Merge branch 'k2-fsa:master' into master

.flake8

@@ -4,10 +4,20 @@ statistics=true
 max-line-length = 80
 per-file-ignores =
     # line too long
-    egs/librispeech/ASR/*/conformer.py: E501,
+    icefall/diagnostics.py: E501,
-    egs/aishell/ASR/*/conformer.py: E501,
+    egs/*/ASR/*/conformer.py: E501,
+    egs/*/ASR/pruned_transducer_stateless*/*.py: E501,
+    egs/*/ASR/*/optim.py: E501,
+    egs/*/ASR/*/scaling.py: E501,
+    egs/librispeech/ASR/conv_emformer_transducer_stateless*/*.py: E501, E203,
+    egs/librispeech/ASR/conformer_ctc2/*py: E501,
+    egs/librispeech/ASR/RESULTS.md: E999,
+
+    # invalid escape sequence (cause by tex formular), W605
+    icefall/utils.py: E501, W605
 
 exclude =
   .git,
   **/data/**,
-  icefall/shared/make_kn_lm.py
+  icefall/shared/make_kn_lm.py,
+  icefall/__init__.py

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

@@ -0,0 +1,17 @@
+#!/usr/bin/env bash
+
+# This script computes fbank features for the test-clean and test-other datasets.
+# The computed features are saved to ~/tmp/fbank-libri and are
+# cached for later runs
+
+export PYTHONPATH=$PWD:$PYTHONPATH
+echo $PYTHONPATH
+
+mkdir ~/tmp/fbank-libri
+cd egs/librispeech/ASR
+mkdir -p data
+cd data
+[ ! -e fbank ] && ln -s ~/tmp/fbank-libri fbank
+cd ..
+./local/compute_fbank_librispeech.py
+ls -lh data/fbank/

.github/scripts/download-gigaspeech-dev-test-dataset.sh

@@ -0,0 +1,15 @@
+#!/usr/bin/env bash
+
+# This script downloads the pre-computed fbank features for
+# dev and test datasets of GigaSpeech.
+#
+# You will find directories `~/tmp/giga-dev-dataset-fbank` after running
+# this script.
+
+mkdir -p ~/tmp
+cd ~/tmp
+
+git lfs install
+git clone https://huggingface.co/csukuangfj/giga-dev-dataset-fbank
+
+ls -lh giga-dev-dataset-fbank/data/fbank

.github/scripts/download-librispeech-test-clean-and-test-other-dataset.sh

@@ -0,0 +1,23 @@
+#!/usr/bin/env bash
+
+# This script downloads the test-clean and test-other datasets
+# of LibriSpeech and unzip them to the folder ~/tmp/download,
+# which is cached by GitHub actions for later runs.
+#
+# You will find directories ~/tmp/download/LibriSpeech after running
+# this script.
+
+mkdir ~/tmp/download
+cd egs/librispeech/ASR
+ln -s ~/tmp/download .
+cd download
+wget -q --no-check-certificate https://www.openslr.org/resources/12/test-clean.tar.gz
+tar xf test-clean.tar.gz
+rm test-clean.tar.gz
+
+wget -q --no-check-certificate https://www.openslr.org/resources/12/test-other.tar.gz
+tar xf test-other.tar.gz
+rm test-other.tar.gz
+pwd
+ls -lh
+ls -lh LibriSpeech

.github/scripts/install-kaldifeat.sh

@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+
+# This script installs kaldifeat into the directory ~/tmp/kaldifeat
+# which is cached by GitHub actions for later runs.
+
+mkdir -p ~/tmp
+cd ~/tmp
+git clone https://github.com/csukuangfj/kaldifeat
+cd kaldifeat
+mkdir build
+cd build
+cmake -DCMAKE_BUILD_TYPE=Release ..
+make -j2 _kaldifeat

.github/scripts/prepare-librispeech-test-clean-and-test-other-manifests.sh

@@ -0,0 +1,11 @@
+#!/usr/bin/env bash
+
+# This script assumes that test-clean and test-other are downloaded
+# to egs/librispeech/ASR/download/LibriSpeech and generates manifest
+# files in egs/librispeech/ASR/data/manifests
+
+cd egs/librispeech/ASR
+[ ! -e download ] && ln -s ~/tmp/download .
+mkdir -p data/manifests
+lhotse prepare librispeech -j 2 -p test-clean -p test-other ./download/LibriSpeech data/manifests
+ls -lh data/manifests

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

@@ -0,0 +1,86 @@
+#!/usr/bin/env bash
+
+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/aishell/ASR
+
+git lfs install
+
+fbank_url=https://huggingface.co/csukuangfj/aishell-test-dev-manifests
+log "Downloading pre-commputed fbank from $fbank_url"
+
+git clone https://huggingface.co/csukuangfj/aishell-test-dev-manifests
+ln -s $PWD/aishell-test-dev-manifests/data .
+
+log "Downloading pre-trained model from $repo_url"
+repo_url=https://huggingface.co/csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20
+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
+ln -s pretrained-epoch-29-avg-5-torch-1.10.0.pt pretrained.pt
+popd
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./pruned_transducer_stateless3/pretrained.py \
+    --method greedy_search \
+    --max-sym-per-frame $sym \
+    --checkpoint $repo/exp/pretrained.pt \
+    --lang-dir $repo/data/lang_char \
+    $repo/test_wavs/BAC009S0764W0121.wav \
+    $repo/test_wavs/BAC009S0764W0122.wav \
+    $rep/test_wavs/BAC009S0764W0123.wav
+done
+
+for method in modified_beam_search beam_search fast_beam_search; do
+  log "$method"
+
+  ./pruned_transducer_stateless3/pretrained.py \
+    --method $method \
+    --beam-size 4 \
+    --checkpoint $repo/exp/pretrained.pt \
+    --lang-dir $repo/data/lang_char \
+    $repo/test_wavs/BAC009S0764W0121.wav \
+    $repo/test_wavs/BAC009S0764W0122.wav \
+    $rep/test_wavs/BAC009S0764W0123.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_stateless3/exp
+  ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless3/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_char data/
+
+  ls -lh data
+  ls -lh pruned_transducer_stateless3/exp
+
+  log "Decoding test and dev"
+
+  # 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_stateless3/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir pruned_transducer_stateless3/exp
+  done
+
+  rm pruned_transducer_stateless3/exp/*.pt
+fi

.github/scripts/run-gigaspeech-pruned-transducer-stateless2-2022-05-12.sh

@@ -0,0 +1,49 @@
+#!/usr/bin/env bash
+
+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/gigaspeech/ASR
+
+repo_url=https://huggingface.co/wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+git clone $repo_url
+repo=$(basename $repo_url)
+
+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_stateless2/exp
+  ln -s $PWD/$repo/exp/pretrained-iter-3488000-avg-20.pt pruned_transducer_stateless2/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh data/lang_bpe_500
+  ls -lh data/fbank
+  ls -lh pruned_transducer_stateless2/exp
+
+  log "Decoding dev and test"
+
+  # use a small value for decoding with CPU
+  max_duration=100
+
+  # Test only greedy_search to reduce CI running time
+  # for method in greedy_search fast_beam_search modified_beam_search; do
+  for method in greedy_search; do
+    log "Decoding with $method"
+
+    ./pruned_transducer_stateless2/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir pruned_transducer_stateless2/exp
+  done
+
+  rm pruned_transducer_stateless2/exp/*.pt
+fi

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

@@ -0,0 +1,76 @@
+#!/usr/bin/env bash
+
+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/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless-2022-03-12
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./pruned_transducer_stateless/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 \
+    $repo/test_wavs/1221-135766-0002.wav
+done
+
+for method in fast_beam_search modified_beam_search beam_search; do
+  log "$method"
+
+  ./pruned_transducer_stateless/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
+
+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_stateless/exp
+  ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh pruned_transducer_stateless/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_stateless/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir pruned_transducer_stateless/exp
+  done
+
+  rm pruned_transducer_stateless/exp/*.pt
+fi

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

@@ -0,0 +1,80 @@
+#!/usr/bin/env bash
+
+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/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless2-2022-04-29
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+ln -s pretrained-epoch-38-avg-10.pt pretrained.pt
+popd
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./pruned_transducer_stateless2/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_stateless2/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
+
+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_stateless2/exp
+  ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless2/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh pruned_transducer_stateless2/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_stateless2/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir pruned_transducer_stateless2/exp
+  done
+
+  rm pruned_transducer_stateless2/exp/*.pt
+fi

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

@@ -0,0 +1,80 @@
+#!/usr/bin/env bash
+
+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/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-04-29
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+ln -s pretrained-epoch-25-avg-6.pt pretrained.pt
+popd
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./pruned_transducer_stateless3/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_stateless3/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
+
+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_stateless3/exp
+  ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless3/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh pruned_transducer_stateless3/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_stateless3/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir pruned_transducer_stateless3/exp
+  done
+
+  rm pruned_transducer_stateless3/exp/*.pt
+fi

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

@@ -0,0 +1,80 @@
+#!/usr/bin/env bash
+
+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/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+ln -s pretrained-iter-1224000-avg-14.pt pretrained.pt
+popd
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./pruned_transducer_stateless3/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_stateless3/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
+
+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_stateless3/exp
+  ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless3/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh pruned_transducer_stateless3/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_stateless3/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir pruned_transducer_stateless3/exp
+  done
+
+  rm pruned_transducer_stateless3/exp/*.pt
+fi

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

@@ -0,0 +1,99 @@
+#!/usr/bin/env bash
+
+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/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless5-2022-05-13
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+ln -s pretrained-epoch-39-avg-7.pt pretrained.pt
+popd
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./pruned_transducer_stateless5/pretrained.py \
+    --method greedy_search \
+    --max-sym-per-frame $sym \
+    --checkpoint $repo/exp/pretrained.pt \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --num-encoder-layers 18 \
+    --dim-feedforward 2048 \
+    --nhead 8 \
+    --encoder-dim 512 \
+    --decoder-dim 512 \
+    --joiner-dim 512 \
+    $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_stateless5/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 \
+    --num-encoder-layers 18 \
+    --dim-feedforward 2048 \
+    --nhead 8 \
+    --encoder-dim 512 \
+    --decoder-dim 512 \
+    --joiner-dim 512
+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_stateless5/exp
+  ln -s $PWD/$repo/exp/pretrained-epoch-39-avg-7.pt pruned_transducer_stateless5/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh pruned_transducer_stateless5/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_stateless5/decode.py \
+      --decoding-method $method \
+      --use-averaged-model 0 \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir pruned_transducer_stateless5/exp \
+      --num-encoder-layers 18 \
+      --dim-feedforward 2048 \
+      --nhead 8 \
+      --encoder-dim 512 \
+      --decoder-dim 512 \
+      --joiner-dim 512
+  done
+
+  rm pruned_transducer_stateless5/exp/*.pt
+fi

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

@@ -0,0 +1,100 @@
+#!/usr/bin/env bash
+
+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/pkufool/icefall_librispeech_streaming_pruned_transducer_stateless2_20220625
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+ln -s pretrained-epoch-24-avg-10.pt pretrained.pt
+popd
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./pruned_transducer_stateless2/pretrained.py \
+    --method greedy_search \
+    --max-sym-per-frame $sym \
+    --checkpoint $repo/exp/pretrained.pt \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --simulate-streaming 1 \
+    --causal-convolution 1 \
+    $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_stateless2/pretrained.py \
+    --method $method \
+    --beam-size 4 \
+    --checkpoint $repo/exp/pretrained.pt \
+    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+    --simulate-streaming 1 \
+    --causal-convolution 1 \
+    $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_stateless2/exp
+  ln -s $PWD/$repo/exp/pretrained-epoch-24-avg-10.pt pruned_transducer_stateless2/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh pruned_transducer_stateless2/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 "Simulate streaming decoding with $method"
+
+    ./pruned_transducer_stateless2/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir pruned_transducer_stateless2/exp \
+      --simulate-streaming 1 \
+      --causal-convolution 1
+  done
+
+  for method in greedy_search fast_beam_search modified_beam_search; do
+    log "Real streaming decoding with $method"
+
+    ./pruned_transducer_stateless2/streaming_decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --num-decode-streams 100 \
+      --exp-dir pruned_transducer_stateless2/exp \
+      --left-context 32 \
+      --decode-chunk-size 8 \
+      --right-context 0
+  done
+
+  rm pruned_transducer_stateless2/exp/*.pt
+fi

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

@@ -0,0 +1,76 @@
+#!/usr/bin/env bash
+
+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/csukuangfj/icefall-asr-librispeech-transducer-stateless2-torchaudio-2022-04-19
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./transducer_stateless2/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 \
+    $repo/test_wavs/1221-135766-0002.wav
+done
+
+for method in fast_beam_search modified_beam_search beam_search; do
+  log "$method"
+
+  ./transducer_stateless2/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
+
+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 transducer_stateless2/exp
+  ln -s $PWD/$repo/exp/pretrained.pt transducer_stateless2/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh transducer_stateless2/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"
+
+    ./transducer_stateless2/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir transducer_stateless2/exp
+  done
+
+  rm transducer_stateless2/exp/*.pt
+fi

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

@@ -0,0 +1,46 @@
+#!/usr/bin/env bash
+
+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://github.com/csukuangfj/icefall-asr-conformer-ctc-bpe-500
+git lfs install
+git clone $repo
+
+log "Downloading pre-trained model from $repo_url"
+git clone $repo_url
+repo=$(basename $repo_url)
+
+log "Display test files"
+tree $repo/
+soxi $repo/test_wavs/*.flac
+ls -lh $repo/test_wavs/*.flac
+
+log "CTC decoding"
+
+./conformer_ctc/pretrained.py \
+  --method ctc-decoding \
+  --num-classes 500 \
+  --checkpoint $repo/exp/pretrained.pt \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  $repo/test_wavs/1089-134686-0001.flac \
+  $repo/test_wavs/1221-135766-0001.flac \
+  $repo/test_wavs/1221-135766-0002.flac
+
+log "HLG decoding"
+
+./conformer_ctc/pretrained.py \
+  --method 1best \
+  --num-classes 500 \
+  --checkpoint $repo/exp/pretrained.pt \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --words-file $repo/data/lang_bpe_500/words.txt \
+  --HLG $repo/data/lang_bpe_500/HLG.pt \
+  $repo/test_wavs/1089-134686-0001.flac \
+  $repo/test_wavs/1221-135766-0001.flac \
+  $repo/test_wavs/1221-135766-0002.flac

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

@@ -0,0 +1,76 @@
+#!/usr/bin/env bash
+
+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/csukuangfj/icefall-asr-librispeech-100h-transducer-stateless-multi-datasets-bpe-500-2022-02-21
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./transducer_stateless_multi_datasets/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"
+
+  ./transducer_stateless_multi_datasets/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
+
+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 transducer_stateless_multi_datasets/exp
+  ln -s $PWD/$repo/exp/pretrained.pt transducer_stateless_multi_datasets/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh transducer_stateless_multi_datasets/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"
+
+    ./transducer_stateless_multi_datasets/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir transducer_stateless_multi_datasets/exp
+  done
+
+  rm transducer_stateless_multi_datasets/exp/*.pt
+fi

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

@@ -0,0 +1,76 @@
+#!/usr/bin/env bash
+
+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/csukuangfj/icefall-asr-librispeech-transducer-stateless-multi-datasets-bpe-500-2022-03-01
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./transducer_stateless_multi_datasets/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"
+
+  ./transducer_stateless_multi_datasets/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
+
+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 transducer_stateless_multi_datasets/exp
+  ln -s $PWD/$repo/exp/pretrained.pt transducer_stateless_multi_datasets/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh transducer_stateless_multi_datasets/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"
+
+    ./transducer_stateless_multi_datasets/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir transducer_stateless_multi_datasets/exp
+  done
+
+  rm transducer_stateless_multi_datasets/exp/*.pt
+fi

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

@@ -0,0 +1,47 @@
+#!/usr/bin/env bash
+
+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/aishell/ASR
+
+repo_url=https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2-2022-03-01
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./transducer_stateless_modified-2/pretrained.py \
+    --method greedy_search \
+    --max-sym-per-frame $sym \
+    --checkpoint $repo/exp/pretrained.pt \
+    --lang-dir $repo/data/lang_char \
+    $repo/test_wavs/BAC009S0764W0121.wav \
+    $repo/test_wavs/BAC009S0764W0122.wav \
+    $repo/test_wavs/BAC009S0764W0123.wav
+done
+
+for method in modified_beam_search beam_search; do
+  log "$method"
+
+  ./transducer_stateless_modified-2/pretrained.py \
+    --method $method \
+    --beam-size 4 \
+    --checkpoint $repo/exp/pretrained.pt \
+    --lang-dir $repo/data/lang_char \
+    $repo/test_wavs/BAC009S0764W0121.wav \
+    $repo/test_wavs/BAC009S0764W0122.wav \
+    $repo/test_wavs/BAC009S0764W0123.wav
+done

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

@@ -0,0 +1,47 @@
+#!/usr/bin/env bash
+
+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/aishell/ASR
+
+repo_url=https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./transducer_stateless_modified/pretrained.py \
+    --method greedy_search \
+    --max-sym-per-frame $sym \
+    --checkpoint $repo/exp/pretrained.pt \
+    --lang-dir $repo/data/lang_char \
+    $repo/test_wavs/BAC009S0764W0121.wav \
+    $repo/test_wavs/BAC009S0764W0122.wav \
+    $repo/test_wavs/BAC009S0764W0123.wav
+done
+
+for method in modified_beam_search beam_search; do
+  log "$method"
+
+  ./transducer_stateless_modified/pretrained.py \
+    --method $method \
+    --beam-size 4 \
+    --checkpoint $repo/exp/pretrained.pt \
+    --lang-dir $repo/data/lang_char \
+    $repo/test_wavs/BAC009S0764W0121.wav \
+    $repo/test_wavs/BAC009S0764W0122.wav \
+    $repo/test_wavs/BAC009S0764W0123.wav
+done

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

@@ -0,0 +1,76 @@
+#!/usr/bin/env bash
+
+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/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2022-02-07
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+
+for sym in 1 2 3; do
+  log "Greedy search with --max-sym-per-frame $sym"
+
+  ./transducer_stateless/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 \
+    $repo/test_wavs/1221-135766-0002.wav
+done
+
+for method in fast_beam_search modified_beam_search beam_search; do
+  log "$method"
+
+  ./transducer_stateless/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
+
+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 transducer_stateless/exp
+  ln -s $PWD/$repo/exp/pretrained.pt transducer_stateless/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh transducer_stateless/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"
+
+    ./transducer_stateless/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir transducer_stateless/exp
+  done
+
+  rm transducer_stateless/exp/*.pt
+fi

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

@@ -0,0 +1,32 @@
+#!/usr/bin/env bash
+
+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/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23
+
+log "Downloading pre-trained model from $repo_url"
+git lfs install
+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
+
+log "Beam search decoding"
+
+./transducer/pretrained.py \
+  --method beam_search \
+  --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

.github/workflows/build-doc.yml

@@ -0,0 +1,65 @@
+# Copyright      2022  Xiaomi Corp.       (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.
+
+# refer to https://github.com/actions/starter-workflows/pull/47/files
+
+# You can access it at https://k2-fsa.github.io/icefall/
+name: Generate doc
+on:
+  push:
+    branches:
+    - master
+    - doc
+  pull_request:
+    types: [labeled]
+
+jobs:
+  build-doc:
+    if: github.event.label.name == 'doc' || github.event_name == 'push'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ["3.8"]
+    steps:
+      # refer to https://github.com/actions/checkout
+      - 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 }}
+
+      - name: Display Python version
+        run: python -c "import sys; print(sys.version)"
+
+      - name: Build doc
+        shell: bash
+        run: |
+          cd docs
+          python3 -m pip install -r ./requirements.txt
+          make html
+          touch build/html/.nojekyll
+
+      - name: Deploy
+        uses: peaceiris/actions-gh-pages@v3
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: ./docs/build/html
+          publish_branch: gh-pages

