Merge branch 'master' of https://github.com/k2-fsa/icefall

.flake8

@@ -11,7 +11,7 @@ per-file-ignores =
     egs/*/ASR/*/scaling.py: E501,
     egs/librispeech/ASR/lstm_transducer_stateless*/*.py: E501, E203
     egs/librispeech/ASR/conv_emformer_transducer_stateless*/*.py: E501, E203
-    egs/librispeech/ASR/conformer_ctc2/*py: E501,
+    egs/librispeech/ASR/conformer_ctc*/*py: E501,
     egs/librispeech/ASR/RESULTS.md: E999,
 
     # invalid escape sequence (cause by tex formular), W605

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

@@ -0,0 +1,119 @@
+#!/usr/bin/env bash
+
+set -e
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-conformer-ctc3-2022-11-27
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+repo=$(basename $repo_url)
+
+log "Display test files"
+tree $repo/
+soxi $repo/test_wavs/*.wav
+ls -lh $repo/test_wavs/*.wav
+
+pushd $repo/exp
+git lfs pull --include "data/*"
+git lfs pull --include "exp/jit_trace.pt"
+git lfs pull --include "exp/pretrained.pt"
+ln -s pretrained.pt epoch-99.pt
+ls -lh *.pt
+popd
+
+log "Decode with models exported by torch.jit.trace()"
+
+for m in ctc-decoding 1best; do
+  ./conformer_ctc3/jit_pretrained.py \
+    --model-filename $repo/exp/jit_trace.pt \
+    --words-file $repo/data/lang_bpe_500/words.txt  \
+    --HLG $repo/data/lang_bpe_500/HLG.pt \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --G $repo/data/lm/G_4_gram.pt \
+    --method $m \
+    --sample-rate 16000 \
+    $repo/test_wavs/1089-134686-0001.wav \
+    $repo/test_wavs/1221-135766-0001.wav \
+    $repo/test_wavs/1221-135766-0002.wav
+done
+
+log "Export to torchscript model"
+
+./conformer_ctc3/export.py \
+  --exp-dir $repo/exp \
+  --lang-dir $repo/data/lang_bpe_500 \
+  --jit-trace 1 \
+  --epoch 99 \
+  --avg 1 \
+  --use-averaged-model 0
+
+ls -lh $repo/exp/*.pt
+
+log "Decode with models exported by torch.jit.trace()"
+
+for m in ctc-decoding 1best; do
+  ./conformer_ctc3/jit_pretrained.py \
+    --model-filename $repo/exp/jit_trace.pt \
+    --words-file $repo/data/lang_bpe_500/words.txt  \
+    --HLG $repo/data/lang_bpe_500/HLG.pt \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --G $repo/data/lm/G_4_gram.pt \
+    --method $m \
+    --sample-rate 16000 \
+    $repo/test_wavs/1089-134686-0001.wav \
+    $repo/test_wavs/1221-135766-0001.wav \
+    $repo/test_wavs/1221-135766-0002.wav
+done
+
+for m in ctc-decoding 1best; do
+  ./conformer_ctc3/pretrained.py \
+    --checkpoint $repo/exp/pretrained.pt \
+    --words-file $repo/data/lang_bpe_500/words.txt  \
+    --HLG $repo/data/lang_bpe_500/HLG.pt \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --G $repo/data/lm/G_4_gram.pt \
+    --method $m \
+    --sample-rate 16000 \
+    $repo/test_wavs/1089-134686-0001.wav \
+    $repo/test_wavs/1221-135766-0001.wav \
+    $repo/test_wavs/1221-135766-0002.wav
+done
+
+echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
+echo "GITHUB_EVENT_LABEL_NAME: ${GITHUB_EVENT_LABEL_NAME}"
+if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_LABEL_NAME}" == x"run-decode"  ]]; then
+  mkdir -p conformer_ctc3/exp
+  ln -s $PWD/$repo/exp/pretrained.pt conformer_ctc3/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh conformer_ctc3/exp
+
+  log "Decoding test-clean and test-other"
+
+  # use a small value for decoding with CPU
+  max_duration=100
+
+  for method in ctc-decoding 1best; do
+    log "Decoding with $method"
+    ./conformer_ctc3/decode.py \
+      --epoch 999 \
+      --avg 1 \
+      --use-averaged-model 0 \
+      --exp-dir conformer_ctc3/exp/ \
+      --max-duration $max_duration \
+      --decoding-method $method \
+      --lm-dir data/lm
+  done
+
+  rm conformer_ctc3/exp/*.pt
+fi

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

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

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

@@ -16,6 +16,7 @@ log "Downloading pre-trained model from $repo_url"
 git lfs install
 git clone $repo_url
 repo=$(basename $repo_url)
+abs_repo=$(realpath $repo)
 
 log "Display test files"
 tree $repo/
@@ -178,21 +179,27 @@ echo "GITHUB_EVENT_LABEL_NAME: ${GITHUB_EVENT_LABEL_NAME}"
 if [[ x"${GITHUB_EVENT_LABEL_NAME}" == x"shallow-fusion" ]]; then
   lm_repo_url=https://huggingface.co/ezerhouni/icefall-librispeech-rnn-lm
   log "Download pre-trained RNN-LM model from ${lm_repo_url}"
-  git clone $lm_repo_url
+  GIT_LFS_SKIP_SMUDGE=1 git clone $lm_repo_url
   lm_repo=$(basename $lm_repo_url)
   pushd $lm_repo
   git lfs pull --include "exp/pretrained.pt"
-  cd exp
+  mv exp/pretrained.pt exp/epoch-88.pt
-  ln -s pretrained.pt epoch-88.pt
   popd
 
+  mkdir -p lstm_transducer_stateless2/exp
+  ln -sf $PWD/$repo/exp/pretrained.pt lstm_transducer_stateless2/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh lstm_transducer_stateless2/exp
+
+  log "Decoding test-clean and test-other"
+
   ./lstm_transducer_stateless2/decode.py \
     --use-averaged-model 0 \
-    --epoch 99 \
+    --epoch 999 \
     --avg 1 \
-    --exp-dir $repo/exp \
+    --exp-dir lstm_transducer_stateless2/exp \
-    --lang-dir $repo/data/lang_bpe_500 \
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
     --max-duration 600 \
     --decoding-method modified_beam_search_rnnlm_shallow_fusion \
     --beam 4 \
@@ -204,6 +211,52 @@ if [[ x"${GITHUB_EVENT_LABEL_NAME}" == x"shallow-fusion" ]]; then
     --rnn-lm-tie-weights 1
 fi
 
+if [[ x"${GITHUB_EVENT_LABEL_NAME}" == x"LODR" ]]; then
+  bigram_repo_url=https://huggingface.co/marcoyang/librispeech_bigram
+  log "Download bi-gram LM from ${bigram_repo_url}"
+  GIT_LFS_SKIP_SMUDGE=1 git clone $bigram_repo_url
+  bigramlm_repo=$(basename $bigram_repo_url)
+  pushd $bigramlm_repo
+  git lfs pull --include "2gram.fst.txt"
+  cp 2gram.fst.txt $abs_repo/data/lang_bpe_500/.
+  popd
+
+  lm_repo_url=https://huggingface.co/ezerhouni/icefall-librispeech-rnn-lm
+  log "Download pre-trained RNN-LM model from ${lm_repo_url}"
+  GIT_LFS_SKIP_SMUDGE=1 git clone $lm_repo_url
+  lm_repo=$(basename $lm_repo_url)
+  pushd $lm_repo
+  git lfs pull --include "exp/pretrained.pt"
+  mv exp/pretrained.pt exp/epoch-88.pt
+  popd
+
+  mkdir -p lstm_transducer_stateless2/exp
+  ln -sf $PWD/$repo/exp/pretrained.pt lstm_transducer_stateless2/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh lstm_transducer_stateless2/exp
+
+  log "Decoding test-clean and test-other"
+
+  ./lstm_transducer_stateless2/decode.py \
+    --use-averaged-model 0 \
+    --epoch 999 \
+    --avg 1 \
+    --exp-dir lstm_transducer_stateless2/exp \
+    --max-duration 600 \
+    --decoding-method modified_beam_search_rnnlm_LODR \
+    --beam 4 \
+    --rnn-lm-scale 0.3 \
+    --rnn-lm-exp-dir $lm_repo/exp \
+    --rnn-lm-epoch 88 \
+    --rnn-lm-avg 1 \
+    --rnn-lm-num-layers 3 \
+    --rnn-lm-tie-weights 1 \
+    --tokens-ngram 2 \
+    --ngram-lm-scale -0.16
+fi
+
 if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
   mkdir -p lstm_transducer_stateless2/exp
   ln -s $PWD/$repo/exp/pretrained.pt lstm_transducer_stateless2/exp/epoch-999.pt

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

@@ -0,0 +1,147 @@
+#!/usr/bin/env bash
+
+set -e
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-ctc-2022-12-01
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+repo=$(basename $repo_url)
+
+log "Display test files"
+tree $repo/
+soxi $repo/test_wavs/*.wav
+ls -lh $repo/test_wavs/*.wav
+
+pushd $repo/exp
+git lfs pull --include "data/*"
+git lfs pull --include "exp/cpu_jit.pt"
+git lfs pull --include "exp/pretrained.pt"
+ln -s pretrained.pt epoch-99.pt
+ls -lh *.pt
+popd
+
+log "Export to torchscript model"
+./pruned_transducer_stateless7_ctc/export.py \
+  --exp-dir $repo/exp \
+  --use-averaged-model false \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --epoch 99 \
+  --avg 1 \
+  --jit 1
+
+ls -lh $repo/exp/*.pt
+
+log "Decode with models exported by torch.jit.script()"
+
+./pruned_transducer_stateless7_ctc/jit_pretrained.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --nn-model-filename $repo/exp/cpu_jit.pt \
+  $repo/test_wavs/1089-134686-0001.wav \
+  $repo/test_wavs/1221-135766-0001.wav \
+  $repo/test_wavs/1221-135766-0002.wav
+
+for m in ctc-decoding 1best; do
+  ./pruned_transducer_stateless7_ctc/jit_pretrained_ctc.py \
+    --model-filename $repo/exp/cpu_jit.pt \
+    --words-file $repo/data/lang_bpe_500/words.txt  \
+    --HLG $repo/data/lang_bpe_500/HLG.pt \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --G $repo/data/lm/G_4_gram.pt \
+    --method $m \
+    --sample-rate 16000 \
+    $repo/test_wavs/1089-134686-0001.wav \
+    $repo/test_wavs/1221-135766-0001.wav \
+    $repo/test_wavs/1221-135766-0002.wav
+done
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./pruned_transducer_stateless7_ctc/pretrained.py \
+    --method greedy_search \
+    --max-sym-per-frame $sym \
+    --checkpoint $repo/exp/pretrained.pt \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    $repo/test_wavs/1089-134686-0001.wav \
+    $repo/test_wavs/1221-135766-0001.wav \
+    $repo/test_wavs/1221-135766-0002.wav
+done
+
+for method in modified_beam_search beam_search fast_beam_search; do
+  log "$method"
+
+  ./pruned_transducer_stateless7_ctc/pretrained.py \
+    --method $method \
+    --beam-size 4 \
+    --checkpoint $repo/exp/pretrained.pt \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    $repo/test_wavs/1089-134686-0001.wav \
+    $repo/test_wavs/1221-135766-0001.wav \
+    $repo/test_wavs/1221-135766-0002.wav
+done
+
+for m in ctc-decoding 1best; do
+  ./pruned_transducer_stateless7_ctc/pretrained_ctc.py \
+    --checkpoint $repo/exp/pretrained.pt \
+    --words-file $repo/data/lang_bpe_500/words.txt  \
+    --HLG $repo/data/lang_bpe_500/HLG.pt \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --G $repo/data/lm/G_4_gram.pt \
+    --method $m \
+    --sample-rate 16000 \
+    $repo/test_wavs/1089-134686-0001.wav \
+    $repo/test_wavs/1221-135766-0001.wav \
+    $repo/test_wavs/1221-135766-0002.wav
+done
+
+echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
+echo "GITHUB_EVENT_LABEL_NAME: ${GITHUB_EVENT_LABEL_NAME}"
+if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_LABEL_NAME}" == x"run-decode"  ]]; then
+  mkdir -p pruned_transducer_stateless7_ctc/exp
+  ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless7_ctc/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh pruned_transducer_stateless7_ctc/exp
+
+  log "Decoding test-clean and test-other"
+
+  # use a small value for decoding with CPU
+  max_duration=100
+
+  for method in greedy_search fast_beam_search modified_beam_search; do
+    log "Decoding with $method"
+
+    ./pruned_transducer_stateless7_ctc/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --use-averaged-model 0 \
+      --max-duration $max_duration \
+      --exp-dir pruned_transducer_stateless7_ctc/exp
+  done
+
+  for m in ctc-decoding 1best; do
+    ./pruned_transducer_stateless7_ctc/ctc_decode.py \
+        --epoch 999 \
+        --avg 1 \
+        --exp-dir ./pruned_transducer_stateless7_ctc/exp \
+        --max-duration $max_duration \
+        --use-averaged-model 0 \
+        --decoding-method $m \
+        --hlg-scale 0.6 \
+        --lm-dir data/lm
+  done
+
+  rm pruned_transducer_stateless7_ctc/exp/*.pt
+fi

.github/workflows/build-doc.yml

@@ -26,6 +26,10 @@ on:
   pull_request:
     types: [labeled]
 
+concurrency:
+  group: build_doc-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   build-doc:
     if: github.event.label.name == 'doc' || github.event_name == 'push'

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

@@ -34,6 +34,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_aishell_2022_06_20-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_aishell_2022_06_20:
     if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -33,6 +33,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_gigaspeech_2022_05_13-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_gigaspeech_2022_05_13:
     if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -33,6 +33,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_librispeech_2022_03_12-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_librispeech_2022_03_12:
     if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -33,6 +33,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_librispeech_2022_04_29-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_librispeech_2022_04_29:
     if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -33,6 +33,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_librispeech_2022_05_13-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_librispeech_2022_05_13:
     if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -33,6 +33,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_librispeech_2022_11_11_zipformer-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_librispeech_2022_11_11_zipformer:
     if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -33,6 +33,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_librispeech_2022_11_14_zipformer_stateless8-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_librispeech_2022_11_14_zipformer_stateless8:
     if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -0,0 +1,163 @@
+# Copyright      2022  Fangjun Kuang (csukuangfj@gmail.com)
+
+# 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.
+
+name: run-librispeech-2022-12-01-stateless7-ctc
+# zipformer
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    types: [labeled]
+
+  schedule:
+    # minute (0-59)
+    # hour (0-23)
+    # day of the month (1-31)
+    # month (1-12)
+    # day of the week (0-6)
+    # nightly build at 15:50 UTC time every day
+    - cron: "50 15 * * *"
+
+jobs:
+  run_librispeech_2022_11_11_zipformer:
+    if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python-version: [3.8]
+
+      fail-fast: false
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: '**/requirements-ci.txt'
+
+      - name: Install Python dependencies
+        run: |
+          grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
+          pip uninstall -y protobuf
+          pip install --no-binary protobuf protobuf
+
+      - name: Cache kaldifeat
+        id: my-cache
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/kaldifeat
+          key: cache-tmp-${{ matrix.python-version }}-2022-09-25
+
+      - name: Install kaldifeat
+        if: steps.my-cache.outputs.cache-hit != 'true'
+        shell: bash
+        run: |
+          .github/scripts/install-kaldifeat.sh
+
+      - name: Cache LibriSpeech test-clean and test-other datasets
+        id: libri-test-clean-and-test-other-data
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/download
+          key: cache-libri-test-clean-and-test-other
+
+      - name: Download LibriSpeech test-clean and test-other
+        if: steps.libri-test-clean-and-test-other-data.outputs.cache-hit != 'true'
+        shell: bash
+        run: |
+          .github/scripts/download-librispeech-test-clean-and-test-other-dataset.sh
+
+      - name: Prepare manifests for LibriSpeech test-clean and test-other
+        shell: bash
+        run: |
+          .github/scripts/prepare-librispeech-test-clean-and-test-other-manifests.sh
+
+      - name: Cache LibriSpeech test-clean and test-other fbank features
+        id: libri-test-clean-and-test-other-fbank
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/fbank-libri
+          key: cache-libri-fbank-test-clean-and-test-other-v2
+
+      - name: Compute fbank for LibriSpeech test-clean and test-other
+        if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true'
+        shell: bash
+        run: |
+          .github/scripts/compute-fbank-librispeech-test-clean-and-test-other.sh
+
+      - name: Inference with pre-trained model
+        shell: bash
+        env:
+          GITHUB_EVENT_NAME: ${{ github.event_name }}
+          GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
+        run: |
+          mkdir -p egs/librispeech/ASR/data
+          ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank
+          ls -lh egs/librispeech/ASR/data/*
+
+          sudo apt-get -qq install git-lfs tree sox
+          export PYTHONPATH=$PWD:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
+
+          .github/scripts/run-librispeech-pruned-transducer-stateless7-ctc-2022-12-01.sh
+
+      - name: Display decoding results for librispeech pruned_transducer_stateless7_ctc
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR/
+          tree ./pruned_transducer_stateless7_ctc/exp
+
+          cd pruned_transducer_stateless7_ctc
+          echo "results for pruned_transducer_stateless7_ctc"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===modified beam search==="
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===ctc decoding==="
+          find exp/ctc-decoding -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/ctc-decoding -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===1best==="
+          find exp/1best -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/1best -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for librispeech pruned_transducer_stateless7_ctc
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless7-ctc-2022-12-01
+          path: egs/librispeech/ASR/pruned_transducer_stateless7_ctc/exp/

