Merge branch 'master' into context_biasing

.github/scripts/compute-fbank-librispeech-test-clean-and-test-other.sh

@@ -15,5 +15,5 @@ mkdir -p data
 cd data
 [ ! -e fbank ] && ln -s ~/tmp/fbank-libri fbank
 cd ..
-./local/compute_fbank_librispeech.py
+./local/compute_fbank_librispeech.py --dataset 'test-clean test-other'
 ls -lh data/fbank/

.github/scripts/run-aishell-pruned-transducer-stateless3-2022-06-20.sh

@@ -25,7 +25,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

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

@@ -18,7 +18,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

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

@@ -20,7 +20,6 @@ abs_repo=$(realpath $repo)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

.github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 for sym in 1 2 3; do

.github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh

@@ -23,7 +23,6 @@ popd
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh

@@ -22,7 +22,6 @@ popd
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-05-13.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

.github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

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

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-2022-12-01.sh

@@ -18,7 +18,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

.github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-bs-2022-12-15.sh

@@ -10,7 +10,7 @@ log() {
 
 cd egs/librispeech/ASR
 
-repo_url=https://huggingface.co/yfyeung/icefall-asr-librispeech-pruned_transducer_stateless7_ctc_bs-2022-12-14
+repo_url=https://huggingface.co/yfyeung/icefall-asr-librispeech-pruned_transducer_stateless7_ctc_bs-2023-01-29
 
 log "Downloading pre-trained model from $repo_url"
 GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
@@ -18,7 +18,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

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

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo

.github/scripts/run-librispeech-pruned-transducer-stateless8-2022-11-14.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

.github/scripts/run-librispeech-streaming-pruned-transducer-stateless2-2022-06-26.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

.github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 for sym in 1 2 3; do

.github/scripts/run-librispeech-zipformer-mmi-2022-12-08.sh

@@ -18,7 +18,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

.github/scripts/run-pre-trained-conformer-ctc.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.flac
 ls -lh $repo/test_wavs/*.flac
 
 log "CTC decoding"

.github/scripts/run-pre-trained-transducer-stateless-librispeech-100h.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 for sym in 1 2 3; do

.github/scripts/run-pre-trained-transducer-stateless-librispeech-960h.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 for sym in 1 2 3; do

.github/scripts/run-pre-trained-transducer-stateless-modified-2-aishell.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 for sym in 1 2 3; do

.github/scripts/run-pre-trained-transducer-stateless-modified-aishell.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 for sym in 1 2 3; do

.github/scripts/run-pre-trained-transducer-stateless.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 for sym in 1 2 3; do

.github/scripts/run-pre-trained-transducer.sh

@@ -19,7 +19,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 log "Beam search decoding"

.github/scripts/run-wenetspeech-pruned-transducer-stateless2.sh

@@ -20,7 +20,6 @@ repo=$(basename $repo_url)
 
 log "Display test files"
 tree $repo/
-soxi $repo/test_wavs/*.wav
 ls -lh $repo/test_wavs/*.wav
 
 pushd $repo/exp

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

@@ -232,70 +232,3 @@ python3 ./pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py \
 
 rm -rf $repo
 log "--------------------------------------------------------------------------"
-
-# Go back to the root directory of icefall repo
-popd
-
-pushd egs/csj/ASR
-
-log "=========================================================================="
-repo_url=https://huggingface.co/TeoWenShen/icefall-asr-csj-pruned-transducer-stateless7-streaming-230208
-GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
-repo=$(basename $repo_url)
-
-pushd $repo
-git lfs pull --include "exp_fluent/pretrained.pt"
-git lfs pull --include "exp_disfluent/pretrained.pt"
-
-cd exp_fluent
-ln -s pretrained.pt epoch-99.pt
-
-cd ../exp_disfluent
-ln -s pretrained.pt epoch-99.pt
-
-cd ../test_wavs
-git lfs pull --include "*.wav"
-popd
-
-log "Export via torch.jit.trace()"
-
-for exp in exp_fluent exp_disfluent; do
-  ./pruned_transducer_stateless7_streaming/export-for-ncnn.py \
-    --exp-dir $repo/$exp/ \
-    --lang $repo/data/lang_char \
-    --epoch 99 \
-    --avg 1 \
-    --use-averaged-model 0 \
-    \
-    --decode-chunk-len 32 \
-    --num-left-chunks 4 \
-    --num-encoder-layers "2,4,3,2,4" \
-    --feedforward-dims  "1024,1024,2048,2048,1024" \
-    --nhead "8,8,8,8,8" \
-    --encoder-dims "384,384,384,384,384" \
-    --attention-dims "192,192,192,192,192" \
-    --encoder-unmasked-dims "256,256,256,256,256" \
-    --zipformer-downsampling-factors "1,2,4,8,2" \
-    --cnn-module-kernels "31,31,31,31,31" \
-    --decoder-dim 512 \
-    --joiner-dim 512
-
-  pnnx $repo/$exp/encoder_jit_trace-pnnx.pt
-  pnnx $repo/$exp/decoder_jit_trace-pnnx.pt
-  pnnx $repo/$exp/joiner_jit_trace-pnnx.pt
-
-  for wav in aps-smp.wav interview_aps-smp.wav reproduction-smp.wav sps-smp.wav; do
-    python3 ./pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py \
-      --tokens $repo/data/lang_char/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 \
-      $repo/test_wavs/$wav
-  done
-done
-
-rm -rf $repo
-log "--------------------------------------------------------------------------"

.github/workflows/run-aishell-2022-06-20.yml

@@ -65,7 +65,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -87,7 +87,7 @@ jobs:
           GITHUB_EVENT_NAME: ${{ github.event_name }}
           GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
         run: |
-          sudo apt-get -qq install git-lfs tree sox
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-gigaspeech-2022-05-13.yml