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

@@ -0,0 +1,119 @@
+# 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-aishell-2022-06-20
+# pruned RNN-T + reworked model with random combiner
+# https://huggingface.co/csukuangfj/icefall-aishell-pruned-transducer-stateless3-2022-06-20
+
+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_aishell_2022_06_20:
+    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-18.04]
+        python-version: [3.7, 3.8, 3.9]
+
+      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 }}
+
+      - 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: ${{ github.event.label.name }}
+        run: |
+          sudo apt-get -qq install git-lfs tree sox
+          export PYTHONPATH=$PWD:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
+
+          .github/scripts/run-aishell-pruned-transducer-stateless3-2022-06-20.sh
+
+      - name: Display decoding results for aishell pruned_transducer_stateless3
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/aishell/ASR/
+          tree ./pruned_transducer_stateless3/exp
+
+          cd pruned_transducer_stateless3
+          echo "results for pruned_transducer_stateless3"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for dev" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test" {} + | sort -n -k2
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for dev" {} + | sort -n -k2
+
+          echo "===modified beam search==="
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test" {} + | sort -n -k2
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for dev" {} + | sort -n -k2
+
+      - name: Upload decoding results for aishell pruned_transducer_stateless3
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: aishell-torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless3-2022-06-20
+          path: egs/aishell/ASR/pruned_transducer_stateless3/exp/

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

@@ -0,0 +1,122 @@
+# Copyright      2021  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-gigaspeech-2022-05-13
+# stateless transducer + k2 pruned rnnt-loss + reworked conformer
+
+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_gigaspeech_2022_05_13:
+    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-18.04]
+        python-version: [3.7, 3.8, 3.9]
+
+      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 }}
+
+      - name: Install kaldifeat
+        if: steps.my-cache.outputs.cache-hit != 'true'
+        shell: bash
+        run: |
+          .github/scripts/install-kaldifeat.sh
+
+      - name: Download GigaSpeech dev/test dataset
+        shell: bash
+        run: |
+          sudo apt-get install -y -q git-lfs
+
+          .github/scripts/download-gigaspeech-dev-test-dataset.sh
+
+      - name: Inference with pre-trained model
+        shell: bash
+        env:
+          GITHUB_EVENT_NAME: ${{ github.event_name }}
+          GITHUB_EVENT_LABEL_NAME: ${{ github.event.label.name }}
+        run: |
+          ln -s ~/tmp/giga-dev-dataset-fbank/data egs/gigaspeech/ASR/
+
+          ls -lh egs/gigaspeech/ASR/data/fbank
+
+          export PYTHONPATH=$PWD:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
+          export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
+
+          .github/scripts/run-gigaspeech-pruned-transducer-stateless2-2022-05-12.sh
+
+      - name: Display decoding results for gigaspeech pruned_transducer_stateless2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/gigaspeech/ASR/
+          tree ./pruned_transducer_stateless2/exp
+
+          sudo apt-get -qq install tree
+
+          cd pruned_transducer_stateless2
+          echo "results for pruned_transducer_stateless2"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for dev" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test" {} + | sort -n -k2
+
+      - name: Upload decoding results for gigaspeech pruned_transducer_stateless2
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-gigaspeech-pruned_transducer_stateless2-2022-05-12
+          path: egs/gigaspeech/ASR/pruned_transducer_stateless2/exp/

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

@@ -0,0 +1,155 @@
+# Copyright      2021  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-2022-03-12
+# stateless transducer + k2 pruned rnnt-loss
+
+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_03_12:
+    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-18.04]
+        python-version: [3.7, 3.8, 3.9]
+
+      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 }}
+
+      - 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:$PYTHONPATH
+
+          .github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh
+
+      - name: Display decoding results for pruned_transducer_stateless
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR/
+          tree ./pruned_transducer_stateless/exp
+
+          cd pruned_transducer_stateless
+          echo "results for pruned_transducer_stateless"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===modified beam search==="
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for pruned_transducer_stateless
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless-2022-03-12
+          path: egs/librispeech/ASR/pruned_transducer_stateless/exp/

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

@@ -0,0 +1,181 @@
+# Copyright      2021  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-2022-04-29
+# stateless pruned transducer (reworked model) + giga speech
+
+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_04_29:
+    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-18.04]
+        python-version: [3.7, 3.8, 3.9]
+
+      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 }}
+
+      - 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-stateless2-2022-04-29.sh
+
+          .github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh
+
+      - name: Display decoding results for pruned_transducer_stateless2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR
+          tree pruned_transducer_stateless2/exp
+          cd pruned_transducer_stateless2/exp
+          echo "===greedy search==="
+          find greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===modified beam search==="
+          find modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Display decoding results for pruned_transducer_stateless3
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR
+          tree pruned_transducer_stateless3/exp
+          cd pruned_transducer_stateless3/exp
+          echo "===greedy search==="
+          find greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===modified beam search==="
+          find modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for pruned_transducer_stateless2
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless2-2022-04-29
+          path: egs/librispeech/ASR/pruned_transducer_stateless2/exp/
+
+      - name: Upload decoding results for pruned_transducer_stateless3
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless3-2022-04-29
+          path: egs/librispeech/ASR/pruned_transducer_stateless3/exp/

.github/workflows/run-librispeech-2022-05-13.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-2022-05-13
+# stateless transducer + k2 pruned rnnt-loss + deeper model
+
+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_05_13:
+    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-18.04]
+        python-version: [3.7, 3.8, 3.9]
+
+      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 }}
+
+      - 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:$PYTHONPATH
+
+          .github/scripts/run-librispeech-pruned-transducer-stateless5-2022-05-13.sh
+
+      - name: Display decoding results for librispeech pruned_transducer_stateless5
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR/
+          tree ./pruned_transducer_stateless5/exp
+
+          cd pruned_transducer_stateless5
+          echo "results for pruned_transducer_stateless5"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===modified beam search==="
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for librispeech pruned_transducer_stateless5
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless5-2022-05-13
+          path: egs/librispeech/ASR/pruned_transducer_stateless5/exp/

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

@@ -0,0 +1,153 @@
+# Copyright      2021  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-pruned-transducer-stateless3-2022-05-13
+# stateless pruned transducer (reworked model) + giga speech
+
+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_pruned_transducer_stateless3_2022_05_13:
+    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-18.04]
+        python-version: [3.7, 3.8, 3.9]
+
+      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 }}
+
+      - 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:$PYTHONPATH
+
+          .github/scripts/run-librispeech-pruned-transducer-stateless3-2022-05-13.sh
+
+      - name: Display decoding results for pruned_transducer_stateless3
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR
+          tree pruned_transducer_stateless3/exp
+          cd pruned_transducer_stateless3/exp
+          echo "===greedy search==="
+          find greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===modified beam search==="
+          find modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for pruned_transducer_stateless3
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless3-2022-04-29
+          path: egs/librispeech/ASR/pruned_transducer_stateless3/exp/

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

@@ -0,0 +1,155 @@
+# Copyright      2021  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-streaming-2022-06-26
+# streaming conformer stateless transducer2
+
+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_streaming_2022_06_26:
+    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-18.04]
+        python-version: [3.7, 3.8, 3.9]
+
+      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 }}
+
+      - 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:$PYTHONPATH
+
+          .github/scripts/run-librispeech-streaming-pruned-transducer-stateless2-2022-06-26.sh
+
+      - name: Display decoding results
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR/
+          tree ./pruned_transducer_stateless2/exp
+
+          cd pruned_transducer_stateless2
+          echo "results for pruned_transducer_stateless2"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===modified_beam_search==="
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for pruned_transducer_stateless2
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless2-2022-06-26
+          path: egs/librispeech/ASR/pruned_transducer_stateless2/exp/

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

@@ -0,0 +1,155 @@
+# Copyright      2021  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-2022-04-19
+# stateless transducer + torchaudio rnn-t loss
+
+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_04_19:
+    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-18.04]
+        python-version: [3.7, 3.8, 3.9]
+
+      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 }}
+
+      - 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:$PYTHONPATH
+
+          .github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh
+
+      - name: Display decoding results
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR/
+          tree ./transducer_stateless2/exp
+
+          cd transducer_stateless2
+          echo "results for transducer_stateless2"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===modified_beam_search==="
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for transducer_stateless2
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-transducer_stateless2-2022-04-19
+          path: egs/librispeech/ASR/transducer_stateless2/exp/

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

@@ -31,9 +31,6 @@ jobs:
       matrix:
         os: [ubuntu-18.04]
         python-version: [3.7, 3.8, 3.9]
-        torch: ["1.10.0"]
-        torchaudio: ["0.10.0"]
-        k2-version: ["1.9.dev20211101"]
 
       fail-fast: false
 
@@ -43,67 +40,37 @@ jobs:
           fetch-depth: 0
 
       - name: Setup Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v1
+        uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: '**/requirements-ci.txt'
 
       - name: Install Python dependencies
         run: |
-          python3 -m pip install --upgrade pip pytest
+          grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
-          # numpy 1.20.x does not support python 3.6
+          pip uninstall -y protobuf
-          pip install numpy==1.19
+          pip install --no-binary protobuf protobuf
-          pip install torch==${{ matrix.torch }}+cpu torchaudio==${{ matrix.torchaudio }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
-          pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/
 
-          python3 -m pip install git+https://github.com/lhotse-speech/lhotse
+      - name: Cache kaldifeat
-          python3 -m pip install kaldifeat
+        id: my-cache
-          # We are in ./icefall and there is a file: requirements.txt in it
+        uses: actions/cache@v2
-          pip install -r requirements.txt
+        with:
+          path: |
+            ~/tmp/kaldifeat
+          key: cache-tmp-${{ matrix.python-version }}
 
-      - name: Install graphviz
+      - name: Install kaldifeat
+        if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          python3 -m pip install -qq graphviz
+          .github/scripts/install-kaldifeat.sh
-          sudo apt-get -qq install graphviz
 
-      - name: Download pre-trained model
+      - name: Inference with pre-trained model
         shell: bash
         run: |
           sudo apt-get -qq install git-lfs tree sox
-          cd egs/librispeech/ASR
-          mkdir tmp
-          cd tmp
-          git lfs install
-          git clone https://github.com/csukuangfj/icefall-asr-conformer-ctc-bpe-500
-          cd ..
-          tree tmp
-          soxi tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/*.flac
-          ls -lh tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/*.flac
-
-      - name: Run CTC decoding
-        shell: bash
-        run: |
-          export PYTHONPATH=$PWD:PYTHONPATH
-          cd egs/librispeech/ASR
-          ./conformer_ctc/pretrained.py \
-            --num-classes 500 \
-            --checkpoint ./tmp/icefall-asr-conformer-ctc-bpe-500/exp/pretrained.pt \
-            --bpe-model ./tmp/icefall-asr-conformer-ctc-bpe-500/data/lang_bpe_500/bpe.model \
-            --method ctc-decoding \
-            ./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1089-134686-0001.flac \
-            ./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1221-135766-0001.flac \
-            ./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1221-135766-0002.flac
-
-      - name: Run HLG decoding
-        shell: bash
-        run: |
           export PYTHONPATH=$PWD:$PYTHONPATH
-          cd egs/librispeech/ASR
+          export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
-          ./conformer_ctc/pretrained.py \
+          export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
-            --num-classes 500 \
+          .github/scripts/run-pre-trained-conformer-ctc.sh
-            --checkpoint ./tmp/icefall-asr-conformer-ctc-bpe-500/exp/pretrained.pt \
-            --words-file ./tmp/icefall-asr-conformer-ctc-bpe-500/data/lang_bpe_500/words.txt \
-            --HLG ./tmp/icefall-asr-conformer-ctc-bpe-500/data/lang_bpe_500/HLG.pt \
-            ./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1089-134686-0001.flac \
-            ./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1221-135766-0001.flac \
-            ./tmp/icefall-asr-conformer-ctc-bpe-500/test_wavs/1221-135766-0002.flac

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

@@ -0,0 +1,154 @@
+# Copyright      2021  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-pre-trained-trandsucer-stateless-multi-datasets-librispeech-100h
+
+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_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'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-18.04]
+        python-version: [3.7, 3.8, 3.9]
+
+      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 }}
+
+      - 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:$PYTHONPATH
+
+          .github/scripts/run-pre-trained-transducer-stateless-librispeech-100h.sh
+
+      - name: Display decoding results for transducer_stateless_multi_datasets
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR/
+          tree ./transducer_stateless_multi_datasets/exp
+
+          cd transducer_stateless_multi_datasets
+          echo "results for transducer_stateless_multi_datasets"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===modified beam search==="
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for transducer_stateless_multi_datasets
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-transducer_stateless_multi_datasets-100h-2022-02-21
+          path: egs/librispeech/ASR/transducer_stateless_multi_datasets/exp/

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

@@ -0,0 +1,154 @@
+# Copyright      2021  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-pre-trained-trandsucer-stateless-multi-datasets-librispeech-960h
+
+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_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'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-18.04]
+        python-version: [3.7, 3.8, 3.9]
+
+      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 }}
+
+      - 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:$PYTHONPATH
+
+          .github/scripts/run-pre-trained-transducer-stateless-librispeech-960h.sh
+
+      - name: Display decoding results for transducer_stateless_multi_datasets
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR/
+          tree ./transducer_stateless_multi_datasets/exp
+
+          cd transducer_stateless_multi_datasets
+          echo "results for transducer_stateless_multi_datasets"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===modified beam search==="
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for transducer_stateless_multi_datasets
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-transducer_stateless_multi_datasets-100h-2022-03-01
+          path: egs/librispeech/ASR/transducer_stateless_multi_datasets/exp/

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

@@ -0,0 +1,76 @@
+# Copyright      2021  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-pre-trained-trandsucer-stateless-modified-2-aishell
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    types: [labeled]
+
+jobs:
+  run_pre_trained_transducer_stateless_modified_2_aishell:
+    if: github.event.label.name == 'ready' || github.event_name == 'push'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-18.04]
+        python-version: [3.7, 3.8, 3.9]
+
+      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 }}
+
+      - 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
+        run: |
+          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-pre-trained-transducer-stateless-modified-2-aishell.sh

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

@@ -0,0 +1,76 @@
+# Copyright      2021  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-pre-trained-trandsucer-stateless-modified-aishell
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    types: [labeled]
+
+jobs:
+  run_pre_trained_transducer_stateless_modified_aishell:
+    if: github.event.label.name == 'ready' || github.event_name == 'push'
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-18.04]
+        python-version: [3.7, 3.8, 3.9]
+
+      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 }}
+
+      - 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
+        run: |
+          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-pre-trained-transducer-stateless-modified-aishell.sh

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

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-name: run-pre-trained-trandsucer-stateless
+name: run-pre-trained-transducer-stateless
 
 on:
   push:
@@ -23,17 +23,23 @@ on:
   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_pre_trained_transducer_stateless:
-    if: github.event.label.name == 'ready' || github.event_name == 'push'
+    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-18.04]
         python-version: [3.7, 3.8, 3.9]
-        torch: ["1.10.0"]
-        torchaudio: ["0.10.0"]
-        k2-version: ["1.9.dev20211101"]
 
       fail-fast: false
 
@@ -43,66 +49,106 @@ jobs:
           fetch-depth: 0
 
       - name: Setup Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v1
+        uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: '**/requirements-ci.txt'
 
       - name: Install Python dependencies
         run: |
-          python3 -m pip install --upgrade pip pytest
+          grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
-          # numpy 1.20.x does not support python 3.6
+          pip uninstall -y protobuf
-          pip install numpy==1.19
+          pip install --no-binary protobuf protobuf
-          pip install torch==${{ matrix.torch }}+cpu torchaudio==${{ matrix.torchaudio }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
-          pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/
 
-          python3 -m pip install git+https://github.com/lhotse-speech/lhotse
+      - name: Cache kaldifeat
-          python3 -m pip install kaldifeat
+        id: my-cache
-          # We are in ./icefall and there is a file: requirements.txt in it
+        uses: actions/cache@v2
-          pip install -r requirements.txt
+        with:
+          path: |
+            ~/tmp/kaldifeat
+          key: cache-tmp-${{ matrix.python-version }}
 
-      - name: Install graphviz
+      - name: Install kaldifeat
+        if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          python3 -m pip install -qq graphviz
+          .github/scripts/install-kaldifeat.sh
-          sudo apt-get -qq install graphviz
 
-      - name: Download pre-trained model
+      - 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: ${{ github.event.label.name }}
+        run: |
+          mkdir -p egs/librispeech/ASR/data
+          ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank
+          ls -lh egs/librispeech/ASR/data/*
+
           sudo apt-get -qq install git-lfs tree sox
-          cd egs/librispeech/ASR
-          mkdir tmp
-          cd tmp
-          git lfs install
-          git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27
-          cd ..
-          tree tmp
-          soxi tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/*.wav
-          ls -lh tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/*.wav
-
-      - name: Run greedy search decoding
-        shell: bash
-        run: |
-          export PYTHONPATH=$PWD:PYTHONPATH
-          cd egs/librispeech/ASR
-          ./transducer_stateless/pretrained.py \
-            --method greedy_search \
-            --checkpoint ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/exp/pretrained.pt \
-            --bpe-model ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/data/lang_bpe_500/bpe.model \
-            ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/1089-134686-0001.wav \
-            ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/1221-135766-0001.wav \
-            ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/1221-135766-0002.wav
-
-      - name: Run beam search decoding
-        shell: bash
-        run: |
           export PYTHONPATH=$PWD:$PYTHONPATH
-          cd egs/librispeech/ASR
+          export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
-          ./transducer_stateless/pretrained.py \
+          export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
-            --method beam_search \
+
-            --beam-size 4 \
+          .github/scripts/run-pre-trained-transducer-stateless.sh
-            --checkpoint ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/exp/pretrained.pt \
+
-            --bpe-model ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/data/lang_bpe_500/bpe.model \
+      - name: Display decoding results for transducer_stateless
-            ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/1089-134686-0001.wav \
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
-            ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/1221-135766-0001.wav \
+        shell: bash
-            ./tmp/icefall-asr-librispeech-transducer-stateless-bpe-500-2021-12-27/test_wavs/1221-135766-0002.wav
+        run: |
+          cd egs/librispeech/ASR/
+          tree ./transducer_stateless/exp
+
+          cd transducer_stateless
+          echo "results for transducer_stateless"
+          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
+
+      - name: Upload decoding results for transducer_stateless
+        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-transducer_stateless-2022-02-07
+          path: egs/librispeech/ASR/transducer_stateless/exp/

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

@@ -31,9 +31,6 @@ jobs:
       matrix:
         os: [ubuntu-18.04]
         python-version: [3.7, 3.8, 3.9]
-        torch: ["1.10.0"]
-        torchaudio: ["0.10.0"]
-        k2-version: ["1.9.dev20211101"]
 
       fail-fast: false
 
@@ -43,67 +40,37 @@ jobs:
           fetch-depth: 0
 
       - name: Setup Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v1
+        uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: '**/requirements-ci.txt'
 
       - name: Install Python dependencies
         run: |
-          python3 -m pip install --upgrade pip pytest
+          grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
-          # numpy 1.20.x does not support python 3.6
+          pip uninstall -y protobuf
-          pip install numpy==1.19
+          pip install --no-binary protobuf protobuf
-          pip install torch==${{ matrix.torch }}+cpu torchaudio==${{ matrix.torchaudio }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
-          pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/
 
-          python3 -m pip install git+https://github.com/lhotse-speech/lhotse
+      - name: Cache kaldifeat
-          python3 -m pip install kaldifeat
+        id: my-cache
-          # We are in ./icefall and there is a file: requirements.txt in it
+        uses: actions/cache@v2
-          pip install -r requirements.txt
+        with:
+          path: |
+            ~/tmp/kaldifeat
+          key: cache-tmp-${{ matrix.python-version }}
 
-      - name: Install graphviz
+      - name: Install kaldifeat
+        if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          python3 -m pip install -qq graphviz
+          make -j2 _kaldifeat
-          sudo apt-get -qq install graphviz
 