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

@@ -0,0 +1,155 @@
+# Copyright      2022  Fangjun Kuang (csukuangfj@gmail.com)
+
+# 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.
+
+name: run-librispeech-conformer-ctc3-2022-11-28
+# zipformer
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    types: [labeled]
+
+  schedule:
+    # minute (0-59)
+    # hour (0-23)
+    # day of the month (1-31)
+    # month (1-12)
+    # day of the week (0-6)
+    # nightly build at 15:50 UTC time every day
+    - cron: "50 15 * * *"
+
+concurrency:
+  group: run_librispeech_2022_11_28_conformer_ctc3-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  run_librispeech_2022_11_28_conformer_ctc3:
+    if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python-version: [3.8]
+
+      fail-fast: false
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: '**/requirements-ci.txt'
+
+      - name: Install Python dependencies
+        run: |
+          grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
+          pip uninstall -y protobuf
+          pip install --no-binary protobuf protobuf
+
+      - name: Cache kaldifeat
+        id: my-cache
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/kaldifeat
+          key: cache-tmp-${{ matrix.python-version }}-2022-09-25
+
+      - name: Install kaldifeat
+        if: steps.my-cache.outputs.cache-hit != 'true'
+        shell: bash
+        run: |
+          .github/scripts/install-kaldifeat.sh
+
+      - name: Cache LibriSpeech test-clean and test-other datasets
+        id: libri-test-clean-and-test-other-data
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/download
+          key: cache-libri-test-clean-and-test-other
+
+      - name: Download LibriSpeech test-clean and test-other
+        if: steps.libri-test-clean-and-test-other-data.outputs.cache-hit != 'true'
+        shell: bash
+        run: |
+          .github/scripts/download-librispeech-test-clean-and-test-other-dataset.sh
+
+      - name: Prepare manifests for LibriSpeech test-clean and test-other
+        shell: bash
+        run: |
+          .github/scripts/prepare-librispeech-test-clean-and-test-other-manifests.sh
+
+      - name: Cache LibriSpeech test-clean and test-other fbank features
+        id: libri-test-clean-and-test-other-fbank
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/fbank-libri
+          key: cache-libri-fbank-test-clean-and-test-other-v2
+
+      - name: Compute fbank for LibriSpeech test-clean and test-other
+        if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true'
+        shell: bash
+        run: |
+          .github/scripts/compute-fbank-librispeech-test-clean-and-test-other.sh
+
+      - name: Inference with pre-trained model
+        shell: bash
+        env:
+          GITHUB_EVENT_NAME: ${{ github.event_name }}
+          GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
+        run: |
+          mkdir -p egs/librispeech/ASR/data
+          ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank
+          ls -lh egs/librispeech/ASR/data/*
+
+          sudo apt-get -qq install git-lfs tree sox
+          export PYTHONPATH=$PWD:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
+
+          .github/scripts/run-librispeech-conformer-ctc3-2022-11-28.sh
+
+      - name: Display decoding results for librispeech conformer_ctc3
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR/
+          tree ./conformer_ctc3/exp
+
+          cd conformer_ctc3
+          echo "results for conformer_ctc3"
+          echo "===ctc-decoding==="
+          find exp/ctc-decoding -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/ctc-decoding -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===1best==="
+          find exp/1best -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/1best -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for librispeech conformer_ctc3
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-conformer_ctc3-2022-11-28
+          path: egs/librispeech/ASR/conformer_ctc3/exp/

.github/workflows/run-librispeech-conv-emformer-transducer-stateless2-2022-12-05.yml

@@ -0,0 +1,77 @@
+name: run-librispeech-conv-emformer-transducer-stateless2-2022-12-05
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    types: [labeled]
+
+  schedule:
+    # minute (0-59)
+    # hour (0-23)
+    # day of the month (1-31)
+    # month (1-12)
+    # day of the week (0-6)
+    # nightly build at 15:50 UTC time every day
+    - cron: "50 15 * * *"
+
+jobs:
+  run_librispeech_conv_emformer_transducer_stateless2_2022_12_05:
+    if: github.event.label.name == 'ready' || github.event.label.name == 'ncnn' || github.event_name == 'push' || github.event_name == 'schedule'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python-version: [3.8]
+
+      fail-fast: false
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: '**/requirements-ci.txt'
+
+      - name: Install Python dependencies
+        run: |
+          grep -v '^#' ./requirements-ci.txt  | grep -v kaldifst | xargs -n 1 -L 1 pip install
+          pip uninstall -y protobuf
+          pip install --no-binary protobuf protobuf
+
+      - name: Cache kaldifeat
+        id: my-cache
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/kaldifeat
+          key: cache-tmp-${{ matrix.python-version }}-2022-09-25
+
+      - name: Install kaldifeat
+        if: steps.my-cache.outputs.cache-hit != 'true'
+        shell: bash
+        run: |
+          .github/scripts/install-kaldifeat.sh
+
+      - name: Inference with pre-trained model
+        shell: bash
+        env:
+          GITHUB_EVENT_NAME: ${{ github.event_name }}
+          GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
+        run: |
+          mkdir -p egs/librispeech/ASR/data
+          ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank
+          ls -lh egs/librispeech/ASR/data/*
+
+          sudo apt-get -qq install git-lfs tree sox
+          export PYTHONPATH=$PWD:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
+
+          .github/scripts/run-librispeech-conv-emformer-transducer-stateless2-2022-12-05.sh

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

@@ -16,9 +16,13 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_librispeech_lstm_transducer_stateless2_2022_09_03-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_librispeech_lstm_transducer_stateless2_2022_09_03:
-    if: github.event.label.name == 'ready' || github.event.label.name == 'shallow-fusion' || github.event.label.name == 'ncnn' || github.event.label.name == 'onnx' || github.event_name == 'push' || github.event_name == 'schedule'
+    if: github.event.label.name == 'ready' || github.event.label.name == 'LODR' || github.event.label.name == 'shallow-fusion' || github.event.label.name == 'ncnn' || github.event.label.name == 'onnx' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -107,7 +111,7 @@ jobs:
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
 
-          .github/scripts/run-librispeech-lstm-transducer-stateless2-2022-09-03.yml
+          .github/scripts/run-librispeech-lstm-transducer-stateless2-2022-09-03.sh
 
       - name: Display decoding results for lstm_transducer_stateless2
         if: github.event_name == 'schedule'
@@ -139,9 +143,20 @@ jobs:
           find modified_beam_search_rnnlm_shallow_fusion  -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
           find modified_beam_search_rnnlm_shallow_fusion  -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
 
+      - name: Display decoding results for lstm_transducer_stateless2
+        if: github.event.label.name == 'LODR'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR
+          tree lstm_transducer_stateless2/exp
+          cd lstm_transducer_stateless2/exp
+          echo "===modified_beam_search_rnnlm_LODR==="
+          find modified_beam_search_rnnlm_LODR  -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find modified_beam_search_rnnlm_LODR  -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
       - name: Upload decoding results for lstm_transducer_stateless2
         uses: actions/upload-artifact@v2
-        if: github.event_name == 'schedule' || github.event.label.name == 'shallow-fusion'
+        if: github.event_name == 'schedule' || github.event.label.name == 'shallow-fusion' || github.event.label.name == 'LODR'
         with:
           name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-lstm_transducer_stateless2-2022-09-03
           path: egs/librispeech/ASR/lstm_transducer_stateless2/exp/

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

@@ -33,6 +33,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_librispeech_pruned_transducer_stateless3_2022_05_13-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_librispeech_pruned_transducer_stateless3_2022_05_13:
     if: github.event.label.name == 'onnx' || github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -33,6 +33,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_librispeech_streaming_2022_06_26-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_librispeech_streaming_2022_06_26:
     if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -33,6 +33,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_librispeech_2022_04_19-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_librispeech_2022_04_19:
     if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -23,6 +23,10 @@ on:
   pull_request:
     types: [labeled]
 
+concurrency:
+  group: run_pre_trained_conformer_ctc-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_pre_trained_conformer_ctc:
     if: github.event.label.name == 'ready' || github.event_name == 'push'

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

@@ -32,6 +32,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_pre_trained_transducer_stateless_multi_datasets_librispeech_100h-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_pre_trained_transducer_stateless_multi_datasets_librispeech_100h:
     if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -32,6 +32,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_pre_trained_transducer_stateless_multi_datasets_librispeech_960h-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_pre_trained_transducer_stateless_multi_datasets_librispeech_960h:
     if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -23,6 +23,10 @@ on:
   pull_request:
     types: [labeled]
 
+concurrency:
+  group: run_pre_trained_transducer_stateless_modified_2_aishell-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_pre_trained_transducer_stateless_modified_2_aishell:
     if: github.event.label.name == 'ready' || github.event_name == 'push'

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

@@ -23,6 +23,10 @@ on:
   pull_request:
     types: [labeled]
 
+concurrency:
+  group: run_pre_trained_transducer_stateless_modified_aishell-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_pre_trained_transducer_stateless_modified_aishell:
     if: github.event.label.name == 'ready' || github.event_name == 'push'

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

@@ -32,6 +32,10 @@ on:
     # nightly build at 15:50 UTC time every day
     - cron: "50 15 * * *"
 
+concurrency:
+  group: run_pre_trained_transducer_stateless-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_pre_trained_transducer_stateless:
     if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'

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

@@ -23,6 +23,10 @@ on:
   pull_request:
     types: [labeled]
 
+concurrency:
+  group: run_pre_trained_transducer-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   run_pre_trained_transducer:
     if: github.event.label.name == 'ready' || github.event_name == 'push'

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

@@ -0,0 +1,71 @@
+name: run-ptb-rnn-lm-training
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    types: [labeled]
+
+  schedule:
+    # minute (0-59)
+    # hour (0-23)
+    # day of the month (1-31)
+    # month (1-12)
+    # day of the week (0-6)
+    # nightly build at 15:50 UTC time every day
+    - cron: "50 15 * * *"
+
+concurrency:
+  group: run_ptb_rnn_lm_training-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  run_ptb_rnn_lm_training:
+    if: github.event.label.name == 'ready' || github.event.label.name == 'rnnlm' || github.event_name == 'push' || github.event_name == 'schedule'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ["3.8"]
+
+      fail-fast: false
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: '**/requirements-ci.txt'
+
+      - name: Install Python dependencies
+        run: |
+          grep -v '^#' ./requirements-ci.txt  | grep -v kaldifst | xargs -n 1 -L 1 pip install
+          pip uninstall -y protobuf
+          pip install --no-binary protobuf protobuf
+
+      - name: Prepare data
+        shell: bash
+        run: |
+          export PYTHONPATH=$PWD:$PYTHONPATH
+          cd egs/ptb/LM
+          ./prepare.sh
+
+      - name: Run training
+        shell: bash
+        run: |
+          export PYTHONPATH=$PWD:$PYTHONPATH
+          cd egs/ptb/LM
+          ./train-rnn-lm.sh --world-size 1 --num-epochs 5 --use-epoch 4 --use-avg 2
+
+      - name: Upload pretrained models
+        uses: actions/upload-artifact@v2
+        if: github.event.label.name == 'ready' || github.event.label.name == 'rnnlm' || github.event_name == 'push' || github.event_name == 'schedule'
+        with:
+          name: python-${{ matrix.python-version }}-ubuntu-rnn-lm-ptb
+          path: egs/ptb/LM/my-rnnlm-exp/

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

@@ -23,8 +23,12 @@ on:
   pull_request:
     types: [labeled]
 
+concurrency:
+  group: run_wenetspeech_pruned_transducer_stateless2-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
-  run_librispeech_pruned_transducer_stateless3_2022_05_13:
+  run_wenetspeech_pruned_transducer_stateless2:
     if: github.event.label.name == 'onnx' || github.event.label.name == 'ready' || github.event_name == 'push' || github.event.label.name == 'wenetspeech'
     runs-on: ${{ matrix.os }}
     strategy:

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

@@ -21,11 +21,15 @@ on:
     branches:
       - master
   pull_request:
-    types: [labeled]
+    branches:
+      - master
+
+concurrency:
+  group: run-yesno-recipe-${{ github.ref }}
+  cancel-in-progress: true
 
 jobs:
   run-yesno-recipe:
-    if: github.event.label.name == 'ready' || github.event_name == 'push'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -61,7 +65,7 @@ jobs:
 
       - name: Install Python dependencies
         run: |
-          grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
+          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
 

.github/workflows/style_check.yml

@@ -24,6 +24,10 @@ on:
     branches:
       - master
 
+concurrency:
+  group: style_check-${{ github.ref }}
+  cancel-in-progress: true
+
 jobs:
   style_check:
     runs-on: ${{ matrix.os }}

.github/workflows/test.yml

@@ -21,26 +21,23 @@ on:
     branches:
       - master
   pull_request:
-    types: [labeled]
+    branches:
+      - master
+
+concurrency:
+  group: test-${{ github.ref }}
+  cancel-in-progress: true
 
 jobs:
   test:
-    if: github.event.label.name == 'ready' || github.event_name == 'push'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        # os: [ubuntu-18.04, macos-10.15]
+        os: [ubuntu-latest]
-        # disable macOS test for now.
+        python-version: ["3.8"]
-        os: [ubuntu-18.04]
+        torch: ["1.10.0"]
-        python-version: [3.7, 3.8]
+        torchaudio: ["0.10.0"]
-        torch: ["1.8.0", "1.11.0"]
+        k2-version: ["1.23.2.dev20221201"]
-        torchaudio: ["0.8.0", "0.11.0"]
-        k2-version: ["1.15.1.dev20220427"]
-        exclude:
-          - torch: "1.8.0"
-            torchaudio: "0.11.0"
-          - torch: "1.11.0"
-            torchaudio: "0.8.0"
 
       fail-fast: false
 
@@ -67,11 +64,7 @@ jobs:
           # numpy 1.20.x does not support python 3.6
           pip install numpy==1.19
           pip install torch==${{ matrix.torch }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
-          if [[ ${{ matrix.torchaudio }} == "0.11.0" ]]; then
           pip install torchaudio==${{ matrix.torchaudio }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
-          else
-            pip install torchaudio==${{ matrix.torchaudio }}
-          fi
 
           pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/
           pip install git+https://github.com/lhotse-speech/lhotse
@@ -81,7 +74,6 @@ jobs:
 
           pip install kaldifst
           pip install onnxruntime
-
           pip install -r requirements.txt
 
       - name: Install graphviz
@@ -124,7 +116,6 @@ jobs:
           cd ../transducer_stateless
           pytest -v -s
 
-          if [[ ${{ matrix.torchaudio }} == "0.10.0" ]]; then
           cd ../transducer
           pytest -v -s
 
@@ -133,7 +124,6 @@ jobs:
 
           cd ../transducer_lstm
           pytest -v -s
-          fi
 
       - name: Run tests
         if: startsWith(matrix.os, 'macos')
@@ -164,7 +154,6 @@ jobs:
           cd ../transducer_stateless
           pytest -v -s
 
-          if [[ ${{ matrix.torchaudio }} == "0.10.0" ]]; then
           cd ../transducer
           pytest -v -s
 
@@ -173,4 +162,3 @@ jobs:
 
           cd ../transducer_lstm
           pytest -v -s
-          fi

.gitignore

@@ -11,5 +11,25 @@ log
 *.bak
 *-bak
 *bak.py
+
+# Ignore Mac system files
+.DS_store
+
+# Ignore node_modules folder
+node_modules
+
+# ignore .nfs
+
+.nfs*
+
+# Ignore all text files
+*.txt
+
+# Ignore files related to API keys
+.env
+
+# Ignore SASS config files
+.sass-cache
+
 *.param
 *.bin

docs/source/installation/index.rst

@@ -393,6 +393,17 @@ Now let us run the training part:
   We use ``export CUDA_VISIBLE_DEVICES=""`` so that ``icefall`` uses CPU
   even if there are GPUs available.
 
+.. hint::
+
+   In case you get a ``Segmentation fault (core dump)`` error, please use:
+
+      .. code-block:: bash
+
+        export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+   See more at `<https://github.com/k2-fsa/icefall/issues/674>` if you are
+   interested.
+
 The training log is given below:
 
 .. code-block::

egs/aidatatang_200zh/ASR/prepare.sh

@@ -1,5 +1,8 @@
 #!/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

egs/aishell/ASR/prepare.sh

@@ -1,5 +1,8 @@
 #!/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
 
 nj=15

egs/aishell2/ASR/prepare.sh

@@ -1,5 +1,8 @@
 #!/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
 
 nj=30

egs/aishell4/ASR/prepare.sh

@@ -1,5 +1,8 @@
 #!/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

egs/alimeeting/ASR/prepare.sh

@@ -1,5 +1,8 @@
 #!/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

egs/ami/ASR/README.md

@@ -0,0 +1,48 @@
+# AMI
+
+This is an ASR recipe for the AMI corpus. AMI provides recordings from the speaker's
+headset and lapel microphones, and also 2 array microphones containing 8 channels each.
+We pool data in the following 4 ways and train a single model on the pooled data:
+
+(i) individual headset microphone (IHM)
+(ii) IHM with simulated reverb
+(iii) Single distant microphone (SDM)
+(iv) GSS-enhanced array microphones
+
+Speed perturbation and MUSAN noise augmentation are additionally performed on the pooled
+data. Here are the statistics of the combined training data:
+
+```python
+>>> cuts_train.describe()
+Cuts count: 1222053
+Total duration (hh:mm:ss): 905:00:28
+Speech duration (hh:mm:ss): 905:00:28 (99.9%)
+Duration statistics (seconds):
+mean    2.7
+std     2.8
+min     0.0
+25%     0.6
+50%     1.6
+75%     3.8
+99%     12.3
+99.5%   13.9
+99.9%   18.4
+max     36.8
+```
+
+**Note:** This recipe additionally uses [GSS](https://github.com/desh2608/gss) for enhancement
+of far-field array microphones, but this is optional (see `prepare.sh` for details).
+
+## Performance Record
+
+### pruned_transducer_stateless7
+
+The following are decoded using `modified_beam_search`:
+
+| Evaluation set           | dev WER    | test WER |
+|--------------------------|------------|---------|
+| IHM                      |  18.92  | 17.40 |
+| SDM                      |  31.25  | 32.21 |
+| MDM (GSS-enhanced)       |  21.67  | 22.43 |
+
+See [RESULTS](/egs/ami/ASR/RESULTS.md) for details.