@@ -64,7 +64,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache

.github/workflows/run-librispeech-2022-03-12.yml

@@ -64,7 +64,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -123,7 +123,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-librispeech-2022-04-29.yml

@@ -64,7 +64,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -123,7 +123,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-librispeech-2022-05-13.yml

@@ -64,7 +64,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -123,7 +123,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

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

@@ -64,7 +64,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -123,7 +123,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-librispeech-2022-11-14-stateless8.yml

@@ -64,7 +64,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -123,7 +123,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-librispeech-2022-12-01-stateless7-ctc.yml

@@ -60,7 +60,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -119,7 +119,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-librispeech-2022-12-08-zipformer-mmi.yml

@@ -64,7 +64,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -123,7 +123,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-librispeech-2022-12-15-stateless7-ctc-bs.yml

@@ -35,7 +35,7 @@ on:
 
 jobs:
   run_librispeech_2022_12_15_zipformer_ctc_bs:
-    if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event.label.name == 'blank-skip' || github.event_name == 'push' || github.event_name == 'schedule'
+    if: github.event.label.name == 'run-decode' || github.event.label.name == 'blank-skip' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -60,7 +60,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -119,7 +119,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

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

@@ -64,7 +64,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -123,7 +123,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-librispeech-conformer-ctc3-2022-11-28.yml

@@ -64,7 +64,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -123,7 +123,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

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

@@ -47,7 +47,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -106,7 +106,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-librispeech-pruned-transducer-stateless3-2022-05-13.yml

@@ -64,7 +64,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -123,7 +123,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-librispeech-streaming-transducer-stateless2-2022-06-26.yml

@@ -64,7 +64,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -123,7 +123,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml

@@ -64,7 +64,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -123,7 +123,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-pretrained-conformer-ctc.yml

@@ -54,7 +54,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -73,7 +73,7 @@ jobs:
       - name: Inference with pre-trained model
         shell: bash
         run: |
-          sudo apt-get -qq install git-lfs tree sox
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml

@@ -63,7 +63,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -122,7 +122,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml

@@ -63,7 +63,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -122,7 +122,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-pretrained-transducer-stateless-modified-2-aishell.yml

@@ -54,7 +54,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -73,7 +73,7 @@ jobs:
       - name: Inference with pre-trained model
         shell: bash
         run: |
-          sudo apt-get -qq install git-lfs tree sox
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-pretrained-transducer-stateless-modified-aishell.yml

@@ -54,7 +54,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -73,7 +73,7 @@ jobs:
       - name: Inference with pre-trained model
         shell: bash
         run: |
-          sudo apt-get -qq install git-lfs tree sox
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-pretrained-transducer-stateless.yml

@@ -63,7 +63,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -122,7 +122,7 @@ jobs:
           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
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-pretrained-transducer.yml

@@ -54,7 +54,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -73,7 +73,7 @@ jobs:
       - name: Inference with pre-trained model
         shell: bash
         run: |
-          sudo apt-get -qq install git-lfs tree sox
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-ptb-rnn-lm.yml

@@ -47,7 +47,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | grep -v kaldifst | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Prepare data
         shell: bash

.github/workflows/run-wenetspeech-pruned-transducer-stateless2.yml

@@ -54,7 +54,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache
@@ -76,7 +76,7 @@ jobs:
           GITHUB_EVENT_NAME: ${{ github.event_name }}
           GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
         run: |
-          sudo apt-get -qq install git-lfs tree sox
+          sudo apt-get -qq install git-lfs tree
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH

.github/workflows/run-yesno-recipe.yml

@@ -67,7 +67,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | grep -v kaldifst | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Run yesno recipe
         shell: bash

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

@@ -46,7 +46,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache

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

@@ -46,7 +46,7 @@ jobs:
         run: |
           grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
       - name: Cache kaldifeat
         id: my-cache

.github/workflows/test.yml

@@ -56,7 +56,7 @@ jobs:
         run: |
           sudo apt update
           sudo apt install -q -y libsndfile1-dev libsndfile1 ffmpeg
-          sudo apt install -q -y --fix-missing sox libsox-dev libsox-fmt-all
+          sudo apt install -q -y --fix-missing libsox-dev libsox-fmt-all
 
       - name: Install Python dependencies
         run: |
@@ -70,7 +70,7 @@ jobs:
           pip install git+https://github.com/lhotse-speech/lhotse
           # icefall requirements
           pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf
+          pip install --no-binary protobuf protobuf==3.20.*
 
           pip install kaldifst
           pip install onnxruntime
@@ -119,8 +119,8 @@ jobs:
           cd ../transducer_stateless
           pytest -v -s
 
-          cd ../transducer
+          # cd ../transducer
-          pytest -v -s
+          # pytest -v -s
 
           cd ../transducer_stateless2
           pytest -v -s
@@ -157,8 +157,8 @@ jobs:
           cd ../transducer_stateless
           pytest -v -s
 
-          cd ../transducer
+          # cd ../transducer
-          pytest -v -s
+          # pytest -v -s
 
           cd ../transducer_stateless2
           pytest -v -s

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py

@@ -391,18 +391,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -412,9 +408,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/aishell/ASR/local/prepare_char_lm_training_data.py

