = False
-    opts.frame_opts.samp_freq = args.sample_rate
-    opts.mel_opts.num_bins = 80
-
-    fbank = kaldifeat.Fbank(opts)
-
-    logging.info(f"Reading sound files: {args.sound_files}")
-    waves = read_sound_files(
-        filenames=args.sound_files,
-        expected_sample_rate=args.sample_rate,
-    )
-
-    logging.info("Decoding started")
-    features = fbank(waves)
-    feature_lengths = [f.size(0) for f in features]
-
-    features = pad_sequence(
-        features,
-        batch_first=True,
-        padding_value=math.log(1e-10),
-    )
-
-    feature_lengths = torch.tensor(feature_lengths, dtype=torch.int64)
-
-    encoder_input_nodes = encoder.get_inputs()
-    encoder_out_nodes = encoder.get_outputs()
-    encoder_out, encoder_out_lens = encoder.run(
-        [encoder_out_nodes[0].name, encoder_out_nodes[1].name],
-        {
-            encoder_input_nodes[0].name: features.numpy(),
-            encoder_input_nodes[1].name: feature_lengths.numpy(),
-        },
-    )
-
-    hyps = greedy_search(
-        decoder=decoder,
-        joiner=joiner,
-        joiner_encoder_proj=joiner_encoder_proj,
-        joiner_decoder_proj=joiner_decoder_proj,
-        encoder_out=encoder_out,
-        encoder_out_lens=encoder_out_lens,
-        context_size=args.context_size,
-    )
-    s = "\n"
-    for filename, hyp in zip(args.sound_files, hyps):
-        words = sp.decode(hyp)
-        s += f"{filename}:\n{words}\n\n"
-    logging.info(s)
-
-    logging.info("Decoding Done")
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()

egs/librispeech/ASR/pruned_transducer_stateless7/onnx_pretrained.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless3/onnx_pretrained.py

egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py

@@ -44,7 +44,7 @@ from scaling import (
 from torch import Tensor, nn
 
 from icefall.dist import get_rank
-from icefall.utils import make_pad_mask
+from icefall.utils import is_jit_tracing, make_pad_mask
 
 
 class Zipformer(EncoderInterface):
@@ -792,6 +792,7 @@ class AttentionDownsample(torch.nn.Module):
         src = src.reshape(d_seq_len, ds, batch_size, in_channels)
         scores = (src * self.query).sum(dim=-1, keepdim=True)
 
+        if not torch.jit.is_scripting() and not torch.jit.is_tracing():
             scores = penalize_abs_values_gt(scores, limit=10.0, penalty=1.0e-04)
 
         weights = scores.softmax(dim=1)
@@ -904,6 +905,13 @@ class RelPositionalEncoding(torch.nn.Module):
     def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000) -> None:
         """Construct a PositionalEncoding object."""
         super(RelPositionalEncoding, self).__init__()
+        if is_jit_tracing():
+            # 10k frames correspond to ~100k ms, e.g., 100 seconds, i.e.,
+            # It assumes that the maximum input won't have more than
+            # 10k frames.
+            #
+            # TODO(fangjun): Use torch.jit.script() for this module
+            max_len = 10000
         self.d_model = d_model
         self.dropout = torch.nn.Dropout(dropout_rate)
         self.pe = None

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-onnx.py

@@ -306,11 +306,11 @@ def export_encoder_model_onnx(
     left_context_len = ",".join(map(str, left_context_len))
 
     meta_data = {
-        "model_type": "streaming_zipformer",
+        "model_type": "zipformer",
         "version": "1",
         "model_author": "k2-fsa",
         "decode_chunk_len": str(decode_chunk_len),  # 32
-        "pad_length": str(pad_length),  # 7
+        "T": str(T),  # 39
         "num_encoder_layers": num_encoder_layers,
         "encoder_dims": encoder_dims,
         "attention_dims": attention_dims,
@@ -362,8 +362,8 @@ def export_encoder_model_onnx(
         input_names=input_names,
         output_names=output_names,
         dynamic_axes={
-            "x": {0: "N", 1: "T"},
+            "x": {0: "N"},
-            "encoder_out": {0: "N", 1: "T"},
+            "encoder_out": {0: "N"},
             **inputs,
             **outputs,
         },

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/onnx_pretrained.py

@@ -136,8 +136,11 @@ class OnnxModel:
     def init_encoder_states(self, batch_size: int = 1):
         encoder_meta = self.encoder.get_modelmeta().custom_metadata_map
 
+        model_type = encoder_meta["model_type"]
+        assert model_type == "zipformer", model_type
+
         decode_chunk_len = int(encoder_meta["decode_chunk_len"])
-        pad_length = int(encoder_meta["pad_length"])
+        T = int(encoder_meta["T"])
 
         num_encoder_layers = encoder_meta["num_encoder_layers"]
         encoder_dims = encoder_meta["encoder_dims"]
@@ -155,7 +158,7 @@ class OnnxModel:
         left_context_len = to_int_list(left_context_len)
 
         logging.info(f"decode_chunk_len: {decode_chunk_len}")
-        logging.info(f"pad_length: {pad_length}")
+        logging.info(f"T: {T}")
         logging.info(f"num_encoder_layers: {num_encoder_layers}")
         logging.info(f"encoder_dims: {encoder_dims}")
         logging.info(f"attention_dims: {attention_dims}")
@@ -219,7 +222,7 @@ class OnnxModel:
 
         self.num_encoders = num_encoders
 
-        self.segment = decode_chunk_len + pad_length
+        self.segment = T
         self.offset = decode_chunk_len
 
     def init_decoder(self, decoder_model_filename: str):