egs/ami/ASR/RESULTS.md

@@ -0,0 +1,92 @@
+## Results
+
+### AMI training results (Pruned Transducer)
+
+#### 2022-11-20
+
+#### Zipformer (pruned_transducer_stateless7)
+
+Zipformer encoder + non-current decoder. The decoder
+contains only an embedding layer, a Conv1d (with kernel size 2) and a linear
+layer (to transform tensor dim).
+
+All the results below are using a single model that is trained by combining the following
+data: IHM, IHM+reverb, SDM, and GSS-enhanced MDM. Speed perturbation and MUSAN noise
+augmentation are applied on top of the pooled data.
+
+**WERs for IHM:**
+
+|                           | dev | test | comment                                  |
+|---------------------------|------------|------------|------------------------------------------|
+| greedy search             |  19.25  |  17.83  | --epoch 14 --avg 8 --max-duration 500 |
+| modified beam search      |  18.92  |  17.40  | --epoch 14 --avg 8 --max-duration 500 --beam-size 4 |
+| fast beam search          |  19.44  |  18.04  | --epoch 14 --avg 8 --max-duration 500 --beam-size 4 --max-contexts 4 --max-states 8 |
+
+**WERs for SDM:**
+
+|                           | dev | test | comment                                  |
+|---------------------------|------------|------------|------------------------------------------|
+| greedy search             |  31.32  |  32.38  | --epoch 14 --avg 8 --max-duration 500 |
+| modified beam search      |  31.25  |  32.21  | --epoch 14 --avg 8 --max-duration 500 --beam-size 4 |
+| fast beam search          |  31.11  |  32.10  | --epoch 14 --avg 8 --max-duration 500 --beam-size 4 --max-contexts 4 --max-states 8 |
+
+**WERs for GSS-enhanced MDM:**
+
+|                           | dev | test | comment                                  |
+|---------------------------|------------|------------|------------------------------------------|
+| greedy search             |  22.05  |  22.93  | --epoch 14 --avg 8 --max-duration 500 |
+| modified beam search      |  21.67  |  22.43  | --epoch 14 --avg 8 --max-duration 500 --beam-size 4 |
+| fast beam search          |  22.21  |  22.83  | --epoch 14 --avg 8 --max-duration 500 --beam-size 4 --max-contexts 4 --max-states 8 |
+
+The training command for reproducing is given below:
+
+```
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+./pruned_transducer_stateless7/train.py \
+  --world-size 4 \
+  --num-epochs 15 \
+  --exp-dir pruned_transducer_stateless7/exp \
+  --max-duration 150 \
+  --max-cuts 150 \
+  --prune-range 5 \
+  --lr-factor 5 \
+  --lm-scale 0.25 \
+  --use-fp16 True
+```
+
+The decoding command is:
+```
+# greedy search
+./pruned_transducer_stateless7/decode.py \
+        --epoch 14 \
+        --avg 8 \
+        --exp-dir ./pruned_transducer_stateless7/exp \
+        --max-duration 500 \
+        --decoding-method greedy_search
+
+# modified beam search
+./pruned_transducer_stateless7/decode.py \
+        --iter 105000 \
+        --avg 10 \
+        --exp-dir ./pruned_transducer_stateless7/exp \
+        --max-duration 500 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+
+# fast beam search
+./pruned_transducer_stateless7/decode.py \
+        --iter 105000 \
+        --avg 10 \
+        --exp-dir ./pruned_transducer_stateless5/exp \
+        --max-duration 500 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
+```
+
+Pretrained model is available at <https://huggingface.co/desh2608/icefall-asr-ami-pruned-transducer-stateless7>
+
+The tensorboard training log can be found at
+<https://tensorboard.dev/experiment/VH10QOTBTbuYpWx994Onrg/#scalars>

egs/ami/ASR/local/compute_fbank_musan.py

@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+This file computes fbank features of the musan dataset.
+It looks for manifests in the directory data/manifests.
+
+The generated fbank features are saved in data/fbank.
+"""
+
+import logging
+from pathlib import Path
+
+import torch
+from lhotse import CutSet, LilcomChunkyWriter, combine
+from lhotse.features.kaldifeat import (
+    KaldifeatFbank,
+    KaldifeatFbankConfig,
+    KaldifeatFrameOptions,
+    KaldifeatMelOptions,
+)
+from lhotse.recipes.utils import read_manifests_if_cached
+
+# Torch's multithreaded behavior needs to be disabled or
+# it wastes a lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
+# even when we are not invoking the main (e.g. when spawning subprocesses).
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+
+def compute_fbank_musan():
+    src_dir = Path("data/manifests")
+    output_dir = Path("data/fbank")
+
+    sampling_rate = 16000
+    num_mel_bins = 80
+
+    dataset_parts = (
+        "music",
+        "speech",
+        "noise",
+    )
+    prefix = "musan"
+    suffix = "jsonl.gz"
+    manifests = read_manifests_if_cached(
+        dataset_parts=dataset_parts,
+        output_dir=src_dir,
+        prefix=prefix,
+        suffix=suffix,
+    )
+    assert manifests is not None
+
+    assert len(manifests) == len(dataset_parts), (
+        len(manifests),
+        len(dataset_parts),
+        list(manifests.keys()),
+        dataset_parts,
+    )
+
+    musan_cuts_path = src_dir / "musan_cuts.jsonl.gz"
+
+    if musan_cuts_path.is_file():
+        logging.info(f"{musan_cuts_path} already exists - skipping")
+        return
+
+    logging.info("Extracting features for Musan")
+
+    extractor = KaldifeatFbank(
+        KaldifeatFbankConfig(
+            frame_opts=KaldifeatFrameOptions(sampling_rate=sampling_rate),
+            mel_opts=KaldifeatMelOptions(num_bins=num_mel_bins),
+            device="cuda",
+        )
+    )
+
+    # create chunks of Musan with duration 5 - 10 seconds
+    _ = (
+        CutSet.from_manifests(
+            recordings=combine(part["recordings"] for part in manifests.values())
+        )
+        .cut_into_windows(10.0)
+        .filter(lambda c: c.duration > 5)
+        .compute_and_store_features_batch(
+            extractor=extractor,
+            storage_path=output_dir / "musan_feats",
+            manifest_path=musan_cuts_path,
+            batch_duration=500,
+            num_workers=4,
+            storage_type=LilcomChunkyWriter,
+        )
+    )
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    compute_fbank_musan()

egs/ami/ASR/local/prepare_ami_enhanced.py

@@ -0,0 +1,158 @@
+#!/usr/local/bin/python
+# -*- coding: utf-8 -*-
+# Data preparation for AMI GSS-enhanced dataset.
+
+import logging
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+
+from lhotse import Recording, RecordingSet, SupervisionSet
+from lhotse.qa import fix_manifests
+from lhotse.recipes.utils import read_manifests_if_cached
+from lhotse.utils import fastcopy
+from tqdm import tqdm
+
+logging.basicConfig(
+    format="%(asctime)s %(levelname)-8s %(message)s",
+    level=logging.INFO,
+    datefmt="%Y-%m-%d %H:%M:%S",
+)
+
+
+def get_args():
+    import argparse
+
+    parser = argparse.ArgumentParser(description="AMI enhanced dataset preparation.")
+    parser.add_argument(
+        "manifests_dir",
+        type=Path,
+        help="Path to directory containing AMI manifests.",
+    )
+    parser.add_argument(
+        "enhanced_dir",
+        type=Path,
+        help="Path to enhanced data directory.",
+    )
+    parser.add_argument(
+        "--num-jobs",
+        "-j",
+        type=int,
+        default=1,
+        help="Number of parallel jobs to run.",
+    )
+    parser.add_argument(
+        "--min-segment-duration",
+        "-d",
+        type=float,
+        default=0.0,
+        help="Minimum duration of a segment in seconds.",
+    )
+    return parser.parse_args()
+
+
+def find_recording_and_create_new_supervision(enhanced_dir, supervision):
+    """
+    Given a supervision (corresponding to original AMI recording), this function finds the
+    enhanced recording correspoding to the supervision, and returns this recording and
+    a new supervision whose start and end times are adjusted to match the enhanced recording.
+    """
+    file_name = Path(
+        f"{supervision.recording_id}-{supervision.speaker}-{int(100*supervision.start):06d}_{int(100*supervision.end):06d}.flac"
+    )
+    save_path = enhanced_dir / f"{supervision.recording_id}" / file_name
+    if save_path.exists():
+        recording = Recording.from_file(save_path)
+        if recording.duration == 0:
+            logging.warning(f"Skipping {save_path} which has duration 0 seconds.")
+            return None
+
+        # Old supervision is wrt to the original recording, we create new supervision
+        # wrt to the enhanced segment
+        new_supervision = fastcopy(
+            supervision,
+            recording_id=recording.id,
+            start=0,
+            duration=recording.duration,
+        )
+        return recording, new_supervision
+    else:
+        logging.warning(f"{save_path} does not exist.")
+        return None
+
+
+def main(args):
+    # Get arguments
+    manifests_dir = args.manifests_dir
+    enhanced_dir = args.enhanced_dir
+
+    # Load manifests from cache if they exist (saves time)
+    manifests = read_manifests_if_cached(
+        dataset_parts=["train", "dev", "test"],
+        output_dir=manifests_dir,
+        prefix="ami-sdm",
+        suffix="jsonl.gz",
+    )
+    if not manifests:
+        raise ValueError("AMI SDM manifests not found in {}".format(manifests_dir))
+
+    with ThreadPoolExecutor(args.num_jobs) as ex:
+        for part in ["train", "dev", "test"]:
+            logging.info(f"Processing {part}...")
+            supervisions_orig = manifests[part]["supervisions"].filter(
+                lambda s: s.duration >= args.min_segment_duration
+            )
+            # Remove TS3009d supervisions since they are not present in the enhanced data
+            supervisions_orig = supervisions_orig.filter(
+                lambda s: s.recording_id != "TS3009d"
+            )
+            futures = []
+
+            for supervision in tqdm(
+                supervisions_orig,
+                desc="Distributing tasks",
+            ):
+                futures.append(
+                    ex.submit(
+                        find_recording_and_create_new_supervision,
+                        enhanced_dir,
+                        supervision,
+                    )
+                )
+
+            recordings = []
+            supervisions = []
+            for future in tqdm(
+                futures,
+                total=len(futures),
+                desc="Processing tasks",
+            ):
+                result = future.result()
+                if result is not None:
+                    recording, new_supervision = result
+                    recordings.append(recording)
+                    supervisions.append(new_supervision)
+
+            # Remove duplicates from the recordings
+            recordings_nodup = {}
+            for recording in recordings:
+                if recording.id not in recordings_nodup:
+                    recordings_nodup[recording.id] = recording
+                else:
+                    logging.warning("Recording {} is duplicated.".format(recording.id))
+            recordings = RecordingSet.from_recordings(recordings_nodup.values())
+            supervisions = SupervisionSet.from_segments(supervisions)
+
+            recordings, supervisions = fix_manifests(
+                recordings=recordings, supervisions=supervisions
+            )
+
+            logging.info(f"Writing {part} enhanced manifests")
+            recordings.to_file(manifests_dir / f"ami-gss_recordings_{part}.jsonl.gz")
+            supervisions.to_file(
+                manifests_dir / f"ami-gss_supervisions_{part}.jsonl.gz"
+            )
+
+
+if __name__ == "__main__":
+    args = get_args()
+    main(args)

egs/ami/ASR/local/prepare_ami_gss.sh

@@ -0,0 +1,98 @@
+#!/bin/bash
+# This script is used to run GSS-based enhancement on AMI data.
+set -euo pipefail
+nj=4
+stage=0
+
+. shared/parse_options.sh || exit 1
+
+if [ $# != 2 ]; then
+   echo "Wrong #arguments ($#, expected 2)"
+   echo "Usage: local/prepare_ami_gss.sh [options] <data-dir> <exp-dir>"
+   echo "e.g. local/prepare_ami_gss.sh data/manifests exp/ami_gss"
+   echo "main options (for others, see top of script file)"
+   echo "  --nj <nj>                                # number of parallel jobs"
+   echo "  --stage <stage>                          # stage to start running from"
+   exit 1;
+fi
+
+DATA_DIR=$1
+EXP_DIR=$2
+
+mkdir -p $EXP_DIR
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+if [ $stage -le 1 ]; then
+  log "Stage 1: Prepare cut sets"
+  for part in train dev test; do
+    lhotse cut simple \
+      -r $DATA_DIR/ami-mdm_recordings_${part}.jsonl.gz \
+      -s $DATA_DIR/ami-mdm_supervisions_${part}.jsonl.gz \
+      $EXP_DIR/cuts_${part}.jsonl.gz
+  done
+fi
+
+if [ $stage -le 2 ]; then
+  log "Stage 2: Trim cuts to supervisions (1 cut per supervision segment)"
+  for part in train dev test; do
+    lhotse cut trim-to-supervisions --discard-overlapping \
+        $EXP_DIR/cuts_${part}.jsonl.gz $EXP_DIR/cuts_per_segment_${part}.jsonl.gz
+  done
+fi
+
+if [ $stage -le 3 ]; then
+  log "Stage 3: Split manifests for multi-GPU processing (optional)"
+  for part in train; do
+    gss utils split $nj $EXP_DIR/cuts_per_segment_${part}.jsonl.gz \
+      $EXP_DIR/cuts_per_segment_${part}_split$nj
+  done
+fi
+
+if [ $stage -le 4 ]; then
+  log "Stage 4: Enhance train segments using GSS (requires GPU)"
+  # for train, we use smaller context and larger batches to speed-up processing
+  for JOB in $(seq $nj); do
+    gss enhance cuts $EXP_DIR/cuts_train.jsonl.gz \
+      $EXP_DIR/cuts_per_segment_train_split$nj/cuts_per_segment_train.JOB.jsonl.gz $EXP_DIR/enhanced \
+      --bss-iterations 10 \
+      --context-duration 5.0 \
+      --use-garbage-class \
+      --channels 0,1,2,3,4,5,6,7 \
+      --min-segment-length 0.05 \
+      --max-segment-length 35.0 \
+      --max-batch-duration 60.0 \
+      --num-buckets 3 \
+      --num-workers 2
+  done
+fi
+
+if [ $stage -le 5 ]; then
+  log "Stage 5: Enhance dev/test segments using GSS (using GPU)"
+  # for dev/test, we use larger context and smaller batches to get better quality
+  for part in dev test; do
+    for JOB in $(seq $nj); do
+      gss enhance cuts $EXP_DIR/cuts_${part}.jsonl.gz \
+      $EXP_DIR/cuts_per_segment_${part}_split$nj/cuts_per_segment_${part}.JOB.jsonl.gz \
+      $EXP_DIR/enhanced \
+      --bss-iterations 10 \
+      --context-duration 15.0 \
+      --use-garbage-class \
+      --channels 0,1,2,3,4,5,6,7 \
+      --min-segment-length 0.05 \
+      --max-segment-length 30.0 \
+      --max-batch-duration 45.0 \
+      --num-buckets 3 \
+      --num-workers 2
+    done
+  done
+fi
+
+if [ $stage -le 6 ]; then
+  log "Stage 6: Prepare manifests for GSS-enhanced data"
+  python local/prepare_ami_enhanced.py $DATA_DIR $EXP_DIR/enhanced -j $nj --min-segment-duration 0.05
+fi

egs/ami/ASR/local/prepare_lang_bpe.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/local/prepare_lang_bpe.py

egs/ami/ASR/local/train_bpe_model.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/local/train_bpe_model.py

egs/ami/ASR/pruned_transducer_stateless7/asr_datamodule.py

@@ -0,0 +1,430 @@
+# Copyright      2021  Piotr Żelasko
+#
+# 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.
+
+
+import argparse
+import logging
+import re
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import torch
+from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
+from lhotse.cut import Cut
+from lhotse.dataset import (
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    K2SpeechRecognitionDataset,
+    PrecomputedFeatures,
+    SpecAugment,
+)
+from lhotse.dataset.input_strategies import OnTheFlyFeatures
+from lhotse.utils import fix_random_seed
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+from icefall.utils import str2bool
+
+
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+
+class AmiAsrDataModule:
+    """
+    DataModule for k2 ASR experiments.
+    It assumes there is always one train and valid dataloader,
+    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
+    and test-other).
+    It contains all the common data pipeline modules used in ASR
+    experiments, e.g.:
+    - dynamic batch size,
+    - bucketing samplers,
+    - cut concatenation,
+    - augmentation,
+    - on-the-fly feature extraction
+    This class should be derived for specific corpora used in ASR tasks.
+    """
+
+    def __init__(self, args: argparse.Namespace):
+        self.args = args
+
+    @classmethod
+    def add_arguments(cls, parser: argparse.ArgumentParser):
+        group = parser.add_argument_group(
+            title="ASR data related options",
+            description=(
+                "These options are used for the preparation of "
+                "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
+                "effective batch sizes, sampling strategies, applied data "
+                "augmentations, etc."
+            ),
+        )
+        group.add_argument(
+            "--manifest-dir",
+            type=Path,
+            default=Path("data/manifests"),
+            help="Path to directory with train/valid/test cuts.",
+        )
+        group.add_argument(
+            "--enable-musan",
+            type=str2bool,
+            default=True,
+            help=(
+                "When enabled, select noise from MUSAN and mix it "
+                "with training dataset. "
+            ),
+        )
+        group.add_argument(
+            "--concatenate-cuts",
+            type=str2bool,
+            default=False,
+            help=(
+                "When enabled, utterances (cuts) will be concatenated "
+                "to minimize the amount of padding."
+            ),
+        )
+        group.add_argument(
+            "--duration-factor",
+            type=float,
+            default=1.0,
+            help=(
+                "Determines the maximum duration of a concatenated cut "
+                "relative to the duration of the longest cut in a batch."
+            ),
+        )
+        group.add_argument(
+            "--gap",
+            type=float,
+            default=1.0,
+            help=(
+                "The amount of padding (in seconds) inserted between "
+                "concatenated cuts. This padding is filled with noise when "
+                "noise augmentation is used."
+            ),
+        )
+        group.add_argument(
+            "--max-duration",
+            type=int,
+            default=100.0,
+            help=(
+                "Maximum pooled recordings duration (seconds) in a "
+                "single batch. You can reduce it if it causes CUDA OOM."
+            ),
+        )
+        group.add_argument(
+            "--max-cuts", type=int, default=None, help="Maximum cuts in a single batch."
+        )
+        group.add_argument(
+            "--num-buckets",
+            type=int,
+            default=50,
+            help=(
+                "The number of buckets for the BucketingSampler"
+                "(you might want to increase it for larger datasets)."
+            ),
+        )
+        group.add_argument(
+            "--on-the-fly-feats",
+            type=str2bool,
+            default=False,
+            help=(
+                "When enabled, use on-the-fly cut mixing and feature "
+                "extraction. Will drop existing precomputed feature manifests "
+                "if available."
+            ),
+        )
+        group.add_argument(
+            "--shuffle",
+            type=str2bool,
+            default=True,
+            help=(
+                "When enabled (=default), the examples will be "
+                "shuffled for each epoch."
+            ),
+        )
+
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=8,
+            help=(
+                "The number of training dataloader workers that " "collect the batches."
+            ),
+        )
+        group.add_argument(
+            "--enable-spec-aug",
+            type=str2bool,
+            default=True,
+            help="When enabled, use SpecAugment for training dataset.",
+        )
+        group.add_argument(
+            "--spec-aug-time-warp-factor",
+            type=int,
+            default=80,
+            help=(
+                "Used only when --enable-spec-aug is True. "
+                "It specifies the factor for time warping in SpecAugment. "
+                "Larger values mean more warping. "
+                "A value less than 1 means to disable time warp."
+            ),
+        )
+        group.add_argument(
+            "--ihm-only",
+            type=str2bool,
+            default=False,
+            help="When enabled, only use IHM data for training.",
+        )
+
+    def train_dataloaders(
+        self,
+        cuts_train: CutSet,
+        sampler_state_dict: Optional[Dict[str, Any]] = None,
+    ) -> DataLoader:
+        """
+        Args:
+          cuts_train:
+            CutSet for training.
+          sampler_state_dict:
+            The state dict for the training sampler.
+        """
+        logging.info("About to get Musan cuts")
+
+        transforms = []
+        if self.args.enable_musan:
+            logging.info("Enable MUSAN")
+            cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
+            transforms.append(
+                CutMix(cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True)
+            )
+        else:
+            logging.info("Disable MUSAN")
+
+        if self.args.concatenate_cuts:
+            logging.info(
+                "Using cut concatenation with duration factor "
+                f"{self.args.duration_factor} and gap {self.args.gap}."
+            )
+            # Cut concatenation should be the first transform in the list,
+            # so that if we e.g. mix noise in, it will fill the gaps between
+            # different utterances.
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + transforms
+
+        input_transforms = []
+        if self.args.enable_spec_aug:
+            logging.info("Enable SpecAugment")
+            logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
+            input_transforms.append(
+                SpecAugment(
+                    time_warp_factor=self.args.spec_aug_time_warp_factor,
+                    num_frame_masks=2,
+                    features_mask_size=27,
+                    num_feature_masks=2,
+                    frames_mask_size=100,
+                )
+            )
+        else:
+            logging.info("Disable SpecAugment")
+
+        logging.info("About to create train dataset")
+        if self.args.on_the_fly_feats:
+            train = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_transforms=input_transforms,
+            )
+        else:
+            train = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_transforms=input_transforms,
+            )
+
+        logging.info("Using DynamicBucketingSampler.")
+        train_sampler = DynamicBucketingSampler(
+            cuts_train,
+            max_duration=self.args.max_duration,
+            max_cuts=self.args.max_cuts,
+            shuffle=False,
+            num_buckets=self.args.num_buckets,
+            drop_last=True,
+        )
+        logging.info("About to create train dataloader")
+
+        if sampler_state_dict is not None:
+            logging.info("Loading sampler state dict")
+            train_sampler.load_state_dict(sampler_state_dict)
+
+        # 'seed' is derived from the current random state, which will have
+        # previously been set in the main process.
+        seed = torch.randint(0, 100000, ()).item()
+        worker_init_fn = _SeedWorkers(seed)
+
+        train_dl = DataLoader(
+            train,
+            sampler=train_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+            worker_init_fn=worker_init_fn,
+        )
+
+        return train_dl
+
+    def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
+
+        transforms = []
+        if self.args.concatenate_cuts:
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + transforms
+
+        logging.info("About to create dev dataset")
+        if self.args.on_the_fly_feats:
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+            )
+        else:
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+            )
+        valid_sampler = DynamicBucketingSampler(
+            cuts_valid,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.info("About to create dev dataloader")
+        valid_dl = DataLoader(
+            validate,
+            sampler=valid_sampler,
+            batch_size=None,
+            num_workers=2,
+            persistent_workers=False,
+        )
+
+        return valid_dl
+
+    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
+        logging.debug("About to create test dataset")
+        test = K2SpeechRecognitionDataset(
+            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+            if self.args.on_the_fly_feats
+            else PrecomputedFeatures(),
+            return_cuts=True,
+        )
+        sampler = DynamicBucketingSampler(
+            cuts, max_duration=self.args.max_duration, shuffle=False
+        )
+        logging.debug("About to create test dataloader")
+        test_dl = DataLoader(
+            test,
+            batch_size=None,
+            sampler=sampler,
+            num_workers=self.args.num_workers,
+        )
+        return test_dl
+
+    def remove_short_cuts(self, cut: Cut) -> bool:
+        """
+        See: https://github.com/k2-fsa/icefall/issues/500
+        Basically, the zipformer model subsamples the input using the following formula:
+        num_out_frames = (num_in_frames - 7)//2
+        For num_out_frames to be at least 1, num_in_frames must be at least 9.
+        """
+        return cut.duration >= 0.09
+
+    @lru_cache()
+    def train_cuts(self, sp: Optional[Any] = None) -> CutSet:
+        logging.info("About to get AMI train cuts")
+
+        def _remove_short_and_long_utt(c: Cut):
+            if c.duration < 0.2 or c.duration > 25.0:
+                return False
+
+            # In pruned RNN-T, we require that T >= S
+            # where T is the number of feature frames after subsampling
+            # and S is the number of tokens in the utterance
+
+            # In ./zipformer.py, the conv module uses the following expression
+            # for subsampling
+            T = ((c.num_frames - 7) // 2 + 1) // 2
+            tokens = sp.encode(c.supervisions[0].text, out_type=str)
+            return T >= len(tokens)
+
+        if self.args.ihm_only:
+            cuts_train = load_manifest_lazy(
+                self.args.manifest_dir / "cuts_train_ihm.jsonl.gz"
+            )
+        else:
+            cuts_train = load_manifest_lazy(
+                self.args.manifest_dir / "cuts_train_all.jsonl.gz"
+            )
+
+        return cuts_train.filter(_remove_short_and_long_utt)
+
+    @lru_cache()
+    def dev_ihm_cuts(self) -> CutSet:
+        logging.info("About to get AMI IHM dev cuts")
+        cs = load_manifest_lazy(self.args.manifest_dir / "cuts_dev_ihm.jsonl.gz")
+        return cs.filter(self.remove_short_cuts)
+
+    @lru_cache()
+    def dev_sdm_cuts(self) -> CutSet:
+        logging.info("About to get AMI SDM dev cuts")
+        cs = load_manifest_lazy(self.args.manifest_dir / "cuts_dev_sdm.jsonl.gz")
+        return cs.filter(self.remove_short_cuts)
+
+    @lru_cache()
+    def dev_gss_cuts(self) -> CutSet:
+        if not (self.args.manifest_dir / "cuts_dev_gss.jsonl.gz").exists():
+            logging.info("No GSS dev cuts found")
+            return None
+        logging.info("About to get AMI GSS-enhanced dev cuts")
+        cs = load_manifest_lazy(self.args.manifest_dir / "cuts_dev_gss.jsonl.gz")
+        return cs.filter(self.remove_short_cuts)
+
+    @lru_cache()
+    def test_ihm_cuts(self) -> CutSet:
+        logging.info("About to get AMI IHM test cuts")
+        cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_ihm.jsonl.gz")
+        return cs.filter(self.remove_short_cuts)
+
+    @lru_cache()
+    def test_sdm_cuts(self) -> CutSet:
+        logging.info("About to get AMI SDM test cuts")
+        cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_sdm.jsonl.gz")
+        return cs.filter(self.remove_short_cuts)
+
+    @lru_cache()
+    def test_gss_cuts(self) -> CutSet:
+        if not (self.args.manifest_dir / "cuts_test_gss.jsonl.gz").exists():
+            logging.info("No GSS test cuts found")
+            return None
+        logging.info("About to get AMI GSS-enhanced test cuts")
+        cs = load_manifest_lazy(self.args.manifest_dir / "cuts_test_gss.jsonl.gz")
+        return cs.filter(self.remove_short_cuts)