-      - name: Download pre-trained model
+      - name: Inference with pre-trained model
         shell: bash
         run: |
           sudo apt-get -qq install git-lfs tree sox
-          cd egs/librispeech/ASR
-          mkdir tmp
-          cd tmp
-          git lfs install
-          git clone https://huggingface.co/csukuangfj/icefall-asr-librispeech-transducer-bpe-500-2021-12-23
-
-          cd ..
-          tree tmp
-          soxi tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/*.wav
-          ls -lh tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/*.wav
-
-      - name: Run greedy search decoding
-        shell: bash
-        run: |
-          export PYTHONPATH=$PWD:PYTHONPATH
-          cd egs/librispeech/ASR
-          ./transducer/pretrained.py \
-            --method greedy_search \
-            --checkpoint ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/exp/pretrained.pt \
-            --bpe-model ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/data/lang_bpe_500/bpe.model \
-            ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/1089-134686-0001.wav \
-            ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/1221-135766-0001.wav \
-            ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/1221-135766-0002.wav
-
-      - name: Run beam search decoding
-        shell: bash
-        run: |
           export PYTHONPATH=$PWD:$PYTHONPATH
-          cd egs/librispeech/ASR
+          export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
-          ./transducer/pretrained.py \
+          export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
-            --method beam_search \
+          .github/scripts/run-pre-trained-transducer.sh
-            --beam-size 4 \
-            --checkpoint ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/exp/pretrained.pt \
-            --bpe-model ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/data/lang_bpe_500/bpe.model \
-            ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/1089-134686-0001.wav \
-            ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/1221-135766-0001.wav \
-            ./tmp/icefall-asr-librispeech-transducer-bpe-500-2021-12-23/test_wavs/1221-135766-0002.wav

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

@@ -33,9 +33,6 @@ jobs:
         # TODO: enable macOS for CPU testing
         os: [ubuntu-18.04]
         python-version: [3.8]
-        torch: ["1.10.0"]
-        torchaudio: ["0.10.0"]
-        k2-version: ["1.9.dev20211101"]
       fail-fast: false
 
     steps:
@@ -43,10 +40,17 @@ jobs:
         with:
           fetch-depth: 0
 
+      - name: Install graphviz
+        shell: bash
+        run: |
+          sudo apt-get -qq install graphviz
+
       - name: Setup Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v1
+        uses: actions/setup-python@v2
         with:
           python-version: ${{ matrix.python-version }}
+          cache: 'pip'
+          cache-dependency-path: '**/requirements-ci.txt'
 
       - name: Install libnsdfile and libsox
         if: startsWith(matrix.os, 'ubuntu')
@@ -57,13 +61,9 @@ jobs:
 
       - name: Install Python dependencies
         run: |
-          python3 -m pip install -U pip
+          grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
-          pip install torch==${{ matrix.torch }}+cpu torchaudio==${{ matrix.torchaudio }}+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
+          pip uninstall -y protobuf
-          pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/
+          pip install --no-binary protobuf protobuf
-          python3 -m pip install git+https://github.com/lhotse-speech/lhotse
-
-          # We are in ./icefall and there is a file: requirements.txt in it
-          python3 -m pip install -r requirements.txt
 
       - name: Run yesno recipe
         shell: bash

.github/workflows/style_check.yml

@@ -29,7 +29,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-18.04, macos-10.15]
+        os: [ubuntu-18.04, macos-latest]
         python-version: [3.7, 3.9]
       fail-fast: false
 
@@ -45,7 +45,9 @@ jobs:
 
       - name: Install Python dependencies
         run: |
-          python3 -m pip install --upgrade pip black==21.6b0 flake8==3.9.2
+          python3 -m pip install --upgrade pip black==21.6b0 flake8==3.9.2 click==8.0.4
+          # See https://github.com/psf/black/issues/2964
+          # The version of click should be selected from 8.0.0, 8.0.1, 8.0.2, 8.0.3, and 8.0.4
 
       - name: Run flake8
         shell: bash

.github/workflows/test.yml

@@ -33,13 +33,13 @@ jobs:
         # disable macOS test for now.
         os: [ubuntu-18.04]
         python-version: [3.7, 3.8]
-        torch: ["1.8.0", "1.10.0"]
+        torch: ["1.8.0", "1.11.0"]
-        torchaudio: ["0.8.0", "0.10.0"]
+        torchaudio: ["0.8.0", "0.11.0"]
-        k2-version: ["1.9.dev20211101"]
+        k2-version: ["1.15.1.dev20220427"]
         exclude:
           - torch: "1.8.0"
-            torchaudio: "0.10.0"
+            torchaudio: "0.11.0"
-          - torch: "1.10.0"
+          - torch: "1.11.0"
             torchaudio: "0.8.0"
 
       fail-fast: false
@@ -67,7 +67,7 @@ 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 }} == "0.10.0" ]]; then
+          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==${{ matrix.torchaudio }}
@@ -76,6 +76,9 @@ jobs:
           pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/
           pip install git+https://github.com/lhotse-speech/lhotse
           # icefall requirements
+          pip uninstall -y protobuf
+          pip install --no-binary protobuf protobuf
+
           pip install -r requirements.txt
 
       - name: Install graphviz
@@ -103,11 +106,26 @@ jobs:
           cd egs/librispeech/ASR/conformer_ctc
           pytest -v -s
 
+          cd ../pruned_transducer_stateless
+          pytest -v -s
+
+          cd ../pruned_transducer_stateless2
+          pytest -v -s
+
+          cd ../pruned_transducer_stateless3
+          pytest -v -s
+
+          cd ../pruned_transducer_stateless4
+          pytest -v -s
+
+          cd ../transducer_stateless
+          pytest -v -s
+
           if [[ ${{ matrix.torchaudio }} == "0.10.0" ]]; then
             cd ../transducer
             pytest -v -s
 
-            cd ../transducer_stateless
+            cd ../transducer_stateless2
             pytest -v -s
 
             cd ../transducer_lstm
@@ -128,13 +146,28 @@ jobs:
           cd egs/librispeech/ASR/conformer_ctc
           pytest -v -s
 
-          if [[ ${{ matrix.torchaudio }} == "0.10.0" ]]; then
+          cd ../pruned_transducer_stateless
-            cd ../transducer
+          pytest -v -s
+
+          cd ../pruned_transducer_stateless2
+          pytest -v -s
+
+          cd ../pruned_transducer_stateless3
+          pytest -v -s
+
+          cd ../pruned_transducer_stateless4
           pytest -v -s
 
           cd ../transducer_stateless
           pytest -v -s
 
+          if [[ ${{ matrix.torchaudio }} == "0.10.0" ]]; then
+            cd ../transducer
+            pytest -v -s
+
+            cd ../transducer_stateless2
+            pytest -v -s
+
             cd ../transducer_lstm
             pytest -v -s
           fi

.gitignore

@@ -6,6 +6,8 @@ exp
 exp*/
 *.pt
 download
+dask-worker-space
+log
 *.bak
 *-bak
 *bak.py

.pre-commit-config.yaml

@@ -4,6 +4,8 @@ repos:
     hooks:
       - id: black
         args: [--line-length=80]
+        additional_dependencies: ['click==8.0.1']
+        exclude: icefall\/__init__\.py
 
   - repo: https://github.com/PyCQA/flake8
     rev: 3.9.2

README.md

@@ -2,6 +2,18 @@
 <img src="https://raw.githubusercontent.com/k2-fsa/icefall/master/docs/source/_static/logo.png" width=168>
 </div>
 
+## Introduction
+
+icefall contains ASR recipes for various datasets
+using <https://github.com/k2-fsa/k2>.
+
+You can use <https://github.com/k2-fsa/sherpa> to deploy models
+trained with icefall.
+
+You can try pre-trained models from within your browser without the need
+to download or install anything by visiting <https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>
+See <https://k2-fsa.github.io/icefall/huggingface/spaces.html> for more details.
+
 ## Installation
 
 Please refer to <https://icefall.readthedocs.io/en/latest/installation/index.html>
@@ -12,12 +24,19 @@ for installation.
 Please refer to <https://icefall.readthedocs.io/en/latest/recipes/index.html>
 for more information.
 
-We provide four recipes at present:
+We provide the following recipes:
 
   - [yesno][yesno]
   - [LibriSpeech][librispeech]
   - [Aishell][aishell]
   - [TIMIT][timit]
+  - [TED-LIUM3][tedlium3]
+  - [GigaSpeech][gigaspeech]
+  - [Aidatatang_200zh][aidatatang_200zh]
+  - [WenetSpeech][wenetspeech]
+  - [Alimeeting][alimeeting]
+  - [Aishell4][aishell4]
+  - [TAL_CSASR][tal_csasr]
 
 ### yesno
 
@@ -34,6 +53,9 @@ We do provide a Colab notebook for this recipe.
 
 ### LibriSpeech
 
+Please see <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md>
+for the **latest** results.
+
 We provide 4 models for this recipe:
 
 - [conformer CTC model][LibriSpeech_conformer_ctc]
@@ -80,16 +102,30 @@ We provide a Colab notebook to run a pre-trained RNN-T conformer model: [![Open
 Using Conformer as encoder. The decoder consists of 1 embedding layer
 and 1 convolutional layer.
 
-The best WER using beam search with beam size 4 is:
+The best WER using modified beam search with beam size 4 is:
 
 |     | test-clean | test-other |
 |-----|------------|------------|
-| WER | 2.83       | 7.19       |
+| WER | 2.56       | 6.27       |
 
 Note: No auxiliary losses are used in the training and no LMs are used
 in the decoding.
 
-We provide a Colab notebook to run a pre-trained transducer conformer + stateless decoder model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Lm37sNajIpkV4HTzMDF7sn9l0JpfmekN?usp=sharing)
+We provide a Colab notebook to run a pre-trained transducer conformer + stateless decoder model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CO1bXJ-2khDckZIW8zjOPHGSKLHpTDlp?usp=sharing)
+
+
+#### k2 pruned RNN-T
+
+|     | test-clean | test-other |
+|-----|------------|------------|
+| WER | 2.57       | 5.95       |
+
+#### k2 pruned RNN-T + GigaSpeech
+
+|     | test-clean | test-other |
+|-----|------------|------------|
+| WER | 2.00       | 4.63       |
+
 
 ### Aishell
 
@@ -105,7 +141,7 @@ The best CER we currently have is:
 | CER | 4.26 |
 
 
-We provide a Colab notebook to run a pre-trained conformer CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1WnG17io5HEZ0Gn_cnh_VzK5QYOoiiklC?usp=sharing)
+We provide a Colab notebook to run a pre-trained conformer CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg](https://colab.research.google.com/drive/1WnG17io5HEZ0Gn_cnh_VzK5QYOoiiklC?usp=sharing)
 
 #### Transducer Stateless Model
 
@@ -113,7 +149,7 @@ The best CER we currently have is:
 
 |     | test |
 |-----|------|
-| CER | 5.7 |
+| CER | 4.68 |
 
 
 We provide a Colab notebook to run a pre-trained TransducerStateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/14XaT2MhnBkK-3_RqqWq3K90Xlbin-GZC?usp=sharing)
@@ -153,6 +189,130 @@ The PER for this model is:
 
 We provide a Colab notebook to run a pre-trained TDNN LiGRU CTC model:  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/11IT-k4HQIgQngXz1uvWsEYktjqQt7Tmb?usp=sharing)
 
+### TED-LIUM3
+
+We provide two models for this recipe: [Transducer Stateless: Conformer encoder + Embedding decoder][TED-LIUM3_transducer_stateless] and [Pruned Transducer Stateless: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][TED-LIUM3_pruned_transducer_stateless].
+
+#### Transducer Stateless:  Conformer encoder + Embedding decoder
+
+The best WER using modified beam search with beam size 4 is:
+
+|     |  dev  |  test  |
+|-----|-------|--------|
+| WER |  6.91 |  6.33  |
+
+Note: No auxiliary losses are used in the training and no LMs are used in the decoding.
+
+We provide a Colab notebook to run a pre-trained Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1MmY5bBxwvKLNT4A2DJnwiqRXhdchUqPN?usp=sharing)
+
+#### Pruned Transducer Stateless: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
+
+The best WER using modified beam search with beam size 4 is:
+
+|     |  dev  |  test  |
+|-----|-------|--------|
+| WER |  6.77 |  6.14  |
+
+We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1je_1zGrOkGVVd4WLzgkXRHxl-I27yWtz?usp=sharing)
+
+### GigaSpeech
+
+We provide two models for this recipe: [Conformer CTC model][GigaSpeech_conformer_ctc]
+and [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][GigaSpeech_pruned_transducer_stateless2].
+
+#### Conformer CTC
+
+|     |  Dev  | Test  |
+|-----|-------|-------|
+| WER | 10.47 | 10.58 |
+
+#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
+
+|                      |  Dev  | Test  |
+|----------------------|-------|-------|
+|    greedy search     | 10.51 | 10.73 |
+|   fast beam search   | 10.50 | 10.69 |
+| modified beam search | 10.40 | 10.51 |
+
+### Aidatatang_200zh
+
+We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Aidatatang_200zh_pruned_transducer_stateless2].
+
+#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
+
+|                      |  Dev  | Test  |
+|----------------------|-------|-------|
+|    greedy search     | 5.53  | 6.59  |
+|   fast beam search   | 5.30  | 6.34  |
+| modified beam search | 5.27  | 6.33  |
+
+We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1wNSnSj3T5oOctbh5IGCa393gKOoQw2GH?usp=sharing)
+
+### WenetSpeech
+
+We provide some models for this recipe: [Pruned stateless RNN-T_2: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][WenetSpeech_pruned_transducer_stateless2] and [Pruned stateless RNN-T_5: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][WenetSpeech_pruned_transducer_stateless5].
+
+#### Pruned stateless RNN-T_2: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with L subset, offline ASR)
+
+|                      |  Dev  | Test-Net | Test-Meeting |
+|----------------------|-------|----------|--------------|
+|    greedy search     | 7.80  |  8.75    |  13.49       |
+|   fast beam search   | 7.94  |  8.74    |  13.80       |
+| modified beam search | 7.76  |  8.71    |  13.41       |
+
+#### Pruned stateless RNN-T_5: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with L subset)
+**Streaming**:
+|                      |  Dev  | Test-Net | Test-Meeting |
+|----------------------|-------|----------|--------------|
+| greedy_search | 8.78 | 10.12 | 16.16 |
+| modified_beam_search | 8.53| 9.95 | 15.81 |
+| fast_beam_search| 9.01 | 10.47 | 16.28 |
+
+We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless2 model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EV4e1CHa1GZgEF-bZgizqI9RyFFehIiN?usp=sharing)
+
+### Alimeeting
+
+We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Alimeeting_pruned_transducer_stateless2].
+
+#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with far subset)
+
+|                      |  Eval  | Test-Net |
+|----------------------|--------|----------|
+|    greedy search     | 31.77  |  34.66   |
+|   fast beam search   | 31.39  |  33.02   |
+| modified beam search | 30.38  |  34.25   |
+
+We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tKr3f0mL17uO_ljdHGKtR7HOmthYHwJG?usp=sharing)
+
+### Aishell4
+
+We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][Aishell4_pruned_transducer_stateless5].
+
+#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss (trained with all subsets)
+
+The best CER(%) results:
+|                      |  test  |
+|----------------------|--------|
+|    greedy search     | 29.89  |
+|   fast beam search   | 28.91  |
+| modified beam search | 29.08  |
+
+We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1z3lkURVv9M7uTiIgf3Np9IntMHEknaks?usp=sharing)
+
+### TAL_CSASR
+
+We provide one model for this recipe: [Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss][TAL_CSASR_pruned_transducer_stateless5].
+
+#### Pruned stateless RNN-T: Conformer encoder + Embedding decoder + k2 pruned RNN-T loss
+
+The best results for Chinese CER(%) and English WER(%) respectivly (zh: Chinese, en: English):
+|decoding-method | dev | dev_zh | dev_en | test | test_zh | test_en |
+|--|--|--|--|--|--|--|
+|greedy_search| 7.30 | 6.48 | 19.19 |7.39| 6.66 | 19.13|
+|modified_beam_search| 7.15 | 6.35 | 18.95 | 7.22| 6.50 | 18.70 |
+|fast_beam_search| 7.18 | 6.39| 18.90 |  7.27| 6.55 | 18.77|
+
+We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DmIx-NloI1CMU5GdZrlse7TRu4y3Dpf8?usp=sharing)
 
 ## Deployment with C++
 
@@ -175,8 +335,25 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad
 [Aishell_conformer_ctc]: egs/aishell/ASR/conformer_ctc
 [TIMIT_tdnn_lstm_ctc]: egs/timit/ASR/tdnn_lstm_ctc
 [TIMIT_tdnn_ligru_ctc]: egs/timit/ASR/tdnn_ligru_ctc
+[TED-LIUM3_transducer_stateless]: egs/tedlium3/ASR/transducer_stateless
+[TED-LIUM3_pruned_transducer_stateless]: egs/tedlium3/ASR/pruned_transducer_stateless
+[GigaSpeech_conformer_ctc]: egs/gigaspeech/ASR/conformer_ctc
+[GigaSpeech_pruned_transducer_stateless2]: egs/gigaspeech/ASR/pruned_transducer_stateless2
+[Aidatatang_200zh_pruned_transducer_stateless2]: egs/aidatatang_200zh/ASR/pruned_transducer_stateless2
+[WenetSpeech_pruned_transducer_stateless2]: egs/wenetspeech/ASR/pruned_transducer_stateless2
+[WenetSpeech_pruned_transducer_stateless5]: egs/wenetspeech/ASR/pruned_transducer_stateless5
+[Alimeeting_pruned_transducer_stateless2]: egs/alimeeting/ASR/pruned_transducer_stateless2
+[Aishell4_pruned_transducer_stateless5]: egs/aishell4/ASR/pruned_transducer_stateless5
+[TAL_CSASR_pruned_transducer_stateless5]: egs/tal_csasr/ASR/pruned_transducer_stateless5
 [yesno]: egs/yesno/ASR
 [librispeech]: egs/librispeech/ASR
 [aishell]: egs/aishell/ASR
 [timit]: egs/timit/ASR
+[tedlium3]: egs/tedlium3/ASR
+[gigaspeech]: egs/gigaspeech/ASR
+[aidatatang_200zh]: egs/aidatatang_200zh/ASR
+[wenetspeech]: egs/wenetspeech/ASR
+[alimeeting]: egs/alimeeting/ASR
+[aishell4]: egs/aishell4/ASR
+[tal_csasr]: egs/tal_csasr/ASR
 [k2]: https://github.com/k2-fsa/k2

docs/requirements.txt

@@ -1,2 +1,3 @@
 sphinx_rtd_theme
 sphinx
+sphinxcontrib-youtube==1.1.0

docs/source/conf.py

@@ -32,7 +32,9 @@ release = "0.1"
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
+    "sphinx.ext.todo",
     "sphinx_rtd_theme",
+    "sphinxcontrib.youtube",
 ]
 