@@ -0,0 +1,164 @@
+#!/usr/bin/env python3
+
+# Copyright (c)  2021  Xiaomi Corporation (authors: Daniel Povey
+#                                                   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 takes a `tokens.txt` and a text file such as
+./download/lm/aishell-transcript.txt
+and outputs the LM training data to a supplied directory such
+as data/lm_training_char.  The format is as follows:
+It creates a PyTorch archive (.pt file), say data/lm_training.pt, which is a
+representation of a dict with the same format with librispeech receipe
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import k2
+import torch
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lang-char",
+        type=str,
+        help="""Lang dir of asr model, e.g. data/lang_char""",
+    )
+    parser.add_argument(
+        "--lm-data",
+        type=str,
+        help="""Input LM training data as text, e.g.
+        download/lm/aishell-train-word.txt""",
+    )
+    parser.add_argument(
+        "--lm-archive",
+        type=str,
+        help="""Path to output archive, e.g. data/lm_training_char/lm_data.pt;
+        look at the source of this script to see the format.""",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+
+    if Path(args.lm_archive).exists():
+        logging.warning(f"{args.lm_archive} exists - skipping")
+        return
+
+    # make token_dict from tokens.txt in order to map characters to tokens.
+    token_dict = {}
+    token_file = args.lang_char + "/tokens.txt"
+
+    with open(token_file, "r") as f:
+        for line in f.readlines():
+            line_list = line.split()
+            token_dict[line_list[0]] = int(line_list[1])
+
+    # word2index is a dictionary from words to integer ids.  No need to reserve
+    # space for epsilon, etc.; the words are just used as a convenient way to
+    # compress the sequences of tokens.
+    word2index = dict()
+
+    word2token = []  # Will be a list-of-list-of-int, representing tokens.
+    sentences = []  # Will be a list-of-list-of-int, representing word-ids.
+
+    if "aishell-lm" in args.lm_data:
+        num_lines_in_total = 120098.0
+        step = 50000
+    elif "valid" in args.lm_data:
+        num_lines_in_total = 14326.0
+        step = 3000
+    elif "test" in args.lm_data:
+        num_lines_in_total = 7176.0
+        step = 3000
+    else:
+        num_lines_in_total = None
+        step = None
+
+    processed = 0
+
+    with open(args.lm_data) as f:
+        while True:
+            line = f.readline()
+            if line == "":
+                break
+
+            if step and processed % step == 0:
+                logging.info(
+                    f"Processed number of lines: {processed} "
+                    f"({processed / num_lines_in_total * 100: .3f}%)"
+                )
+            processed += 1
+
+            line_words = line.split()
+            for w in line_words:
+                if w not in word2index:
+                    w_token = []
+                    for t in w:
+                        if t in token_dict:
+                            w_token.append(token_dict[t])
+                        else:
+                            w_token.append(token_dict["<unk>"])
+                    word2index[w] = len(word2token)
+                    word2token.append(w_token)
+            sentences.append([word2index[w] for w in line_words])
+
+    logging.info("Constructing ragged tensors")
+    words = k2.ragged.RaggedTensor(word2token)
+    sentences = k2.ragged.RaggedTensor(sentences)
+
+    output = dict(words=words, sentences=sentences)
+
+    num_sentences = sentences.dim0
+    logging.info(f"Computing sentence lengths, num_sentences: {num_sentences}")
+    sentence_lengths = [0] * num_sentences
+    for i in range(num_sentences):
+        if step and i % step == 0:
+            logging.info(
+                f"Processed number of lines: {i} ({i / num_sentences * 100: .3f}%)"
+            )
+
+        word_ids = sentences[i]
+
+        # NOTE: If word_ids is a tensor with only 1 entry,
+        # token_ids is a torch.Tensor
+        token_ids = words[word_ids]
+        if isinstance(token_ids, k2.RaggedTensor):
+            token_ids = token_ids.values
+
+        # token_ids is a 1-D tensor containing the BPE tokens
+        # of the current sentence
+
+        sentence_lengths[i] = token_ids.numel()
+
+    output["sentence_lengths"] = torch.tensor(sentence_lengths, dtype=torch.int32)
+
+    torch.save(output, args.lm_archive)
+    logging.info(f"Saved to {args.lm_archive}")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    main()