egs/ami/ASR/pruned_transducer_stateless7/beam_search.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/beam_search.py

egs/ami/ASR/pruned_transducer_stateless7/decode.py

@@ -0,0 +1,747 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Usage:
+(1) greedy search
+./pruned_transducer_stateless7/decode.py \
+        --iter 105000 \
+        --avg 10 \
+        --exp-dir ./pruned_transducer_stateless7/exp \
+        --max-duration 100 \
+        --decoding-method greedy_search
+
+(2) beam search
+./pruned_transducer_stateless7/decode.py \
+        --iter 105000 \
+        --avg 10 \
+        --exp-dir ./pruned_transducer_stateless7/exp \
+        --max-duration 500 \
+        --decoding-method beam_search \
+        --beam-size 4
+
+(3) modified beam search
+./pruned_transducer_stateless7/decode.py \
+        --iter 105000 \
+        --avg 10 \
+        --exp-dir ./pruned_transducer_stateless7/exp \
+        --max-duration 500 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+
+(4) fast beam search
+./pruned_transducer_stateless7/decode.py \
+        --iter 105000 \
+        --avg 10 \
+        --exp-dir ./pruned_transducer_stateless5/exp \
+        --max-duration 500 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
+"""
+
+
+import argparse
+import logging
+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 AmiAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search_nbest_LG,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from train import add_model_arguments, get_params, get_transducer_model
+
+from icefall import NgramLm
+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,
+)
+
+
+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 0.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=10,
+        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_stateless2/exp",
+        help="The experiment dir",
+    )
+
+    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 interger 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=4,
+        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""",
+    )
+
+    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""",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    decoding_graph: Optional[k2.Fsa] = None,
+    word_table: Optional[k2.SymbolTable] = 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`.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
+      word_table:
+        The word symbol table.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict.
+    """
+    device = model.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)
+
+    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 == "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 params.decoding_method == "fast_beam_search":
+        return {
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): 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,
+    decoding_graph: Optional[k2.Fsa] = None,
+    word_table: Optional[k2.SymbolTable] = None,
+) -> Dict[str, List[Tuple[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.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
+    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 = 100
+    else:
+        log_interval = 2
+
+    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[List[int], List[int]]]],
+):
+    test_set_wers = dict()
+    test_set_cers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            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 = (
+            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
+            )
+            test_set_wers[key] = wer
+
+        # we also compute CER for AMI dataset.
+        results_char = []
+        for res in results:
+            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
+        cers_filename = (
+            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
+            )
+            test_set_cers[key] = cer
+
+        logging.info("Wrote detailed error stats to {}".format(wers_filename))
+
+    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 = (
+        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:
+            print(
+                "{}\t{}\t{}".format(key, test_set_wers[key], test_set_cers[key]),
+                file=f,
+            )
+
+    s = "\nFor {}, WER/CER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key in test_set_wers:
+        s += "{}\t{}\t{}{}\n".format(key, test_set_wers[key], test_set_cers[key], note)
+        note = ""
+    logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    AmiAsrDataModule.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_LG",
+        "modified_beam_search",
+    )
+    params.res_dir = params.exp_dir / params.decoding_method
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    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}"
+
+    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(f"{params.lang_dir}/bpe.model")
+
+    # <blk> is 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()
+
+    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()
+    model.device = device
+
+    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}")
+
+    ami = AmiAsrDataModule(args)
+
+    dev_ihm_cuts = ami.dev_ihm_cuts()
+    test_ihm_cuts = ami.test_ihm_cuts()
+    dev_sdm_cuts = ami.dev_sdm_cuts()
+    test_sdm_cuts = ami.test_sdm_cuts()
+    dev_gss_cuts = ami.dev_gss_cuts()
+    test_gss_cuts = ami.test_gss_cuts()
+
+    dev_ihm_dl = ami.test_dataloaders(dev_ihm_cuts)
+    test_ihm_dl = ami.test_dataloaders(test_ihm_cuts)
+    dev_sdm_dl = ami.test_dataloaders(dev_sdm_cuts)
+    test_sdm_dl = ami.test_dataloaders(test_sdm_cuts)
+    if dev_gss_cuts is not None:
+        dev_gss_dl = ami.test_dataloaders(dev_gss_cuts)
+    if test_gss_cuts is not None:
+        test_gss_dl = ami.test_dataloaders(test_gss_cuts)
+
+    test_sets = {
+        "dev_ihm": (dev_ihm_dl, dev_ihm_cuts),
+        "test_ihm": (test_ihm_dl, test_ihm_cuts),
+        "dev_sdm": (dev_sdm_dl, dev_sdm_cuts),
+        "test_sdm": (test_sdm_dl, test_sdm_cuts),
+    }
+    if dev_gss_cuts is not None:
+        test_sets["dev_gss"] = (dev_gss_dl, dev_gss_cuts)
+    if test_gss_cuts is not None:
+        test_sets["test_gss"] = (test_gss_dl, test_gss_cuts)
+
+    for test_set in test_sets:
+        logging.info(f"Decoding {test_set}")
+        dl, cuts = test_sets[test_set]
+        results_dict = decode_dataset(
+            dl=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()

egs/ami/ASR/pruned_transducer_stateless7/decoder.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/decoder.py

egs/ami/ASR/pruned_transducer_stateless7/encoder_interface.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/encoder_interface.py

egs/ami/ASR/pruned_transducer_stateless7/export.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/export.py

egs/ami/ASR/pruned_transducer_stateless7/joiner.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/joiner.py

egs/ami/ASR/pruned_transducer_stateless7/model.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/model.py

egs/ami/ASR/pruned_transducer_stateless7/optim.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/optim.py

egs/ami/ASR/pruned_transducer_stateless7/scaling.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/scaling.py

egs/ami/ASR/pruned_transducer_stateless7/scaling_converter.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/scaling_converter.py

egs/ami/ASR/pruned_transducer_stateless7/zipformer.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless7/zipformer.py

egs/ami/ASR/shared

@@ -0,0 +1 @@
+../../../icefall/shared

egs/csj/ASR/prepare.sh

@@ -35,6 +35,9 @@
 # can generate other transcript formats by supplying your own config files. A few examples of these
 # config files can be found in local/conf.
 
+# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
 set -eou pipefail
 
 nj=8

egs/gigaspeech/ASR/prepare.sh

@@ -1,5 +1,8 @@
 #!/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
 
 nj=15

egs/librispeech/ASR/README.md

@@ -23,6 +23,7 @@ The following table lists the differences among them.
 | `pruned_transducer_stateless5`        | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + more layers + random combiner|
 | `pruned_transducer_stateless6`        | Conformer(modified) | Embedding + Conv1d | same as pruned_transducer_stateless4 + distillation with hubert|
 | `pruned_transducer_stateless7`        | Zipformer | Embedding + Conv1d | First experiment with Zipformer from Dan|
+| `pruned_transducer_stateless7_ctc`    | Zipformer | Embedding + Conv1d | Same as pruned_transducer_stateless7, but with extra CTC head|
 | `pruned_transducer_stateless8`        | Zipformer | Embedding + Conv1d | Same as pruned_transducer_stateless7, but using extra data from GigaSpeech|
 | `pruned_stateless_emformer_rnnt2`     | Emformer(from torchaudio) | Embedding + Conv1d | Using Emformer from torchaudio for streaming ASR|
 | `conv_emformer_transducer_stateless`  | ConvEmformer | Embedding + Conv1d | Using ConvEmformer for streaming ASR + mechanisms in reworked model |

egs/librispeech/ASR/RESULTS.md

@@ -1,5 +1,185 @@
 ## Results
 
+### pruned_transducer_stateless7_ctc (zipformer with transducer loss and ctc loss)
+
+See <https://github.com/k2-fsa/icefall/pull/683> for more details.
+
+[pruned_transducer_stateless7_ctc](./pruned_transducer_stateless7_ctc)
+
+The tensorboard log can be found at
+<https://tensorboard.dev/experiment/hxlGAhOPToGmRLZFnAzPWw/>
+
+You can find a pretrained model, training logs, decoding logs, and decoding
+results at:
+<https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-ctc-2022-12-01>
+
+Number of model parameters: 70561891, i.e., 70.56 M
+
+|                          | test-clean | test-other  | comment            |
+|--------------------------|------------|-------------|--------------------|
+| greedy search            | 2.23       | 5.19        | --epoch 30 --avg 8 |
+| modified beam search     | 2.21       | 5.12        | --epoch 30 --avg 8 |
+| fast beam search         | 2.23       | 5.18        | --epoch 30 --avg 8 |
+| ctc decoding             | 2.48       | 5.82        | --epoch 30 --avg 9 |
+| 1best                    | 2.43       | 5.22        | --epoch 30 --avg 9 |
+| nbest                    | 2.43       | 5.22        | --epoch 30 --avg 9 |
+| nbest rescoring          | 2.34       | 5.05        | --epoch 30 --avg 9 |
+| whole lattice rescoring  | 2.34       | 5.04        | --epoch 30 --avg 9 |
+
+The training commands are:
+```bash
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+./pruned_transducer_stateless7_ctc/train.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --full-libri 1 \
+  --use-fp16 1 \
+  --max-duration 750 \
+  --exp-dir pruned_transducer_stateless7_ctc/exp \
+  --feedforward-dims  "1024,1024,2048,2048,1024" \
+  --ctc-loss-scale 0.2 \
+  --master-port 12535
+```
+
+The decoding commands for the transducer branch are:
+```bash
+for m in greedy_search fast_beam_search modified_beam_search ; do
+  for epoch in 30; do
+    for avg in 8; do
+      ./pruned_transducer_stateless7_ctc/decode.py \
+          --epoch $epoch \
+          --avg $avg \
+          --use-averaged-model 1 \
+          --exp-dir ./pruned_transducer_stateless7_ctc/exp \
+          --feedforward-dims  "1024,1024,2048,2048,1024" \
+          --max-duration 600 \
+          --decoding-method $m
+    done
+  done
+done
+```
+
+The decoding commands for the ctc branch are:
+```bash
+for m in ctc-decoding nbest nbest-rescoring whole-lattice-rescoring; do
+  for epoch in 30; do
+    for avg in 9; do
+      ./pruned_transducer_stateless7_ctc/ctc_decode.py \
+          --epoch $epoch \
+          --avg $avg \
+          --exp-dir ./pruned_transducer_stateless7_ctc/exp \
+          --max-duration 100 \
+          --decoding-method $m \
+          --hlg-scale 0.6 \
+          --lm-dir data/lm
+    done
+  done
+done
+```
+
+
+### LibriSpeech BPE training results (Conformer CTC, supporting delay penalty)
+
+#### [conformer_ctc3](./conformer_ctc3)
+
+It implements Conformer model training with CTC loss.
+For streaming mode, it supports symbol delay penalty.
+
+See <https://github.com/k2-fsa/icefall/pull/669> for more details.
+
+##### training on full librispeech
+
+This model contains 12 encoder layers. The number of model parameters is 77352694.
+
+The WERs are:
+
+|                                     | test-clean | test-other | comment              |
+|-------------------------------------|------------|------------|----------------------|
+| ctc-decoding                        | 3.09       | 7.62       | --epoch 25 --avg 7   |
+| 1best                               | 2.87       | 6.44       | --epoch 25 --avg 7   |
+| nbest                               | 2.88       | 6.5        | --epoch 25 --avg 7   |
+| nbest-rescoring                     | 2.71       | 6.1        | --epoch 25 --avg 7   |
+| whole-lattice-rescoring             | 2.71       | 6.04       | --epoch 25 --avg 7   |
+
+The training command is:
+
+```bash
+./conformer_ctc3/train.py \
+  --world-size 4 \
+  --num-epochs 25 \
+  --start-epoch 1 \
+  --exp-dir conformer_ctc3/full \
+  --full-libri 1 \
+  --max-duration 300 \
+  --master-port 12345
+```
+
+The tensorboard log can be found at
+<https://tensorboard.dev/experiment/4jbxIQ2SQIaQeRqsR6bOSA>
+
+The decoding command using different methods is:
+```bash
+for method in ctc-decoding 1best nbest nbest-rescoring whole-lattice-rescoring; do
+  ./conformer_ctc3/decode.py \
+    --epoch 25 \
+    --avg 7 \
+    --exp-dir conformer_ctc3/exp \
+    --max-duration 300 \
+    --decoding-method $method \
+    --manifest-dir data/fbank \
+    --lm-dir data/lm \
+done
+```
+
+Pretrained models, training logs, decoding logs, and decoding results
+are available at
+<https://huggingface.co/Zengwei/icefall-asr-librispeech-conformer-ctc3-2022-11-27>
+
+The command to train a streaming model with symbol delay penalty is:
+```bash
+./conformer_ctc3/train.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 1 \
+  --exp-dir conformer_ctc3/exp \
+  --full-libri 1 \
+  --dynamic-chunk-training 1 \
+  --causal-convolution 1 \
+  --short-chunk-size 25 \
+  --num-left-chunks 4 \
+  --max-duration 300 \
+  --delay-penalty 0.1
+```
+To evaluate symbol delay, you should:
+(1) Generate cuts with word-time alignments:
+```bash
+./local/add_alignment_librispeech.py \
+  --alignments-dir data/alignment \
+  --cuts-in-dir data/fbank \
+  --cuts-out-dir data/fbank_ali
+```
+(2) Set the argument "--manifest-dir data/fbank_ali" while decoding.
+For example:
+```bash
+./conformer_ctc3/decode.py \
+  --epoch 25 \
+  --avg 7 \
+  --exp-dir ./conformer_ctc3/exp \
+  --max-duration 300 \
+  --decoding-method ctc-decoding \
+  --simulate-streaming 1 \
+  --causal-convolution 1 \
+  --decode-chunk-size 16 \
+  --left-context 64 \
+  --manifest-dir data/fbank_ali
+```
+Note: It supports to calculate symbol delay with following decoding methods:
+  - ctc-greedy-search
+  - ctc-decoding
+  - 1best
+
+
 ### pruned_transducer_stateless8 (zipformer + multidataset)
 
 See <https://github.com/k2-fsa/icefall/pull/675> for more details.
@@ -7,21 +187,25 @@ See <https://github.com/k2-fsa/icefall/pull/675> for more details.
 [pruned_transducer_stateless8](./pruned_transducer_stateless8)
 
 The tensorboard log can be found at
-<https://tensorboard.dev/experiment/y6kAPnN3S3OwvQxQqKQzsQ>
+<https://tensorboard.dev/experiment/3e9AfOcgRwOXpLQlZvHZrQ>
 
 You can find a pretrained model, training logs, decoding logs, and decoding
 results at:
-<https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless8-2022-11-14>
+<https://huggingface.co/WeijiZhuang/icefall-asr-librispeech-pruned-transducer-stateless8-2022-12-02>
 
 You can use <https://github.com/k2-fsa/sherpa> to deploy it.
 