 # Add any paths that contain templates here, relative to this directory.
@@ -74,3 +76,5 @@ html_context = {
     "github_version": "master",
     "conf_py_path": "/icefall/docs/source/",
 }
+
+todo_include_todos = True

docs/source/huggingface/index.rst

@@ -0,0 +1,13 @@
+Huggingface
+===========
+
+This section describes how to find pre-trained models.
+It also demonstrates how to try them from within your browser
+without installing anything by using
+`Huggingface spaces <https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>`_.
+
+.. toctree::
+   :maxdepth: 2
+
+   pretrained-models
+   spaces

docs/source/huggingface/pretrained-models.rst

@@ -0,0 +1,17 @@
+Pre-trained models
+==================
+
+We have uploaded pre-trained models for all recipes in ``icefall``
+to `<https://huggingface.co/>`_.
+
+You can find them by visiting the following link:
+
+`<https://huggingface.co/models?search=icefall>`_.
+
+You can also find links of pre-trained models for a specific recipe
+by looking at the corresponding ``RESULTS.md``. For instance:
+
+  - `<https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md>`_
+  - `<https://github.com/k2-fsa/icefall/blob/master/egs/aishell/ASR/RESULTS.md>`_
+  - `<https://github.com/k2-fsa/icefall/blob/master/egs/gigaspeech/ASR/RESULTS.md>`_
+  - `<https://github.com/k2-fsa/icefall/blob/master/egs/wenetspeech/ASR/RESULTS.md>`_

docs/source/huggingface/spaces.rst

@@ -0,0 +1,65 @@
+Huggingface spaces
+==================
+
+We have integrated the server framework
+`sherpa <http://github.com/k2-fsa/sherpa>`_
+with `Huggingface spaces <https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>`_
+so that you can try pre-trained models from within your browser
+without the need to download or install anything.
+
+All you need is a browser, which can be run on Windows, macOS, Linux, or even on your
+iPad and your phone.
+
+Start your browser and visit the following address:
+
+`<https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>`_
+
+and you will see a page like the following screenshot:
+
+.. image:: ./pic/hugging-face-sherpa.png
+   :alt: screenshot of `<https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>`_
+   :target: https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition
+
+You can:
+
+  1. Select a language for recognition. Currently, we provide pre-trained models
+     from ``icefall`` for the following languages: ``Chinese``, ``English``, and
+     ``Chinese+English``.
+  2. After selecting the target language, you can select a pre-trained model
+     corresponding to the language.
+  3. Select the decoding method. Currently, it provides ``greedy search``
+     and ``modified_beam_search``.
+  4. If you selected ``modified_beam_search``, you can choose the number of
+     active paths during the search.
+  5. Either upload a file or record your speech for recognition.
+  6. Click the button ``Submit for recognition``.
+  7. Wait for a moment and you will get the recognition results.
+
+The following screenshot shows an example when selecting ``Chinese+English``:
+
+.. image:: ./pic/hugging-face-sherpa-3.png
+   :alt: screenshot of `<https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>`_
+   :target: https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition
+
+
+In the bottom part of the page, you can find a table of examples. You can click
+one of them and then click ``Submit for recognition``.
+
+.. image:: ./pic/hugging-face-sherpa-2.png
+   :alt: screenshot of `<https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>`_
+   :target: https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition
+
+YouTube Video
+-------------
+
+We provide the following YouTube video demonstrating how to use
+`<https://huggingface.co/spaces/k2-fsa/automatic-speech-recognition>`_.
+
+.. note::
+
+   To get the latest news of `next-gen Kaldi <https://github.com/k2-fsa>`_, please subscribe
+   the following YouTube channel by `Nadira Povey <https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_:
+
+      `<https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_
+
+..  youtube:: ElN3r9dkKE4

docs/source/index.rst

@@ -23,3 +23,4 @@ speech recognition recipes using `k2 <https://github.com/k2-fsa/k2>`_.
    installation/index
    recipes/index
    contributing/index
+   huggingface/index

docs/source/installation/images/README.md

@@ -0,0 +1,4 @@
+
+# Introduction
+
+<https://shields.io/> is used to generate files in this directory.

docs/source/installation/images/k2-gt-v1.9-blueviolet.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="80" height="20" role="img" aria-label="k2: &gt;= v1.9"><title>k2: &gt;= v1.9</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="80" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="23" height="20" fill="#555"/><rect x="23" width="57" height="20" fill="blueviolet"/><rect width="80" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="125" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="130">k2</text><text x="125" y="140" transform="scale(.1)" fill="#fff" textLength="130">k2</text><text aria-hidden="true" x="505" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="470">&gt;= v1.9</text><text x="505" y="140" transform="scale(.1)" fill="#fff" textLength="470">&gt;= v1.9</text></g></svg>

docs/source/installation/images/k2-v1.9-blueviolet.svg

@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="58" height="20" role="img" aria-label="k2: v1.9"><title>k2: v1.9</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="58" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="23" height="20" fill="#555"/><rect x="23" width="35" height="20" fill="blueviolet"/><rect width="58" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="125" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="130">k2</text><text x="125" y="140" transform="scale(.1)" fill="#fff" textLength="130">k2</text><text aria-hidden="true" x="395" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="250">v1.9</text><text x="395" y="140" transform="scale(.1)" fill="#fff" textLength="250">v1.9</text></g></svg>

docs/source/installation/images/python-3.6_3.7_3.8_3.9-blue.svg

@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="170" height="20" role="img" aria-label="python: 3.6 | 3.7 | 3.8 | 3.9"><title>python: 3.6 | 3.7 | 3.8 | 3.9</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="170" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="49" height="20" fill="#555"/><rect x="49" width="121" height="20" fill="#007ec6"/><rect width="170" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="255" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="390">python</text><text x="255" y="140" transform="scale(.1)" fill="#fff" textLength="390">python</text><text aria-hidden="true" x="1085" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="1110">3.6 | 3.7 | 3.8 | 3.9</text><text x="1085" y="140" transform="scale(.1)" fill="#fff" textLength="1110">3.6 | 3.7 | 3.8 | 3.9</text></g></svg>

docs/source/installation/images/python-gt-v3.6-blue.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="98" height="20" role="img" aria-label="python: &gt;= 3.6"><title>python: &gt;= 3.6</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="98" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="49" height="20" fill="#555"/><rect x="49" width="49" height="20" fill="#007ec6"/><rect width="98" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="255" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="390">python</text><text x="255" y="140" transform="scale(.1)" fill="#fff" textLength="390">python</text><text aria-hidden="true" x="725" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="390">&gt;= 3.6</text><text x="725" y="140" transform="scale(.1)" fill="#fff" textLength="390">&gt;= 3.6</text></g></svg>

docs/source/installation/images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg

@@ -1 +0,0 @@
-<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="286" height="20" role="img" aria-label="torch: 1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0"><title>torch: 1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="286" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="39" height="20" fill="#555"/><rect x="39" width="247" height="20" fill="#97ca00"/><rect width="286" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="205" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="290">torch</text><text x="205" y="140" transform="scale(.1)" fill="#fff" textLength="290">torch</text><text aria-hidden="true" x="1615" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="2370">1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</text><text x="1615" y="140" transform="scale(.1)" fill="#fff" textLength="2370">1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</text></g></svg>

docs/source/installation/images/torch-gt-v1.6.0-green.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="100" height="20" role="img" aria-label="torch: &gt;= 1.6.0"><title>torch: &gt;= 1.6.0</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="100" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="39" height="20" fill="#555"/><rect x="39" width="61" height="20" fill="#97ca00"/><rect width="100" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="205" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="290">torch</text><text x="205" y="140" transform="scale(.1)" fill="#fff" textLength="290">torch</text><text aria-hidden="true" x="685" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="510">&gt;= 1.6.0</text><text x="685" y="140" transform="scale(.1)" fill="#fff" textLength="510">&gt;= 1.6.0</text></g></svg>

docs/source/installation/index.rst

@@ -15,21 +15,33 @@ Installation
 .. |device| image:: ./images/device-CPU_CUDA-orange.svg
   :alt: Supported devices
 
-.. |python_versions| image:: ./images/python-3.6_3.7_3.8_3.9-blue.svg
+.. |python_versions| image:: ./images/python-gt-v3.6-blue.svg
   :alt: Supported python versions
 
-.. |torch_versions| image:: ./images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg
+.. |torch_versions| image:: ./images/torch-gt-v1.6.0-green.svg
   :alt: Supported PyTorch versions
 
-.. |k2_versions| image:: ./images/k2-v1.9-blueviolet.svg
+.. |k2_versions| image:: ./images/k2-gt-v1.9-blueviolet.svg
   :alt: Supported k2 versions
 
 ``icefall`` depends on `k2 <https://github.com/k2-fsa/k2>`_ and
 `lhotse <https://github.com/lhotse-speech/lhotse>`_.
 
-We recommend you to install ``k2`` first, as ``k2`` is bound to
+We recommend you to use the following steps to install the dependencies.
-a specific version of PyTorch after compilation. Install ``k2`` also
+
-installs its dependency PyTorch, which can be reused by ``lhotse``.
+- (0) Install PyTorch and torchaudio
+- (1) Install k2
+- (2) Install lhotse
+
+.. caution::
+
+  Installation order matters.
+
+(0) Install PyTorch and torchaudio
+----------------------------------
+
+Please refer `<https://pytorch.org/>`_ to install PyTorch
+and torchaudio.
 
 
 (1) Install k2
@@ -54,14 +66,15 @@ to install ``k2``.
 Please refer to `<https://lhotse.readthedocs.io/en/latest/getting-started.html#installation>`_
 to install ``lhotse``.
 
-.. HINT::
 
-  Install ``lhotse`` also installs its dependency `torchaudio <https://github.com/pytorch/audio>`_.
+.. hint::
 
-.. CAUTION::
+    We strongly recommend you to use::
+
+      pip install git+https://github.com/lhotse-speech/lhotse
+
+    to install the latest version of lhotse.
 
-  If you have installed ``torchaudio``, please consider uninstalling it before
-  installing ``lhotse``. Otherwise, it may update your already installed PyTorch.
 
 (3) Download icefall
 --------------------
@@ -461,3 +474,19 @@ The decoding log is:
 **Congratulations!** You have successfully setup the environment and have run the first recipe in ``icefall``.
 
 Have fun with ``icefall``!
+
+YouTube Video
+-------------
+
+We provide the following YouTube video showing how to install ``icefall``.
+It also shows how to debug various problems that you may encounter while
+using ``icefall``.
+
+.. note::
+
+   To get the latest news of `next-gen Kaldi <https://github.com/k2-fsa>`_, please subscribe
+   the following YouTube channel by `Nadira Povey <https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_:
+
+      `<https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_
+
+..  youtube:: LVmrBD0tLfE

docs/source/recipes/aishell.rst

@@ -1,10 +0,0 @@
-Aishell
-=======
-
-We provide the following models for the Aishell dataset:
-
-.. toctree::
-   :maxdepth: 2
-
-   aishell/conformer_ctc
-   aishell/tdnn_lstm_ctc

docs/source/recipes/aishell/conformer_ctc.rst

@@ -1,4 +1,4 @@
-Confromer CTC
+Conformer CTC
 =============
 
 This tutorial shows you how to run a conformer ctc model

docs/source/recipes/aishell/index.rst

@@ -0,0 +1,22 @@
+aishell
+=======
+
+Aishell is an open-source Chinese Mandarin speech corpus published by Beijing
+Shell Shell Technology Co.,Ltd.
+
+400 people from different accent areas in China are invited to participate in
+the recording, which is conducted in a quiet indoor environment using high
+fidelity microphone and downsampled to 16kHz. The manual transcription accuracy
+is above 95%, through professional speech annotation and strict quality
+inspection. The data is free for academic use. We hope to provide moderate
+amount of data for new researchers in the field of speech recognition.
+
+It can be downloaded from `<https://www.openslr.org/33/>`_
+
+.. toctree::
+   :maxdepth: 1
+
+   tdnn_lstm_ctc
+   conformer_ctc
+   stateless_transducer
+