egs/aishell/ASR/prepare.sh

@@ -7,7 +7,7 @@ set -eou pipefail
 
 nj=15
 stage=-1
-stop_stage=10
+stop_stage=11
 
 # We assume dl_dir (download dir) contains the following
 # directories and files. If not, they will be downloaded
@@ -219,3 +219,93 @@ if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
   ./local/compile_hlg.py --lang-dir $lang_phone_dir
   ./local/compile_hlg.py --lang-dir $lang_char_dir
 fi
+
+if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
+  log "Stage 9: Generate LM training data"
+
+  log "Processing char based data"
+  out_dir=data/lm_training_char
+  mkdir -p $out_dir $dl_dir/lm
+
+  if [ ! -f $dl_dir/lm/aishell-train-word.txt ]; then
+    cp $lang_phone_dir/transcript_words.txt $dl_dir/lm/aishell-train-word.txt
+  fi
+
+  ./local/prepare_char_lm_training_data.py \
+    --lang-char data/lang_char \
+    --lm-data $dl_dir/lm/aishell-train-word.txt \
+    --lm-archive $out_dir/lm_data.pt
+
+  if [ ! -f $dl_dir/lm/aishell-valid-word.txt ]; then
+    aishell_text=$dl_dir/aishell/data_aishell/transcript/aishell_transcript_v0.8.txt
+    aishell_valid_uid=$dl_dir/aishell/data_aishell/transcript/aishell_valid_uid
+    find $dl_dir/aishell/data_aishell/wav/dev -name "*.wav" | sed 's/\.wav//g' | awk -F '/' '{print $NF}' > $aishell_valid_uid
+    awk 'NR==FNR{uid[$1]=$1} NR!=FNR{if($1 in uid) print $0}' $aishell_valid_uid $aishell_text |
+	    cut -d " " -f 2- > $dl_dir/lm/aishell-valid-word.txt
+  fi
+
+  ./local/prepare_char_lm_training_data.py \
+    --lang-char data/lang_char \
+    --lm-data $dl_dir/lm/aishell-valid-word.txt \
+    --lm-archive $out_dir/lm_data_valid.pt
+
+  if [ ! -f $dl_dir/lm/aishell-test-word.txt ]; then
+    aishell_text=$dl_dir/aishell/data_aishell/transcript/aishell_transcript_v0.8.txt
+    aishell_test_uid=$dl_dir/aishell/data_aishell/transcript/aishell_test_uid
+    find $dl_dir/aishell/data_aishell/wav/test -name "*.wav" | sed 's/\.wav//g' | awk -F '/' '{print $NF}' > $aishell_test_uid
+    awk 'NR==FNR{uid[$1]=$1} NR!=FNR{if($1 in uid) print $0}' $aishell_test_uid $aishell_text |
+	    cut -d " " -f 2- > $dl_dir/lm/aishell-test-word.txt
+  fi
+
+  ./local/prepare_char_lm_training_data.py \
+    --lang-char data/lang_char \
+    --lm-data $dl_dir/lm/aishell-test-word.txt \
+    --lm-archive $out_dir/lm_data_test.pt
+fi
+
+
+if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then
+  log "Stage 10: Sort LM training data"
+  # Sort LM training data by sentence length in descending order
+  # for ease of training.
+  #
+  # Sentence length equals to the number of tokens
+  # in a sentence.
+
+  out_dir=data/lm_training_char
+  mkdir -p $out_dir
+  ln -snf ../../../librispeech/ASR/local/sort_lm_training_data.py local/
+  
+  ./local/sort_lm_training_data.py \
+    --in-lm-data $out_dir/lm_data.pt \
+    --out-lm-data $out_dir/sorted_lm_data.pt \
+    --out-statistics $out_dir/statistics.txt
+
+  ./local/sort_lm_training_data.py \
+    --in-lm-data $out_dir/lm_data_valid.pt \
+    --out-lm-data $out_dir/sorted_lm_data-valid.pt \
+    --out-statistics $out_dir/statistics-valid.txt
+
+  ./local/sort_lm_training_data.py \
+    --in-lm-data $out_dir/lm_data_test.pt \
+    --out-lm-data $out_dir/sorted_lm_data-test.pt \
+    --out-statistics $out_dir/statistics-test.txt
+fi
+
+if [ $stage -le 11 ] && [ $stop_stage -ge 11 ]; then
+  log "Stage 11: Train RNN LM model"
+  python ../../../icefall/rnn_lm/train.py \
+    --start-epoch 0 \
+    --world-size 1 \
+    --num-epochs 20 \
+    --use-fp16 0 \
+    --embedding-dim 512 \
+    --hidden-dim 512 \
+    --num-layers 2 \
+    --batch-size 400 \
+    --exp-dir rnnlm_char/exp \
+    --lm-data data/lm_training_char/sorted_lm_data.pt \
+    --lm-data-valid data/lm_training_char/sorted_lm_data-valid.pt \
+    --vocab-size 4336 \
+    --master-port 12345
+fi

egs/aishell/ASR/pruned_transducer_stateless2/decode.py

@@ -388,18 +388,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         # we compute CER for aishell dataset.
         results_char = []
         for res in results:
@@ -413,9 +409,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/aishell/ASR/pruned_transducer_stateless3/decode.py

@@ -406,18 +406,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         # we compute CER for aishell dataset.
         results_char = []
         for res in results:
@@ -431,9 +427,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tCER", file=f)
         for key, val in test_set_wers:

egs/aishell/ASR/transducer_stateless/decode.py

@@ -325,17 +325,13 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         # we compute CER for aishell dataset.
         results_char = []
         for res in results:
@@ -349,9 +345,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tCER", file=f)
         for key, val in test_set_wers:

egs/aishell/ASR/transducer_stateless_modified-2/decode.py

@@ -370,18 +370,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         # we compute CER for aishell dataset.
         results_char = []
         for res in results:
@@ -395,9 +391,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tCER", file=f)
         for key, val in test_set_wers:

egs/aishell/ASR/transducer_stateless_modified/decode.py

@@ -374,18 +374,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         # we compute CER for aishell dataset.
         results_char = []
         for res in results:
@@ -399,9 +395,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tCER", file=f)
         for key, val in test_set_wers:

egs/aishell2/ASR/pruned_transducer_stateless5/decode.py

@@ -543,18 +543,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -564,9 +560,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/aishell4/ASR/pruned_transducer_stateless5/decode.py

@@ -406,18 +406,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -427,9 +423,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/alimeeting/ASR/pruned_transducer_stateless2/decode.py

@@ -391,18 +391,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -412,9 +408,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/alimeeting/ASR_v2/pruned_transducer_stateless7/decode.py

@@ -462,18 +462,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -483,9 +479,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/ami/ASR/pruned_transducer_stateless7/decode.py

@@ -478,17 +478,13 @@ def save_results(
     test_set_wers = dict()
     test_set_cers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        wers_filename = (
+        wers_filename = params.res_dir / f"wers-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"wers-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(wers_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -499,9 +495,7 @@ def save_results(
         results_char = []
         for res in results:
             results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
-        cers_filename = (
+        cers_filename = params.res_dir / f"cers-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"cers-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(cers_filename, "w") as f:
             cer = write_error_stats(
                 f, f"{test_set_name}-{key}", results_char, enable_log=True
@@ -512,9 +506,7 @@ def save_results(
 
     test_set_wers = {k: v for k, v in sorted(test_set_wers.items(), key=lambda x: x[1])}
     test_set_cers = {k: v for k, v in sorted(test_set_cers.items(), key=lambda x: x[1])}
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER\tCER", file=f)
         for key in test_set_wers:

egs/csj/ASR/pruned_transducer_stateless7_streaming/decode.py

@@ -599,9 +599,7 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
 
@@ -609,9 +607,7 @@ def save_results(
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -621,9 +617,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py