 Number of model parameters: 70369391, i.e., 70.37 M
 
-|                      | test-clean | test-other  | comment                                |
+| decoding method      | test-clean | test-other | comment            |
-|----------------------|------------|-------------|----------------------------------------|
+|----------------------|------------|------------|--------------------|
-| greedy search        | 1.87       | 4.38        | --epoch 16 --avg 2 --max-duration 600  |
+| greedy_search        | 1.81       | 4.18       | --epoch 20 --avg 4 |
-| modified beam search | 1.81       | 4.34        | --epoch 16 --avg 2 --max-duration 600  |
+| fast_beam_search     | 1.82       | 4.15       | --epoch 20 --avg 4 |
-| fast beam search     | 1.91       | 4.33        | --epoch 16 --avg 2 --max-duration 600  |
+| modified_beam_search | 1.78       | **4.08**   | --epoch 20 --avg 4 |
+| greedy_search        | 1.84       | 4.3        | --epoch 19 --avg 8 |
+| fast_beam_search     |**1.77**    | 4.25       | --epoch 19 --avg 8 |
+| modified_beam_search | 1.81       | 4.16       | --epoch 19 --avg 8 |
+
 
 The training commands are:
 ```bash
@@ -41,9 +225,9 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
 
 The decoding commands are:
 ```bash
-for m in greedy_search fast_beam_search modified_beam_search ; do
+for m in greedy_search fast_beam_search modified_beam_search; do
-  for epoch in 16; do
+  for epoch in $(seq 20 -1 10); do
-    for avg in 2; do
+    for avg in $(seq 9 -1 1); do
       ./pruned_transducer_stateless8/decode.py \
           --epoch $epoch \
           --avg $avg \
@@ -115,7 +299,6 @@ done
 ```
 
 
-
 ### LibriSpeech BPE training results (Pruned Stateless LSTM RNN-T + gradient filter)
 
 #### [lstm_transducer_stateless3](./lstm_transducer_stateless3)
@@ -218,6 +401,7 @@ The WERs are:
 | greedy search (max sym per frame 1) | 2.78       | 7.36       | --iter 468000 --avg 16  |
 | modified_beam_search                | 2.73       | 7.15       | --iter 468000 --avg 16  |
 | modified_beam_search + RNNLM shallow fusion   | 2.42     |  6.46      | --iter 468000 --avg 16  |
+| modified_beam_search + RNNLM shallow fusion   | 2.28     |  5.94      | --iter 468000 --avg 16  |
 | fast_beam_search                    | 2.76       | 7.31       | --iter 468000 --avg 16  |
 | greedy search (max sym per frame 1) | 2.77       | 7.35       | --iter 472000 --avg 18  |
 | modified_beam_search                | 2.75       | 7.08       | --iter 472000 --avg 18  |
@@ -293,6 +477,32 @@ for iter in 472000; do
     done
 done
 
+You may also decode using LODR + RNNLM shallow fusion. This decoding method is proposed in <https://arxiv.org/pdf/2203.16776.pdf>.
+It subtracts the internal language model score during shallow fusion, which is approximated by a bi-gram model. The bi-gram can be
+generated by `generate-lm.sh`, or you may download it from <https://huggingface.co/marcoyang/librispeech_bigram>.
+
+The decoding command is as follows:
+
+for iter in 472000; do
+    for avg in 8 10 12 14 16 18; do
+        ./lstm_transducer_stateless2/decode.py \
+                --iter $iter \
+                --avg $avg \
+                --exp-dir ./lstm_transducer_stateless2/exp \
+                --max-duration 600 \
+                --decoding-method modified_beam_search_rnnlm_LODR \
+                --beam 4 \
+                --rnn-lm-scale 0.4 \
+                --rnn-lm-exp-dir /path/to/RNNLM \
+                --rnn-lm-epoch 99 \
+                --rnn-lm-avg 1 \
+                --rnn-lm-num-layers 3 \
+                --rnn-lm-tie-weights 1 \
+                --token-ngram 2 \
+                --ngram-lm-scale -0.16
+    done
+done
+
 Pretrained models, training logs, decoding logs, and decoding results
 are available at
 <https://huggingface.co/csukuangfj/icefall-asr-librispeech-lstm-transducer-stateless2-2022-09-03>
@@ -1812,6 +2022,8 @@ subset so that the gigaspeech dataloader never exhausts.
 |-------------------------------------|------------|------------|---------------------------------------------|
 | greedy search (max sym per frame 1) | 2.03       | 4.70       | --iter 1224000 --avg 14  --max-duration 600 |
 | modified beam search                | 2.00       | 4.63       | --iter 1224000 --avg 14  --max-duration 600 |
+| modified beam search + rnnlm shallow fusion  | 1.94     |  4.2    | --iter 1224000 --avg 14  --max-duration 600 |
+| modified beam search + LODR         | 1.83       | 4.03       | --iter 1224000 --avg 14  --max-duration 600 |
 | fast beam search                    | 2.10       | 4.68       | --iter 1224000 --avg 14 --max-duration 600 |
 
 The training commands are:
@@ -1857,6 +2069,64 @@ for iter in 1224000; do
   done
 done
 ```
+You may also decode using shallow fusion with external RNNLM. To do so you need to
+download a well-trained RNNLM from this link <https://huggingface.co/ezerhouni/icefall-librispeech-rnn-lm/tree/main>
+
+```bash
+rnn_lm_scale=0.3
+
+for iter in 1224000; do
+  for avg in 14; do
+    for method in modified_beam_search_rnnlm_shallow_fusion ; do
+      ./pruned_transducer_stateless3/decode.py \
+        --iter $iter \
+        --avg $avg \
+        --exp-dir ./pruned_transducer_stateless3/exp-0.9/ \
+        --max-duration 600 \
+        --decoding-method $method \
+        --max-sym-per-frame 1 \
+        --beam 4 \
+        --max-contexts 32 \
+        --rnn-lm-scale $rnn_lm_scale \
+        --rnn-lm-exp-dir /path/to/RNNLM \
+        --rnn-lm-epoch 99 \
+        --rnn-lm-avg 1 \
+        --rnn-lm-num-layers 3 \
+        --rnn-lm-tie-weights 1
+    done
+  done
+done
+```
+
+If you want to try out with LODR decoding, use the following command. This assums you have a bi-gram LM trained on LibriSpeech text. You can also download the bi-gram LM from here <https://huggingface.co/marcoyang/librispeech_bigram/tree/main> and put it under the directory `data/lang_bpe_500`.
+
+```bash
+rnn_lm_scale=0.4
+
+for iter in 1224000; do
+  for avg in 14; do
+    for method in modified_beam_search_rnnlm_LODR ; do
+      ./pruned_transducer_stateless3/decode.py \
+        --iter $iter \
+        --avg $avg \
+        --exp-dir ./pruned_transducer_stateless3/exp-0.9/ \
+        --max-duration 600 \
+        --decoding-method $method \
+        --max-sym-per-frame 1 \
+        --beam 4 \
+        --max-contexts 32 \
+        --rnn-lm-scale $rnn_lm_scale \
+        --rnn-lm-exp-dir /path/to/RNNLM \
+        --rnn-lm-epoch 99 \
+        --rnn-lm-avg 1 \
+        --rnn-lm-num-layers 3 \
+        --rnn-lm-tie-weights 1 \
+        --tokens-ngram 2 \
+        --ngram-lm-scale -0.14
+    done
+  done
+done
+```
 
 The pretrained models, training logs, decoding logs, and decoding results
 can be found at

egs/librispeech/ASR/conformer_ctc/train.py

@@ -687,10 +687,10 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
+        train_cuts = librispeech.train_all_shuf_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+    else:
+        train_cuts = librispeech.train_clean_100_cuts()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds

egs/librispeech/ASR/conformer_ctc2/train.py

@@ -928,10 +928,10 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
+        train_cuts = librispeech.train_all_shuf_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+    else:
+        train_cuts = librispeech.train_clean_100_cuts()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds

egs/librispeech/ASR/conformer_ctc3/__init__.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/__init__.py

egs/librispeech/ASR/conformer_ctc3/asr_datamodule.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/asr_datamodule.py

egs/librispeech/ASR/conformer_ctc3/conformer.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/conformer.py

egs/librispeech/ASR/conformer_ctc3/encoder_interface.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/encoder_interface.py

egs/librispeech/ASR/conformer_ctc3/export.py

@@ -0,0 +1,292 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script converts several saved checkpoints
+# to a single one using model averaging.
+"""
+Usage:
+
+(1) Export to torchscript model using torch.jit.trace()
+
+./conformer_ctc3/export.py \
+  --exp-dir ./conformer_ctc3/exp \
+  --lang-dir data/lang_bpe_500 \
+  --epoch 20 \
+  --avg 10 \
+  --jit-trace 1
+
+It will generates the file: `jit_trace.pt`.
+
+(2) Export `model.state_dict()`
+
+./conformer_ctc3/export.py \
+  --exp-dir ./conformer_ctc3/exp \
+  --lang-dir data/lang_bpe_500 \
+  --epoch 20 \
+  --avg 10
+
+It will generate a file `pretrained.pt` in the given `exp_dir`. You can later
+load it by `icefall.checkpoint.load_checkpoint()`.
+
+To use the generated file with `conformer_ctc3/decode.py`,
+you can do:
+
+    cd /path/to/exp_dir
+    ln -s pretrained.pt epoch-9999.pt
+
+    cd /path/to/egs/librispeech/ASR
+    ./conformer_ctc3/decode.py \
+        --exp-dir ./conformer_ctc3/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 100 \
+        --lang-dir data/lang_bpe_500
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import torch
+from scaling_converter import convert_scaled_to_non_scaled
+from train import add_model_arguments, get_ctc_model, get_params
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.lexicon import Lexicon
+from icefall.utils import str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="""It specifies the checkpoint to use for averaging.
+        Note: Epoch counts from 0.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=True,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless4/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    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(
+        "--jit-trace",
+        type=str2bool,
+        default=False,
+        help="""True to save a model after applying torch.jit.script.
+        """,
+    )
+
+    parser.add_argument(
+        "--streaming-model",
+        type=str2bool,
+        default=False,
+        help="""Whether to export a streaming model, if the models in exp-dir
+        are streaming model, this should be True.
+        """,
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    lexicon = Lexicon(params.lang_dir)
+    max_token_id = max(lexicon.tokens)
+    num_classes = max_token_id + 1  # +1 for the blank
+    params.vocab_size = num_classes
+
+    if params.streaming_model:
+        assert params.causal_convolution
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_ctc_model(params)
+
+    model.to(device)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.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.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.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.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to("cpu")
+    model.eval()
+
+    if params.jit_trace:
+        # TODO: will support streaming mode
+        assert not params.streaming_model
+        convert_scaled_to_non_scaled(model, inplace=True)
+
+        logging.info("Using torch.jit.trace()")
+
+        x = torch.zeros(1, 100, 80, dtype=torch.float32)
+        x_lens = torch.tensor([100], dtype=torch.int64)
+        traced_model = torch.jit.trace(model, (x, x_lens))
+
+        filename = params.exp_dir / "jit_trace.pt"
+        traced_model.save(str(filename))
+        logging.info(f"Saved to {filename}")
+    else:
+        logging.info("Not using torch.jit.trace()")
+        # Save it using a format so that it can be loaded
+        # by :func:`load_checkpoint`
+        filename = params.exp_dir / "pretrained.pt"
+        torch.save({"model": model.state_dict()}, str(filename))
+        logging.info(f"Saved to {filename}")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/librispeech/ASR/conformer_ctc3/jit_pretrained.py

@@ -0,0 +1,410 @@
+#!/usr/bin/env python3
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                    Mingshuang Luo,)
+#                                                    Zengwei Yao)
+#
+# 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 (for non-streaming mode):
+
+(1) ctc-decoding
+./conformer_ctc3/pretrained.py \
+  --nn-model-filename ./conformer_ctc3/exp/cpu_jit.pt \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --method ctc-decoding \
+  --sample-rate 16000 \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+(2) 1best
+./conformer_ctc3/pretrained.py \
+  --nn-model-filename ./conformer_ctc3/exp/cpu_jit.pt \
+  --HLG data/lang_bpe_500/HLG.pt \
+  --words-file data/lang_bpe_500/words.txt  \
+  --method 1best \
+  --sample-rate 16000 \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+(3) nbest-rescoring
+./conformer_ctc3/pretrained.py \
+  --nn-model-filename ./conformer_ctc3/exp/cpu_jit.pt \
+  --HLG data/lang_bpe_500/HLG.pt \
+  --words-file data/lang_bpe_500/words.txt  \
+  --G data/lm/G_4_gram.pt \
+  --method nbest-rescoring \
+  --sample-rate 16000 \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+
+(4) whole-lattice-rescoring
+./conformer_ctc3/pretrained.py \
+  --nn-model-filename ./conformer_ctc3/exp/cpu_jit.pt \
+  --HLG data/lang_bpe_500/HLG.pt \
+  --words-file data/lang_bpe_500/words.txt  \
+  --G data/lm/G_4_gram.pt \
+  --method whole-lattice-rescoring \
+  --sample-rate 16000 \
+  /path/to/foo.wav \
+  /path/to/bar.wav
+"""
+
+
+import argparse
+import logging
+import math
+from typing import List
+
+import k2
+import kaldifeat
+import sentencepiece as spm
+import torch
+import torchaudio
+from decode import get_decoding_params
+from torch.nn.utils.rnn import pad_sequence
+from train import add_model_arguments, get_params
+
+from icefall.decode import (
+    get_lattice,
+    one_best_decoding,
+    rescore_with_n_best_list,
+    rescore_with_whole_lattice,
+)
+from icefall.utils import get_texts
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--model-filename",
+        type=str,
+        required=True,
+        help="Path to the torchscript model.",
+    )
+
+    parser.add_argument(
+        "--words-file",
+        type=str,
+        help="""Path to words.txt.
+        Used only when method is not ctc-decoding.
+        """,
+    )
+
+    parser.add_argument(
+        "--HLG",
+        type=str,
+        help="""Path to HLG.pt.
+        Used only when method is not ctc-decoding.
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        help="""Path to bpe.model.
+        Used only when method is ctc-decoding.
+        """,
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="1best",
+        help="""Decoding method.
+        Possible values are:
+        (0) ctc-decoding - Use CTC decoding. It uses a sentence
+            piece model, i.e., lang_dir/bpe.model, to convert
+            word pieces to words. It needs neither a lexicon
+            nor an n-gram LM.
+        (1) 1best - Use the best path as decoding output. Only
+            the transformer encoder output is used for decoding.
+            We call it HLG decoding.
+        (2) nbest-rescoring. Extract n paths from the decoding lattice,
+            rescore them with an LM, the path with
+            the highest score is the decoding result.
+            We call it HLG decoding + n-gram LM rescoring.
+        (3) whole-lattice-rescoring - Use an LM to rescore the
+            decoding lattice and then use 1best to decode the
+            rescored lattice.
+            We call it HLG decoding + n-gram LM rescoring.
+        """,
+    )
+
+    parser.add_argument(
+        "--G",
+        type=str,
+        help="""An LM for rescoring.
+        Used only when method is
+        whole-lattice-rescoring or nbest-rescoring.
+        It's usually a 4-gram LM.
+        """,
+    )
+
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=100,
+        help="""
+        Used only when method is attention-decoder.
+        It specifies the size of n-best list.""",
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=1.3,
+        help="""
+        Used only when method is whole-lattice-rescoring and nbest-rescoring.
+        It specifies the scale for n-gram LM scores.
+        (Note: You need to tune it on a dataset.)
+        """,
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""
+        Used only when method is nbest-rescoring.
+        It specifies the scale for lattice.scores when
+        extracting n-best lists. A smaller value results in
+        more unique number of paths with the risk of missing
+        the best path.
+        """,
+    )
+
+    parser.add_argument(
+        "--num-classes",
+        type=int,
+        default=500,
+        help="""
+        Vocab size in the BPE model.
+        """,
+    )
+
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="The sample rate of the input sound file",
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        help="The input sound file(s) to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. " f"Given: {sample_rate}"
+        )
+        # We use only the first channel
+        ans.append(wave[0])
+    return ans
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+    # add decoding params
+    params.update(get_decoding_params())
+    params.update(vars(args))
+    params.vocab_size = params.num_classes
+
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+
+    logging.info(f"device: {device}")
+
+    model = torch.jit.load(args.model_filename)
+    model.to(device)
+    model.eval()
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = params.sample_rate
+    opts.mel_opts.num_bins = params.feature_dim
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {params.sound_files}")
+    waves = read_sound_files(
+        filenames=params.sound_files, expected_sample_rate=params.sample_rate
+    )
+    waves = [w.to(device) for w in waves]
+
+    logging.info("Decoding started")
+    features = fbank(waves)
+    feature_lengths = [f.size(0) for f in features]
+
+    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    feature_lengths = torch.tensor(feature_lengths, device=device)
+
+    nnet_output, _ = model(features, feature_lengths)
+
+    batch_size = nnet_output.shape[0]
+    supervision_segments = torch.tensor(
+        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
+        dtype=torch.int32,
+    )
+
+    if params.method == "ctc-decoding":
+        logging.info("Use CTC decoding")
+        bpe_model = spm.SentencePieceProcessor()
+        bpe_model.load(params.bpe_model)
+        max_token_id = params.num_classes - 1
+
+        H = k2.ctc_topo(
+            max_token=max_token_id,
+            modified=False,
+            device=device,
+        )
+
+        lattice = get_lattice(
+            nnet_output=nnet_output,
+            decoding_graph=H,
+            supervision_segments=supervision_segments,
+            search_beam=params.search_beam,
+            output_beam=params.output_beam,
+            min_active_states=params.min_active_states,
+            max_active_states=params.max_active_states,
+            subsampling_factor=params.subsampling_factor,
+        )
+
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+        token_ids = get_texts(best_path)
+        hyps = bpe_model.decode(token_ids)
+        hyps = [s.split() for s in hyps]
+    elif params.method in [
+        "1best",
+        "nbest-rescoring",
+        "whole-lattice-rescoring",
+    ]:
+        logging.info(f"Loading HLG from {params.HLG}")
+        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = HLG.to(device)
+        if not hasattr(HLG, "lm_scores"):
+            # For whole-lattice-rescoring and attention-decoder
+            HLG.lm_scores = HLG.scores.clone()
+
+        if params.method in [
+            "nbest-rescoring",
+            "whole-lattice-rescoring",
+        ]:
+            logging.info(f"Loading G from {params.G}")
+            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = G.to(device)
+            if params.method == "whole-lattice-rescoring":
+                # Add epsilon self-loops to G as we will compose
+                # it with the whole lattice later
+                G = k2.add_epsilon_self_loops(G)
+                G = k2.arc_sort(G)
+
+            # G.lm_scores is used to replace HLG.lm_scores during
+            # LM rescoring.
+            G.lm_scores = G.scores.clone()
+
+        lattice = get_lattice(
+            nnet_output=nnet_output,
+            decoding_graph=HLG,
+            supervision_segments=supervision_segments,
+            search_beam=params.search_beam,
+            output_beam=params.output_beam,
+            min_active_states=params.min_active_states,
+            max_active_states=params.max_active_states,
+            subsampling_factor=params.subsampling_factor,
+        )
+
+        if params.method == "1best":
+            logging.info("Use HLG decoding")
+            best_path = one_best_decoding(
+                lattice=lattice, use_double_scores=params.use_double_scores
+            )
+        if params.method == "nbest-rescoring":
+            logging.info("Use HLG decoding + LM rescoring")
+            best_path_dict = rescore_with_n_best_list(
+                lattice=lattice,
+                G=G,
+                num_paths=params.num_paths,
+                lm_scale_list=[params.ngram_lm_scale],
+                nbest_scale=params.nbest_scale,
+            )
+            best_path = next(iter(best_path_dict.values()))
+        elif params.method == "whole-lattice-rescoring":
+            logging.info("Use HLG decoding + LM rescoring")
+            best_path_dict = rescore_with_whole_lattice(
+                lattice=lattice,
+                G_with_epsilon_loops=G,
+                lm_scale_list=[params.ngram_lm_scale],
+            )
+            best_path = next(iter(best_path_dict.values()))
+
+        hyps = get_texts(best_path)
+        word_sym_table = k2.SymbolTable.from_file(params.words_file)
+        hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
+    else:
+        raise ValueError(f"Unsupported decoding method: {params.method}")
+
+    s = "\n"
+    for filename, hyp in zip(params.sound_files, hyps):
+        words = " ".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/librispeech/ASR/conformer_ctc3/lstmp.py