docs/source/recipes/aishell/stateless_transducer.rst

@@ -0,0 +1,714 @@
+Stateless Transducer
+====================
+
+This tutorial shows you how to do transducer training in ``icefall``.
+
+.. HINT::
+
+  Instead of using RNN-T or RNN transducer, we only use transducer
+  here. As you will see, there are no RNNs in the model.
+
+.. HINT::
+
+  We assume you have read the page :ref:`install icefall` and have setup
+  the environment for ``icefall``.
+
+.. HINT::
+
+  We recommend you to use a GPU or several GPUs to run this recipe.
+
+In this tutorial, you will learn:
+
+  - (1) What does the transducer model look like
+  - (2) How to prepare data for training and decoding
+  - (3) How to start the training, either with a single GPU or with multiple GPUs
+  - (4) How to do decoding after training, with greedy search, beam search and, **modified beam search**
+  - (5) How to use a pre-trained model provided by us to transcribe sound files
+
+
+The Model
+---------
+
+The transducer model consists of 3 parts:
+
+- **Encoder**: It is a conformer encoder with the following parameters
+
+    - Number of heads: 8
+    - Attention dim: 512
+    - Number of layers: 12
+    - Feedforward dim: 2048
+
+- **Decoder**: We use a stateless model consisting of:
+
+    - An embedding layer with embedding dim 512
+    - A Conv1d layer with a default kernel size 2 (i.e. it sees 2
+      symbols of left-context by default)
+
+- **Joiner**: It consists of a ``nn.tanh()`` and a ``nn.Linear()``.
+
+.. Caution::
+
+  The decoder is stateless and very simple. It is borrowed from
+  `<https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419>`_
+  (Rnn-Transducer with Stateless Prediction Network)
+
+  We make one modification to it: Place a Conv1d layer right after
+  the embedding layer.
+
+When using Chinese characters as modelling unit, whose vocabulary size
+is 4336 in this specific dataset,
+the number of parameters of the model is ``87939824``, i.e., about ``88 M``.
+
+The Loss
+--------
+
+We are using `<https://github.com/csukuangfj/optimized_transducer>`_
+to compute the transducer loss, which removes extra paddings
+in loss computation to save memory.
+
+.. Hint::
+
+  ``optimized_transducer`` implements the technqiues proposed
+  in `Improving RNN Transducer Modeling for End-to-End Speech Recognition <https://arxiv.org/abs/1909.12415>`_ to save memory.
+
+  Furthermore, it supports ``modified transducer``, limiting the maximum
+  number of symbols that can be emitted per frame to 1, which simplifies
+  the decoding process significantly. Also, the experiment results
+  show that it does not degrade the performance.
+
+  See `<https://github.com/csukuangfj/optimized_transducer#modified-transducer>`_
+  for what exactly modified transducer is.
+
+  `<https://github.com/csukuangfj/transducer-loss-benchmarking>`_ shows that
+  in the unpruned case ``optimized_transducer`` has the advantage about minimizing
+  memory usage.
+
+.. todo::
+
+  Add tutorial about ``pruned_transducer_stateless`` that uses k2
+  pruned transducer loss.
+
+.. hint::
+
+  You can use::
+
+    pip install optimized_transducer
+
+  to install ``optimized_transducer``. Refer to
+  `<https://github.com/csukuangfj/optimized_transducer>`_ for other
+  alternatives.
+
+Data Preparation
+----------------
+
+To prepare the data for training, please use the following commands:
+
+.. code-block:: bash
+
+  cd egs/aishell/ASR
+  ./prepare.sh --stop-stage 4
+  ./prepare.sh --stage 6 --stop-stage 6
+
+.. note::
+
+  You can use ``./prepare.sh``, though it will generate FSTs that
+  are not used in transducer training.
+
+When you finish running the script, you will get the following two folders:
+
+  - ``data/fbank``: It saves the pre-computed features
+  - ``data/lang_char``: It contains tokens that will be used in the training
+
+Training
+--------
+
+.. code-block:: bash
+
+  cd egs/aishell/ASR
+  ./transducer_stateless_modified/train.py --help
+
+shows you the training options that can be passed from the commandline.
+The following options are used quite often:
+
+  - ``--exp-dir``
+
+    The experiment folder to save logs and model checkpoints,
+    defaults to ``./transducer_stateless_modified/exp``.
+
+  - ``--num-epochs``
+
+    It is the number of epochs to train. For instance,
+    ``./transducer_stateless_modified/train.py --num-epochs 30`` trains for 30
+    epochs and generates ``epoch-0.pt``, ``epoch-1.pt``, ..., ``epoch-29.pt``
+    in the folder set by ``--exp-dir``.
+
+  - ``--start-epoch``
+
+    It's used to resume training.
+    ``./transducer_stateless_modified/train.py --start-epoch 10`` loads the
+    checkpoint from ``exp_dir/epoch-9.pt`` and starts
+    training from epoch 10, based on the state from epoch 9.
+
+  - ``--world-size``
+
+    It is used for single-machine multi-GPU DDP training.
+
+      - (a) If it is 1, then no DDP training is used.
+
+      - (b) If it is 2, then GPU 0 and GPU 1 are used for DDP training.
+
+    The following shows some use cases with it.
+
+      **Use case 1**: You have 4 GPUs, but you only want to use GPU 0 and
+      GPU 2 for training. You can do the following:
+
+        .. code-block:: bash
+
+          $ cd egs/aishell/ASR
+          $ export CUDA_VISIBLE_DEVICES="0,2"
+          $ ./transducer_stateless_modified/train.py --world-size 2
+
+      **Use case 2**: You have 4 GPUs and you want to use all of them
+      for training. You can do the following:
+
+        .. code-block:: bash
+
+          $ cd egs/aishell/ASR
+          $ ./transducer_stateless_modified/train.py --world-size 4
+
+      **Use case 3**: You have 4 GPUs but you only want to use GPU 3
+      for training. You can do the following:
+
+        .. code-block:: bash
+
+          $ cd egs/aishell/ASR
+          $ export CUDA_VISIBLE_DEVICES="3"
+          $ ./transducer_stateless_modified/train.py --world-size 1
+
+    .. CAUTION::
+
+      Only single-machine multi-GPU DDP training is implemented at present.
+      There is an on-going PR `<https://github.com/k2-fsa/icefall/pull/63>`_
+      that adds support for multi-machine multi-GPU DDP training.
+
+  - ``--max-duration``
+
+    It specifies the number of seconds over all utterances in a
+    batch **before padding**.
+    If you encounter CUDA OOM, please reduce it. For instance, if
+    your are using V100 NVIDIA GPU with 32 GB RAM, we recommend you
+    to set it to ``300`` when the vocabulary size is 500.
+
+    .. HINT::
+
+      Due to padding, the number of seconds of all utterances in a
+      batch will usually be larger than ``--max-duration``.
+
+      A larger value for ``--max-duration`` may cause OOM during training,
+      while a smaller value may increase the training time. You have to
+      tune it.
+
+  - ``--lr-factor``
+
+    It controls the learning rate. If you use a single GPU for training, you
+    may want to use a small value for it. If you use multiple GPUs for training,
+    you may increase it.
+
+  - ``--context-size``
+
+    It specifies the kernel size in the decoder. The default value 2 means it
+    functions as a tri-gram LM.
+
+  - ``--modified-transducer-prob``
+
+    It specifies the probability to use modified transducer loss.
+    If it is 0, then no modified transducer is used; if it is 1,
+    then it uses modified transducer loss for all batches. If it is
+    ``p``, it applies modified transducer with probability ``p``.
+
+There are some training options, e.g.,
+number of warmup steps,
+that are not passed from the commandline.
+They are pre-configured by the function ``get_params()`` in
+`transducer_stateless_modified/train.py <https://github.com/k2-fsa/icefall/blob/master/egs/aishell/ASR/transducer_stateless_modified/train.py#L162>`_
+
+If you need to change them, please modify ``./transducer_stateless_modified/train.py`` directly.
+
+.. CAUTION::
+
+  The training set is perturbed by speed with two factors: 0.9 and 1.1.
+  Each epoch actually processes ``3x150 == 450`` hours of data.
+
+Training logs
+~~~~~~~~~~~~~
+
+Training logs and checkpoints are saved in the folder set by ``--exp-dir``
+(defaults to ``transducer_stateless_modified/exp``). You will find the following files in that directory:
+
+  - ``epoch-0.pt``, ``epoch-1.pt``, ...
+
+    These are checkpoint files, containing model ``state_dict`` and optimizer ``state_dict``.
+    To resume training from some checkpoint, say ``epoch-10.pt``, you can use:
+
+      .. code-block:: bash
+
+        $ ./transducer_stateless_modified/train.py --start-epoch 11
+
+  - ``tensorboard/``
+
+    This folder contains TensorBoard logs. Training loss, validation loss, learning
+    rate, etc, are recorded in these logs. You can visualize them by:
+
+      .. code-block:: bash
+
+        $ cd transducer_stateless_modified/exp/tensorboard
+        $ tensorboard dev upload --logdir . --name "Aishell transducer training with icefall" --description "Training modified transducer, see https://github.com/k2-fsa/icefall/pull/219"
+
+    It will print something like below:
+
+      .. code-block::
+
+        TensorFlow installation not found - running with reduced feature set.
+        Upload started and will continue reading any new data as it's added to the logdir.
+
+        To stop uploading, press Ctrl-C.
+
+        New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/laGZ6HrcQxOigbFD5E0Y3Q/
+
+        [2022-03-03T14:29:45] Started scanning logdir.
+        [2022-03-03T14:29:48] Total uploaded: 8477 scalars, 0 tensors, 0 binary objects
+        Listening for new data in logdir...
+
+    Note there is a `URL <https://tensorboard.dev/experiment/laGZ6HrcQxOigbFD5E0Y3Q/>`_ in the
+    above output, click it and you will see the following screenshot:
+
+      .. figure:: images/aishell-transducer_stateless_modified-tensorboard-log.png
+         :width: 600
+         :alt: TensorBoard screenshot
+         :align: center
+         :target: https://tensorboard.dev/experiment/laGZ6HrcQxOigbFD5E0Y3Q
+
+         TensorBoard screenshot.
+
+  - ``log/log-train-xxxx``
+
+    It is the detailed training log in text format, same as the one
+    you saw printed to the console during training.
+
+Usage examples
+~~~~~~~~~~~~~~
+
+The following shows typical use cases:
+
+**Case 1**
+^^^^^^^^^^
+
+.. code-block:: bash
+
+  $ cd egs/aishell/ASR
+  $ ./transducer_stateless_modified/train.py --max-duration 250
+
+It uses ``--max-duration`` of 250 to avoid OOM.
+
+
+**Case 2**
+^^^^^^^^^^
+
+.. code-block:: bash
+
+  $ cd egs/aishell/ASR
+  $ export CUDA_VISIBLE_DEVICES="0,3"
+  $ ./transducer_stateless_modified/train.py --world-size 2
+
+It uses GPU 0 and GPU 3 for DDP training.
+
+**Case 3**
+^^^^^^^^^^
+
+.. code-block:: bash
+
+  $ cd egs/aishell/ASR
+  $ ./transducer_stateless_modified/train.py --num-epochs 10 --start-epoch 3
+
+It loads checkpoint ``./transducer_stateless_modified/exp/epoch-2.pt`` and starts
+training from epoch 3. Also, it trains for 10 epochs.
+
+Decoding
+--------
+
+The decoding part uses checkpoints saved by the training part, so you have
+to run the training part first.
+
+.. code-block:: bash
+
+  $ cd egs/aishell/ASR
+  $ ./transducer_stateless_modified/decode.py --help
+
+shows the options for decoding.
+
+The commonly used options are:
+
+  - ``--method``
+
+    This specifies the decoding method. Currently, it supports:
+
+      - **greedy_search**. You can provide the commandline option ``--max-sym-per-frame``
+        to limit the maximum number of symbols that can be emitted per frame.
+
+      - **beam_search**. You can provide the commandline option ``--beam-size``.
+
+      - **modified_beam_search**. You can also provide the commandline option ``--beam-size``.
+        To use this method, we assume that you have trained your model with modified transducer,
+        i.e., used the option ``--modified-transducer-prob`` in the training.
+
+    The following command uses greedy search for decoding
+
+    .. code-block::
+
+      $ cd egs/aishell/ASR
+      $ ./transducer_stateless_modified/decode.py \
+              --epoch 64 \
+              --avg 33 \
+              --exp-dir ./transducer_stateless_modified/exp \
+              --max-duration 100 \
+              --decoding-method greedy_search \
+              --max-sym-per-frame 1
+
+    The following command uses beam search for decoding
+
+    .. code-block::
+
+      $ cd egs/aishell/ASR
+      $ ./transducer_stateless_modified/decode.py \
+              --epoch 64 \
+              --avg 33 \
+              --exp-dir ./transducer_stateless_modified/exp \
+              --max-duration 100 \
+              --decoding-method beam_search \
+              --beam-size 4
+
+    The following command uses ``modified`` beam search for decoding
+
+    .. code-block::
+
+      $ cd egs/aishell/ASR
+      $ ./transducer_stateless_modified/decode.py \
+              --epoch 64 \
+              --avg 33 \
+              --exp-dir ./transducer_stateless_modified/exp \
+              --max-duration 100 \
+              --decoding-method modified_beam_search \
+              --beam-size 4
+
+  - ``--max-duration``
+
+    It has the same meaning as the one used in training. A larger
+    value may cause OOM.
+
+  - ``--epoch``
+
+    It specifies the checkpoint from which epoch that should be used for decoding.
+
+  - ``--avg``
+
+    It specifies the number of models to average. For instance, if it is 3 and if
+    ``--epoch=10``, then it averages the checkpoints ``epoch-8.pt``, ``epoch-9.pt``,
+    and ``epoch-10.pt`` and the averaged checkpoint is used for decoding.
+
+After decoding, you can find the decoding logs and results in `exp_dir/log/<decoding_method>`, e.g.,
+``exp_dir/log/greedy_search``.
+
+Pre-trained Model
+-----------------
+
+We have uploaded a pre-trained model to
+`<https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01>`_
+
+We describe how to use the pre-trained model to transcribe a sound file or
+multiple sound files in the following.
+
+Install kaldifeat
+~~~~~~~~~~~~~~~~~
+
+`kaldifeat <https://github.com/csukuangfj/kaldifeat>`_ is used to
+extract features for a single sound file or multiple sound files
+at the same time.
+
+Please refer to `<https://github.com/csukuangfj/kaldifeat>`_ for installation.
+
+Download the pre-trained model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The following commands describe how to download the pre-trained model:
+
+.. code-block::
+
+  $ cd egs/aishell/ASR
+  $ mkdir tmp
+  $ cd tmp
+  $ git lfs install
+  $ git clone https://huggingface.co/csukuangfj/icefall-aishell-transducer-stateless-modified-2022-03-01
+
+
+.. CAUTION::
+
+  You have to use ``git lfs`` to download the pre-trained model.
+
+After downloading, you will have the following files:
+
+.. code-block:: bash
+
+  $ cd egs/aishell/ASR
+  $ tree tmp/icefall-aishell-transducer-stateless-modified-2022-03-01
+
+
+.. code-block:: bash
+
+  tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/
+  |-- README.md
+  |-- data
+  |   `-- lang_char
+  |       |-- L.pt
+  |       |-- lexicon.txt
+  |       |-- tokens.txt
+  |       `-- words.txt
+  |-- exp
+  |   `-- pretrained.pt
+  |-- log
+  |   |-- errs-test-beam_4-epoch-64-avg-33-beam-4.txt
+  |   |-- errs-test-greedy_search-epoch-64-avg-33-context-2-max-sym-per-frame-1.txt
+  |   |-- log-decode-epoch-64-avg-33-beam-4-2022-03-02-12-05-03
+  |   |-- log-decode-epoch-64-avg-33-context-2-max-sym-per-frame-1-2022-02-28-18-13-07
+  |   |-- recogs-test-beam_4-epoch-64-avg-33-beam-4.txt
+  |   `-- recogs-test-greedy_search-epoch-64-avg-33-context-2-max-sym-per-frame-1.txt
+  `-- test_wavs
+      |-- BAC009S0764W0121.wav
+      |-- BAC009S0764W0122.wav
+      |-- BAC009S0764W0123.wav
+      `-- transcript.txt
+
+  5 directories, 16 files
+
+
+**File descriptions**:
+
+  - ``data/lang_char``
+
+    It contains language related files. You can find the vocabulary size in ``tokens.txt``.
+
+  - ``exp/pretrained.pt``
+
+      It contains pre-trained model parameters, obtained by averaging
+      checkpoints from ``epoch-32.pt`` to ``epoch-64.pt``.
+      Note: We have removed optimizer ``state_dict`` to reduce file size.
+
+  - ``log``
+
+      It contains decoding logs and decoded results.
+
+  - ``test_wavs``
+
+      It contains some test sound files from Aishell ``test`` dataset.
+
+The information of the test sound files is listed below:
+
+.. code-block:: bash
+
+  $ soxi tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/*.wav
+
+  Input File     : 'tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav'
+  Channels       : 1
+  Sample Rate    : 16000
+  Precision      : 16-bit
+  Duration       : 00:00:04.20 = 67263 samples ~ 315.295 CDDA sectors
+  File Size      : 135k
+  Bit Rate       : 256k
+  Sample Encoding: 16-bit Signed Integer PCM
+
+
+  Input File     : 'tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav'
+  Channels       : 1
+  Sample Rate    : 16000
+  Precision      : 16-bit
+  Duration       : 00:00:04.12 = 65840 samples ~ 308.625 CDDA sectors
+  File Size      : 132k
+  Bit Rate       : 256k
+  Sample Encoding: 16-bit Signed Integer PCM
+
+
+  Input File     : 'tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav'
+  Channels       : 1
+  Sample Rate    : 16000
+  Precision      : 16-bit
+  Duration       : 00:00:04.00 = 64000 samples ~ 300 CDDA sectors
+  File Size      : 128k
+  Bit Rate       : 256k
+  Sample Encoding: 16-bit Signed Integer PCM
+
+  Total Duration of 3 files: 00:00:12.32
+
+Usage
+~~~~~
+
+.. code-block::
+
+  $ cd egs/aishell/ASR
+  $ ./transducer_stateless_modified/pretrained.py --help
+
+displays the help information.
+
+It supports three decoding methods:
+
+  - greedy search
+  - beam search
+  - modified beam search
+
+.. note::
+
+  In modified beam search, it limits the maximum number of symbols that can be
+  emitted per frame to 1. To use this method, you have to ensure that your model
+  has been trained with the option ``--modified-transducer-prob``. Otherwise,
+  it may give you poor results.
+
+Greedy search
+^^^^^^^^^^^^^
+
+The command to run greedy search is given below:
+
+.. code-block:: bash
+
+
+  $ cd egs/aishell/ASR
+  $ ./transducer_stateless_modified/pretrained.py \
+      --checkpoint ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/exp/pretrained.pt \
+      --lang-dir ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/data/lang_char \
+      --method greedy_search \
+      ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav \
+      ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav \
+      ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav
+
+The output is as follows:
+
+.. code-block::
+
+  2022-03-03 15:35:26,531 INFO [pretrained.py:239] device: cuda:0
+  2022-03-03 15:35:26,994 INFO [lexicon.py:176] Loading pre-compiled tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/data/lang_char/Linv.pt
+  2022-03-03 15:35:27,027 INFO [pretrained.py:246] {'feature_dim': 80, 'encoder_out_dim': 512, 'subsampling_factor': 4, 'attention_dim': 512, 'nhead': 8, 'dim_feedforward': 2048, 'num_encoder_layers': 12, 'vgg_frontend': False, 'env_info': {'k2-version': '1.13', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': 'f4fefe4882bc0ae59af951da3f47335d5495ef71', 'k2-git-date': 'Thu Feb 10 15:16:02 2022', 'lhotse-version': '1.0.0.dev+missing.version.file', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'master', 'icefall-git-sha1': '50d2281-clean', 'icefall-git-date': 'Wed Mar 2 16:02:38 2022', 'icefall-path': '/ceph-fj/fangjun/open-source-2/icefall-aishell', 'k2-path': '/ceph-fj/fangjun/open-source-2/k2-multi-datasets/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-fj/fangjun/open-source-2/lhotse-aishell/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-2-0815224919-75d558775b-mmnv8', 'IP address': '10.177.72.138'}, 'sample_rate': 16000, 'checkpoint': './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/exp/pretrained.pt', 'lang_dir': PosixPath('tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/data/lang_char'), 'method': 'greedy_search', 'sound_files': ['./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav', './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav', './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav'], 'beam_size': 4, 'context_size': 2, 'max_sym_per_frame': 3, 'blank_id': 0, 'vocab_size': 4336}
+  2022-03-03 15:35:27,027 INFO [pretrained.py:248] About to create model
+  2022-03-03 15:35:36,878 INFO [pretrained.py:257] Constructing Fbank computer
+  2022-03-03 15:35:36,880 INFO [pretrained.py:267] Reading sound files: ['./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav', './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav', './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav']
+  2022-03-03 15:35:36,891 INFO [pretrained.py:273] Decoding started
+  /ceph-fj/fangjun/open-source-2/icefall-aishell/egs/aishell/ASR/transducer_stateless_modified/conformer.py:113: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
+    lengths = ((x_lens - 1) // 2 - 1) // 2
+  2022-03-03 15:35:37,163 INFO [pretrained.py:320]
+  ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav:
+  甚 至 出 现 交 易 几 乎 停 滞 的 情 况
+
+  ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav:
+  一 二 线 城 市 虽 然 也 处 于 调 整 中
+
+  ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav:
+  但 因 为 聚 集 了 过 多 公 共 资 源
+
+  2022-03-03 15:35:37,163 INFO [pretrained.py:322] Decoding Done
+
+Beam search
+^^^^^^^^^^^
+
+The command to run beam search is given below:
+
+.. code-block:: bash
+
+
+  $ cd egs/aishell/ASR
+
+  $ ./transducer_stateless_modified/pretrained.py \
+      --checkpoint ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/exp/pretrained.pt \
+      --lang-dir ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/data/lang_char \
+      --method beam_search \
+      --beam-size 4 \
+      ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav \
+      ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav \
+      ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav
+
+The output is as follows:
+
+.. code-block::
+
+  2022-03-03 15:39:09,285 INFO [pretrained.py:239] device: cuda:0
+  2022-03-03 15:39:09,708 INFO [lexicon.py:176] Loading pre-compiled tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/data/lang_char/Linv.pt
+  2022-03-03 15:39:09,759 INFO [pretrained.py:246] {'feature_dim': 80, 'encoder_out_dim': 512, 'subsampling_factor': 4, 'attention_dim': 512, 'nhead': 8, 'dim_feedforward': 2048, 'num_encoder_layers': 12, 'vgg_frontend': False, 'env_info': {'k2-version': '1.13', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': 'f4fefe4882bc0ae59af951da3f47335d5495ef71', 'k2-git-date': 'Thu Feb 10 15:16:02 2022', 'lhotse-version': '1.0.0.dev+missing.version.file', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'master', 'icefall-git-sha1': '50d2281-clean', 'icefall-git-date': 'Wed Mar 2 16:02:38 2022', 'icefall-path': '/ceph-fj/fangjun/open-source-2/icefall-aishell', 'k2-path': '/ceph-fj/fangjun/open-source-2/k2-multi-datasets/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-fj/fangjun/open-source-2/lhotse-aishell/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-2-0815224919-75d558775b-mmnv8', 'IP address': '10.177.72.138'}, 'sample_rate': 16000, 'checkpoint': './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/exp/pretrained.pt', 'lang_dir': PosixPath('tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/data/lang_char'), 'method': 'beam_search', 'sound_files': ['./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav', './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav', './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav'], 'beam_size': 4, 'context_size': 2, 'max_sym_per_frame': 3, 'blank_id': 0, 'vocab_size': 4336}
+  2022-03-03 15:39:09,760 INFO [pretrained.py:248] About to create model
+  2022-03-03 15:39:18,919 INFO [pretrained.py:257] Constructing Fbank computer
+  2022-03-03 15:39:18,922 INFO [pretrained.py:267] Reading sound files: ['./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav', './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav', './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav']
+  2022-03-03 15:39:18,929 INFO [pretrained.py:273] Decoding started
+  /ceph-fj/fangjun/open-source-2/icefall-aishell/egs/aishell/ASR/transducer_stateless_modified/conformer.py:113: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
+    lengths = ((x_lens - 1) // 2 - 1) // 2
+  2022-03-03 15:39:21,046 INFO [pretrained.py:320]
+  ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav:
+  甚 至 出 现 交 易 几 乎 停 滞 的 情 况
+
+  ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav:
+  一 二 线 城 市 虽 然 也 处 于 调 整 中
+
+  ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav:
+  但 因 为 聚 集 了 过 多 公 共 资 源
+
+  2022-03-03 15:39:21,047 INFO [pretrained.py:322] Decoding Done
+
+Modified Beam search
+^^^^^^^^^^^^^^^^^^^^
+
+The command to run modified beam search is given below:
+
+.. code-block:: bash
+
+
+  $ cd egs/aishell/ASR
+
+  $ ./transducer_stateless_modified/pretrained.py \
+      --checkpoint ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/exp/pretrained.pt \
+      --lang-dir ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/data/lang_char \
+      --method modified_beam_search \
+      --beam-size 4 \
+      ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav \
+      ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav \
+      ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav
+
+The output is as follows:
+
+.. code-block::
+
+  2022-03-03 15:41:23,319 INFO [pretrained.py:239] device: cuda:0
+  2022-03-03 15:41:23,798 INFO [lexicon.py:176] Loading pre-compiled tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/data/lang_char/Linv.pt
+  2022-03-03 15:41:23,831 INFO [pretrained.py:246] {'feature_dim': 80, 'encoder_out_dim': 512, 'subsampling_factor': 4, 'attention_dim': 512, 'nhead': 8, 'dim_feedforward': 2048, 'num_encoder_layers': 12, 'vgg_frontend': False, 'env_info': {'k2-version': '1.13', 'k2-build-type': 'Release', 'k2-with-cuda': True, 'k2-git-sha1': 'f4fefe4882bc0ae59af951da3f47335d5495ef71', 'k2-git-date': 'Thu Feb 10 15:16:02 2022', 'lhotse-version': '1.0.0.dev+missing.version.file', 'torch-cuda-available': True, 'torch-cuda-version': '10.2', 'python-version': '3.8', 'icefall-git-branch': 'master', 'icefall-git-sha1': '50d2281-clean', 'icefall-git-date': 'Wed Mar 2 16:02:38 2022', 'icefall-path': '/ceph-fj/fangjun/open-source-2/icefall-aishell', 'k2-path': '/ceph-fj/fangjun/open-source-2/k2-multi-datasets/k2/python/k2/__init__.py', 'lhotse-path': '/ceph-fj/fangjun/open-source-2/lhotse-aishell/lhotse/__init__.py', 'hostname': 'de-74279-k2-train-2-0815224919-75d558775b-mmnv8', 'IP address': '10.177.72.138'}, 'sample_rate': 16000, 'checkpoint': './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/exp/pretrained.pt', 'lang_dir': PosixPath('tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/data/lang_char'), 'method': 'modified_beam_search', 'sound_files': ['./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav', './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav', './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav'], 'beam_size': 4, 'context_size': 2, 'max_sym_per_frame': 3, 'blank_id': 0, 'vocab_size': 4336}
+  2022-03-03 15:41:23,831 INFO [pretrained.py:248] About to create model
+  2022-03-03 15:41:32,214 INFO [pretrained.py:257] Constructing Fbank computer
+  2022-03-03 15:41:32,215 INFO [pretrained.py:267] Reading sound files: ['./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav', './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav', './tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav']
+  2022-03-03 15:41:32,220 INFO [pretrained.py:273] Decoding started
+  /ceph-fj/fangjun/open-source-2/icefall-aishell/egs/aishell/ASR/transducer_stateless_modified/conformer.py:113: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
+    lengths = ((x_lens - 1) // 2 - 1) // 2
+  /ceph-fj/fangjun/open-source-2/icefall-aishell/egs/aishell/ASR/transducer_stateless_modified/beam_search.py:402: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
+    topk_hyp_indexes = topk_indexes // logits.size(-1)
+  2022-03-03 15:41:32,583 INFO [pretrained.py:320]
+  ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0121.wav:
+  甚 至 出 现 交 易 几 乎 停 滞 的 情 况
+
+  ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0122.wav:
+  一 二 线 城 市 虽 然 也 处 于 调 整 中
+
+  ./tmp/icefall-aishell-transducer-stateless-modified-2022-03-01/test_wavs/BAC009S0764W0123.wav:
+  但 因 为 聚 集 了 过 多 公 共 资 源
+
+  2022-03-03 15:41:32,583 INFO [pretrained.py:322] Decoding Done
+
+Colab notebook
+--------------
+
+We provide a colab notebook for this recipe showing how to use a pre-trained model to
+transcribe sound files.
+
+|aishell asr stateless modified transducer colab notebook|
+
+.. |aishell asr stateless modified transducer colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/drive/12jpTxJB44vzwtcmJl2DTdznW0OawPb9H?usp=sharing

docs/source/recipes/index.rst

@@ -10,12 +10,10 @@ We may add recipes for other tasks as well in the future.
 .. Other recipes are listed in a alphabetical order.
 