@@ -399,9 +399,7 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = post_processing(results)
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
@@ -409,9 +407,7 @@ def save_results(
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -421,9 +417,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/RESULTS.md

@@ -540,6 +540,10 @@ for m in greedy_search fast_beam_search modified_beam_search ; do
 done
 ```
 
+Note that a small change is made to the `pruned_transducer_stateless7/decoder.py` in 
+this [PR](/ceph-data4/yangxiaoyu/softwares/icefall_development/icefall_random_padding/egs/librispeech/ASR/pruned_transducer_stateless7/exp_960h_no_paddingidx_ngpu4/tensorboard) to address the 
+problem of emitting the first symbol at the very beginning. If you need a 
+model without this issue, please download the model from here: <https://huggingface.co/marcoyang/icefall-asr-librispeech-pruned-transducer-stateless7-2023-03-10>
 
 ### LibriSpeech BPE training results (Pruned Stateless LSTM RNN-T + gradient filter)
 

egs/librispeech/ASR/conformer_ctc3/decode.py

@@ -728,18 +728,14 @@ def save_results(
     test_set_wers = dict()
     test_set_delays = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts_and_timestamps(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer, mean_delay, var_delay = write_error_stats_with_timestamps(
                 f,
@@ -754,9 +750,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:
@@ -765,8 +759,7 @@ def save_results(
     # sort according to the mean start symbol delay
     test_set_delays = sorted(test_set_delays.items(), key=lambda x: x[1][0][0])
     delays_info = (
-        params.res_dir
+        params.res_dir / f"symbol-delay-summary-{test_set_name}-{params.suffix}.txt"
-        / f"symbol-delay-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(delays_info, "w") as f:
         print("settings\t(start, end) symbol-delay (s) (start, end)", file=f)

egs/librispeech/ASR/conv_emformer_transducer_stateless/decode.py

@@ -432,18 +432,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -453,9 +449,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/conv_emformer_transducer_stateless/streaming_decode.py

@@ -750,17 +750,13 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         store_transcripts(filename=recog_path, texts=sorted(results))
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -770,9 +766,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/conv_emformer_transducer_stateless2/decode.py

@@ -432,18 +432,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -453,9 +449,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/conv_emformer_transducer_stateless2/streaming_decode.py

@@ -750,17 +750,13 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         store_transcripts(filename=recog_path, texts=sorted(results))
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -770,9 +766,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/finetune.sh

@@ -0,0 +1,85 @@
+#!/usr/bin/env bash
+
+# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+set -eou pipefail
+
+stage=-1
+stop_stage=100
+
+# This is an example script for fine-tuning. Here, we fine-tune a model trained
+# on Librispeech on GigaSpeech. The model used for fine-tuning is 
+# pruned_transducer_stateless7 (zipformer). If you want to fine-tune model 
+# from another recipe, you can adapt ./pruned_transducer_stateless7/finetune.py 
+# for that recipe. If you have any problem, please open up an issue in https://github.com/k2-fsa/icefall/issues.
+
+# We assume that you have already prepared the GigaSpeech manfiest&features under ./data.
+# If you haven't done that, please see https://github.com/k2-fsa/icefall/blob/master/egs/gigaspeech/ASR/prepare.sh.
+
+dl_dir=$PWD/download
+
+. shared/parse_options.sh || exit 1
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+if [ $stage -le -1 ] && [ $stop_stage -ge -1 ]; then
+  log "Stage -1: Download Pre-trained model"
+  
+  # clone from huggingface
+  git lfs install
+  git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11
+
+fi
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Start fine-tuning"
+  
+  # The following configuration of lr schedule should work well
+  # You may also tune the following parameters to adjust learning rate schedule
+  base_lr=0.005
+  lr_epochs=100
+  lr_batches=100000
+
+  # We recommend to start from an averaged model
+  finetune_ckpt=icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11/exp/pretrained.pt
+  export CUDA_VISIBLE_DEVICES="0,1"
+
+  ./pruned_transducer_stateless7/finetune.py \
+    --world-size 2 \
+    --master-port 18180 \
+    --num-epochs 20 \
+    --start-epoch 1 \
+    --exp-dir pruned_transducer_stateless7/exp_giga_finetune \
+    --subset S \
+    --use-fp16 1 \
+    --base-lr $base_lr \
+    --lr-epochs $lr_epochs \
+    --lr-batches $lr_batches \
+    --bpe-model icefall-asr-librispeech-pruned-transducer-stateless7-2022-11-11/data/lang_bpe_500/bpe.model \
+    --do-finetune True \
+    --finetune-ckpt $finetune_ckpt \
+    --max-duration 500
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Decoding"
+
+  epoch=15
+  avg=10
+
+  for m in greedy_search modified_beam_search; do
+    python pruned_transducer_stateless7/decode_gigaspeech.py \
+    --epoch $epoch \
+    --avg $avg \
+    --use-averaged-model True \
+    --beam-size 4 \
+    --exp-dir pruned_transducer_stateless7/exp_giga_finetune \
+    --max-duration 400 \
+    --decoding-method $m
+  done
+fi

egs/librispeech/ASR/local/compute_fbank_librispeech.py

@@ -54,10 +54,20 @@ def get_args():
         help="""Path to the bpe.model. If not None, we will remove short and
         long utterances before extracting features""",
     )
+
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        help="""Dataset parts to compute fbank. If None, we will use all""",
+    )
+
     return parser.parse_args()
 
 
-def compute_fbank_librispeech(bpe_model: Optional[str] = None):
+def compute_fbank_librispeech(
+    bpe_model: Optional[str] = None,
+    dataset: Optional[str] = None,
+):
     src_dir = Path("data/manifests")
     output_dir = Path("data/fbank")
     num_jobs = min(15, os.cpu_count())
@@ -68,15 +78,19 @@ def compute_fbank_librispeech(bpe_model: Optional[str] = None):
         sp = spm.SentencePieceProcessor()
         sp.load(bpe_model)
 
-    dataset_parts = (
+    if dataset is None:
-        "dev-clean",
+        dataset_parts = (
-        "dev-other",
+            "dev-clean",
-        "test-clean",
+            "dev-other",
-        "test-other",
+            "test-clean",
-        "train-clean-100",
+            "test-other",
-        "train-clean-360",
+            "train-clean-100",
-        "train-other-500",
+            "train-clean-360",
-    )
+            "train-other-500",
+        )
+    else:
+        dataset_parts = dataset.split(" ", -1)
+
     prefix = "librispeech"
     suffix = "jsonl.gz"
     manifests = read_manifests_if_cached(
@@ -131,4 +145,4 @@ if __name__ == "__main__":
     logging.basicConfig(format=formatter, level=logging.INFO)
     args = get_args()
     logging.info(vars(args))
-    compute_fbank_librispeech(bpe_model=args.bpe_model)
+    compute_fbank_librispeech(bpe_model=args.bpe_model, dataset=args.dataset)

egs/librispeech/ASR/lstm_transducer_stateless/decode.py

@@ -566,18 +566,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -587,9 +583,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/lstm_transducer_stateless/streaming_decode.py

@@ -742,17 +742,13 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         store_transcripts(filename=recog_path, texts=sorted(results))
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -762,9 +758,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/lstm_transducer_stateless2/decode.py

@@ -702,18 +702,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -723,9 +719,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/lstm_transducer_stateless3/decode.py

@@ -611,18 +611,14 @@ def save_results(
     test_set_wers = dict()
     test_set_delays = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts_and_timestamps(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer, mean_delay, var_delay = write_error_stats_with_timestamps(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -633,9 +629,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:
@@ -643,8 +637,7 @@ def save_results(
 
     test_set_delays = sorted(test_set_delays.items(), key=lambda x: x[1][0])
     delays_info = (
-        params.res_dir
+        params.res_dir / f"symbol-delay-summary-{test_set_name}-{params.suffix}.txt"
-        / f"symbol-delay-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(delays_info, "w") as f:
         print("settings\tsymbol-delay", file=f)

egs/librispeech/ASR/lstm_transducer_stateless3/streaming_decode.py

@@ -742,17 +742,13 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         store_transcripts(filename=recog_path, texts=sorted(results))
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -762,9 +758,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned2_knowledge/decode.py

@@ -386,17 +386,13 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -406,9 +402,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/decode.py

@@ -420,18 +420,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -441,9 +437,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_transducer_stateless/decode.py

@@ -585,18 +585,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -606,9 +602,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_transducer_stateless/decoder.py

@@ -58,7 +58,6 @@ class Decoder(nn.Module):
         self.embedding = nn.Embedding(
             num_embeddings=vocab_size,
             embedding_dim=embedding_dim,
-            padding_idx=blank_id,
         )
         self.blank_id = blank_id
         self.unk_id = unk_id

egs/librispeech/ASR/pruned_transducer_stateless/streaming_decode.py

@@ -423,9 +423,7 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         # sort results so we can easily compare the difference between two
         # recognition results
         results = sorted(results)
@@ -434,9 +432,7 @@ def save_results(
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -446,9 +442,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_transducer_stateless2/decode.py

@@ -609,18 +609,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -630,9 +626,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_transducer_stateless2/decoder.py

@@ -59,7 +59,6 @@ class Decoder(nn.Module):
         self.embedding = ScaledEmbedding(
             num_embeddings=vocab_size,
             embedding_dim=decoder_dim,
-            padding_idx=blank_id,
         )
         self.blank_id = blank_id
 

egs/librispeech/ASR/pruned_transducer_stateless2/streaming_decode.py

@@ -425,9 +425,7 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         # sort results so we can easily compare the difference between two
         # recognition results
         results = sorted(results)
@@ -436,9 +434,7 @@ def save_results(
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -448,9 +444,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_transducer_stateless3/decode.py

@@ -869,18 +869,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -890,9 +886,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_transducer_stateless3/streaming_decode.py

@@ -426,18 +426,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -447,9 +443,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_transducer_stateless4/decode.py

@@ -676,18 +676,14 @@ def save_results(
     test_set_wers = dict()
     test_set_delays = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts_and_timestamps(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer, mean_delay, var_delay = write_error_stats_with_timestamps(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -698,9 +694,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:
@@ -708,8 +702,7 @@ def save_results(
 
     test_set_delays = sorted(test_set_delays.items(), key=lambda x: x[1][0])
     delays_info = (
-        params.res_dir
+        params.res_dir / f"symbol-delay-summary-{test_set_name}-{params.suffix}.txt"
-        / f"symbol-delay-summary-{test_set_name}-{key}-{params.suffix}.txt"
     )
     with open(delays_info, "w") as f:
         print("settings\tsymbol-delay", file=f)

egs/librispeech/ASR/pruned_transducer_stateless4/streaming_decode.py

@@ -442,18 +442,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -463,9 +459,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_transducer_stateless5/decode.py

@@ -735,18 +735,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -756,9 +752,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_transducer_stateless5/streaming_decode.py

@@ -442,18 +442,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -463,9 +459,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_transducer_stateless6/decode.py

@@ -416,18 +416,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -437,9 +433,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_transducer_stateless7/decode.py

@@ -722,18 +722,14 @@ def save_results(
 ):
     test_set_wers = dict()
     for key, results in results_dict.items():
-        recog_path = (
+        recog_path = params.res_dir / f"recogs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         results = sorted(results)
         store_transcripts(filename=recog_path, texts=results)
         logging.info(f"The transcripts are stored in {recog_path}")
 
         # The following prints out WERs, per-word error statistics and aligned
         # ref/hyp pairs.
-        errs_filename = (
+        errs_filename = params.res_dir / f"errs-{test_set_name}-{params.suffix}.txt"
-            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
-        )
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
                 f, f"{test_set_name}-{key}", results, enable_log=True
@@ -743,9 +739,7 @@ def save_results(
         logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = (
+    errs_info = params.res_dir / f"wer-summary-{test_set_name}-{params.suffix}.txt"
-        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
-    )
     with open(errs_info, "w") as f:
         print("settings\tWER", file=f)
         for key, val in test_set_wers:

egs/librispeech/ASR/pruned_transducer_stateless7/decode_gigaspeech.py

@@ -0,0 +1,861 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Zengwei Yao,
+#                                                 Xiaoyu Yang)
+#
+# 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.
+"""
+Usage:
+(1) greedy search
+./pruned_transducer_stateless7/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless7/exp \
+    --max-duration 600 \
+    --decoding-method greedy_search
+
+(2) beam search (not recommended)
+./pruned_transducer_stateless7/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless7/exp \
+    --max-duration 600 \
+    --decoding-method beam_search \
+    --beam-size 4
+
+(3) modified beam search
+./pruned_transducer_stateless7/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless7/exp \
+    --max-duration 600 \
+    --decoding-method modified_beam_search \
+    --beam-size 4
+
+(4) fast beam search (one best)
+./pruned_transducer_stateless7/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless7/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64
+
+(5) fast beam search (nbest)
+./pruned_transducer_stateless7/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless7/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(6) fast beam search (nbest oracle WER)
+./pruned_transducer_stateless7/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless7/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_oracle \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64 \
+    --num-paths 200 \
+    --nbest-scale 0.5
+
+(7) fast beam search (with LG)
+./pruned_transducer_stateless7/decode.py \
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless7/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search_nbest_LG \
+    --beam 20.0 \
+    --max-contexts 8 \
+    --max-states 64
+"""
+
+
+import argparse
+import logging
+import math
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+
+# from asr_datamodule import LibriSpeechAsrDataModule
+from gigaspeech import GigaSpeechAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search_nbest,
+    fast_beam_search_nbest_LG,
+    fast_beam_search_nbest_oracle,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from gigaspeech_scoring import asr_text_post_processing
+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.lexicon import Lexicon
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+LOG_EPS = math.log(1e-10)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=30,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        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=9,
+        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_stateless7/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_bpe_500",
+        help="The lang dir containing word table and LG graph",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_beam_search
+          - fast_beam_search
+          - fast_beam_search_nbest
+          - fast_beam_search_nbest_oracle
+          - fast_beam_search_nbest_LG
+        If you use fast_beam_search_nbest_LG, you have to specify
+        `--lang-dir`, which should contain `LG.pt`.
+        """,
+    )
+
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=4,
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=20.0,
+        help="""A floating point value to calculate the cutoff score during beam
+        search (i.e., `cutoff = max-score - beam`), which is the same as the
+        `beam` in Kaldi.
+        Used only when --decoding-method is fast_beam_search,
+        fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.01,
+        help="""
+        Used only when --decoding_method is fast_beam_search_nbest_LG.
+        It specifies the scale for n-gram LM scores.
+        """,
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=64,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
+    )
+
+    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(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame.
+        Used only when --decoding_method is greedy_search""",
+    )
+
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=200,
+        help="""Number of paths for nbest decoding.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""Scale applied to lattice scores when computing nbest paths.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--simulate-streaming",
+        type=str2bool,
+        default=False,
+        help="""Whether to simulate streaming in decoding, this is a good way to
+        test a streaming model.
+        """,
+    )
+
+    parser.add_argument(
+        "--decode-chunk-size",
+        type=int,
+        default=16,
+        help="The chunk size for decoding (in frames after subsampling)",
+    )
+
+    parser.add_argument(
+        "--left-context",
+        type=int,
+        default=64,
+        help="left context can be seen during decoding (in frames after subsampling)",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def post_processing(
+    results: List[Tuple[str, List[str], List[str]]],
+) -> List[Tuple[str, List[str], List[str]]]:
+    new_results = []
+    for key, ref, hyp in results:
+        new_ref = asr_text_post_processing(" ".join(ref)).split()
+        new_hyp = asr_text_post_processing(" ".join(hyp)).split()
+        new_results.append((key, new_ref, new_hyp))
+    return new_results
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[List[str]]]:
+    """Decode one batch and return the result in a dict. The dict has the
+    following format:
+
+        - key: It indicates the setting used for decoding. For example,
+               if greedy_search is used, it would be "greedy_search"
+               If beam search with a beam size of 7 is used, it would be
+               "beam_7"
+        - value: It contains the decoding result. `len(value)` equals to
+                 batch size. `value[i]` is the decoding result for the i-th
+                 utterance in the given batch.
+    Args:
+      params:
+        It's the return value of :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+      word_table:
+        The word symbol table.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict.
+    """
+    device = next(model.parameters()).device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+    feature_lens = supervisions["num_frames"].to(device)
+
+    if params.simulate_streaming:
+        feature_lens += params.left_context
+        feature = torch.nn.functional.pad(
+            feature,
+            pad=(0, 0, 0, params.left_context),
+            value=LOG_EPS,
+        )
+        encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
+            x=feature,
+            x_lens=feature_lens,
+            chunk_size=params.decode_chunk_size,
+            left_context=params.left_context,
+            simulate_streaming=True,
+        )
+    else:
+        encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens)
+
+    hyps = []
+
+    if params.decoding_method == "fast_beam_search":
+        hyp_tokens = fast_beam_search_one_best(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_LG":
+        hyp_tokens = fast_beam_search_nbest_LG(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in hyp_tokens:
+            hyps.append([word_table[i] for i in hyp])
+    elif params.decoding_method == "fast_beam_search_nbest":
+        hyp_tokens = fast_beam_search_nbest(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        hyp_tokens = fast_beam_search_nbest_oracle(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            ref_texts=sp.encode(supervisions["text"]),
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+        hyp_tokens = greedy_search_batch(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "modified_beam_search":
+        hyp_tokens = modified_beam_search(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    else:
+        batch_size = encoder_out.size(0)
+
+        for i in range(batch_size):
+            # fmt: off
+            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
+            # fmt: on
+            if params.decoding_method == "greedy_search":
+                hyp = greedy_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    max_sym_per_frame=params.max_sym_per_frame,
+                )
+            elif params.decoding_method == "beam_search":
+                hyp = beam_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
+                )
+            else:
+                raise ValueError(
+                    f"Unsupported decoding method: {params.decoding_method}"
+                )
+            hyps.append(sp.decode(hyp).split())
+
+    if params.decoding_method == "greedy_search":
+        return {"greedy_search": hyps}
+    elif "fast_beam_search" in params.decoding_method:
+        key = f"beam_{params.beam}_"
+        key += f"max_contexts_{params.max_contexts}_"
+        key += f"max_states_{params.max_states}"
+        if "nbest" in params.decoding_method:
+            key += f"_num_paths_{params.num_paths}_"
+            key += f"nbest_scale_{params.nbest_scale}"
+            if "LG" in params.decoding_method:
+                key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
+
+        return {key: hyps}
+    else:
+        return {f"beam_size_{params.beam_size}": hyps}
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
+    """Decode dataset.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      word_table:
+        The word symbol table.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+    Returns:
+      Return a dict, whose key may be "greedy_search" if greedy search
+      is used, or it may be "beam_7" if beam size of 7 is used.
+      Its value is a list of tuples. Each tuple contains two elements:
+      The first is the reference transcript, and the second is the
+      predicted result.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    if params.decoding_method == "greedy_search":
+        log_interval = 50
+    else:
+        log_interval = 20
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+            word_table=word_table,
+            batch=batch,
+        )
+
+        for name, hyps in hyps_dict.items():
+            this_batch = []
+            assert len(hyps) == len(texts)
+            for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
+                ref_words = ref_text.split()
+                this_batch.append((cut_id, ref_words, hyp_words))
+
+            results[name].extend(this_batch)
+
+        num_cuts += len(texts)
+
+        if batch_idx % log_interval == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
+            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+    return results
+
+
+def save_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
+):
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        results = post_processing(results)
+        results = sorted(results)
+        store_transcripts(filename=recog_path, texts=results)
+        logging.info(f"The transcripts are stored in {recog_path}")
+
+        # The following prints out WERs, per-word error statistics and aligned
+        # ref/hyp pairs.
+        errs_filename = (
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+    test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
+    errs_info = (
+        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
+    with open(errs_info, "w") as f:
+        print("settings\tWER", file=f)
+        for key, val in test_set_wers:
+            print("{}\t{}".format(key, val), file=f)
+
+    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key, val in test_set_wers:
+        s += "{}\t{}{}\n".format(key, val, note)
+        note = ""
+    logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    """
+    This scripts test a libri model with libri BPE
+    on Gigaspeech.
+    """
+    parser = get_parser()
+    GigaSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    assert params.decoding_method in (
+        "greedy_search",
+        "beam_search",
+        "fast_beam_search",
+        "fast_beam_search_nbest",
+        "fast_beam_search_nbest_LG",
+        "fast_beam_search_nbest_oracle",
+        "modified_beam_search",
+    )
+    params.res_dir = params.exp_dir / (params.decoding_method + "_gigaspeech")
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    if params.simulate_streaming:
+        params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
+        params.suffix += f"-left-context-{params.left_context}"
+
+    if "fast_beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+        if "nbest" in params.decoding_method:
+            params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"-num-paths-{params.num_paths}"
+            if "LG" in params.decoding_method:
+                params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
+    elif "beam_search" in params.decoding_method:
+        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    if params.use_averaged_model:
+        params.suffix += "-use-averaged-model"
+
+    setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
+    logging.info("Decoding started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> and <unk> are defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    if params.simulate_streaming:
+        assert (
+            params.causal_convolution
+        ), "Decoding in streaming requires causal convolution"
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    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(device)
+    model.eval()
+
+    if "fast_beam_search" in params.decoding_method:
+        if params.decoding_method == "fast_beam_search_nbest_LG":
+            lexicon = Lexicon(params.lang_dir)
+            word_table = lexicon.word_table
+            lg_filename = params.lang_dir / "LG.pt"
+            logging.info(f"Loading {lg_filename}")
+            decoding_graph = k2.Fsa.from_dict(
+                torch.load(lg_filename, map_location=device)
+            )
+            decoding_graph.scores *= params.ngram_lm_scale
+        else:
+            word_table = None
+            decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+        word_table = None
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    # we need cut ids to display recognition results.
+    args.return_cuts = True
+    gigaspeech = GigaSpeechAsrDataModule(args)
+
+    dev_cuts = gigaspeech.dev_cuts()
+    test_cuts = gigaspeech.test_cuts()
+
+    dev_dl = gigaspeech.test_dataloaders(dev_cuts)
+    test_dl = gigaspeech.test_dataloaders(test_cuts)
+
+    test_sets = ["dev", "test"]
+    test_dls = [dev_dl, test_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dls):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            word_table=word_table,
+            decoding_graph=decoding_graph,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
+        )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()