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

egs/librispeech/ASR/conformer_ctc3/model.py

@@ -0,0 +1,122 @@
+# Copyright  2021-2022  Xiaomi Corp.     (authors: Fangjun Kuang,
+#                                                  Wei Kang,
+#                                                  Zengwei Yao)
+#
+# 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.
+
+
+import math
+from typing import Tuple
+
+import torch
+import torch.nn as nn
+from encoder_interface import EncoderInterface
+from scaling import ScaledLinear
+
+
+class CTCModel(nn.Module):
+    """It implements https://www.cs.toronto.edu/~graves/icml_2006.pdf
+    "Connectionist Temporal Classification: Labelling Unsegmented
+    Sequence Data with Recurrent Neural Networks"
+    """
+
+    def __init__(
+        self,
+        encoder: EncoderInterface,
+        encoder_dim: int,
+        vocab_size: int,
+    ):
+        """
+        Args:
+          encoder:
+            It is the transcription network in the paper. Its accepts
+            two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,).
+            It returns two tensors: `logits` of shape (N, T, encoder_dm) and
+            `logit_lens` of shape (N,).
+          encoder_dim:
+            The feature embedding dimension.
+          vocab_size:
+            The vocabulary size.
+        """
+        super().__init__()
+        assert isinstance(encoder, EncoderInterface), type(encoder)
+
+        self.encoder = encoder
+        self.ctc_output_module = nn.Sequential(
+            nn.Dropout(p=0.1),
+            ScaledLinear(encoder_dim, vocab_size),
+        )
+
+    def get_ctc_output(
+        self,
+        encoder_out: torch.Tensor,
+        delay_penalty: float = 0.0,
+        blank_threshold: float = 0.99,
+    ):
+        """Compute ctc log-prob and optionally (delay_penalty > 0) apply delay penalty.
+        We first split utterance into sub-utterances according to the
+        blank probs, and then add sawtooth-like "blank-bonus" values to
+        the blank probs.
+        See https://github.com/k2-fsa/icefall/pull/669 for details.
+
+        Args:
+          encoder_out:
+            A tensor with shape of (N, T, C).
+          delay_penalty:
+            A constant used to scale the delay penalty score.
+          blank_threshold:
+            The threshold used to split utterance into sub-utterances.
+        """
+        output = self.ctc_output_module(encoder_out)
+        log_prob = nn.functional.log_softmax(output, dim=-1)
+
+        if self.training and delay_penalty > 0:
+            T_arange = torch.arange(encoder_out.shape[1]).to(device=encoder_out.device)
+            # split into sub-utterances using the blank-id
+            mask = log_prob[:, :, 0] >= math.log(blank_threshold)  # (B, T)
+            mask[:, 0] = True
+            cummax_out = (T_arange * mask).cummax(dim=-1)[0]  # (B, T)
+            # the sawtooth "blank-bonus" value
+            penalty = T_arange - cummax_out  # (B, T)
+            penalty_all = torch.zeros_like(log_prob)
+            penalty_all[:, :, 0] = delay_penalty * penalty
+            # apply latency penalty on probs
+            log_prob = log_prob + penalty_all
+
+        return log_prob
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        warmup: float = 1.0,
+        delay_penalty: float = 0.0,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C).
+          x_lens:
+            A 1-D tensor of shape (N,). It contains the number of frames in `x`
+            before padding.
+          warmup: a floating point value which increases throughout training;
+            values >= 1.0 are fully warmed up and have all modules present.
+          delay_penalty:
+            A constant used to scale the delay penalty score.
+        """
+        encoder_out, encoder_out_lens = self.encoder(x, x_lens, warmup=warmup)
+        assert torch.all(encoder_out_lens > 0)
+        nnet_output = self.get_ctc_output(encoder_out, delay_penalty=delay_penalty)
+        return nnet_output, encoder_out_lens

egs/librispeech/ASR/conformer_ctc3/optim.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/optim.py

egs/librispeech/ASR/conformer_ctc3/pretrained.py

@@ -0,0 +1,458 @@
+#!/usr/bin/env python3
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                    Mingshuang Luo,)
+#                                                    Zengwei Yao)
+#
+# 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 (for non-streaming mode):
+
+(1) ctc-decoding
+./conformer_ctc3/pretrained.py \
+  --checkpoint conformer_ctc3/exp/pretrained.pt \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --method ctc-decoding \
+  --sample-rate 16000 \
+  test_wavs/1089-134686-0001.wav
+
+(2) 1best
+./conformer_ctc3/pretrained.py \
+  --checkpoint conformer_ctc3/exp/pretrained.pt \
+  --HLG data/lang_bpe_500/HLG.pt \
+  --words-file data/lang_bpe_500/words.txt  \
+  --method 1best \
+  --sample-rate 16000 \
+  test_wavs/1089-134686-0001.wav
+
+(3) nbest-rescoring
+./conformer_ctc3/pretrained.py \
+  --checkpoint conformer_ctc3/exp/pretrained.pt \
+  --HLG data/lang_bpe_500/HLG.pt \
+  --words-file data/lang_bpe_500/words.txt  \
+  --G data/lm/G_4_gram.pt \
+  --method nbest-rescoring \
+  --sample-rate 16000 \
+  test_wavs/1089-134686-0001.wav
+
+(4) whole-lattice-rescoring
+./conformer_ctc3/pretrained.py \
+  --checkpoint conformer_ctc3/exp/pretrained.pt \
+  --HLG data/lang_bpe_500/HLG.pt \
+  --words-file data/lang_bpe_500/words.txt  \
+  --G data/lm/G_4_gram.pt \
+  --method whole-lattice-rescoring \
+  --sample-rate 16000 \
+  test_wavs/1089-134686-0001.wav
+"""
+
+
+import argparse
+import logging
+import math
+from typing import List
+
+import k2
+import kaldifeat
+import sentencepiece as spm
+import torch
+import torchaudio
+from decode import get_decoding_params
+from torch.nn.utils.rnn import pad_sequence
+from train import add_model_arguments, get_ctc_model, get_params
+
+from icefall.decode import (
+    get_lattice,
+    one_best_decoding,
+    rescore_with_n_best_list,
+    rescore_with_whole_lattice,
+)
+from icefall.utils import get_texts, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="Path to the checkpoint. "
+        "The checkpoint is assumed to be saved by "
+        "icefall.checkpoint.save_checkpoint().",
+    )
+
+    parser.add_argument(
+        "--words-file",
+        type=str,
+        help="""Path to words.txt.
+        Used only when method is not ctc-decoding.
+        """,
+    )
+
+    parser.add_argument(
+        "--HLG",
+        type=str,
+        help="""Path to HLG.pt.
+        Used only when method is not ctc-decoding.
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        help="""Path to bpe.model.
+        Used only when method is ctc-decoding.
+        """,
+    )
+
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="1best",
+        help="""Decoding method.
+        Possible values are:
+        (0) ctc-decoding - Use CTC decoding. It uses a sentence
+            piece model, i.e., lang_dir/bpe.model, to convert
+            word pieces to words. It needs neither a lexicon
+            nor an n-gram LM.
+        (1) 1best - Use the best path as decoding output. Only
+            the transformer encoder output is used for decoding.
+            We call it HLG decoding.
+        (2) nbest-rescoring. Extract n paths from the decoding lattice,
+            rescore them with an LM, the path with
+            the highest score is the decoding result.
+            We call it HLG decoding + n-gram LM rescoring.
+        (3) whole-lattice-rescoring - Use an LM to rescore the
+            decoding lattice and then use 1best to decode the
+            rescored lattice.
+            We call it HLG decoding + n-gram LM rescoring.
+        """,
+    )
+
+    parser.add_argument(
+        "--G",
+        type=str,
+        help="""An LM for rescoring.
+        Used only when method is
+        whole-lattice-rescoring or nbest-rescoring.
+        It's usually a 4-gram LM.
+        """,
+    )
+
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=100,
+        help="""
+        Used only when method is attention-decoder.
+        It specifies the size of n-best list.""",
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=1.3,
+        help="""
+        Used only when method is whole-lattice-rescoring and nbest-rescoring.
+        It specifies the scale for n-gram LM scores.
+        (Note: You need to tune it on a dataset.)
+        """,
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""
+        Used only when method is nbest-rescoring.
+        It specifies the scale for lattice.scores when
+        extracting n-best lists. A smaller value results in
+        more unique number of paths with the risk of missing
+        the best path.
+        """,
+    )
+
+    parser.add_argument(
+        "--num-classes",
+        type=int,
+        default=500,
+        help="""
+        Vocab size in the BPE model.
+        """,
+    )
+
+    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)",
+    )
+
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="The sample rate of the input sound file",
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        help="The input sound file(s) to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. " f"Given: {sample_rate}"
+        )
+        # We use only the first channel
+        ans.append(wave[0])
+    return ans
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+    # add decoding params
+    params.update(get_decoding_params())
+    params.update(vars(args))
+    params.vocab_size = params.num_classes
+
+    if params.simulate_streaming:
+        assert (
+            params.causal_convolution
+        ), "Decoding in streaming requires causal convolution"
+
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    logging.info("About to create model")
+    model = get_ctc_model(params)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"], strict=False)
+    model.to(device)
+    model.eval()
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = params.sample_rate
+    opts.mel_opts.num_bins = params.feature_dim
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {params.sound_files}")
+    waves = read_sound_files(
+        filenames=params.sound_files, expected_sample_rate=params.sample_rate
+    )
+    waves = [w.to(device) for w in waves]
+
+    logging.info("Decoding started")
+    features = fbank(waves)
+    feature_lengths = [f.size(0) for f in features]
+
+    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
+    feature_lengths = torch.tensor(feature_lengths, device=device)
+
+    # model forward
+    if params.simulate_streaming:
+        encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
+            x=features,
+            x_lens=feature_lengths,
+            chunk_size=params.decode_chunk_size,
+            left_context=params.left_context,
+            simulate_streaming=True,
+        )
+    else:
+        encoder_out, encoder_out_lens = model.encoder(
+            x=features, x_lens=feature_lengths
+        )
+    nnet_output = model.get_ctc_output(encoder_out)
+
+    batch_size = nnet_output.shape[0]
+    supervision_segments = torch.tensor(
+        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
+        dtype=torch.int32,
+    )
+
+    if params.method == "ctc-decoding":
+        logging.info("Use CTC decoding")
+        bpe_model = spm.SentencePieceProcessor()
+        bpe_model.load(params.bpe_model)
+        max_token_id = params.num_classes - 1
+
+        H = k2.ctc_topo(
+            max_token=max_token_id,
+            modified=False,
+            device=device,
+        )
+
+        lattice = get_lattice(
+            nnet_output=nnet_output,
+            decoding_graph=H,
+            supervision_segments=supervision_segments,
+            search_beam=params.search_beam,
+            output_beam=params.output_beam,
+            min_active_states=params.min_active_states,
+            max_active_states=params.max_active_states,
+            subsampling_factor=params.subsampling_factor,
+        )
+
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+        token_ids = get_texts(best_path)
+        hyps = bpe_model.decode(token_ids)
+        hyps = [s.split() for s in hyps]
+    elif params.method in [
+        "1best",
+        "nbest-rescoring",
+        "whole-lattice-rescoring",
+    ]:
+        logging.info(f"Loading HLG from {params.HLG}")
+        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = HLG.to(device)
+        if not hasattr(HLG, "lm_scores"):
+            # For whole-lattice-rescoring and attention-decoder
+            HLG.lm_scores = HLG.scores.clone()
+
+        if params.method in [
+            "nbest-rescoring",
+            "whole-lattice-rescoring",
+        ]:
+            logging.info(f"Loading G from {params.G}")
+            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = G.to(device)
+            if params.method == "whole-lattice-rescoring":
+                # Add epsilon self-loops to G as we will compose
+                # it with the whole lattice later
+                G = k2.add_epsilon_self_loops(G)
+                G = k2.arc_sort(G)
+
+            # G.lm_scores is used to replace HLG.lm_scores during
+            # LM rescoring.
+            G.lm_scores = G.scores.clone()
+
+        lattice = get_lattice(
+            nnet_output=nnet_output,
+            decoding_graph=HLG,
+            supervision_segments=supervision_segments,
+            search_beam=params.search_beam,
+            output_beam=params.output_beam,
+            min_active_states=params.min_active_states,
+            max_active_states=params.max_active_states,
+            subsampling_factor=params.subsampling_factor,
+        )
+
+        if params.method == "1best":
+            logging.info("Use HLG decoding")
+            best_path = one_best_decoding(
+                lattice=lattice, use_double_scores=params.use_double_scores
+            )
+        if params.method == "nbest-rescoring":
+            logging.info("Use HLG decoding + LM rescoring")
+            best_path_dict = rescore_with_n_best_list(
+                lattice=lattice,
+                G=G,
+                num_paths=params.num_paths,
+                lm_scale_list=[params.ngram_lm_scale],
+                nbest_scale=params.nbest_scale,
+            )
+            best_path = next(iter(best_path_dict.values()))
+        elif params.method == "whole-lattice-rescoring":
+            logging.info("Use HLG decoding + LM rescoring")
+            best_path_dict = rescore_with_whole_lattice(
+                lattice=lattice,
+                G_with_epsilon_loops=G,
+                lm_scale_list=[params.ngram_lm_scale],
+            )
+            best_path = next(iter(best_path_dict.values()))
+
+        hyps = get_texts(best_path)
+        word_sym_table = k2.SymbolTable.from_file(params.words_file)
+        hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
+    else:
+        raise ValueError(f"Unsupported decoding method: {params.method}")
+
+    s = "\n"
+    for filename, hyp in zip(params.sound_files, hyps):
+        words = " ".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/librispeech/ASR/conformer_ctc3/scaling.py

@@ -0,0 +1 @@
+../pruned_transducer_stateless2/scaling.py

egs/librispeech/ASR/conformer_ctc3/scaling_converter.py

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

egs/librispeech/ASR/conformer_ctc3/test_model.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+# Copyright    2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+To run this file, do:
+
+    cd icefall/egs/librispeech/ASR
+    python ./conformer_ctc3/test_model.py
+"""
+
+import torch
+
+from train import get_params, get_ctc_model
+
+
+def test_model():
+    params = get_params()
+    params.vocab_size = 500
+    params.blank_id = 0
+    params.context_size = 2
+    params.unk_id = 2
+
+    params.dynamic_chunk_training = False
+    params.short_chunk_size = 25
+    params.num_left_chunks = 4
+    params.causal_convolution = False
+
+    model = get_ctc_model(params)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    print(f"Number of model parameters: {num_param}")
+
+    features = torch.randn(2, 100, 80)
+    feature_lengths = torch.full((2,), 100)
+    model(x=features, x_lens=feature_lengths)
+
+
+def test_model_streaming():
+    params = get_params()
+    params.vocab_size = 500
+    params.blank_id = 0
+    params.context_size = 2
+    params.unk_id = 2
+
+    params.dynamic_chunk_training = True
+    params.short_chunk_size = 25
+    params.num_left_chunks = 4
+    params.causal_convolution = True
+
+    model = get_ctc_model(params)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    print(f"Number of model parameters: {num_param}")
+
+    features = torch.randn(2, 100, 80)
+    feature_lengths = torch.full((2,), 100)
+    encoder_out, _ = model.encoder(x=features, x_lens=feature_lengths)
+    model.get_ctc_output(encoder_out)
+
+
+def main():
+    test_model()
+    test_model_streaming()
+
+
+if __name__ == "__main__":
+    main()

egs/librispeech/ASR/conv_emformer_transducer_stateless/train.py

@@ -970,10 +970,10 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
+        train_cuts = librispeech.train_all_shuf_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+    else:
+        train_cuts = librispeech.train_clean_100_cuts()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds

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

@@ -0,0 +1,335 @@
+#!/usr/bin/env python3
+
+"""
+Usage:
+./conv_emformer_transducer_stateless2/export-for-ncnn.py \
+  --exp-dir ./conv_emformer_transducer_stateless2/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --epoch 30 \
+  --avg 10 \
+  --use-averaged-model=True \
+  --num-encoder-layers 12 \
+  --chunk-length 32 \
+  --cnn-module-kernel 31 \
+  --left-context-length 32 \
+  --right-context-length 8 \
+  --memory-size 32 \
+
+cd ./conv_emformer_transducer_stateless2/exp
+pnnx encoder_jit_trace-pnnx.pt
+pnnx decoder_jit_trace-pnnx.pt
+pnnx joiner_jit_trace-pnnx.pt
+
+You can find converted models at
+https://huggingface.co/csukuangfj/sherpa-ncnn-conv-emformer-transducer-2022-12-04
+
+See ./streaming-ncnn-decode.py
+and
+https://github.com/k2-fsa/sherpa-ncnn
+for usage.
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import sentencepiece as spm
+import torch
+from scaling_converter import convert_scaled_to_non_scaled
+from train2 import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="""It specifies the checkpoint to use for averaging.
+        Note: Epoch counts from 0.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless2/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--jit",
+        type=str2bool,
+        default=False,
+        help="""True to save a model after applying torch.jit.script.
+        """,
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=True,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def export_encoder_model_jit_trace(
+    encoder_model: torch.nn.Module,
+    encoder_filename: str,
+) -> None:
+    """Export the given encoder model with torch.jit.trace()
+
+    Note: The warmup argument is fixed to 1.
+
+    Args:
+      encoder_model:
+        The input encoder model
+      encoder_filename:
+        The filename to save the exported model.
+    """
+    chunk_length = encoder_model.chunk_length  # before subsampling
+    right_context_length = encoder_model.right_context_length  # before subsampling
+    pad_length = right_context_length + 2 * 4 + 3
+    s = f"chunk_length: {chunk_length}, "
+    s += f"right_context_length: {right_context_length}\n"
+    logging.info(s)
+
+    T = chunk_length + pad_length
+
+    x = torch.zeros(1, T, 80, dtype=torch.float32)
+    states = encoder_model.init_states()
+    states = encoder_model.init_states()
+
+    traced_model = torch.jit.trace(encoder_model, (x, states))
+    traced_model.save(encoder_filename)
+    logging.info(f"Saved to {encoder_filename}")
+
+
+def export_decoder_model_jit_trace(
+    decoder_model: torch.nn.Module,
+    decoder_filename: str,
+) -> None:
+    """Export the given decoder model with torch.jit.trace()
+
+    Note: The argument need_pad is fixed to False.
+
+    Args:
+      decoder_model:
+        The input decoder model
+      decoder_filename:
+        The filename to save the exported model.
+    """
+    y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64)
+    need_pad = torch.tensor([False])
+
+    traced_model = torch.jit.trace(decoder_model, (y, need_pad))
+    traced_model.save(decoder_filename)
+    logging.info(f"Saved to {decoder_filename}")
+
+
+def export_joiner_model_jit_trace(
+    joiner_model: torch.nn.Module,
+    joiner_filename: str,
+) -> None:
+    """Export the given joiner model with torch.jit.trace()
+
+    Note: The argument project_input is fixed to True. A user should not
+    project the encoder_out/decoder_out by himself/herself. The exported joiner
+    will do that for the user.
+
+    Args:
+      joiner_model:
+        The input joiner model
+      joiner_filename:
+        The filename to save the exported model.
+
+    """
+    encoder_out_dim = joiner_model.encoder_proj.weight.shape[1]
+    decoder_out_dim = joiner_model.decoder_proj.weight.shape[1]
+    encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32)
+    decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32)
+
+    traced_model = torch.jit.trace(joiner_model, (encoder_out, decoder_out))
+    traced_model.save(joiner_filename)
+    logging.info(f"Saved to {joiner_filename}")
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+
+    logging.info(f"device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg + 1
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0, params.avg
+            start = params.epoch - params.avg
+            assert start >= 1, start
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to("cpu")
+    model.eval()
+
+    convert_scaled_to_non_scaled(model, inplace=True)
+    logging.info("Using torch.jit.trace()")
+
+    logging.info("Exporting encoder")
+    encoder_filename = params.exp_dir / "encoder_jit_trace-pnnx.pt"
+    export_encoder_model_jit_trace(model.encoder, encoder_filename)
+
+    logging.info("Exporting decoder")
+    decoder_filename = params.exp_dir / "decoder_jit_trace-pnnx.pt"
+    export_decoder_model_jit_trace(model.decoder, decoder_filename)
+
+    logging.info("Exporting joiner")
+    joiner_filename = params.exp_dir / "joiner_jit_trace-pnnx.pt"
+    export_joiner_model_jit_trace(model.joiner, joiner_filename)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/librispeech/ASR/conv_emformer_transducer_stateless2/jit_pretrained.py

@@ -0,0 +1,292 @@
+#!/usr/bin/env python3
+# flake8: noqa
+# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang, Zengwei Yao)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This script loads torchscript models exported by `torch.jit.trace()`
+and uses them to decode waves.
+You can use the following command to get the exported models:
+
+./conv_emformer_transducer_stateless2/export-for-ncnn.py \
+  --exp-dir ./conv_emformer_transducer_stateless2/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --epoch 20 \
+  --avg 10
+
+Usage of this script:
+
+./conv_emformer_transducer_stateless2/jit_pretrained.py \
+  --encoder-model-filename ./conv_emformer_transducer_stateless2/exp/encoder_jit_trace-pnnx.pt \
+  --decoder-model-filename ./conv_emformer_transducer_stateless2/exp/decoder_jit_trace-pnnx.pt \
+  --joiner-model-filename ./conv_emformer_transducer_stateless2/exp/joiner_jit_trace-pnnx.pt \
+  --bpe-model ./data/lang_bpe_500/bpe.model \
+  /path/to/foo.wav \
+"""
+
+import argparse
+import logging
+import math
+from typing import List
+
+import kaldifeat
+import sentencepiece as spm
+import torch
+import torchaudio
+from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
+from torch.nn.utils.rnn import pad_sequence
+from typing import Optional, List
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--encoder-model-filename",
+        type=str,
+        required=True,
+        help="Path to the encoder torchscript model. ",
+    )
+
+    parser.add_argument(
+        "--decoder-model-filename",
+        type=str,
+        required=True,
+        help="Path to the decoder torchscript model. ",
+    )
+
+    parser.add_argument(
+        "--joiner-model-filename",
+        type=str,
+        required=True,
+        help="Path to the joiner torchscript model. ",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        help="""Path to bpe.model.""",
+    )
+
+    parser.add_argument(
+        "sound_file",
+        type=str,
+        help="The input sound file(s) to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="The sample rate of the input sound file",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="Context size of the decoder model",
+    )
+
+    return parser
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert (
+            sample_rate == expected_sample_rate
+        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        # We use only the first channel
+        ans.append(wave[0])
+    return ans
+
+
+def greedy_search(
+    decoder: torch.jit.ScriptModule,
+    joiner: torch.jit.ScriptModule,
+    encoder_out: torch.Tensor,
+    decoder_out: Optional[torch.Tensor] = None,
+    hyp: Optional[List[int]] = None,
+):
+    assert encoder_out.ndim == 2
+    context_size = 2
+    blank_id = 0
+
+    if decoder_out is None:
+        assert hyp is None, hyp
+        hyp = [blank_id] * context_size
+        decoder_input = torch.tensor(hyp, dtype=torch.int32).unsqueeze(0)
+        # decoder_input.shape (1,, 1 context_size)
+        decoder_out = decoder(decoder_input, torch.tensor([0])).squeeze(1)
+    else:
+        assert decoder_out.ndim == 2
+        assert hyp is not None, hyp
+
+    T = encoder_out.size(0)
+    for i in range(T):
+        cur_encoder_out = encoder_out[i : i + 1]
+        joiner_out = joiner(cur_encoder_out, decoder_out).squeeze(0)
+        y = joiner_out.argmax(dim=0).item()
+
+        if y != blank_id:
+            hyp.append(y)
+            decoder_input = hyp[-context_size:]
+
+            decoder_input = torch.tensor(decoder_input, dtype=torch.int32).unsqueeze(0)
+            decoder_out = decoder(decoder_input, torch.tensor([0])).squeeze(1)
+
+    return hyp, decoder_out
+
+
+def create_streaming_feature_extractor(sample_rate) -> OnlineFeature:
+    """Create a CPU streaming feature extractor.
+
+    At present, we assume it returns a fbank feature extractor with
+    fixed options. In the future, we will support passing in the options
+    from outside.
+
+    Returns:
+      Return a CPU streaming feature extractor.
+    """
+    opts = FbankOptions()
+    opts.device = "cpu"
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = sample_rate
+    opts.mel_opts.num_bins = 80
+    return OnlineFbank(opts)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+    logging.info(vars(args))
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    encoder = torch.jit.load(args.encoder_model_filename)
+    decoder = torch.jit.load(args.decoder_model_filename)
+    joiner = torch.jit.load(args.joiner_model_filename)
+
+    encoder.eval()
+    decoder.eval()
+    joiner.eval()
+
+    encoder.to(device)
+    decoder.to(device)
+    joiner.to(device)
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(args.bpe_model)
+
+    logging.info("Constructing Fbank computer")
+    online_fbank = create_streaming_feature_extractor(args.sample_rate)
+
+    logging.info(f"Reading sound files: {args.sound_file}")
+    wave_samples = read_sound_files(
+        filenames=[args.sound_file],
+        expected_sample_rate=args.sample_rate,
+    )[0]
+    logging.info(wave_samples.shape)
+
+    logging.info("Decoding started")
+    chunk_length = encoder.chunk_length
+    right_context_length = encoder.right_context_length
+
+    # Assume the subsampling factor is 4
+    pad_length = right_context_length + 2 * 4 + 3
+    T = chunk_length + pad_length
+
+    logging.info(f"chunk_length: {chunk_length}")
+    logging.info(f"right_context_length: {right_context_length}")
+
+    states = encoder.init_states(device)
+    logging.info(f"num layers: {len(states)//4}")
+
+    tail_padding = torch.zeros(int(0.3 * args.sample_rate), dtype=torch.float32)
+
+    wave_samples = torch.cat([wave_samples, tail_padding])
+
+    chunk = int(0.25 * args.sample_rate)  # 0.2 second
+    num_processed_frames = 0
+
+    hyp = None
+    decoder_out = None
+
+    start = 0
+    while start < wave_samples.numel():
+        logging.info(f"{start}/{wave_samples.numel()}")
+        end = min(start + chunk, wave_samples.numel())
+        samples = wave_samples[start:end]
+        start += chunk
+        online_fbank.accept_waveform(
+            sampling_rate=args.sample_rate,
+            waveform=samples,
+        )
+        while online_fbank.num_frames_ready - num_processed_frames >= T:
+            frames = []
+            for i in range(T):
+                frames.append(online_fbank.get_frame(num_processed_frames + i))
+            num_processed_frames += chunk_length
+            frames = torch.cat(frames, dim=0).unsqueeze(0)
+            # TODO(fangjun): remove x_lens
+            x_lens = torch.tensor([T])
+            encoder_out, _, states = encoder(frames, x_lens, states)
+
+            hyp, decoder_out = greedy_search(
+                decoder, joiner, encoder_out.squeeze(0), decoder_out, hyp
+            )
+
+    context_size = 2
+
+    logging.info(args.sound_file)
+    logging.info(sp.decode(hyp[context_size:]))
+
+    logging.info("Decoding Done")
+
+
+torch.set_num_threads(4)
+torch.set_num_interop_threads(1)
+torch._C._jit_set_profiling_executor(False)
+torch._C._jit_set_profiling_mode(False)
+torch._C._set_graph_executor_optimize(False)
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/librispeech/ASR/conv_emformer_transducer_stateless2/lstmp.py

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

egs/librispeech/ASR/conv_emformer_transducer_stateless2/scaling_converter.py

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

egs/librispeech/ASR/conv_emformer_transducer_stateless2/streaming-ncnn-decode.py

@@ -0,0 +1,387 @@
+#!/usr/bin/env python3
+#
+# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang, Zengwei Yao)
+#
+# 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:
+
+./conv_emformer_transducer_stateless2/streaming-ncnn-decode.py \
+  --tokens ./sherpa-ncnn-conv-emformer-transducer-2022-12-04/tokens.txt \
+  --encoder-param-filename ./sherpa-ncnn-conv-emformer-transducer-2022-12-04/encoder_jit_trace-epoch-30-avg-10-pnnx.ncnn.param \
+  --encoder-bin-filename ./sherpa-ncnn-conv-emformer-transducer-2022-12-04/encoder_jit_trace-epoch-30-avg-10-pnnx.ncnn.bin \
+  --decoder-param-filename ./sherpa-ncnn-conv-emformer-transducer-2022-12-04/decoder_jit_trace-epoch-30-avg-10-pnnx.ncnn.param \
+  --decoder-bin-filename ./sherpa-ncnn-conv-emformer-transducer-2022-12-04/decoder_jit_trace-epoch-30-avg-10-pnnx.ncnn.bin \
+  --joiner-param-filename ./sherpa-ncnn-conv-emformer-transducer-2022-12-04/joiner_jit_trace-epoch-30-avg-10-pnnx.ncnn.param \
+  --joiner-bin-filename ./sherpa-ncnn-conv-emformer-transducer-2022-12-04/joiner_jit_trace-epoch-30-avg-10-pnnx.ncnn.bin \
+  ./sherpa-ncnn-conv-emformer-transducer-2022-12-04/test_wavs/1089-134686-0001.wav
+
+You can find pretrained models at
+https://huggingface.co/csukuangfj/sherpa-ncnn-conv-emformer-transducer-2022-12-04
+"""
+
+import argparse
+import logging
+from typing import List, Optional, Tuple
+
+import k2
+import ncnn
+import torch
+import torchaudio
+from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        help="Path to tokens.txt",
+    )
+
+    parser.add_argument(
+        "--encoder-param-filename",
+        type=str,
+        help="Path to encoder.ncnn.param",
+    )
+
+    parser.add_argument(
+        "--encoder-bin-filename",
+        type=str,
+        help="Path to encoder.ncnn.bin",
+    )
+
+    parser.add_argument(
+        "--decoder-param-filename",
+        type=str,
+        help="Path to decoder.ncnn.param",
+    )
+
+    parser.add_argument(
+        "--decoder-bin-filename",
+        type=str,
+        help="Path to decoder.ncnn.bin",
+    )
+
+    parser.add_argument(
+        "--joiner-param-filename",
+        type=str,
+        help="Path to joiner.ncnn.param",
+    )
+
+    parser.add_argument(
+        "--joiner-bin-filename",
+        type=str,
+        help="Path to joiner.ncnn.bin",
+    )
+
+    parser.add_argument(
+        "sound_filename",
+        type=str,
+        help="Path to foo.wav",
+    )
+
+    return parser.parse_args()
+
+
+class Model:
+    def __init__(self, args):
+        self.init_encoder(args)
+        self.init_decoder(args)
+        self.init_joiner(args)
+
+        self.num_layers = 12
+        self.memory_size = 32
+        self.d_model = 512
+        self.cnn_module_kernel = 31
+
+        self.left_context_length = 32 // 4  # after subsampling
+        self.chunk_length = 32  # before subsampling
+        right_context_length = 8  # before subsampling
+        pad_length = right_context_length + 2 * 4 + 3
+        self.T = self.chunk_length + pad_length
+        print("T", self.T, self.chunk_length)
+
+    def get_init_states(self) -> List[torch.Tensor]:
+        states = []
+
+        for i in range(self.num_layers):
+            s0 = torch.zeros(self.memory_size, self.d_model)
+            s1 = torch.zeros(self.left_context_length, self.d_model)
+            s2 = torch.zeros(self.left_context_length, self.d_model)
+            s3 = torch.zeros(self.d_model, self.cnn_module_kernel - 1)
+            states.extend([s0, s1, s2, s3])
+
+        return states
+
+    def init_encoder(self, args):
+        encoder_net = ncnn.Net()
+        encoder_net.opt.use_packing_layout = False
+        encoder_net.opt.use_fp16_storage = False
+        encoder_param = args.encoder_param_filename
+        encoder_model = args.encoder_bin_filename
+
+        encoder_net.load_param(encoder_param)
+        encoder_net.load_model(encoder_model)
+
+        self.encoder_net = encoder_net
+
+    def init_decoder(self, args):
+        decoder_param = args.decoder_param_filename
+        decoder_model = args.decoder_bin_filename
+
+        decoder_net = ncnn.Net()
+
+        decoder_net.load_param(decoder_param)
+        decoder_net.load_model(decoder_model)
+
+        self.decoder_net = decoder_net
+
+    def init_joiner(self, args):
+        joiner_param = args.joiner_param_filename
+        joiner_model = args.joiner_bin_filename
+        joiner_net = ncnn.Net()
+        joiner_net.load_param(joiner_param)
+        joiner_net.load_model(joiner_model)
+
+        self.joiner_net = joiner_net
+
+    def run_encoder(
+        self,
+        x: torch.Tensor,
+        states: List[torch.Tensor],
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        """
+        Args:
+          x:
+            A tensor of shape (T, C)
+          states:
+            A list of tensors. len(states) == self.num_layers * 4
+        Returns:
+          Return a tuple containing:
+           - encoder_out, a tensor of shape (T, encoder_dim).
+           - next_states, a list of tensors containing the next states
+        """
+        with self.encoder_net.create_extractor() as ex:
+            ex.set_num_threads(4)
+            ex.input("in0", ncnn.Mat(x.numpy()).clone())
+
+            # layer0 in2-in5
+            # layer1 in6-in9
+            for i in range(self.num_layers):
+                offset = 1 + i * 4
+                name = f"in{offset}"
+                # (32, 1, 512) -> (32, 512)
+                ex.input(name, ncnn.Mat(states[i * 4 + 0].numpy()).clone())
+
+                name = f"in{offset+1}"
+                #  (8, 1, 512) -> (8, 512)
+                ex.input(name, ncnn.Mat(states[i * 4 + 1].numpy()).clone())
+
+                name = f"in{offset+2}"
+                #  (8, 1, 512) -> (8, 512)
+                ex.input(name, ncnn.Mat(states[i * 4 + 2].numpy()).clone())
+
+                name = f"in{offset+3}"
+                #  (1, 512, 2) -> (512, 2)
+                ex.input(name, ncnn.Mat(states[i * 4 + 3].numpy()).clone())
+
+            import pdb
+
+            #  pdb.set_trace()
+            ret, ncnn_out0 = ex.extract("out0")
+            #  assert ret == 0, ret
+            encoder_out = torch.from_numpy(ncnn_out0.numpy()).clone()
+
+            out_states: List[torch.Tensor] = []
+            for i in range(4 * self.num_layers):
+                name = f"out{i+1}"
+                ret, ncnn_out_state = ex.extract(name)
+                assert ret == 0, ret
+                ncnn_out_state = torch.from_numpy(ncnn_out_state.numpy())
+                out_states.append(ncnn_out_state)
+
+            return encoder_out, out_states
+
+    def run_decoder(self, decoder_input):
+        assert decoder_input.dtype == torch.int32
+
+        with self.decoder_net.create_extractor() as ex:
+            ex.set_num_threads(4)
+            ex.input("in0", ncnn.Mat(decoder_input.numpy()).clone())
+            ret, ncnn_out0 = ex.extract("out0")
+            assert ret == 0, ret
+            decoder_out = torch.from_numpy(ncnn_out0.numpy()).clone()
+            return decoder_out
+
+    def run_joiner(self, encoder_out, decoder_out):
+        with self.joiner_net.create_extractor() as ex:
+            ex.set_num_threads(4)
+            ex.input("in0", ncnn.Mat(encoder_out.numpy()).clone())
+            ex.input("in1", ncnn.Mat(decoder_out.numpy()).clone())
+            ret, ncnn_out0 = ex.extract("out0")
+            assert ret == 0, ret
+            joiner_out = torch.from_numpy(ncnn_out0.numpy()).clone()
+            return joiner_out
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert (
+            sample_rate == expected_sample_rate
+        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        # We use only the first channel
+        ans.append(wave[0])
+    return ans
+
+
+def create_streaming_feature_extractor() -> OnlineFeature:
+    """Create a CPU streaming feature extractor.
+
+    At present, we assume it returns a fbank feature extractor with
+    fixed options. In the future, we will support passing in the options
+    from outside.
+
+    Returns:
+      Return a CPU streaming feature extractor.
+    """
+    opts = FbankOptions()
+    opts.device = "cpu"
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = 16000
+    opts.mel_opts.num_bins = 80
+    return OnlineFbank(opts)
+
+
+def greedy_search(
+    model: Model,
+    encoder_out: torch.Tensor,
+    decoder_out: Optional[torch.Tensor] = None,
+    hyp: Optional[List[int]] = None,
+):
+    context_size = 2
+    blank_id = 0
+
+    if decoder_out is None:
+        assert hyp is None, hyp
+        hyp = [blank_id] * context_size
+        decoder_input = torch.tensor(hyp, dtype=torch.int32)  # (1, context_size)
+        decoder_out = model.run_decoder(decoder_input).squeeze(0)
+    else:
+        assert decoder_out.ndim == 1
+        assert hyp is not None, hyp
+
+    T = encoder_out.size(0)
+    for t in range(T):
+        cur_encoder_out = encoder_out[t]
+
+        joiner_out = model.run_joiner(cur_encoder_out, decoder_out)
+        y = joiner_out.argmax(dim=0).item()
+        if y != blank_id:
+            hyp.append(y)
+            decoder_input = hyp[-context_size:]
+            decoder_input = torch.tensor(decoder_input, dtype=torch.int32)
+            decoder_out = model.run_decoder(decoder_input).squeeze(0)
+
+    return hyp, decoder_out
+
+
+def main():
+    args = get_args()
+    logging.info(vars(args))
+
+    model = Model(args)
+
+    sound_file = args.sound_filename
+
+    sample_rate = 16000
+
+    logging.info("Constructing Fbank computer")
+    online_fbank = create_streaming_feature_extractor()
+
+    logging.info(f"Reading sound files: {sound_file}")
+    wave_samples = read_sound_files(
+        filenames=[sound_file],
+        expected_sample_rate=sample_rate,
+    )[0]
+    logging.info(wave_samples.shape)
+
+    tail_padding = torch.zeros(int(0.3 * sample_rate), dtype=torch.float32)
+
+    wave_samples = torch.cat([wave_samples, tail_padding])
+
+    states = model.get_init_states()
+
+    hyp = None
+    decoder_out = None
+
+    num_processed_frames = 0
+    segment = model.T
+    offset = model.chunk_length
+
+    chunk = int(1 * sample_rate)  # 0.2 second
+
+    start = 0
+    while start < wave_samples.numel():
+        end = min(start + chunk, wave_samples.numel())
+        samples = wave_samples[start:end]
+        start += chunk
+
+        online_fbank.accept_waveform(
+            sampling_rate=sample_rate,
+            waveform=samples,
+        )
+        while online_fbank.num_frames_ready - num_processed_frames >= segment:
+            frames = []
+            for i in range(segment):
+                frames.append(online_fbank.get_frame(num_processed_frames + i))
+            num_processed_frames += offset
+            frames = torch.cat(frames, dim=0)
+            encoder_out, states = model.run_encoder(frames, states)
+            hyp, decoder_out = greedy_search(model, encoder_out, decoder_out, hyp)
+
+    symbol_table = k2.SymbolTable.from_file(args.tokens)
+
+    context_size = 2
+    text = ""
+    for i in hyp[context_size:]:
+        text += symbol_table[i]
+    text = text.replace("▁", " ").strip()
+
+    logging.info(sound_file)
+    logging.info(text)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    main()