 .. toctree::
-   :maxdepth: 3
+   :maxdepth: 2
+   :caption: Table of Contents
 
-   yesno
+   aishell/index
-
+   librispeech/index
-   librispeech
+   timit/index
-
+   yesno/index
-   aishell
-
-   timit

docs/source/recipes/librispeech.rst

@@ -1,10 +0,0 @@
-LibriSpeech
-===========
-
-We provide the following models for the LibriSpeech dataset:
-
-.. toctree::
-   :maxdepth: 2
-
-   librispeech/tdnn_lstm_ctc
-   librispeech/conformer_ctc

docs/source/recipes/librispeech/conformer_ctc.rst

@@ -70,6 +70,17 @@ To run stage 2 to stage 5, use:
   All generated files by ``./prepare.sh``, e.g., features, lexicon, etc,
   are saved in ``./data`` directory.
 
+We provide the following YouTube video showing how to run ``./prepare.sh``.
+
+.. note::
+
+   To get the latest news of `next-gen Kaldi <https://github.com/k2-fsa>`_, please subscribe
+   the following YouTube channel by `Nadira Povey <https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_:
+
+      `<https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_
+
+..  youtube:: ofEIoJL-mGM
+
 
 Training
 --------

docs/source/recipes/librispeech/index.rst

@@ -0,0 +1,8 @@
+LibriSpeech
+===========
+
+.. toctree::
+   :maxdepth: 1
+
+   tdnn_lstm_ctc
+   conformer_ctc

docs/source/recipes/librispeech/tdnn_lstm_ctc.rst

@@ -45,6 +45,16 @@ To run stage 2 to stage 5, use:
 
   $ ./prepare.sh --stage 2 --stop-stage 5
 
+We provide the following YouTube video showing how to run ``./prepare.sh``.
+
+.. note::
+
+   To get the latest news of `next-gen Kaldi <https://github.com/k2-fsa>`_, please subscribe
+   the following YouTube channel by `Nadira Povey <https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_:
+
+      `<https://www.youtube.com/channel/UC_VaumpkmINz1pNkFXAN9mw>`_
+
+..  youtube:: ofEIoJL-mGM
 
 Training
 --------

docs/source/recipes/timit.rst

@@ -1,10 +0,0 @@
-TIMIT
-===========
-
-We provide the following models for the TIMIT dataset:
-
-.. toctree::
-   :maxdepth: 2
-
-   timit/tdnn_lstm_ctc
-   timit/tdnn_ligru_ctc

docs/source/recipes/timit/index.rst

@@ -0,0 +1,9 @@
+TIMIT
+=====
+
+.. toctree::
+   :maxdepth: 1
+
+   tdnn_ligru_ctc
+   tdnn_lstm_ctc
+

docs/source/recipes/timit/tdnn_ligru_ctc.rst

@@ -1,5 +1,5 @@
 TDNN-LiGRU-CTC
-=============
+==============
 
 This tutorial shows you how to run a TDNN-LiGRU-CTC model with the `TIMIT <https://data.deepai.org/timit.zip>`_ dataset.
 

docs/source/recipes/yesno/index.rst

@@ -0,0 +1,7 @@
+YesNo
+=====
+
+.. toctree::
+   :maxdepth: 1
+
+   tdnn

docs/source/recipes/yesno/tdnn.rst

@@ -1,5 +1,5 @@
-yesno
+TDNN-CTC
-=====
+========
 
 This page shows you how to run the `yesno <https://www.openslr.org/1>`_ recipe. It contains:
 
@@ -145,7 +145,7 @@ In ``tdnn/exp``, you will find the following files:
     Note there is a URL in the above output, click it and you will see
     the following screenshot:
 
-      .. figure:: images/yesno-tdnn-tensorboard-log.png
+      .. figure:: images/tdnn-tensorboard-log.png
          :width: 600
          :alt: TensorBoard screenshot
          :align: center

egs/aidatatang_200zh/ASR/README.md

@@ -0,0 +1,38 @@
+Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/375
+# Pre-trained Transducer-Stateless2 models for the Aidatatang_200zh dataset with icefall.
+The model was trained on full [Aidatatang_200zh](https://www.openslr.org/62) with the scripts in [icefall](https://github.com/k2-fsa/icefall) based on the latest version k2.
+## Training procedure
+The main repositories are list below, we will update the training and decoding scripts with the update of version.
+k2: https://github.com/k2-fsa/k2
+icefall: https://github.com/k2-fsa/icefall
+lhotse: https://github.com/lhotse-speech/lhotse
+* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
+* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
+```
+git clone https://github.com/k2-fsa/icefall
+cd icefall
+```
+* Preparing data.
+```
+cd egs/aidatatang_200zh/ASR
+bash ./prepare.sh
+```
+* Training
+```
+export CUDA_VISIBLE_DEVICES="0,1"
+./pruned_transducer_stateless2/train.py \
+                  --world-size 2 \
+                  --num-epochs 30 \
+                  --start-epoch 0 \
+                  --exp-dir pruned_transducer_stateless2/exp \
+                  --lang-dir data/lang_char \
+                  --max-duration 250
+```
+## Evaluation results
+The decoding results (WER%) on Aidatatang_200zh(dev and test) are listed below, we got this result by averaging models from epoch 11 to 29.
+The WERs are
+|                                    |     dev    |    test    | comment                                  |
+|------------------------------------|------------|------------|------------------------------------------|
+|          greedy search             | 5.53       | 6.59       | --epoch 29, --avg 19, --max-duration 100 |
+| modified beam search (beam size 4) | 5.27       | 6.33       | --epoch 29, --avg 19, --max-duration 100 |
+| fast beam search (set as default)  | 5.30       | 6.34       | --epoch 29, --avg 19, --max-duration 1500|

egs/aidatatang_200zh/ASR/RESULTS.md

@@ -0,0 +1,72 @@
+## Results
+
+### Aidatatang_200zh Char training results (Pruned Transducer Stateless2)
+
+#### 2022-05-16
+
+Using the codes from this PR https://github.com/k2-fsa/icefall/pull/375.
+
+The WERs are
+
+|                                    |     dev    |    test    | comment                                  |
+|------------------------------------|------------|------------|------------------------------------------|
+|          greedy search             | 5.53       | 6.59       | --epoch 29, --avg 19, --max-duration 100 |
+| modified beam search (beam size 4) | 5.27       | 6.33       | --epoch 29, --avg 19, --max-duration 100 |
+| fast beam search (set as default)  | 5.30       | 6.34       | --epoch 29, --avg 19, --max-duration 1500|
+
+The training command for reproducing is given below:
+
+```
+export CUDA_VISIBLE_DEVICES="0,1"
+
+./pruned_transducer_stateless2/train.py \
+  --world-size 2 \
+  --num-epochs 30 \
+  --start-epoch 0 \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --lang-dir data/lang_char \
+  --max-duration 250 \
+  --save-every-n 1000
+
+```
+
+The tensorboard training log can be found at
+https://tensorboard.dev/experiment/xS7kgYf2RwyDpQAOdS8rAA/#scalars
+
+The decoding command is:
+```
+epoch=29
+avg=19
+
+## greedy search
+./pruned_transducer_stateless2/decode.py \
+  --epoch $epoch \
+  --avg $avg \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --lang-dir ./data/lang_char \
+  --max-duration 100
+
+## modified beam search
+./pruned_transducer_stateless2/decode.py \
+  --epoch $epoch \
+  --avg $avg \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --lang-dir ./data/lang_char \
+  --max-duration 100 \
+  --decoding-method modified_beam_search \
+  --beam-size 4
+
+## fast beam search
+./pruned_transducer_stateless2/decode.py \
+        --epoch $epoch \
+        --avg $avg \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --lang-dir ./data/lang_char \
+        --max-duration 1500 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
+```
+
+A pre-trained model and decoding logs can be found at <https://huggingface.co/luomingshuang/icefall_asr_aidatatang-200zh_pruned_transducer_stateless2>

egs/aidatatang_200zh/ASR/local/compute_fbank_aidatatang_200zh.py

@@ -0,0 +1,119 @@
+#!/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 aidatatang_200zh dataset.
+It looks for manifests in the directory data/manifests.
+
+The generated fbank features are saved in data/fbank.
+"""
+
+import argparse
+import logging
+import os
+from pathlib import Path
+
+import torch
+from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
+from lhotse.recipes.utils import read_manifests_if_cached
+
+from icefall.utils import get_executor
+
+# 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_aidatatang_200zh(num_mel_bins: int = 80):
+    src_dir = Path("data/manifests")
+    output_dir = Path("data/fbank")
+    num_jobs = min(15, os.cpu_count())
+
+    dataset_parts = (
+        "train",
+        "dev",
+        "test",
+    )
+    prefix = "aidatatang"
+    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
+
+    extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
+
+    with get_executor() as ex:  # Initialize the executor only once.
+        for partition, m in manifests.items():
+            if (output_dir / f"{prefix}_cuts_{partition}.{suffix}").is_file():
+                logging.info(f"{partition} already exists - skipping.")
+                continue
+            logging.info(f"Processing {partition}")
+
+            for sup in m["supervisions"]:
+                sup.custom = {"origin": "aidatatang_200zh"}
+
+            cut_set = CutSet.from_manifests(
+                recordings=m["recordings"],
+                supervisions=m["supervisions"],
+            )
+            if "train" in partition:
+                cut_set = (
+                    cut_set
+                    + cut_set.perturb_speed(0.9)
+                    + cut_set.perturb_speed(1.1)
+                )
+            cut_set = cut_set.compute_and_store_features(
+                extractor=extractor,
+                storage_path=f"{output_dir}/{prefix}_feats_{partition}",
+                # when an executor is specified, make more partitions
+                num_jobs=num_jobs if ex is None else 80,
+                executor=ex,
+                storage_type=LilcomChunkyWriter,
+            )
+
+            cut_set.to_file(output_dir / f"{prefix}_cuts_{partition}.{suffix}")
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--num-mel-bins",
+        type=int,
+        default=80,
+        help="""The number of mel bins for Fbank""",
+    )
+
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    args = get_args()
+    compute_fbank_aidatatang_200zh(num_mel_bins=args.num_mel_bins)

egs/aidatatang_200zh/ASR/local/compute_fbank_musan.py

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

egs/aidatatang_200zh/ASR/local/display_manifest_statistics.py

@@ -0,0 +1,96 @@
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang
+# 						                           Mingshuang Luo)
+#
+# 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 displays duration statistics of utterances in a manifest.
+You can use the displayed value to choose minimum/maximum duration
+to remove short and long utterances during the training.
+See the function `remove_short_and_long_utt()`
+in ../../../librispeech/ASR/transducer/train.py
+for usage.
+"""
+
+
+from lhotse import load_manifest_lazy
+
+
+def main():
+    paths = [
+        "./data/fbank/aidatatang_cuts_train.jsonl.gz",
+        "./data/fbank/aidatatang_cuts_dev.jsonl.gz",
+        "./data/fbank/aidatatang_cuts_test.jsonl.gz",
+    ]
+
+    for path in paths:
+        print(f"Starting display the statistics for {path}")
+        cuts = load_manifest_lazy(path)
+        cuts.describe()
+
+
+if __name__ == "__main__":
+    main()
+
+"""
+Starting display the statistics for ./data/fbank/aidatatang_cuts_train.jsonl.gz
+Cuts count: 494715
+Total duration (hours): 422.6
+Speech duration (hours): 422.6 (100.0%)
+***
+Duration statistics (seconds):
+mean    3.1
+std     1.2
+min     1.0
+25%     2.3
+50%     2.7
+75%     3.5
+99%     7.2
+99.5%   8.0
+99.9%   9.5
+max     18.1
+Starting display the statistics for ./data/fbank/aidatatang_cuts_dev.jsonl.gz
+Cuts count: 24216
+Total duration (hours): 20.2
+Speech duration (hours): 20.2 (100.0%)
+***
+Duration statistics (seconds):
+mean    3.0
+std     1.0
+min     1.2
+25%     2.3
+50%     2.7
+75%     3.4
+99%     6.7
+99.5%   7.3
+99.9%   8.8
+max     11.3
+Starting display the statistics for ./data/fbank/aidatatang_cuts_test.jsonl.gz
+Cuts count: 48144
+Total duration (hours): 40.2
+Speech duration (hours): 40.2 (100.0%)
+***
+Duration statistics (seconds):
+mean    3.0
+std     1.1
+min     0.9
+25%     2.3
+50%     2.6
+75%     3.4
+99%     6.9
+99.5%   7.5
+99.9%   9.0
+max     21.8
+"""

egs/aidatatang_200zh/ASR/local/prepare_char.py

@@ -0,0 +1,248 @@
+#!/usr/bin/env python3
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                  Wei Kang)
+#
+# 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`, which should contain::
+
+    - lang_dir/text,
+    - lang_dir/words.txt
+
+and generates the following files in the directory `lang_dir`:
+
+    - lexicon.txt
+    - lexicon_disambig.txt
+    - L.pt
+    - L_disambig.pt
+    - tokens.txt
+"""
+
+import re
+from pathlib import Path
+from typing import Dict, List
+
+import k2
+import torch
+from prepare_lang import (
+    Lexicon,
+    add_disambig_symbols,
+    add_self_loops,
+    write_lexicon,
+    write_mapping,
+)
+
+
+def lexicon_to_fst_no_sil(
+    lexicon: Lexicon,
+    token2id: Dict[str, int],
+    word2id: Dict[str, int],
+    need_self_loops: bool = False,
+) -> k2.Fsa:
+    """Convert a lexicon to an FST (in k2 format).
+
+    Args:
+      lexicon:
+        The input lexicon. See also :func:`read_lexicon`
+      token2id:
+        A dict mapping tokens to IDs.
+      word2id:
+        A dict mapping words to IDs.
+      need_self_loops:
+        If True, add self-loop to states with non-epsilon output symbols
+        on at least one arc out of the state. The input label for this
+        self loop is `token2id["#0"]` and the output label is `word2id["#0"]`.
+    Returns:
+      Return an instance of `k2.Fsa` representing the given lexicon.
+    """
+    loop_state = 0  # words enter and leave from here
+    next_state = 1  # the next un-allocated state, will be incremented as we go
+
+    arcs = []
+
+    # The blank symbol <blk> is defined in local/train_bpe_model.py
+    assert token2id["<blk>"] == 0
+    assert word2id["<eps>"] == 0
+
+    eps = 0
+
+    for word, pieces in lexicon:
+        assert len(pieces) > 0, f"{word} has no pronunciations"
+        cur_state = loop_state
+
+        word = word2id[word]
+        pieces = [
+            token2id[i] if i in token2id else token2id["<unk>"] for i in pieces
+        ]
+
+        for i in range(len(pieces) - 1):
+            w = word if i == 0 else eps
+            arcs.append([cur_state, next_state, pieces[i], w, 0])
+
+            cur_state = next_state
+            next_state += 1
+
+        # now for the last piece of this word
+        i = len(pieces) - 1
+        w = word if i == 0 else eps
+        arcs.append([cur_state, loop_state, pieces[i], w, 0])
+
+    if need_self_loops:
+        disambig_token = token2id["#0"]
+        disambig_word = word2id["#0"]
+        arcs = add_self_loops(
+            arcs,
+            disambig_token=disambig_token,
+            disambig_word=disambig_word,
+        )
+
+    final_state = next_state
+    arcs.append([loop_state, final_state, -1, -1, 0])
+    arcs.append([final_state])
+
+    arcs = sorted(arcs, key=lambda arc: arc[0])
+    arcs = [[str(i) for i in arc] for arc in arcs]
+    arcs = [" ".join(arc) for arc in arcs]
+    arcs = "\n".join(arcs)
+
+    fsa = k2.Fsa.from_str(arcs, acceptor=False)
+    return fsa
+
+
+def contain_oov(token_sym_table: Dict[str, int], tokens: List[str]) -> bool:
+    """Check if all the given tokens are in token symbol table.
+
+    Args:
+      token_sym_table:
+        Token symbol table that contains all the valid tokens.
+      tokens:
+        A list of tokens.
+    Returns:
+      Return True if there is any token not in the token_sym_table,
+      otherwise False.
+    """
+    for tok in tokens:
+        if tok not in token_sym_table:
+            return True
+    return False
+
+
+def generate_lexicon(
+    token_sym_table: Dict[str, int], words: List[str]
+) -> Lexicon:
+    """Generate a lexicon from a word list and token_sym_table.
+
+    Args:
+      token_sym_table:
+        Token symbol table that mapping token to token ids.
+      words:
+        A list of strings representing words.
+    Returns:
+      Return a dict whose keys are words and values are the corresponding
+          tokens.
+    """
+    lexicon = []
+    for word in words:
+        chars = list(word.strip(" \t"))
+        if contain_oov(token_sym_table, chars):
+            continue
+        lexicon.append((word, chars))
+
+    # The OOV word is <UNK>
+    lexicon.append(("<UNK>", ["<unk>"]))
+    return lexicon
+
+
+def generate_tokens(text_file: str) -> Dict[str, int]:
+    """Generate tokens from the given text file.
+
+    Args:
+      text_file:
+        A file that contains text lines to generate tokens.
+    Returns:
+      Return a dict whose keys are tokens and values are token ids ranged
+      from 0 to len(keys) - 1.
+    """
+    tokens: Dict[str, int] = dict()
+    tokens["<blk>"] = 0
+    tokens["<sos/eos>"] = 1
+    tokens["<unk>"] = 2
+    whitespace = re.compile(r"([ \t\r\n]+)")
+    with open(text_file, "r", encoding="utf-8") as f:
+        for line in f:
+            line = re.sub(whitespace, "", line)
+            chars = list(line)
+            for char in chars:
+                if char not in tokens:
+                    tokens[char] = len(tokens)
+    return tokens
+
+
+def main():
+    lang_dir = Path("data/lang_char")
+    text_file = lang_dir / "text"
+
+    word_sym_table = k2.SymbolTable.from_file(lang_dir / "words.txt")
+
+    words = word_sym_table.symbols
+
+    excluded = ["<eps>", "!SIL", "<SPOKEN_NOISE>", "<UNK>", "#0", "<s>", "</s>"]
+    for w in excluded:
+        if w in words:
+            words.remove(w)
+
+    token_sym_table = generate_tokens(text_file)
+
+    lexicon = generate_lexicon(token_sym_table, words)
+
+    lexicon_disambig, max_disambig = add_disambig_symbols(lexicon)
+
+    next_token_id = max(token_sym_table.values()) + 1
+    for i in range(max_disambig + 1):
+        disambig = f"#{i}"
+        assert disambig not in token_sym_table
+        token_sym_table[disambig] = next_token_id
+        next_token_id += 1
+
+    word_sym_table.add("#0")
+    word_sym_table.add("<s>")
+    word_sym_table.add("</s>")
+
+    write_mapping(lang_dir / "tokens.txt", token_sym_table)
+
+    write_lexicon(lang_dir / "lexicon.txt", lexicon)
+    write_lexicon(lang_dir / "lexicon_disambig.txt", lexicon_disambig)
+
+    L = lexicon_to_fst_no_sil(
+        lexicon,
+        token2id=token_sym_table,
+        word2id=word_sym_table,
+    )
+
+    L_disambig = lexicon_to_fst_no_sil(
+        lexicon_disambig,
+        token2id=token_sym_table,
+        word2id=word_sym_table,
+        need_self_loops=True,
+    )
+    torch.save(L.as_dict(), lang_dir / "L.pt")
+    torch.save(L_disambig.as_dict(), lang_dir / "L_disambig.pt")
+
+
+if __name__ == "__main__":
+    main()

egs/aidatatang_200zh/ASR/local/prepare_lang.py

@@ -0,0 +1,390 @@
+#!/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 script takes as input a lexicon file "data/lang_phone/lexicon.txt"
+consisting of words and tokens (i.e., phones) and does the following:
+
+1. Add disambiguation symbols to the lexicon and generate lexicon_disambig.txt
+
+2. Generate tokens.txt, the token table mapping a token to a unique integer.
+
+3. Generate words.txt, the word table mapping a word to a unique integer.
+
+4. Generate L.pt, in k2 format. It can be loaded by
+
+        d = torch.load("L.pt")
+        lexicon = k2.Fsa.from_dict(d)
+
+5. Generate L_disambig.pt, in k2 format.
+"""
+import argparse
+import math
+from collections import defaultdict
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+
+import k2
+import torch
+
+from icefall.lexicon import read_lexicon, write_lexicon
+
+Lexicon = List[Tuple[str, List[str]]]
+
+
+def write_mapping(filename: str, sym2id: Dict[str, int]) -> None:
+    """Write a symbol to ID mapping to a file.
+
+    Note:
+      No need to implement `read_mapping` as it can be done
+      through :func:`k2.SymbolTable.from_file`.
+
+    Args:
+      filename:
+        Filename to save the mapping.
+      sym2id:
+        A dict mapping symbols to IDs.
+    Returns:
+      Return None.
+    """
+    with open(filename, "w", encoding="utf-8") as f:
+        for sym, i in sym2id.items():
+            f.write(f"{sym} {i}\n")
+
+
+def get_tokens(lexicon: Lexicon) -> List[str]:
+    """Get tokens from a lexicon.
+
+    Args:
+      lexicon:
+        It is the return value of :func:`read_lexicon`.
+    Returns:
+      Return a list of unique tokens.
+    """
+    ans = set()
+    for _, tokens in lexicon:
+        ans.update(tokens)
+    sorted_ans = sorted(list(ans))
+    return sorted_ans
+
+
+def get_words(lexicon: Lexicon) -> List[str]:
+    """Get words from a lexicon.
+
+    Args:
+      lexicon:
+        It is the return value of :func:`read_lexicon`.
+    Returns:
+      Return a list of unique words.
+    """
+    ans = set()
+    for word, _ in lexicon:
+        ans.add(word)
+    sorted_ans = sorted(list(ans))
+    return sorted_ans
+
+
+def add_disambig_symbols(lexicon: Lexicon) -> Tuple[Lexicon, int]:
+    """It adds pseudo-token disambiguation symbols #1, #2 and so on
+    at the ends of tokens to ensure that all pronunciations are different,
+    and that none is a prefix of another.
+
+    See also add_lex_disambig.pl from kaldi.
+
+    Args:
+      lexicon:
+        It is returned by :func:`read_lexicon`.
+    Returns:
+      Return a tuple with two elements:
+
+        - The output lexicon with disambiguation symbols
+        - The ID of the max disambiguation symbol that appears
+          in the lexicon
+    """
+
+    # (1) Work out the count of each token-sequence in the
+    # lexicon.
+    count = defaultdict(int)
+    for _, tokens in lexicon:
+        count[" ".join(tokens)] += 1
+
+    # (2) For each left sub-sequence of each token-sequence, note down
+    # that it exists (for identifying prefixes of longer strings).
+    issubseq = defaultdict(int)
+    for _, tokens in lexicon:
+        tokens = tokens.copy()
+        tokens.pop()
+        while tokens:
+            issubseq[" ".join(tokens)] = 1
+            tokens.pop()
+
+    # (3) For each entry in the lexicon:
+    # if the token sequence is unique and is not a
+    # prefix of another word, no disambig symbol.
+    # Else output #1, or #2, #3, ... if the same token-seq
+    # has already been assigned a disambig symbol.
+    ans = []
+
+    # We start with #1 since #0 has its own purpose
+    first_allowed_disambig = 1
+    max_disambig = first_allowed_disambig - 1
+    last_used_disambig_symbol_of = defaultdict(int)
+
+    for word, tokens in lexicon:
+        tokenseq = " ".join(tokens)
+        assert tokenseq != ""
+        if issubseq[tokenseq] == 0 and count[tokenseq] == 1:
+            ans.append((word, tokens))
+            continue
+
+        cur_disambig = last_used_disambig_symbol_of[tokenseq]
+        if cur_disambig == 0:
+            cur_disambig = first_allowed_disambig
+        else:
+            cur_disambig += 1
+
+        if cur_disambig > max_disambig:
+            max_disambig = cur_disambig
+        last_used_disambig_symbol_of[tokenseq] = cur_disambig
+        tokenseq += f" #{cur_disambig}"
+        ans.append((word, tokenseq.split()))
+    return ans, max_disambig
+
+
+def generate_id_map(symbols: List[str]) -> Dict[str, int]:
+    """Generate ID maps, i.e., map a symbol to a unique ID.
+
+    Args:
+      symbols:
+        A list of unique symbols.
+    Returns:
+      A dict containing the mapping between symbols and IDs.
+    """
+    return {sym: i for i, sym in enumerate(symbols)}
+
+
+def add_self_loops(
+    arcs: List[List[Any]], disambig_token: int, disambig_word: int
+) -> List[List[Any]]:
+    """Adds self-loops to states of an FST to propagate disambiguation symbols
+    through it. They are added on each state with non-epsilon output symbols
+    on at least one arc out of the state.
+
+    See also fstaddselfloops.pl from Kaldi. One difference is that
+    Kaldi uses OpenFst style FSTs and it has multiple final states.
+    This function uses k2 style FSTs and it does not need to add self-loops
+    to the final state.
+
+    The input label of a self-loop is `disambig_token`, while the output
+    label is `disambig_word`.
+
+    Args:
+      arcs:
+        A list-of-list. The sublist contains
+        `[src_state, dest_state, label, aux_label, score]`
+      disambig_token:
+        It is the token ID of the symbol `#0`.
+      disambig_word:
+        It is the word ID of the symbol `#0`.
+
+    Return:
+      Return new `arcs` containing self-loops.
+    """
+    states_needs_self_loops = set()
+    for arc in arcs:
+        src, dst, ilabel, olabel, score = arc
+        if olabel != 0:
+            states_needs_self_loops.add(src)
+
+    ans = []
+    for s in states_needs_self_loops:
+        ans.append([s, s, disambig_token, disambig_word, 0])
+
+    return arcs + ans
+
+
+def lexicon_to_fst(
+    lexicon: Lexicon,
+    token2id: Dict[str, int],
+    word2id: Dict[str, int],
+    sil_token: str = "SIL",
+    sil_prob: float = 0.5,
+    need_self_loops: bool = False,
+) -> k2.Fsa:
+    """Convert a lexicon to an FST (in k2 format) with optional silence at
+    the beginning and end of each word.
+
+    Args:
+      lexicon:
+        The input lexicon. See also :func:`read_lexicon`
+      token2id:
+        A dict mapping tokens to IDs.
+      word2id:
+        A dict mapping words to IDs.
+      sil_token:
+        The silence token.
+      sil_prob:
+        The probability for adding a silence at the beginning and end
+        of the word.
+      need_self_loops:
+        If True, add self-loop to states with non-epsilon output symbols
+        on at least one arc out of the state. The input label for this
+        self loop is `token2id["#0"]` and the output label is `word2id["#0"]`.
+    Returns:
+      Return an instance of `k2.Fsa` representing the given lexicon.
+    """
+    assert sil_prob > 0.0 and sil_prob < 1.0
+    # CAUTION: we use score, i.e, negative cost.
+    sil_score = math.log(sil_prob)
+    no_sil_score = math.log(1.0 - sil_prob)
+
+    start_state = 0
+    loop_state = 1  # words enter and leave from here
+    sil_state = 2  # words terminate here when followed by silence; this state
+    # has a silence transition to loop_state.
+    next_state = 3  # the next un-allocated state, will be incremented as we go.
+    arcs = []
+
+    assert token2id["<eps>"] == 0
+    assert word2id["<eps>"] == 0
+
+    eps = 0
+
+    sil_token = token2id[sil_token]
+
+    arcs.append([start_state, loop_state, eps, eps, no_sil_score])
+    arcs.append([start_state, sil_state, eps, eps, sil_score])
+    arcs.append([sil_state, loop_state, sil_token, eps, 0])
+
+    for word, tokens in lexicon:
+        assert len(tokens) > 0, f"{word} has no pronunciations"
+        cur_state = loop_state
+
+        word = word2id[word]
+        tokens = [token2id[i] for i in tokens]
+
+        for i in range(len(tokens) - 1):
+            w = word if i == 0 else eps
+            arcs.append([cur_state, next_state, tokens[i], w, 0])
+
+            cur_state = next_state
+            next_state += 1
+
+        # now for the last token of this word
+        # It has two out-going arcs, one to the loop state,
+        # the other one to the sil_state.
+        i = len(tokens) - 1
+        w = word if i == 0 else eps
+        arcs.append([cur_state, loop_state, tokens[i], w, no_sil_score])
+        arcs.append([cur_state, sil_state, tokens[i], w, sil_score])
+
+    if need_self_loops:
+        disambig_token = token2id["#0"]
+        disambig_word = word2id["#0"]
+        arcs = add_self_loops(
+            arcs,
+            disambig_token=disambig_token,
+            disambig_word=disambig_word,
+        )
+
+    final_state = next_state
+    arcs.append([loop_state, final_state, -1, -1, 0])
+    arcs.append([final_state])
+
+    arcs = sorted(arcs, key=lambda arc: arc[0])
+    arcs = [[str(i) for i in arc] for arc in arcs]
+    arcs = [" ".join(arc) for arc in arcs]
+    arcs = "\n".join(arcs)
+
+    fsa = k2.Fsa.from_str(arcs, acceptor=False)
+    return fsa
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lang-dir", type=str, help="The lang dir, data/lang_phone"
+    )
+    return parser.parse_args()
+
+
+def main():
+    out_dir = Path(get_args().lang_dir)
+    lexicon_filename = out_dir / "lexicon.txt"
+    sil_token = "SIL"
+    sil_prob = 0.5
+
+    lexicon = read_lexicon(lexicon_filename)
+    tokens = get_tokens(lexicon)
+    words = get_words(lexicon)
+
+    lexicon_disambig, max_disambig = add_disambig_symbols(lexicon)
+
+    for i in range(max_disambig + 1):
+        disambig = f"#{i}"
+        assert disambig not in tokens
+        tokens.append(f"#{i}")
+
+    assert "<eps>" not in tokens
+    tokens = ["<eps>"] + tokens
+
+    assert "<eps>" not in words
+    assert "#0" not in words
+    assert "<s>" not in words
+    assert "</s>" not in words
+
+    words = ["<eps>"] + words + ["#0", "<s>", "</s>"]
+
+    token2id = generate_id_map(tokens)
+    word2id = generate_id_map(words)
+
+    write_mapping(out_dir / "tokens.txt", token2id)
+    write_mapping(out_dir / "words.txt", word2id)
+    write_lexicon(out_dir / "lexicon_disambig.txt", lexicon_disambig)
+
+    L = lexicon_to_fst(
+        lexicon,
+        token2id=token2id,
+        word2id=word2id,
+        sil_token=sil_token,
+        sil_prob=sil_prob,
+    )
+
+    L_disambig = lexicon_to_fst(
+        lexicon_disambig,
+        token2id=token2id,
+        word2id=word2id,
+        sil_token=sil_token,
+        sil_prob=sil_prob,
+        need_self_loops=True,
+    )
+    torch.save(L.as_dict(), out_dir / "L.pt")
+    torch.save(L_disambig.as_dict(), out_dir / "L_disambig.pt")
+
+    if False:
+        # Just for debugging, will remove it
+        L.labels_sym = k2.SymbolTable.from_file(out_dir / "tokens.txt")
+        L.aux_labels_sym = k2.SymbolTable.from_file(out_dir / "words.txt")
+        L_disambig.labels_sym = L.labels_sym
+        L_disambig.aux_labels_sym = L.aux_labels_sym
+        L.draw(out_dir / "L.png", title="L")
+        L_disambig.draw(out_dir / "L_disambig.png", title="L_disambig")
+
+
+if __name__ == "__main__":
+    main()

egs/aidatatang_200zh/ASR/local/prepare_words.py

@@ -0,0 +1,84 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+# Copyright    2021  Xiaomi Corp.        (authors: Mingshuang Luo)
+#
+# 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 words.txt without ids:
+    - words_no_ids.txt
+and generates the new words.txt with related ids.
+    - words.txt
+"""
+
+
+import argparse
+import logging
+
+from tqdm import tqdm
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        description="Prepare words.txt",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--input-file",
+        default="data/lang_char/words_no_ids.txt",
+        type=str,
+        help="the words file without ids for WenetSpeech",
+    )
+    parser.add_argument(
+        "--output-file",
+        default="data/lang_char/words.txt",
+        type=str,
+        help="the words file with ids for WenetSpeech",
+    )
+
+    return parser
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    input_file = args.input_file
+    output_file = args.output_file
+
+    f = open(input_file, "r", encoding="utf-8")
+    lines = f.readlines()
+    new_lines = []
+    add_words = ["<eps> 0", "!SIL 1", "<SPOKEN_NOISE> 2", "<UNK> 3"]
+    new_lines.extend(add_words)
+
+    logging.info("Starting reading the input file")
+    for i in tqdm(range(len(lines))):
+        x = lines[i]
+        idx = 4 + i
+        new_line = str(x.strip("\n")) + " " + str(idx)
+        new_lines.append(new_line)
+
+    logging.info("Starting writing the words.txt")
+    f_out = open(output_file, "w", encoding="utf-8")
+    for line in new_lines:
+        f_out.write(line)
+        f_out.write("\n")
+
+
+if __name__ == "__main__":
+    main()

egs/aidatatang_200zh/ASR/local/test_prepare_lang.py

@@ -0,0 +1,106 @@
+#!/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.
+
+
+# Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+import os
+import tempfile
+
+import k2
+from prepare_lang import (
+    add_disambig_symbols,
+    generate_id_map,
+    get_phones,
+    get_words,
+    lexicon_to_fst,
+    read_lexicon,
+    write_lexicon,
+    write_mapping,
+)
+
+
+def generate_lexicon_file() -> str:
+    fd, filename = tempfile.mkstemp()
+    os.close(fd)
+    s = """
+    !SIL SIL
+    <SPOKEN_NOISE> SPN
+    <UNK> SPN
+    f f
+    a a
+    foo f o o
+    bar b a r
+    bark b a r k
+    food f o o d
+    food2 f o o d
+    fo  f o
+    """.strip()
+    with open(filename, "w") as f:
+        f.write(s)
+    return filename
+
+
+def test_read_lexicon(filename: str):
+    lexicon = read_lexicon(filename)
+    phones = get_phones(lexicon)
+    words = get_words(lexicon)
+    print(lexicon)
+    print(phones)
+    print(words)
+    lexicon_disambig, max_disambig = add_disambig_symbols(lexicon)
+    print(lexicon_disambig)
+    print("max disambig:", f"#{max_disambig}")
+
+    phones = ["<eps>", "SIL", "SPN"] + phones
+    for i in range(max_disambig + 1):
+        phones.append(f"#{i}")
+    words = ["<eps>"] + words
+
+    phone2id = generate_id_map(phones)
+    word2id = generate_id_map(words)
+
+    print(phone2id)
+    print(word2id)
+
+    write_mapping("phones.txt", phone2id)
+    write_mapping("words.txt", word2id)
+
+    write_lexicon("a.txt", lexicon)
+    write_lexicon("a_disambig.txt", lexicon_disambig)
+
+    fsa = lexicon_to_fst(lexicon, phone2id=phone2id, word2id=word2id)
+    fsa.labels_sym = k2.SymbolTable.from_file("phones.txt")
+    fsa.aux_labels_sym = k2.SymbolTable.from_file("words.txt")
+    fsa.draw("L.pdf", title="L")
+
+    fsa_disambig = lexicon_to_fst(
+        lexicon_disambig, phone2id=phone2id, word2id=word2id
+    )
+    fsa_disambig.labels_sym = k2.SymbolTable.from_file("phones.txt")
+    fsa_disambig.aux_labels_sym = k2.SymbolTable.from_file("words.txt")
+    fsa_disambig.draw("L_disambig.pdf", title="L_disambig")
+
+
+def main():
+    filename = generate_lexicon_file()
+    test_read_lexicon(filename)
+    os.remove(filename)
+
+
+if __name__ == "__main__":
+    main()

egs/aidatatang_200zh/ASR/local/text2token.py

@@ -0,0 +1,195 @@
+#!/usr/bin/env python3
+# Copyright    2017  Johns Hopkins University   (authors: Shinji Watanabe)
+#              2022  Xiaomi Corp.               (authors: Mingshuang Luo)
+#
+# 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 codecs
+import re
+import sys
+from typing import List
+
+from pypinyin import lazy_pinyin, pinyin
+
+is_python2 = sys.version_info[0] == 2
+
+
+def exist_or_not(i, match_pos):
+    start_pos = None
+    end_pos = None
+    for pos in match_pos:
+        if pos[0] <= i < pos[1]:
+            start_pos = pos[0]
+            end_pos = pos[1]
+            break
+
+    return start_pos, end_pos
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        description="convert raw text to tokenized text",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--nchar",
+        "-n",
+        default=1,
+        type=int,
+        help="number of characters to split, i.e., \
+                        aabb -> a a b b with -n 1 and aa bb with -n 2",
+    )
+    parser.add_argument(
+        "--skip-ncols", "-s", default=0, type=int, help="skip first n columns"
+    )
+    parser.add_argument(
+        "--space", default="<space>", type=str, help="space symbol"
+    )
+    parser.add_argument(
+        "--non-lang-syms",
+        "-l",
+        default=None,
+        type=str,
+        help="list of non-linguistic symobles, e.g., <NOISE> etc.",
+    )
+    parser.add_argument(
+        "text", type=str, default=False, nargs="?", help="input text"
+    )
+    parser.add_argument(
+        "--trans_type",
+        "-t",
+        type=str,
+        default="char",
+        choices=["char", "pinyin", "lazy_pinyin"],
+        help="""Transcript type. char/pinyin/lazy_pinyin""",
+    )
+    return parser
+
+
+def token2id(
+    texts, token_table, token_type: str = "lazy_pinyin", oov: str = "<unk>"
+) -> List[List[int]]:
+    """Convert token to id.
+    Args:
+      texts:
+        The input texts, it refers to the chinese text here.
+      token_table:
+        The token table is built based on "data/lang_xxx/token.txt"
+      token_type:
+        The type of token, such as "pinyin" and "lazy_pinyin".
+      oov:
+        Out of vocabulary token. When a word(token) in the transcript
+        does not exist in the token list, it is replaced with `oov`.
+
+    Returns:
+      The list of ids for the input texts.
+    """
+    if texts is None:
+        raise ValueError("texts can't be None!")
+    else:
+        oov_id = token_table[oov]
+        ids: List[List[int]] = []
+        for text in texts:
+            chars_list = list(str(text))
+            if token_type == "lazy_pinyin":
+                text = lazy_pinyin(chars_list)
+                sub_ids = [
+                    token_table[txt] if txt in token_table else oov_id
+                    for txt in text
+                ]
+                ids.append(sub_ids)
+            else:  # token_type = "pinyin"
+                text = pinyin(chars_list)
+                sub_ids = [
+                    token_table[txt[0]] if txt[0] in token_table else oov_id
+                    for txt in text
+                ]
+                ids.append(sub_ids)
+        return ids
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    rs = []
+    if args.non_lang_syms is not None:
+        with codecs.open(args.non_lang_syms, "r", encoding="utf-8") as f:
+            nls = [x.rstrip() for x in f.readlines()]
+            rs = [re.compile(re.escape(x)) for x in nls]
+
+    if args.text:
+        f = codecs.open(args.text, encoding="utf-8")
+    else:
+        f = codecs.getreader("utf-8")(
+            sys.stdin if is_python2 else sys.stdin.buffer
+        )
+
+    sys.stdout = codecs.getwriter("utf-8")(
+        sys.stdout if is_python2 else sys.stdout.buffer
+    )
+    line = f.readline()
+    n = args.nchar
+    while line:
+        x = line.split()
+        print(" ".join(x[: args.skip_ncols]), end=" ")
+        a = " ".join(x[args.skip_ncols :])  # noqa E203
+
+        # get all matched positions
+        match_pos = []
+        for r in rs:
+            i = 0
+            while i >= 0:
+                m = r.search(a, i)
+                if m:
+                    match_pos.append([m.start(), m.end()])
+                    i = m.end()
+                else:
+                    break
+        if len(match_pos) > 0:
+            chars = []
+            i = 0
+            while i < len(a):
+                start_pos, end_pos = exist_or_not(i, match_pos)
+                if start_pos is not None:
+                    chars.append(a[start_pos:end_pos])
+                    i = end_pos
+                else:
+                    chars.append(a[i])
+                    i += 1
+            a = chars
+
+        if args.trans_type == "pinyin":
+            a = pinyin(list(str(a)))
+            a = [one[0] for one in a]
+
+        if args.trans_type == "lazy_pinyin":
+            a = lazy_pinyin(list(str(a)))
+
+        a = [a[j : j + n] for j in range(0, len(a), n)]  # noqa E203
+
+        a_flat = []
+        for z in a:
+            a_flat.append("".join(z))
+
+        a_chars = "".join(a_flat)
+        print(a_chars)
+        line = f.readline()
+
+
+if __name__ == "__main__":
+    main()

egs/aidatatang_200zh/ASR/prepare.sh

@@ -0,0 +1,118 @@
+#!/usr/bin/env bash
+
+set -eou pipefail
+
+stage=-1
+stop_stage=100
+
+# We assume dl_dir (download dir) contains the following
+# directories and files. If not, they will be downloaded
+# by this script automatically.
+#
+#  - $dl_dir/aidatatang_200zh
+#      You can find "corpus" and "transcript" inside it.
+#      You can download it at
+#       https://openslr.org/62/
+
+dl_dir=$PWD/download
+
+. shared/parse_options.sh || exit 1
+
+# All files generated by this script are saved in "data".
+# You can safely remove "data" and rerun this script to regenerate it.
+mkdir -p data
+
+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]}) $*"
+}
+
+log "dl_dir: $dl_dir"
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Download data"
+
+  if [ ! -f $dl_dir/aidatatang_200zh/transcript/aidatatang_200_zh_transcript.txt ]; then
+    lhotse download aidatatang-200zh $dl_dir
+  fi
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare aidatatang_200zh manifest"
+  # We assume that you have downloaded the aidatatang_200zh corpus
+  # to $dl_dir/aidatatang_200zh
+  if [ ! -f data/manifests/aidatatang_200zh/.manifests.done ]; then
+    mkdir -p data/manifests/aidatatang_200zh
+    lhotse prepare aidatatang-200zh $dl_dir data/manifests/aidatatang_200zh
+    touch data/manifests/aidatatang_200zh/.manifests.done
+  fi
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Process aidatatang_200zh"
+  if [ ! -f data/fbank/aidatatang_200zh/.fbank.done ]; then
+    mkdir -p data/fbank/aidatatang_200zh
+    lhotse prepare aidatatang-200zh $dl_dir data/manifests/aidatatang_200zh
+    touch data/fbank/aidatatang_200zh/.fbank.done
+  fi
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Prepare musan manifest"
+  # We assume that you have downloaded the musan corpus
+  # to data/musan
+  if [ ! -f data/manifests/.musan_manifests.done ]; then
+    log "It may take 6 minutes"
+    mkdir -p data/manifests
+    lhotse prepare musan $dl_dir/musan data/manifests
+    touch data/manifests/.musan_manifests.done
+  fi
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Compute fbank for musan"
+  if [ ! -f data/fbank/.msuan.done ]; then
+    mkdir -p data/fbank
+    ./local/compute_fbank_musan.py
+    touch data/fbank/.msuan.done
+  fi
+fi
+
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  log "Stage 5: Compute fbank for aidatatang_200zh"
+  if [ ! -f data/fbank/.aidatatang_200zh.done ]; then
+    mkdir -p data/fbank
+    ./local/compute_fbank_aidatatang_200zh.py
+    touch data/fbank/.aidatatang_200zh.done
+  fi
+fi
+
+if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
+  log "Stage 6: Prepare char based lang"
+  lang_char_dir=data/lang_char
+  mkdir -p $lang_char_dir
+
+  # Prepare text.
+  grep "\"text\":" data/manifests/aidatatang_200zh/supervisions_train.json \
+    | sed -e 's/["text:\t ]*//g' | sed 's/,//g' \
+    | ./local/text2token.py -t "char" > $lang_char_dir/text
+
+  # Prepare words.txt
+  grep "\"text\":" data/manifests/aidatatang_200zh/supervisions_train.json \
+    | sed -e 's/["text:\t]*//g' | sed 's/,//g' \
+    | ./local/text2token.py -t "char" > $lang_char_dir/text_words
+
+  cat $lang_char_dir/text_words | sed 's/ /\n/g' | sort -u | sed '/^$/d' \
+    | uniq > $lang_char_dir/words_no_ids.txt
+
+  if [ ! -f $lang_char_dir/words.txt ]; then
+    ./local/prepare_words.py \
+      --input-file $lang_char_dir/words_no_ids.txt
+      --output-file $lang_char_dir/words.txt
+  fi
+
+  if [ ! -f $lang_char_dir/L_disambig.pt ]; then
+    ./local/prepare_char.py
+  fi
+fi

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/asr_datamodule.py

@@ -0,0 +1,420 @@
+# 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 inspect
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+import torch
+from lhotse import (
+    CutSet,
+    Fbank,
+    FbankConfig,
+    load_manifest,
+    load_manifest_lazy,
+    set_caching_enabled,
+)
+from lhotse.dataset import (
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    K2SpeechRecognitionDataset,
+    PrecomputedFeatures,
+    SingleCutSampler,
+    SpecAugment,
+)
+from lhotse.dataset.input_strategies import OnTheFlyFeatures
+from lhotse.utils import fix_random_seed
+from torch.utils.data import DataLoader
+
+from icefall.utils import str2bool
+
+set_caching_enabled(False)
+torch.set_num_threads(1)
+
+
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+
+class Aidatatang_200zhAsrDataModule:
+    """
+    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/fbank"),
+            help="Path to directory with train/dev/test cuts.",
+        )
+        group.add_argument(
+            "--max-duration",
+            type=int,
+            default=200.0,
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
+        )
+        group.add_argument(
+            "--bucketing-sampler",
+            type=str2bool,
+            default=True,
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
+        )
+        group.add_argument(
+            "--num-buckets",
+            type=int,
+            default=300,
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
+        )
+        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(
+            "--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(
+            "--return-cuts",
+            type=str2bool,
+            default=True,
+            help="When enabled, each batch will have the "
+            "field: batch['supervisions']['cut'] with the cuts that "
+            "were used to construct it.",
+        )
+
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=2,
+            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(
+            "--enable-musan",
+            type=str2bool,
+            default=True,
+            help="When enabled, select noise from MUSAN and mix it"
+            "with training dataset. ",
+        )
+
+    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")
+        cuts_musan = load_manifest(
+            self.args.manifest_dir / "musan_cuts.jsonl.gz"
+        )
+
+        transforms = []
+        if self.args.enable_musan:
+            logging.info("Enable MUSAN")
+            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(
+                f"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}"
+            )
+            # Set the value of num_frame_masks according to Lhotse's version.
+            # In different Lhotse's versions, the default of num_frame_masks is
+            # different.
+            num_frame_masks = 10
+            num_frame_masks_parameter = inspect.signature(
+                SpecAugment.__init__
+            ).parameters["num_frame_masks"]
+            if num_frame_masks_parameter.default == 1:
+                num_frame_masks = 2
+            logging.info(f"Num frame mask: {num_frame_masks}")
+            input_transforms.append(
+                SpecAugment(
+                    time_warp_factor=self.args.spec_aug_time_warp_factor,
+                    num_frame_masks=num_frame_masks,
+                    features_mask_size=27,
+                    num_feature_masks=2,
+                    frames_mask_size=100,
+                )
+            )
+        else:
+            logging.info("Disable SpecAugment")
+
+        logging.info("About to create train dataset")
+        train = K2SpeechRecognitionDataset(
+            cut_transforms=transforms,
+            input_transforms=input_transforms,
+            return_cuts=self.args.return_cuts,
+        )
+
+        if self.args.on_the_fly_feats:
+            # NOTE: the PerturbSpeed transform should be added only if we
+            # remove it from data prep stage.
+            # Add on-the-fly speed perturbation; since originally it would
+            # have increased epoch size by 3, we will apply prob 2/3 and use
+            # 3x more epochs.
+            # Speed perturbation probably should come first before
+            # concatenation, but in principle the transforms order doesn't have
+            # to be strict (e.g. could be randomized)
+            # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
+            # Drop feats to be on the safe side.
+            train = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
+                input_transforms=input_transforms,
+                return_cuts=self.args.return_cuts,
+            )
+
+        if self.args.bucketing_sampler:
+            logging.info("Using DynamicBucketingSampler.")
+            train_sampler = DynamicBucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                drop_last=True,
+            )
+        else:
+            logging.info("Using SingleCutSampler.")
+            train_sampler = SingleCutSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+            )
+        logging.info("About to create train dataloader")
+
+        # '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,
+        )
+
+        if sampler_state_dict is not None:
+            logging.info("Loading sampler state dict")
+            train_dl.sampler.load_state_dict(sampler_state_dict)
+
+        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))
+                ),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                return_cuts=self.args.return_cuts,
+            )
+        valid_sampler = DynamicBucketingSampler(
+            cuts_valid,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.info("About to create dev dataloader")
+
+        from lhotse.dataset.iterable_dataset import IterableDatasetWrapper
+
+        dev_iter_dataset = IterableDatasetWrapper(
+            dataset=validate,
+            sampler=valid_sampler,
+        )
+        valid_dl = DataLoader(
+            dev_iter_dataset,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            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=self.args.return_cuts,
+        )
+        sampler = DynamicBucketingSampler(
+            cuts,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        from lhotse.dataset.iterable_dataset import IterableDatasetWrapper
+
+        test_iter_dataset = IterableDatasetWrapper(
+            dataset=test,
+            sampler=sampler,
+        )
+        test_dl = DataLoader(
+            test_iter_dataset,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+        )
+        return test_dl
+
+    @lru_cache()
+    def train_cuts(self) -> CutSet:
+        logging.info("About to get train cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "aidatatang_cuts_train.jsonl.gz"
+        )
+
+    @lru_cache()
+    def valid_cuts(self) -> CutSet:
+        logging.info("About to get dev cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "aidatatang_cuts_dev.jsonl.gz"
+        )
+
+    @lru_cache()
+    def test_cuts(self) -> List[CutSet]:
+        logging.info("About to get test cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "aidatatang_cuts_test.jsonl.gz"
+        )

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/beam_search.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless2/beam_search.py

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/conformer.py

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

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/decode.py

@@ -0,0 +1,600 @@
+#!/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.
+"""
+When training with the L subset, usage:
+(1) greedy search
+./pruned_transducer_stateless2/decode.py \
+        --epoch 6 \
+        --avg 3 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --lang-dir data/lang_char \
+        --max-duration 100 \
+        --decoding-method greedy_search
+
+(2) modified beam search
+./pruned_transducer_stateless2/decode.py \
+        --epoch 6 \
+        --avg 3 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --lang-dir data/lang_char \
+        --max-duration 100 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+
+(3) fast beam search
+./pruned_transducer_stateless2/decode.py \
+        --epoch 6 \
+        --avg 3 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --lang-dir data/lang_char \
+        --max-duration 1500 \
+        --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 torch
+import torch.nn as nn
+from asr_datamodule import Aidatatang_200zhAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from train import get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.lexicon import Lexicon
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+    store_transcripts,
+    write_error_stats,
+)
+
+
+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 decoding."
+        "Note: Epoch counts from 0.",
+    )
+
+    parser.add_argument(
+        "--batch",
+        type=int,
+        default=None,
+        help="It specifies the batch checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch'. ",
+    )
+
+    parser.add_argument(
+        "--avg-last-n",
+        type=int,
+        default=0,
+        help="""If positive, --epoch and --avg are ignored and it
+        will use the last n checkpoints exp_dir/checkpoint-xxx.pt
+        where xxx is the number of processed batches while
+        saving that checkpoint.
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless2/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        default="data/lang_char",
+        help="""The lang dir
+        It contains language related input files such as
+        "lexicon.txt"
+        """,
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_beam_search
+          - fast_beam_search
+        """,
+    )
+
+    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(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    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""",
+    )
+
+    return parser
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    lexicon: Lexicon,
+    batch: dict,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[List[str]]]:
+    """Decode one batch and return the result in a dict. The dict has the
+    following format:
+
+        - key: It indicates the setting used for decoding. For example,
+               if greedy_search is used, it would be "greedy_search"
+               If beam search with a beam size of 7 is used, it would be
+               "beam_7"
+        - value: It contains the decoding result. `len(value)` equals to
+                 batch size. `value[i]` is the decoding result for the i-th
+                 utterance in the given batch.
+    Args:
+      params:
+        It's the return value of :func:`get_params`.
+      model:
+        The neural model.
+      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.
+    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 i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
+    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 i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
+    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 i in range(encoder_out.size(0)):
+            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
+    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([lexicon.token_table[idx] for idx in hyp])
+
+    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
+        }
+    else:
+        return {f"beam_size_{params.beam_size}": hyps}
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    lexicon: Lexicon,
+    decoding_graph: Optional[k2.Fsa] = 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.
+      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 = 50
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+        texts = [list(str(text).replace(" ", "")) for text in texts]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            lexicon=lexicon,
+            decoding_graph=decoding_graph,
+            batch=batch,
+        )
+
+        for name, hyps in hyps_dict.items():
+            this_batch = []
+            assert len(hyps) == len(texts)
+            for hyp_words, ref_text in zip(hyps, texts):
+                this_batch.append((ref_text, 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()
+    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.
+        errs_filename = (
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+    test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
+    errs_info = (
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
+    with open(errs_info, "w") as f:
+        print("settings\tWER", file=f)
+        for key, val in test_set_wers:
+            print("{}\t{}".format(key, val), file=f)
+
+    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key, val in test_set_wers:
+        s += "{}\t{}{}\n".format(key, val, note)
+        note = ""
+    logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    Aidatatang_200zhAsrDataModule.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",
+        "modified_beam_search",
+    )
+    params.res_dir = params.exp_dir / params.decoding_method
+
+    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}"
+    elif "beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{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}")
+
+    lexicon = Lexicon(params.lang_dir)
+    params.blank_id = lexicon.token_table["<blk>"]
+    params.vocab_size = max(lexicon.tokens) + 1
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    if params.avg_last_n > 0:
+        filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n]
+        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)
+    elif params.batch is not None:
+        filenames = f"{params.exp_dir}/checkpoint-{params.batch}.pt"
+        logging.info(f"averaging {filenames}")
+        model.to(device)
+        model.load_state_dict(average_checkpoints([filenames], device=device))
+    else:
+        start = params.epoch - params.avg + 1
+        filenames = []
+        for i in range(start, params.epoch + 1):
+            if start >= 0:
+                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))
+
+    model.to(device)
+    model.eval()
+    model.device = device
+
+    if params.decoding_method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    # Note: Please use "pip install webdataset==0.1.103"
+    # for installing the webdataset.
+    import glob
+    import os
+
+    from lhotse import CutSet
+    from lhotse.dataset.webdataset import export_to_webdataset
+
+    aidatatang_200zh = Aidatatang_200zhAsrDataModule(args)
+
+    dev = "dev"
+    test = "test"
+
+    if not os.path.exists(f"{dev}/shared-0.tar"):
+        os.makedirs(dev)
+        dev_cuts = aidatatang_200zh.valid_cuts()
+        export_to_webdataset(
+            dev_cuts,
+            output_path=f"{dev}/shared-%d.tar",
+            shard_size=300,
+        )
+
+    if not os.path.exists(f"{test}/shared-0.tar"):
+        os.makedirs(test)
+        test_cuts = aidatatang_200zh.test_cuts()
+        export_to_webdataset(
+            test_cuts,
+            output_path=f"{test}/shared-%d.tar",
+            shard_size=300,
+        )
+
+    dev_shards = [
+        str(path)
+        for path in sorted(glob.glob(os.path.join(dev, "shared-*.tar")))
+    ]
+    cuts_dev_webdataset = CutSet.from_webdataset(
+        dev_shards,
+        split_by_worker=True,
+        split_by_node=True,
+        shuffle_shards=True,
+    )
+
+    test_shards = [
+        str(path)
+        for path in sorted(glob.glob(os.path.join(test, "shared-*.tar")))
+    ]
+    cuts_test_webdataset = CutSet.from_webdataset(
+        test_shards,
+        split_by_worker=True,
+        split_by_node=True,
+        shuffle_shards=True,
+    )
+
+    dev_dl = aidatatang_200zh.valid_dataloaders(cuts_dev_webdataset)
+    test_dl = aidatatang_200zh.test_dataloaders(cuts_test_webdataset)
+
+    test_sets = ["dev", "test"]
+    test_dl = [dev_dl, test_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            lexicon=lexicon,
+            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/aidatatang_200zh/ASR/pruned_transducer_stateless2/decoder.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/pruned_transducer_stateless2/decoder.py

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/encoder_interface.py

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+../../../librispeech/ASR/transducer_stateless/encoder_interface.py

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/export.py

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+# 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:
+./pruned_transducer_stateless2/export.py \
+  --exp-dir ./pruned_transducer_stateless2/exp \
+  --lang-dir data/lang_char \
+  --epoch 29 \
+  --avg 19
+
+It will generate a file exp_dir/pretrained.pt
+
+To use the generated file with `pruned_transducer_stateless2/decode.py`,
+you can do:
+
+    cd /path/to/exp_dir
+    ln -s pretrained.pt epoch-9999.pt
+
+    cd /path/to/egs/aidatatang_200zh/ASR
+    ./pruned_transducer_stateless2/decode.py \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 100 \
+        --lang-dir data/lang_char
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import torch
+from train import get_params, get_transducer_model
+
+from icefall.checkpoint import average_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 decoding."
+        "Note: Epoch counts from 0.",
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch'. ",
+    )
+
+    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(
+        "--lang-dir",
+        type=str,
+        default="data/lang_char",
+        help="The lang dir",
+    )
+
+    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",
+    )
+
+    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)
+
+    params.blank_id = 0
+    params.vocab_size = max(lexicon.tokens) + 1
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    model.to(device)
+
+    if 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 start >= 0:
+                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))
+
+    model.eval()
+
+    model.to("cpu")
+    model.eval()
+
+    if params.jit:
+        # We won't use the forward() method of the model in C++, so just ignore
+        # it here.
+        # Otherwise, one of its arguments is a ragged tensor and is not
+        # torch scriptabe.
+        model.__class__.forward = torch.jit.ignore(model.__class__.forward)
+        logging.info("Using torch.jit.script")
+        model = torch.jit.script(model)
+        filename = params.exp_dir / "cpu_jit.pt"
+        model.save(str(filename))
+        logging.info(f"Saved to {filename}")
+    else:
+        logging.info("Not using torch.jit.script")
+        # 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/aidatatang_200zh/ASR/pruned_transducer_stateless2/joiner.py

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+../../../librispeech/ASR/pruned_transducer_stateless2/joiner.py

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/model.py

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+../../../librispeech/ASR/pruned_transducer_stateless2/model.py

egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/optim.py

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+../../../librispeech/ASR/pruned_transducer_stateless2/optim.py