egs/librispeech/ASR/conv_emformer_transducer_stateless2/train.py

@@ -970,10 +970,10 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
+        train_cuts = librispeech.train_all_shuf_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+    else:
+        train_cuts = librispeech.train_clean_100_cuts()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds

egs/librispeech/ASR/local/compile_hlg_using_openfst.py

@@ -0,0 +1,184 @@
+#!/usr/bin/env python3
+# Copyright    2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+This script takes as input lang_dir and generates HLG from
+
+    - H, the ctc topology, built from tokens contained in lang_dir/lexicon.txt
+    - L, the lexicon, built from lang_dir/L_disambig.fst
+
+        Caution: We use a lexicon that contains disambiguation symbols
+
+    - G, the LM, built from data/lm/G_3_gram.fst.txt
+
+The generated HLG is saved in $lang_dir/HLG_fst.pt
+
+So when to use this script instead of ./local/compile_hlg.py ?
+If you have a very large G, ./local/compile_hlg.py may throw OOM for
+determinization. In that case, you can use this script to compile HLG.
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import k2
+import kaldifst
+import torch
+
+from icefall.lexicon import Lexicon
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        help="""Input and output directory.
+        """,
+    )
+
+    return parser.parse_args()
+
+
+def compile_HLG(lang_dir: str) -> kaldifst.StdVectorFst:
+    """
+    Args:
+      lang_dir:
+        The language directory, e.g., data/lang_phone or data/lang_bpe_5000.
+
+    Return:
+      An FST representing HLG.
+    """
+
+    L = kaldifst.StdVectorFst.read(f"{lang_dir}/L_disambig.fst")
+    logging.info("Arc sort L")
+    kaldifst.arcsort(L, sort_type="olabel")
+    logging.info(f"L: #states {L.num_states}")
+
+    G_filename_txt = "data/lm/G_3_gram.fst.txt"
+    G_filename_binary = "data/lm/G_3_gram.fst"
+    if Path(G_filename_binary).is_file():
+        logging.info(f"Loading {G_filename_binary}")
+        G = kaldifst.StdVectorFst.read(G_filename_binary)
+    else:
+        logging.info(f"Loading {G_filename_txt}")
+        with open(G_filename_txt) as f:
+            G = kaldifst.compile(s=f.read(), acceptor=False)
+            logging.info(f"Saving G to {G_filename_binary}")
+            G.write(G_filename_binary)
+
+    logging.info("Arc sort G")
+    kaldifst.arcsort(G, sort_type="ilabel")
+
+    logging.info(f"G: #states {G.num_states}")
+
+    logging.info("Compose L and G and connect LG")
+    LG = kaldifst.compose(L, G, connect=True)
+    logging.info(f"LG: #states {LG.num_states}")
+
+    logging.info("Determinizestar LG")
+    kaldifst.determinize_star(LG)
+    logging.info(f"LG after determinize_star: #states {LG.num_states}")
+
+    logging.info("Minimize encoded LG")
+    kaldifst.minimize_encoded(LG)
+    logging.info(f"LG after minimize_encoded: #states {LG.num_states}")
+
+    logging.info("Converting LG to k2 format")
+    LG = k2.Fsa.from_openfst(LG.to_str(is_acceptor=False), acceptor=False)
+    logging.info(f"LG in k2: #states: {LG.shape[0]}, #arcs: {LG.num_arcs}")
+
+    lexicon = Lexicon(lang_dir)
+
+    first_token_disambig_id = lexicon.token_table["#0"]
+    first_word_disambig_id = lexicon.word_table["#0"]
+    logging.info(f"token id for #0: {first_token_disambig_id}")
+    logging.info(f"word id for #0: {first_word_disambig_id}")
+
+    max_token_id = max(lexicon.tokens)
+    modified = False
+    logging.info(
+        f"Building ctc_topo. modified: {modified}, max_token_id: {max_token_id}"
+    )
+
+    H = k2.ctc_topo(max_token_id, modified=modified)
+    logging.info(f"H: #states: {H.shape[0]}, #arcs: {H.num_arcs}")
+
+    logging.info("Removing disambiguation symbols on LG")
+    LG.labels[LG.labels >= first_token_disambig_id] = 0
+    LG.aux_labels[LG.aux_labels >= first_word_disambig_id] = 0
+
+    # See https://github.com/k2-fsa/k2/issues/874
+    # for why we need to set LG.properties to None
+    LG.__dict__["_properties"] = None
+
+    logging.info("Removing epsilons from LG")
+    LG = k2.remove_epsilon(LG)
+    logging.info(
+        f"LG after k2.remove_epsilon: #states: {LG.shape[0]}, #arcs: {LG.num_arcs}"
+    )
+
+    logging.info("Connecting LG after removing epsilons")
+    LG = k2.connect(LG)
+    LG.aux_labels = LG.aux_labels.remove_values_eq(0)
+    logging.info(f"LG after k2.connect: #states: {LG.shape[0]}, #arcs: {LG.num_arcs}")
+
+    logging.info("Arc sorting LG")
+    LG = k2.arc_sort(LG)
+
+    logging.info("Composing H and LG")
+
+    HLG = k2.compose(H, LG, inner_labels="tokens")
+    logging.info(
+        f"HLG after k2.compose: #states: {HLG.shape[0]}, #arcs: {HLG.num_arcs}"
+    )
+
+    logging.info("Connecting HLG")
+    HLG = k2.connect(HLG)
+    logging.info(
+        f"HLG after k2.connect: #states: {HLG.shape[0]}, #arcs: {HLG.num_arcs}"
+    )
+
+    logging.info("Arc sorting LG")
+    HLG = k2.arc_sort(HLG)
+
+    return HLG
+
+
+def main():
+    args = get_args()
+    lang_dir = Path(args.lang_dir)
+
+    filename = lang_dir / "HLG_fst.pt"
+
+    if filename.is_file():
+        logging.info(f"{filename} already exists - skipping")
+        return
+
+    HLG = compile_HLG(lang_dir)
+    logging.info(f"Saving HLG to {filename}")
+    torch.save(HLG.as_dict(), filename)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    main()

egs/librispeech/ASR/local/train_bpe_model.py

@@ -89,6 +89,9 @@ def main():
             bos_id=-1,
             eos_id=-1,
         )
+    else:
+        print(f"{model_file} exists - skipping")
+        return
 
     shutil.copyfile(model_file, f"{lang_dir}/bpe.model")
 

egs/librispeech/ASR/lstm_transducer_stateless/train.py

@@ -954,10 +954,10 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
+        train_cuts = librispeech.train_all_shuf_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+    else:
+        train_cuts = librispeech.train_clean_100_cuts()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds

egs/librispeech/ASR/lstm_transducer_stateless2/decode.py

@@ -107,8 +107,25 @@ Usage:
     --rnn-lm-avg 1 \
     --rnn-lm-num-layers 3 \
     --rnn-lm-tie-weights 1
-"""
 
+(9) modified beam search with RNNLM shallow fusion + LODR
+./lstm_transducer_stateless2/decode.py \
+    --epoch 35 \
+    --avg 15 \
+    --max-duration 600 \
+    --exp-dir ./lstm_transducer_stateless2/exp \
+    --decoding-method modified_beam_search_rnnlm_LODR \
+    --beam 4 \
+    --max-contexts 4 \
+    --rnn-lm-scale 0.4 \
+    --rnn-lm-exp-dir /path/to/RNNLM/exp \
+    --rnn-lm-epoch 99 \
+    --rnn-lm-avg 1 \
+    --rnn-lm-num-layers 3 \
+    --rnn-lm-tie-weights 1 \
+    --tokens-ngram 2 \
+    --ngram-lm-scale -0.16 \
+"""
 
 import argparse
 import logging
@@ -132,6 +149,7 @@ from beam_search import (
     greedy_search_batch,
     modified_beam_search,
     modified_beam_search_ngram_rescoring,
+    modified_beam_search_rnnlm_LODR,
     modified_beam_search_rnnlm_shallow_fusion,
 )
 from librispeech import LibriSpeech
@@ -235,7 +253,8 @@ def get_parser():
           - fast_beam_search_nbest_oracle
           - fast_beam_search_nbest_LG
           - modified_beam_search_ngram_rescoring
-          - modified_beam_search_rnnlm_shallow_fusion # for rnn lm shallow fusion
+          - modified_beam_search_rnnlm_shallow_fusion
+          - modified_beam_search_rnnlm_LODR
         If you use fast_beam_search_nbest_LG, you have to specify
         `--lang-dir`, which should contain `LG.pt`.
         """,
@@ -394,7 +413,8 @@ def get_parser():
         type=int,
         default=3,
         help="""Token Ngram used for rescoring.
-            Used only when the decoding method is modified_beam_search_ngram_rescoring""",
+            Used only when the decoding method is
+            modified_beam_search_ngram_rescoring""",
     )
 
     parser.add_argument(
@@ -402,7 +422,8 @@ def get_parser():
         type=int,
         default=500,
         help="""ID of the backoff symbol.
-                Used only when the decoding method is modified_beam_search_ngram_rescoring""",
+                Used only when the decoding method is
+                modified_beam_search_ngram_rescoring""",
     )
 
     add_model_arguments(parser)
@@ -572,6 +593,20 @@ def decode_one_batch(
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
+    elif params.decoding_method == "modified_beam_search_rnnlm_LODR":
+        hyp_tokens = modified_beam_search_rnnlm_LODR(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+            sp=sp,
+            LODR_lm=ngram_lm,
+            LODR_lm_scale=ngram_lm_scale,
+            rnnlm=rnnlm,
+            rnnlm_scale=rnnlm_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     else:
         batch_size = encoder_out.size(0)
 
@@ -760,6 +795,7 @@ def main():
         "fast_beam_search_nbest_LG",
         "fast_beam_search_nbest_oracle",
         "modified_beam_search",
+        "modified_beam_search_rnnlm_LODR",
         "modified_beam_search_ngram_rescoring",
         "modified_beam_search_rnnlm_shallow_fusion",
     )
@@ -788,6 +824,9 @@ def main():
     if "rnnlm" in params.decoding_method:
         params.suffix += f"-rnnlm-lm-scale-{params.rnn_lm_scale}"
 
+    if "LODR" in params.decoding_method:
+        params.suffix += "-LODR"
+
     if params.use_averaged_model:
         params.suffix += "-use-averaged-model"
 
@@ -901,7 +940,7 @@ def main():
     model.eval()
 
     # only load N-gram LM when needed
-    if "ngram" in params.decoding_method:
+    if "ngram" in params.decoding_method or "LODR" in params.decoding_method:
         lm_filename = f"{params.tokens_ngram}gram.fst.txt"
         logging.info(f"lm filename: {lm_filename}")
         ngram_lm = NgramLm(
@@ -910,6 +949,7 @@ def main():
             is_binary=False,
         )
         logging.info(f"num states: {ngram_lm.lm.num_states}")
+        ngram_lm_scale = params.ngram_lm_scale
     else:
         ngram_lm = None
         ngram_lm_scale = None
@@ -933,7 +973,6 @@ def main():
         )
         rnn_lm_model.to(device)
         rnn_lm_model.eval()
-
     else:
         rnn_lm_model = None
         rnn_lm_scale = 0.0

egs/librispeech/ASR/lstm_transducer_stateless2/train.py

@@ -1108,10 +1108,10 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeech(manifest_dir=args.manifest_dir)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
+        train_cuts = librispeech.train_all_shuf_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+    else:
+        train_cuts = librispeech.train_clean_100_cuts()
 
     train_cuts = filter_short_and_long_utterances(train_cuts, sp)
 

egs/librispeech/ASR/prepare.sh

@@ -1,5 +1,8 @@
 #!/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
 
 nj=15
@@ -41,9 +44,9 @@ dl_dir=$PWD/download
 # It will generate data/lang_bpe_xxx,
 # data/lang_bpe_yyy if the array contains xxx, yyy
 vocab_sizes=(
-  5000
+  # 5000
-  2000
+  # 2000
-  1000
+  # 1000
   500
 )
 
@@ -120,6 +123,11 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
     touch data/fbank/.librispeech.done
   fi
 
+  cat <(gunzip -c data/fbank/librispeech_cuts_train-clean-100.jsonl.gz) \
+    <(gunzip -c data/fbank/librispeech_cuts_train-clean-360.jsonl.gz) \
+    <(gunzip -c data/fbank/librispeech_cuts_train-other-500.jsonl.gz) | \
+    shuf | gzip -c > data/fbank/librispeech_cuts_train-all-shuf.jsonl.gz
+
   if [ ! -e data/fbank/.librispeech-validated.done ]; then
     log "Validating data/fbank for LibriSpeech"
     parts=(
@@ -160,6 +168,22 @@ if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
   if [ ! -f $lang_dir/L_disambig.pt ]; then
     ./local/prepare_lang.py --lang-dir $lang_dir
   fi
+
+  if [ ! -f $lang_dir/L.fst ]; then
+    log "Converting L.pt to L.fst"
+    ./shared/convert-k2-to-openfst.py \
+      --olabels aux_labels \
+      $lang_dir/L.pt \
+      $lang_dir/L.fst
+  fi
+
+  if [ ! -f $lang_dir/L_disambig.fst ]; then
+    log "Converting L_disambig.pt to L_disambig.fst"
+    ./shared/convert-k2-to-openfst.py \
+      --olabels aux_labels \
+      $lang_dir/L_disambig.pt \
+      $lang_dir/disambig_L.fst
+  fi
 fi
 
 
@@ -200,6 +224,22 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
         --lexicon $lang_dir/lexicon.txt \
         --bpe-model $lang_dir/bpe.model
     fi
+
+    if [ ! -f $lang_dir/L.fst ]; then
+      log "Converting L.pt to L.fst"
+      ./shared/convert-k2-to-openfst.py \
+        --olabels aux_labels \
+        $lang_dir/L.pt \
+        $lang_dir/L.fst
+    fi
+
+    if [ ! -f $lang_dir/L_disambig.fst ]; then
+      log "Converting L_disambig.pt to L_disambig.fst"
+      ./shared/convert-k2-to-openfst.py \
+        --olabels aux_labels \
+        $lang_dir/L_disambig.pt \
+        $lang_dir/L_disambig.fst
+    fi
   done
 fi
 
@@ -262,10 +302,13 @@ fi
 if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
   log "Stage 9: Compile HLG"
   ./local/compile_hlg.py --lang-dir data/lang_phone
+  ./local/compile_hlg_using_openfst.py --lang-dir data/lang_phone
 
   for vocab_size in ${vocab_sizes[@]}; do
     lang_dir=data/lang_bpe_${vocab_size}
     ./local/compile_hlg.py --lang-dir $lang_dir
+
+    ./local/compile_hlg_using_openfst.py --lang-dir $lang_dir
   done
 fi
 

egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py

@@ -882,10 +882,10 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
+        train_cuts = librispeech.train_all_shuf_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+    else:
+        train_cuts = librispeech.train_clean_100_cuts()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds

egs/librispeech/ASR/pruned_transducer_stateless/train.py

@@ -873,10 +873,10 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    train_cuts = librispeech.train_clean_100_cuts()
     if params.full_libri:
-        train_cuts += librispeech.train_clean_360_cuts()
+        train_cuts = librispeech.train_all_shuf_cuts()
-        train_cuts += librispeech.train_other_500_cuts()
+    else:
+        train_cuts = librispeech.train_clean_100_cuts()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds