Merge remote-tracking branch 'dan/master' into deeper-conformer

.flake8

@@ -9,6 +9,7 @@ per-file-ignores =
     egs/tedlium3/ASR/*/conformer.py: E501,
     egs/gigaspeech/ASR/*/conformer.py: E501,
     egs/librispeech/ASR/pruned_transducer_stateless2/*.py: E501,
+    egs/librispeech/ASR/pruned_transducer_stateless4/*.py: E501,
     egs/librispeech/ASR/*/optim.py: E501,
     egs/librispeech/ASR/*/scaling.py: E501,
 

.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-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-librispeech-pruned-transducer-stateless-2022-03-12.sh

@@ -33,7 +33,7 @@ for sym in 1 2 3; do
     $repo/test_wavs/1221-135766-0002.wav
 done
 
-for method in modified_beam_search beam_search; do
+for method in fast_beam_search modified_beam_search beam_search; do
   log "$method"
 
   ./pruned_transducer_stateless/pretrained.py \
@@ -45,3 +45,32 @@ for method in modified_beam_search beam_search; do
     $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

@@ -49,3 +49,32 @@ for method in modified_beam_search beam_search fast_beam_search; do
     $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

@@ -49,3 +49,32 @@ for method in modified_beam_search beam_search fast_beam_search; do
     $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-transducer-stateless2-2022-04-19.sh

@@ -33,7 +33,7 @@ for sym in 1 2 3; do
     $repo/test_wavs/1221-135766-0002.wav
 done
 
-for method in modified_beam_search beam_search; do
+for method in fast_beam_search modified_beam_search beam_search; do
   log "$method"
 
   ./transducer_stateless2/pretrained.py \
@@ -45,3 +45,32 @@ for method in modified_beam_search beam_search; do
     $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-transducer-stateless-librispeech-100h.sh

@@ -33,7 +33,7 @@ for sym in 1 2 3; do
     $repo/test_wavs/1221-135766-0002.wav
 done
 
-for method in modified_beam_search beam_search; do
+for method in modified_beam_search beam_search fast_beam_search; do
   log "$method"
 
   ./transducer_stateless_multi_datasets/pretrained.py \
@@ -45,3 +45,32 @@ for method in modified_beam_search beam_search; do
     $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

@@ -33,7 +33,7 @@ for sym in 1 2 3; do
     $repo/test_wavs/1221-135766-0002.wav
 done
 
-for method in modified_beam_search beam_search; do
+for method in modified_beam_search beam_search fast_beam_search; do
   log "$method"
 
   ./transducer_stateless_multi_datasets/pretrained.py \
@@ -45,3 +45,32 @@ for method in modified_beam_search beam_search; do
     $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.sh

@@ -33,7 +33,7 @@ for sym in 1 2 3; do
     $repo/test_wavs/1221-135766-0002.wav
 done
 
-for method in modified_beam_search beam_search; do
+for method in fast_beam_search modified_beam_search beam_search; do
   log "$method"
 
   ./transducer_stateless/pretrained.py \
@@ -46,15 +46,31 @@ for method in modified_beam_search beam_search; do
     $repo/test_wavs/1221-135766-0002.wav
 done
 
-for method in modified_beam_search beam_search; do
-  log "$method"
+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/
 
-  ./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
+  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/workflows/run-librispeech-2022-03-12.yml

@@ -24,9 +24,18 @@ 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_librispeech_2022_03_12:
-    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:
@@ -63,20 +72,82 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          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/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
+
+      - 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

@@ -24,9 +24,18 @@ 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_librispeech_2022_04_29:
-    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:
@@ -63,18 +72,51 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          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/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
+
+      - 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
@@ -83,3 +125,55 @@ jobs:
           .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 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: 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 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_stateless2-2022-04-29
+          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-transducer-stateless2-2022-04-19.yml

@@ -24,9 +24,18 @@ 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_librispeech_2022_04_19:
-    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:
@@ -63,20 +72,82 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          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/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
+
+      - 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

@@ -62,14 +62,7 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          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/install-kaldifeat.sh
 
       - name: Inference with pre-trained model
         shell: bash

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

@@ -23,9 +23,18 @@ 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_multi_datasets_librispeech_100h:
-    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:
@@ -62,20 +71,82 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          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/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
+
+      - 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

@@ -23,9 +23,18 @@ 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_multi_datasets_librispeech_960h:
-    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:
@@ -62,20 +71,82 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          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/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
+
+      - 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

@@ -62,14 +62,7 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          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/install-kaldifeat.sh
 
       - name: Inference with pre-trained model
         shell: bash

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

@@ -62,14 +62,7 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          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/install-kaldifeat.sh
 
       - name: Inference with pre-trained model
         shell: bash

.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:
@@ -23,9 +23,18 @@ 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:
@@ -62,20 +71,82 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          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/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
+
+      - 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:$PYTHONPATH
+
           .github/scripts/run-pre-trained-transducer-stateless.sh
+
+      - name: Display decoding results for transducer_stateless
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR/
+          tree ./transducer_stateless/exp
+
+          cd transducer_stateless
+          echo "results for transducer_stateless"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===modified beam search==="
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for transducer_stateless
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
+        with:
+          name: torch-${{ 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

@@ -62,13 +62,6 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          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
 
       - name: Inference with pre-trained model

egs/aishell/ASR/transducer_stateless/conformer.py

@@ -110,7 +110,9 @@ class Conformer(Transformer):
         x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
 
         # Caution: We assume the subsampling factor is 4!
-        lengths = ((x_lens - 1) // 2 - 1) // 2
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            lengths = ((x_lens - 1) // 2 - 1) // 2
         assert x.size(0) == lengths.max().item()
         mask = make_pad_mask(lengths)
 

egs/aishell/ASR/transducer_stateless/export.py

@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 #
 # Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
+#           2022 Xiaomi Corporation (Author: Mingshuang Luo)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -22,7 +23,7 @@
 Usage:
 ./transducer_stateless/export.py \
   --exp-dir ./transducer_stateless/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --lang-dir data/lang_char \
   --epoch 20 \
   --avg 10
 
@@ -33,20 +34,19 @@ To use the generated file with `transducer_stateless/decode.py`, you can do:
     cd /path/to/exp_dir
     ln -s pretrained.pt epoch-9999.pt
 
-    cd /path/to/egs/librispeech/ASR
+    cd /path/to/egs/aishell/ASR
     ./transducer_stateless/decode.py \
         --exp-dir ./transducer_stateless/exp \
         --epoch 9999 \
         --avg 1 \
         --max-duration 1 \
-        --bpe-model data/lang_bpe_500/bpe.model
+        --lang-dir data/lang_char
 """
 
 import argparse
 import logging
 from pathlib import Path
 
-import sentencepiece as spm
 import torch
 import torch.nn as nn
 from conformer import Conformer
@@ -56,6 +56,7 @@ from model import Transducer
 
 from icefall.checkpoint import average_checkpoints, load_checkpoint
 from icefall.env import get_env_info
+from icefall.lexicon import Lexicon
 from icefall.utils import AttributeDict, str2bool
 
 
@@ -91,10 +92,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--lang-dir",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_char",
+        help="The lang dir",
     )
 
     parser.add_argument(
@@ -194,12 +195,10 @@ def main():
 
     logging.info(f"device: {device}")
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    lexicon = Lexicon(params.lang_dir)
 
-    # <blk> is defined in local/train_bpe_model.py
-    params.blank_id = sp.piece_to_id("<blk>")
-    params.vocab_size = sp.get_piece_size()
+    params.blank_id = 0
+    params.vocab_size = max(lexicon.tokens) + 1
 
     logging.info(params)
 

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

@@ -19,49 +19,62 @@
 Usage:
 (1) greedy search
 ./transducer_stateless_modified-2/decode.py \
-        --epoch 89 \
-        --avg 38 \
-        --exp-dir ./transducer_stateless_modified-2/exp \
-        --max-duration 100 \
-        --decoding-method greedy_search
+    --epoch 89 \
+    --avg 38 \
+    --exp-dir ./transducer_stateless_modified-2/exp \
+    --max-duration 100 \
+    --decoding-method greedy_search
 
-(2) beam search
-./transducer_stateless_modified/decode.py \
-        --epoch 89 \
-        --avg 38 \
-        --exp-dir ./transducer_stateless_modified-2/exp \
-        --max-duration 100 \
-        --decoding-method beam_search \
-        --beam-size 4
+(2) beam search (not recommended)
+./transducer_stateless_modified-2/decode.py \
+    --epoch 89 \
+    --avg 38 \
+    --exp-dir ./transducer_stateless_modified-2/exp \
+    --max-duration 100 \
+    --decoding-method beam_search \
+    --beam-size 4
 
 (3) modified beam search
 ./transducer_stateless_modified-2/decode.py \
-        --epoch 89 \
-        --avg 38 \
-        --exp-dir ./transducer_stateless_modified/exp \
-        --max-duration 100 \
-        --decoding-method modified_beam_search \
-        --beam-size 4
+    --epoch 89 \
+    --avg 38 \
+    --exp-dir ./transducer_stateless_modified-2/exp \
+    --max-duration 100 \
+    --decoding-method modified_beam_search \
+    --beam-size 4
+(4) fast beam search
+./transducer_stateless_modified-2/decode.py \
+    --epoch 89 \
+    --avg 38 \
+    --exp-dir ./transducer_stateless_modified-2/exp \
+    --max-duration 100 \
+    --decoding-method fast_beam_search \
+    --beam-size 4 \
+    --max-contexts 4 \
+    --max-states 8
 """
 
 import argparse
 import logging
 from collections import defaultdict
 from pathlib import Path
-from typing import Dict, List, Tuple
+from typing import Dict, List, Optional, Tuple
 
+import k2
 import torch
 import torch.nn as nn
 from aishell import AIShell
 from asr_datamodule import AsrDataModule
-from beam_search import beam_search, greedy_search, modified_beam_search
-from conformer import Conformer
-from decoder import Decoder
-from joiner import Joiner
-from model import Transducer
+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, load_checkpoint
-from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
@@ -114,6 +127,7 @@ def get_parser():
           - greedy_search
           - beam_search
           - modified_beam_search
+          - fast_beam_search
         """,
     )
 
@@ -121,8 +135,35 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
-        help="Used only when --decoding-method is beam_search "
-        "and modified_beam_search",
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=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(
@@ -132,84 +173,24 @@ def get_parser():
         help="The context size in the decoder. 1 means bigram; "
         "2 means tri-gram",
     )
+
     parser.add_argument(
         "--max-sym-per-frame",
         type=int,
-        default=3,
-        help="Maximum number of symbols per frame",
+        default=1,
+        help="""Maximum number of symbols per frame.
+        Used only when --decoding_method is greedy_search""",
     )
+
     return parser
 
 
-def get_params() -> AttributeDict:
-    params = AttributeDict(
-        {
-            # parameters for conformer
-            "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": get_env_info(),
-        }
-    )
-    return params
-
-
-def get_encoder_model(params: AttributeDict):
-    # TODO: We can add an option to switch between Conformer and Transformer
-    encoder = Conformer(
-        num_features=params.feature_dim,
-        output_dim=params.encoder_out_dim,
-        subsampling_factor=params.subsampling_factor,
-        d_model=params.attention_dim,
-        nhead=params.nhead,
-        dim_feedforward=params.dim_feedforward,
-        num_encoder_layers=params.num_encoder_layers,
-        vgg_frontend=params.vgg_frontend,
-    )
-    return encoder
-
-
-def get_decoder_model(params: AttributeDict):
-    decoder = Decoder(
-        vocab_size=params.vocab_size,
-        embedding_dim=params.encoder_out_dim,
-        blank_id=params.blank_id,
-        context_size=params.context_size,
-    )
-    return decoder
-
-
-def get_joiner_model(params: AttributeDict):
-    joiner = Joiner(
-        input_dim=params.encoder_out_dim,
-        output_dim=params.vocab_size,
-    )
-    return joiner
-
-
-def get_transducer_model(params: AttributeDict):
-    encoder = get_encoder_model(params)
-    decoder = get_decoder_model(params)
-    joiner = get_joiner_model(params)
-
-    model = Transducer(
-        encoder=encoder,
-        decoder=decoder,
-        joiner=joiner,
-    )
-    return model
-
-
 def decode_one_batch(
     params: AttributeDict,
     model: nn.Module,
-    lexicon: Lexicon,
+    token_table: k2.SymbolTable,
     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:
@@ -230,8 +211,8 @@ def decode_one_batch(
         It is the return value from iterating
         `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
         for the format of the `batch`.
-      lexicon:
-        It contains the token symbol table and the word symbol table.
+      token_table:
+        It maps token ID to a string.
     Returns:
       Return the decoding result. See above description for the format of
       the returned dict.
@@ -249,44 +230,80 @@ def decode_one_batch(
     encoder_out, encoder_out_lens = model.encoder(
         x=feature, x_lens=feature_lens
     )
-    hyps = []
-    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
-            )
-        elif params.decoding_method == "modified_beam_search":
-            hyp = modified_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[i] for i in hyp])
+    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,
+        )
+    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,
+        )
+    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,
+        )
+    else:
+        hyp_tokens = []
+        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}"
+                )
+            hyp_tokens.append(hyp)
+
+    hyps = [[token_table[t] for t in tokens] for tokens in hyp_tokens]
 
     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_{params.beam_size}": hyps}
+        return {f"beam_size_{params.beam_size}": hyps}
 
 
 def decode_dataset(
     dl: torch.utils.data.DataLoader,
     params: AttributeDict,
     model: nn.Module,
-    lexicon: Lexicon,
+    token_table: k2.SymbolTable,
+    decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
 
@@ -297,6 +314,11 @@ def decode_dataset(
         It is returned by :func:`get_params`.
       model:
         The neural model.
+      token_table:
+        It maps a token ID to a string.
+      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.
@@ -312,9 +334,9 @@ def decode_dataset(
         num_batches = "?"
 
     if params.decoding_method == "greedy_search":
-        log_interval = 100
+        log_interval = 50
     else:
-        log_interval = 2
+        log_interval = 10
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -323,7 +345,8 @@ def decode_dataset(
         hyps_dict = decode_one_batch(
             params=params,
             model=model,
-            lexicon=lexicon,
+            token_table=token_table,
+            decoding_graph=decoding_graph,
             batch=batch,
         )
 
@@ -358,6 +381,7 @@ def save_results(
             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.
@@ -408,13 +432,21 @@ def main():
     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 "beam_search" in params.decoding_method:
-        params.suffix += f"-beam-{params.beam_size}"
+
+    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"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -456,6 +488,11 @@ def main():
     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}")
 
@@ -472,7 +509,8 @@ def main():
             dl=test_dl,
             params=params,
             model=model,
-            lexicon=lexicon,
+            token_table=lexicon.token_table,
+            decoding_graph=decoding_graph,
         )
 
         save_results(
@@ -484,8 +522,5 @@ def main():
     logging.info("Done!")
 
 
-torch.set_num_threads(1)
-torch.set_num_interop_threads(1)
-
 if __name__ == "__main__":
     main()

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

@@ -19,7 +19,7 @@
 """
 Usage:
 
-# greedy search
+(1) greedy search
 ./transducer_stateless_modified-2/pretrained.py \
   --checkpoint /path/to/pretrained.pt \
   --lang-dir /path/to/lang_char \
@@ -27,7 +27,7 @@ Usage:
   /path/to/foo.wav \
   /path/to/bar.wav
 
-# beam search
+(2) beam search
 ./transducer_stateless_modified-2/pretrained.py \
   --checkpoint /path/to/pretrained.pt \
   --lang-dir /path/to/lang_char \
@@ -36,7 +36,7 @@ Usage:
   /path/to/foo.wav \
   /path/to/bar.wav
 
-# modified beam search
+(3) modified beam search
 ./transducer_stateless_modified-2/pretrained.py \
   --checkpoint /path/to/pretrained.pt \
   --lang-dir /path/to/lang_char \
@@ -45,6 +45,14 @@ Usage:
   /path/to/foo.wav \
   /path/to/bar.wav
 
+(4) fast beam search
+./transducer_stateless_modified-2/pretrained.py \
+  --checkpoint /path/to/pretrained.pt \
+  --lang-dir /path/to/lang_char \
+  --method fast_beam_search \
+  --beam-size 4 \
+  /path/to/foo.wav \
+  /path/to/bar.wav
 """
 
 import argparse
@@ -53,11 +61,13 @@ import math
 from pathlib import Path
 from typing import List
 
+import k2
 import kaldifeat
 import torch
 import torchaudio
 from beam_search import (
     beam_search,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -97,6 +107,7 @@ def get_parser():
           - greedy_search
           - beam_search
           - modified_beam_search
+          - fast_beam_search
         """,
     )
 
@@ -121,7 +132,33 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
-        help="Used only when --method is beam_search and modified_beam_search",
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --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 --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --method is fast_beam_search""",
     )
 
     parser.add_argument(
@@ -134,11 +171,10 @@ def get_parser():
     parser.add_argument(
         "--max-sym-per-frame",
         type=int,
-        default=3,
+        default=1,
         help="Maximum number of symbols per frame. "
         "Use only when --method is greedy_search",
     )
-    return parser
 
     return parser
 
@@ -225,20 +261,37 @@ def main():
     encoder_out, encoder_out_lens = model.encoder(
         x=features, x_lens=feature_lens
     )
+
+    num_waves = encoder_out.size(0)
     hyp_list = []
-    if params.method == "greedy_search" and params.max_sym_per_frame == 1:
+    logging.info(f"Using {params.method}")
+
+    if params.method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+        hyp_list = 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,
+        )
+    elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
         hyp_list = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
     elif params.method == "modified_beam_search":
         hyp_list = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
     else:
-        for i in range(encoder_out.size(0)):
+        for i in range(num_waves):
             # fmt: off
             encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
             # fmt: on

egs/aishell/ASR/transducer_stateless_modified/decode.py

@@ -19,48 +19,63 @@
 Usage:
 (1) greedy search
 ./transducer_stateless_modified/decode.py \
-        --epoch 64 \
-        --avg 33 \
-        --exp-dir ./transducer_stateless_modified/exp \
-        --max-duration 100 \
-        --decoding-method greedy_search
+    --epoch 14 \
+    --avg 7 \
+    --exp-dir ./transducer_stateless_modified/exp \
+    --max-duration 600 \
+    --decoding-method greedy_search
 
-(2) beam search
+(2) beam search (not recommended)
 ./transducer_stateless_modified/decode.py \
-        --epoch 14 \
-        --avg 7 \
-        --exp-dir ./transducer_stateless_modified/exp \
-        --max-duration 100 \
-        --decoding-method beam_search \
-        --beam-size 4
+    --epoch 14 \
+    --avg 7 \
+    --exp-dir ./transducer_stateless_modified/exp \
+    --max-duration 600 \
+    --decoding-method beam_search \
+    --beam-size 4
 
 (3) modified beam search
 ./transducer_stateless_modified/decode.py \
-        --epoch 14 \
-        --avg 7 \
-        --exp-dir ./transducer_stateless_modified/exp \
-        --max-duration 100 \
-        --decoding-method modified_beam_search \
-        --beam-size 4
+    --epoch 14 \
+    --avg 7 \
+    --exp-dir ./transducer_stateless_modified/exp \
+    --max-duration 600 \
+    --decoding-method modified_beam_search \
+    --beam-size 4
+
+(4) fast beam search
+./transducer_stateless_modified/decode.py \
+    --epoch 14 \
+    --avg 7 \
+    --exp-dir ./transducer_stateless_modified/exp \
+    --max-duration 600 \
+    --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, Tuple
+from typing import Dict, List, Optional, Tuple
 
+import k2
 import torch
 import torch.nn as nn
 from asr_datamodule import AishellAsrDataModule
-from beam_search import beam_search, greedy_search, modified_beam_search
-from conformer import Conformer
-from decoder import Decoder
-from joiner import Joiner
-from model import Transducer
+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, load_checkpoint
-from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
@@ -113,6 +128,7 @@ def get_parser():
           - greedy_search
           - beam_search
           - modified_beam_search
+          - fast_beam_search
         """,
     )
 
@@ -120,7 +136,35 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
-        help="Used only when --decoding-method is beam_search",
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=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(
@@ -130,84 +174,24 @@ def get_parser():
         help="The context size in the decoder. 1 means bigram; "
         "2 means tri-gram",
     )
+
     parser.add_argument(
         "--max-sym-per-frame",
         type=int,
-        default=3,
-        help="Maximum number of symbols per frame",
+        default=1,
+        help="""Maximum number of symbols per frame.
+        Used only when --decoding_method is greedy_search""",
     )
+
     return parser
 
 
-def get_params() -> AttributeDict:
-    params = AttributeDict(
-        {
-            # parameters for conformer
-            "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": get_env_info(),
-        }
-    )
-    return params
-
-
-def get_encoder_model(params: AttributeDict):
-    # TODO: We can add an option to switch between Conformer and Transformer
-    encoder = Conformer(
-        num_features=params.feature_dim,
-        output_dim=params.encoder_out_dim,
-        subsampling_factor=params.subsampling_factor,
-        d_model=params.attention_dim,
-        nhead=params.nhead,
-        dim_feedforward=params.dim_feedforward,
-        num_encoder_layers=params.num_encoder_layers,
-        vgg_frontend=params.vgg_frontend,
-    )
-    return encoder
-
-
-def get_decoder_model(params: AttributeDict):
-    decoder = Decoder(
-        vocab_size=params.vocab_size,
-        embedding_dim=params.encoder_out_dim,
-        blank_id=params.blank_id,
-        context_size=params.context_size,
-    )
-    return decoder
-
-
-def get_joiner_model(params: AttributeDict):
-    joiner = Joiner(
-        input_dim=params.encoder_out_dim,
-        output_dim=params.vocab_size,
-    )
-    return joiner
-
-
-def get_transducer_model(params: AttributeDict):
-    encoder = get_encoder_model(params)
-    decoder = get_decoder_model(params)
-    joiner = get_joiner_model(params)
-
-    model = Transducer(
-        encoder=encoder,
-        decoder=decoder,
-        joiner=joiner,
-    )
-    return model
-
-
 def decode_one_batch(
     params: AttributeDict,
     model: nn.Module,
-    lexicon: Lexicon,
+    token_table: k2.SymbolTable,
     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:
@@ -228,8 +212,11 @@ def decode_one_batch(
         It is the return value from iterating
         `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
         for the format of the `batch`.
-      lexicon:
-        It contains the token symbol table and the word symbol table.
+      token_table:
+        It maps token ID to a string.
+      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.
@@ -247,44 +234,80 @@ def decode_one_batch(
     encoder_out, encoder_out_lens = model.encoder(
         x=feature, x_lens=feature_lens
     )
-    hyps = []
-    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
-            )
-        elif params.decoding_method == "modified_beam_search":
-            hyp = modified_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[i] for i in hyp])
+    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,
+        )
+    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,
+        )
+    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,
+        )
+    else:
+        hyp_tokens = []
+        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}"
+                )
+            hyp_tokens.append(hyp)
+
+    hyps = [[token_table[t] for t in tokens] for tokens in hyp_tokens]
 
     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_{params.beam_size}": hyps}
+        return {f"beam_size_{params.beam_size}": hyps}
 
 
 def decode_dataset(
     dl: torch.utils.data.DataLoader,
     params: AttributeDict,
     model: nn.Module,
-    lexicon: Lexicon,
+    token_table: k2.SymbolTable,
+    decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
 
@@ -295,6 +318,11 @@ def decode_dataset(
         It is returned by :func:`get_params`.
       model:
         The neural model.
+      token_table:
+        It maps a token ID to a string.
+      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.
@@ -310,9 +338,9 @@ def decode_dataset(
         num_batches = "?"
 
     if params.decoding_method == "greedy_search":
-        log_interval = 100
+        log_interval = 50
     else:
-        log_interval = 2
+        log_interval = 10
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -321,7 +349,8 @@ def decode_dataset(
         hyps_dict = decode_one_batch(
             params=params,
             model=model,
-            lexicon=lexicon,
+            token_table=token_table,
+            decoding_graph=decoding_graph,
             batch=batch,
         )
 
@@ -356,6 +385,7 @@ def save_results(
             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.
@@ -406,13 +436,21 @@ def main():
     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 "beam_search" in params.decoding_method:
-        params.suffix += f"-beam-{params.beam_size}"
+
+    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"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -452,6 +490,11 @@ def main():
     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}")
 
@@ -467,7 +510,8 @@ def main():
             dl=test_dl,
             params=params,
             model=model,
-            lexicon=lexicon,
+            token_table=lexicon.token_table,
+            decoding_graph=decoding_graph,
         )
 
         save_results(
@@ -479,8 +523,5 @@ def main():
     logging.info("Done!")
 
 
-torch.set_num_threads(1)
-torch.set_num_interop_threads(1)
-
 if __name__ == "__main__":
     main()

egs/aishell/ASR/transducer_stateless_modified/pretrained.py

@@ -19,7 +19,7 @@
 """
 Usage:
 
-# greedy search
+(1) greedy search
 ./transducer_stateless_modified/pretrained.py \
   --checkpoint /path/to/pretrained.pt \
   --lang-dir /path/to/lang_char \
@@ -27,7 +27,7 @@ Usage:
   /path/to/foo.wav \
   /path/to/bar.wav
 
-# beam search
+(2) beam search
 ./transducer_stateless_modified/pretrained.py \
   --checkpoint /path/to/pretrained.pt \
   --lang-dir /path/to/lang_char \
@@ -36,7 +36,7 @@ Usage:
   /path/to/foo.wav \
   /path/to/bar.wav
 
-# modified beam search
+(3) modified beam search
 ./transducer_stateless_modified/pretrained.py \
   --checkpoint /path/to/pretrained.pt \
   --lang-dir /path/to/lang_char \
@@ -45,6 +45,14 @@ Usage:
   /path/to/foo.wav \
   /path/to/bar.wav
 
+(4) fast beam search
+./transducer_stateless_modified/pretrained.py \
+  --checkpoint /path/to/pretrained.pt \
+  --lang-dir /path/to/lang_char \
+  --method fast_beam_search \
+  --beam-size 4 \
+  /path/to/foo.wav \
+  /path/to/bar.wav
 """
 
 import argparse
@@ -53,11 +61,13 @@ import math
 from pathlib import Path
 from typing import List
 
+import k2
 import kaldifeat
 import torch
 import torchaudio
 from beam_search import (
     beam_search,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -97,6 +107,7 @@ def get_parser():
           - greedy_search
           - beam_search
           - modified_beam_search
+          - fast_beam_search
         """,
     )
 
@@ -121,7 +132,33 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
-        help="Used only when --method is beam_search and modified_beam_search",
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --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 --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --method is fast_beam_search""",
     )
 
     parser.add_argument(
@@ -134,11 +171,10 @@ def get_parser():
     parser.add_argument(
         "--max-sym-per-frame",
         type=int,
-        default=3,
+        default=1,
         help="Maximum number of symbols per frame. "
         "Use only when --method is greedy_search",
     )
-    return parser
 
     return parser
 
@@ -225,20 +261,37 @@ def main():
     encoder_out, encoder_out_lens = model.encoder(
         x=features, x_lens=feature_lens
     )
+
+    num_waves = encoder_out.size(0)
     hyp_list = []
-    if params.method == "greedy_search" and params.max_sym_per_frame == 1:
+    logging.info(f"Using {params.method}")
+
+    if params.method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+        hyp_list = 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,
+        )
+    elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
         hyp_list = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
     elif params.method == "modified_beam_search":
         hyp_list = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
     else:
-        for i in range(encoder_out.size(0)):
+        for i in range(num_waves):
             # fmt: off
             encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
             # fmt: on

egs/gigaspeech/ASR/conformer_ctc/decode.py

@@ -177,8 +177,8 @@ def post_processing(
 ) -> List[Tuple[List[str], List[str]]]:
     new_results = []
     for ref, hyp in results:
-        new_ref = asr_text_post_processing(" ".join(ref))
-        new_hyp = asr_text_post_processing(" ".join(hyp))
+        new_ref = asr_text_post_processing(" ".join(ref)).split()
+        new_hyp = asr_text_post_processing(" ".join(hyp)).split()
         new_results.append((new_ref, new_hyp))
     return new_results
 

egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py

@@ -15,6 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import warnings
 from dataclasses import dataclass
 from typing import Dict, List, Optional
 
@@ -26,6 +27,149 @@ from icefall.decode import Nbest, one_best_decoding
 from icefall.utils import get_texts
 
 
+def fast_beam_search_one_best(
+    model: Transducer,
+    decoding_graph: k2.Fsa,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+) -> List[List[int]]:
+    """It limits the maximum number of symbols per frame to 1.
+
+    A lattice is first obtained using modified beam search, and then
+    the shortest path within the lattice is used as the final output.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      decoding_graph:
+        Decoding graph used for decoding, may be a TrivialGraph or a HLG.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      encoder_out_lens:
+        A tensor of shape (N,) containing the number of frames in `encoder_out`
+        before padding.
+      beam:
+        Beam value, similar to the beam used in Kaldi..
+      max_states:
+        Max states per stream per frame.
+      max_contexts:
+        Max contexts pre stream per frame.
+    Returns:
+      Return the decoded result.
+    """
+    lattice = fast_beam_search(
+        model=model,
+        decoding_graph=decoding_graph,
+        encoder_out=encoder_out,
+        encoder_out_lens=encoder_out_lens,
+        beam=beam,
+        max_states=max_states,
+        max_contexts=max_contexts,
+    )
+
+    best_path = one_best_decoding(lattice)
+    hyps = get_texts(best_path)
+    return hyps
+
+
+def fast_beam_search_nbest_oracle(
+    model: Transducer,
+    decoding_graph: k2.Fsa,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+    num_paths: int,
+    ref_texts: List[List[int]],
+    use_double_scores: bool = True,
+    nbest_scale: float = 0.5,
+) -> List[List[int]]:
+    """It limits the maximum number of symbols per frame to 1.
+
+    A lattice is first obtained using modified beam search, and then
+    we select `num_paths` linear paths from the lattice. The path
+    that has the minimum edit distance with the given reference transcript
+    is used as the output.
+
+    This is the best result we can achieve for any nbest based rescoring
+    methods.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      decoding_graph:
+        Decoding graph used for decoding, may be a TrivialGraph or a HLG.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      encoder_out_lens:
+        A tensor of shape (N,) containing the number of frames in `encoder_out`
+        before padding.
+      beam:
+        Beam value, similar to the beam used in Kaldi..
+      max_states:
+        Max states per stream per frame.
+      max_contexts:
+        Max contexts pre stream per frame.
+      num_paths:
+        Number of paths to extract from the decoded lattice.
+      ref_texts:
+        A list-of-list of integers containing the reference transcripts.
+        If the decoding_graph is a trivial_graph, the integer ID is the
+        BPE token ID.
+      use_double_scores:
+        True to use double precision for computation. False to use
+        single precision.
+      nbest_scale:
+        It's the scale applied to the lattice.scores. A smaller value
+        yields more unique paths.
+
+    Returns:
+      Return the decoded result.
+    """
+    lattice = fast_beam_search(
+        model=model,
+        decoding_graph=decoding_graph,
+        encoder_out=encoder_out,
+        encoder_out_lens=encoder_out_lens,
+        beam=beam,
+        max_states=max_states,
+        max_contexts=max_contexts,
+    )
+
+    nbest = Nbest.from_lattice(
+        lattice=lattice,
+        num_paths=num_paths,
+        use_double_scores=use_double_scores,
+        nbest_scale=nbest_scale,
+    )
+
+    hyps = nbest.build_levenshtein_graphs()
+    refs = k2.levenshtein_graph(ref_texts, device=hyps.device)
+
+    levenshtein_alignment = k2.levenshtein_alignment(
+        refs=refs,
+        hyps=hyps,
+        hyp_to_ref_map=nbest.shape.row_ids(1),
+        sorted_match_ref=True,
+    )
+
+    tot_scores = levenshtein_alignment.get_tot_scores(
+        use_double_scores=False, log_semiring=False
+    )
+    ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores)
+
+    max_indexes = ragged_tot_scores.argmax()
+
+    best_path = k2.index_fsa(nbest.fsa, max_indexes)
+
+    hyps = get_texts(best_path)
+    return hyps
+
+
 def fast_beam_search(
     model: Transducer,
     decoding_graph: k2.Fsa,
@@ -34,8 +178,7 @@ def fast_beam_search(
     beam: float,
     max_states: int,
     max_contexts: int,
-    use_max: bool = False,
-) -> List[List[int]]:
+) -> k2.Fsa:
     """It limits the maximum number of symbols per frame to 1.
 
     Args:
@@ -54,11 +197,10 @@ def fast_beam_search(
         Max states per stream per frame.
       max_contexts:
         Max contexts pre stream per frame.
-      use_max:
-        True to use max operation to select the hypothesis with the largest
-        log_prob when there are duplicate hypotheses; False to use log-add.
     Returns:
-      Return the decoded result.
+      Return an FsaVec with axes [utt][state][arc] containing the decoded
+      lattice. Note: When the input graph is a TrivialGraph, the returned
+      lattice is actually an acceptor.
     """
     assert encoder_out.ndim == 3
 
@@ -91,7 +233,7 @@ def fast_beam_search(
         # (shape.NumElements(), 1, encoder_out_dim)
         # fmt: off
         current_encoder_out = torch.index_select(
-            encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).long()
+            encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64)
             # in some old versions of pytorch, the type of index requires
             # to be LongTensor. In the newest version of pytorch, the type
             # of index can be IntTensor or LongTensor. For supporting the
@@ -108,67 +250,7 @@ def fast_beam_search(
     decoding_streams.terminate_and_flush_to_streams()
     lattice = decoding_streams.format_output(encoder_out_lens.tolist())
 
-    if use_max:
-        best_path = one_best_decoding(lattice)
-        hyps = get_texts(best_path)
-        return hyps
-    else:
-        num_paths = 200
-        use_double_scores = True
-        nbest_scale = 0.8
-
-        nbest = Nbest.from_lattice(
-            lattice=lattice,
-            num_paths=num_paths,
-            use_double_scores=use_double_scores,
-            nbest_scale=nbest_scale,
-        )
-        # The following code is modified from nbest.intersect()
-        word_fsa = k2.invert(nbest.fsa)
-        if hasattr(lattice, "aux_labels"):
-            # delete token IDs as it is not needed
-            del word_fsa.aux_labels
-        word_fsa.scores.zero_()
-
-        word_fsa_with_epsilon_loops = k2.linear_fsa_with_self_loops(word_fsa)
-        path_to_utt_map = nbest.shape.row_ids(1)
-
-        if hasattr(lattice, "aux_labels"):
-            # lattice has token IDs as labels and word IDs as aux_labels.
-            # inv_lattice has word IDs as labels and token IDs as aux_labels
-            inv_lattice = k2.invert(lattice)
-            inv_lattice = k2.arc_sort(inv_lattice)
-        else:
-            inv_lattice = k2.arc_sort(lattice)
-
-        if inv_lattice.shape[0] == 1:
-            path_lattice = k2.intersect_device(
-                inv_lattice,
-                word_fsa_with_epsilon_loops,
-                b_to_a_map=torch.zeros_like(path_to_utt_map),
-                sorted_match_a=True,
-            )
-        else:
-            path_lattice = k2.intersect_device(
-                inv_lattice,
-                word_fsa_with_epsilon_loops,
-                b_to_a_map=path_to_utt_map,
-                sorted_match_a=True,
-            )
-
-        # path_lattice has word IDs as labels and token IDs as aux_labels
-        path_lattice = k2.top_sort(k2.connect(path_lattice))
-
-        tot_scores = path_lattice.get_tot_scores(
-            use_double_scores=use_double_scores, log_semiring=True
-        )
-
-        ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores)
-        best_hyp_indexes = ragged_tot_scores.argmax()
-
-        best_path = k2.index_fsa(nbest.fsa, best_hyp_indexes)
-        hyps = get_texts(best_path)
-        return hyps
+    return lattice
 
 
 def greedy_search(
@@ -192,10 +274,10 @@ def greedy_search(
     assert encoder_out.size(0) == 1, encoder_out.size(0)
 
     blank_id = model.decoder.blank_id
-    unk_id = model.decoder.unk_id
     context_size = model.decoder.context_size
+    unk_id = getattr(model, "unk_id", blank_id)
 
-    device = model.device
+    device = next(model.parameters()).device
 
     decoder_input = torch.tensor(
         [blank_id] * context_size, device=device, dtype=torch.int64
@@ -229,7 +311,7 @@ def greedy_search(
         # logits is (1, 1, 1, vocab_size)
 
         y = logits.argmax().item()
-        if y != blank_id and y != unk_id:
+        if y not in (blank_id, unk_id):
             hyp.append(y)
             decoder_input = torch.tensor(
                 [hyp[-context_size:]], device=device
@@ -248,7 +330,9 @@ def greedy_search(
 
 
 def greedy_search_batch(
-    model: Transducer, encoder_out: torch.Tensor
+    model: Transducer,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
 ) -> List[List[int]]:
     """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
     Args:
@@ -256,6 +340,9 @@ def greedy_search_batch(
         The transducer model.
       encoder_out:
         Output from the encoder. Its shape is (N, T, C), where N >= 1.
+      encoder_out_lens:
+        A 1-D tensor of shape (N,), containing number of valid frames in
+        encoder_out before padding.
     Returns:
       Return a list-of-list of token IDs containing the decoded results.
       len(ans) equals to encoder_out.size(0).
@@ -263,28 +350,48 @@ def greedy_search_batch(
     assert encoder_out.ndim == 3
     assert encoder_out.size(0) >= 1, encoder_out.size(0)
 
-    device = model.device
+    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
+        input=encoder_out,
+        lengths=encoder_out_lens.cpu(),
+        batch_first=True,
+        enforce_sorted=False,
+    )
 
-    batch_size = encoder_out.size(0)
-    T = encoder_out.size(1)
+    device = next(model.parameters()).device
 
     blank_id = model.decoder.blank_id
-    unk_id = model.decoder.unk_id
+    unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
 
-    hyps = [[blank_id] * context_size for _ in range(batch_size)]
+    batch_size_list = packed_encoder_out.batch_sizes.tolist()
+    N = encoder_out.size(0)
+    assert torch.all(encoder_out_lens > 0), encoder_out_lens
+    assert N == batch_size_list[0], (N, batch_size_list)
+
+    hyps = [[blank_id] * context_size for _ in range(N)]
 
     decoder_input = torch.tensor(
         hyps,
         device=device,
         dtype=torch.int64,
-    )  # (batch_size, context_size)
+    )  # (N, context_size)
 
     decoder_out = model.decoder(decoder_input, need_pad=False)
-    # decoder_out: (batch_size, 1, decoder_out_dim)
-    for t in range(T):
-        current_encoder_out = encoder_out[:, t : t + 1, :].unsqueeze(2)  # noqa
+    # decoder_out: (N, 1, decoder_out_dim)
+
+    encoder_out = packed_encoder_out.data
+
+    offset = 0
+    for batch_size in batch_size_list:
+        start = offset
+        end = offset + batch_size
+        current_encoder_out = encoder_out.data[start:end]
+        current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1)
         # current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim)
+        offset = end
+
+        decoder_out = decoder_out[:batch_size]
+
         logits = model.joiner(current_encoder_out, decoder_out.unsqueeze(1))
         # logits'shape (batch_size, 1, 1, vocab_size)
 
@@ -293,12 +400,12 @@ def greedy_search_batch(
         y = logits.argmax(dim=1).tolist()
         emitted = False
         for i, v in enumerate(y):
-            if v != blank_id and v != unk_id:
+            if v not in (blank_id, unk_id):
                 hyps[i].append(v)
                 emitted = True
         if emitted:
             # update decoder output
-            decoder_input = [h[-context_size:] for h in hyps]
+            decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
             decoder_input = torch.tensor(
                 decoder_input,
                 device=device,
@@ -306,7 +413,12 @@ def greedy_search_batch(
             )
             decoder_out = model.decoder(decoder_input, need_pad=False)
 
-    ans = [h[context_size:] for h in hyps]
+    sorted_ans = [h[context_size:] for h in hyps]
+    ans = []
+    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
+    for i in range(N):
+        ans.append(sorted_ans[unsorted_indices[i]])
+
     return ans
 
 
@@ -471,6 +583,7 @@ def _get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape:
 def modified_beam_search(
     model: Transducer,
     encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
     beam: int = 4,
     use_max: bool = False,
 ) -> List[List[int]]:
@@ -481,6 +594,9 @@ def modified_beam_search(
         The transducer model.
       encoder_out:
         Output from the encoder. Its shape is (N, T, C).
+      encoder_out_lens:
+        A 1-D tensor of shape (N,), containing number of valid frames in
+        encoder_out before padding.
       beam:
         Number of active paths during the beam search.
       use_max:
@@ -491,16 +607,27 @@ def modified_beam_search(
       for the i-th utterance.
     """
     assert encoder_out.ndim == 3, encoder_out.shape
+    assert encoder_out.size(0) >= 1, encoder_out.size(0)
 
-    batch_size = encoder_out.size(0)
-    T = encoder_out.size(1)
+    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
+        input=encoder_out,
+        lengths=encoder_out_lens.cpu(),
+        batch_first=True,
+        enforce_sorted=False,
+    )
 
     blank_id = model.decoder.blank_id
-    unk_id = model.decoder.unk_id
+    unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
-    device = model.device
-    B = [HypothesisList() for _ in range(batch_size)]
-    for i in range(batch_size):
+    device = next(model.parameters()).device
+
+    batch_size_list = packed_encoder_out.batch_sizes.tolist()
+    N = encoder_out.size(0)
+    assert torch.all(encoder_out_lens > 0), encoder_out_lens
+    assert N == batch_size_list[0], (N, batch_size_list)
+
+    B = [HypothesisList() for _ in range(N)]
+    for i in range(N):
         B[i].add(
             Hypothesis(
                 ys=[blank_id] * context_size,
@@ -509,9 +636,20 @@ def modified_beam_search(
             use_max=use_max,
         )
 
-    for t in range(T):
-        current_encoder_out = encoder_out[:, t : t + 1, :].unsqueeze(2)  # noqa
+    encoder_out = packed_encoder_out.data
+
+    offset = 0
+    finalized_B = []
+    for batch_size in batch_size_list:
+        start = offset
+        end = offset + batch_size
+        current_encoder_out = encoder_out.data[start:end]
+        current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1)
         # current_encoder_out's shape is (batch_size, 1, 1, encoder_out_dim)
+        offset = end
+
+        finalized_B = B[batch_size:] + finalized_B
+        B = B[:batch_size]
 
         hyps_shape = _get_hyps_shape(B).to(device)
 
@@ -565,8 +703,10 @@ def modified_beam_search(
         for i in range(batch_size):
             topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam)
 
-            topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
-            topk_token_indexes = (topk_indexes % vocab_size).tolist()
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
+                topk_token_indexes = (topk_indexes % vocab_size).tolist()
 
             for k in range(len(topk_hyp_indexes)):
                 hyp_idx = topk_hyp_indexes[k]
@@ -574,15 +714,21 @@ def modified_beam_search(
 
                 new_ys = hyp.ys[:]
                 new_token = topk_token_indexes[k]
-                if new_token != blank_id and new_token != unk_id:
+                if new_token not in (blank_id, unk_id):
                     new_ys.append(new_token)
 
                 new_log_prob = topk_log_probs[k]
                 new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob)
                 B[i].add(new_hyp)
 
+    B = B + finalized_B
     best_hyps = [b.get_most_probable(length_norm=True) for b in B]
-    ans = [h.ys[context_size:] for h in best_hyps]
+
+    sorted_ans = [h.ys[context_size:] for h in best_hyps]
+    ans = []
+    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
+    for i in range(N):
+        ans.append(sorted_ans[unsorted_indices[i]])
 
     return ans
 
@@ -619,10 +765,10 @@ def _deprecated_modified_beam_search(
     # support only batch_size == 1 for now
     assert encoder_out.size(0) == 1, encoder_out.size(0)
     blank_id = model.decoder.blank_id
-    unk_id = model.decoder.unk_id
+    unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
 
-    device = model.device
+    device = next(model.parameters()).device
 
     T = encoder_out.size(1)
 
@@ -679,14 +825,16 @@ def _deprecated_modified_beam_search(
         topk_hyp_indexes = topk_indexes // logits.size(-1)
         topk_token_indexes = topk_indexes % logits.size(-1)
 
-        topk_hyp_indexes = topk_hyp_indexes.tolist()
-        topk_token_indexes = topk_token_indexes.tolist()
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            topk_hyp_indexes = topk_hyp_indexes.tolist()
+            topk_token_indexes = topk_token_indexes.tolist()
 
         for i in range(len(topk_hyp_indexes)):
             hyp = A[topk_hyp_indexes[i]]
             new_ys = hyp.ys[:]
             new_token = topk_token_indexes[i]
-            if new_token != blank_id and new_token != unk_id:
+            if new_token not in (blank_id, unk_id):
                 new_ys.append(new_token)
             new_log_prob = topk_log_probs[i]
             new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob)
@@ -727,10 +875,10 @@ def beam_search(
     # support only batch_size == 1 for now
     assert encoder_out.size(0) == 1, encoder_out.size(0)
     blank_id = model.decoder.blank_id
-    unk_id = model.decoder.unk_id
+    unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
 
-    device = model.device
+    device = next(model.parameters()).device
 
     decoder_input = torch.tensor(
         [blank_id] * context_size,
@@ -813,7 +961,7 @@ def beam_search(
             # Second, process other non-blank labels
             values, indices = log_prob.topk(beam + 1)
             for i, v in zip(indices.tolist(), values.tolist()):
-                if i == blank_id or i == unk_id:
+                if i in (blank_id, unk_id):
                     continue
                 new_ys = y_star.ys + [i]
                 new_log_prob = y_star.log_prob + v

egs/librispeech/ASR/pruned_transducer_stateless/decode.py

@@ -19,53 +19,53 @@
 Usage:
 (1) greedy search
 ./pruned_transducer_stateless/decode.py \
-        --epoch 28 \
-        --avg 15 \
-        --exp-dir ./pruned_transducer_stateless/exp \
-        --max-duration 100 \
-        --decoding-method greedy_search
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless/exp \
+    --max-duration 600 \
+    --decoding-method greedy_search
 
-(2) beam search
+(2) beam search (not recommended)
 ./pruned_transducer_stateless/decode.py \
-        --epoch 28 \
-        --avg 15 \
-        --exp-dir ./pruned_transducer_stateless/exp \
-        --max-duration 100 \
-        --decoding-method beam_search \
-        --beam-size 4
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless/exp \
+    --max-duration 600 \
+    --decoding-method beam_search \
+    --beam-size 4
 
 (3) modified beam search
 ./pruned_transducer_stateless/decode.py \
-        --epoch 28 \
-        --avg 15 \
-        --exp-dir ./pruned_transducer_stateless/exp \
-        --max-duration 100 \
-        --decoding-method modified_beam_search \
-        --beam-size 4
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless/exp \
+    --max-duration 600 \
+    --decoding-method modified_beam_search \
+    --beam-size 4
 
 (4) fast beam search
 ./pruned_transducer_stateless/decode.py \
-        --epoch 28 \
-        --avg 15 \
-        --exp-dir ./pruned_transducer_stateless/exp \
-        --max-duration 1500 \
-        --decoding-method fast_beam_search \
-        --beam 4 \
-        --max-contexts 4 \
-        --max-states 8
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search \
+    --beam 4 \
+    --max-contexts 4 \
+    --max-states 8
 
 (5) fast beam search using LG
 ./pruned_transducer_stateless/decode.py \
-        --epoch 28 \
-        --avg 15 \
-        --exp-dir ./pruned_transducer_stateless/exp \
-        --use-LG True \
-        --use-max False \
-        --max-duration 1500 \
-        --decoding-method fast_beam_search \
-        --beam 8 \
-        --max-contexts 8 \
-        --max-states 64
+    --epoch 28 \
+    --avg 15 \
+    --exp-dir ./pruned_transducer_stateless/exp \
+    --use-LG True \
+    --use-max False \
+    --max-duration 600 \
+    --decoding-method fast_beam_search \
+    --beam 8 \
+    --max-contexts 8 \
+    --max-states 64
 """
 
 
@@ -82,7 +82,7 @@ import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
 from beam_search import (
     beam_search,
-    fast_beam_search,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -307,7 +307,7 @@ def decode_one_batch(
     hyps = []
 
     if params.decoding_method == "fast_beam_search":
-        hyp_tokens = fast_beam_search(
+        hyp_tokens = fast_beam_search_one_best(
             model=model,
             decoding_graph=decoding_graph,
             encoder_out=encoder_out,
@@ -315,7 +315,6 @@ def decode_one_batch(
             beam=params.beam,
             max_contexts=params.max_contexts,
             max_states=params.max_states,
-            use_max=params.use_max,
         )
         if params.use_LG:
             for hyp in hyp_tokens:
@@ -330,6 +329,7 @@ def decode_one_batch(
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
@@ -337,6 +337,7 @@ def decode_one_batch(
         hyp_tokens = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
             use_max=params.use_max,
         )
@@ -421,9 +422,9 @@ def decode_dataset(
         num_batches = "?"
 
     if params.decoding_method == "greedy_search":
-        log_interval = 100
+        log_interval = 50
     else:
-        log_interval = 2
+        log_interval = 10
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):

egs/librispeech/ASR/pruned_transducer_stateless/pretrained.py

@@ -25,7 +25,7 @@ Usage:
         /path/to/foo.wav \
         /path/to/bar.wav \
 
-(1) beam search
+(2) beam search
 ./pruned_transducer_stateless/pretrained.py \
         --checkpoint ./pruned_transducer_stateless/exp/pretrained.pt \
         --bpe-model ./data/lang_bpe_500/bpe.model \
@@ -34,6 +34,24 @@ Usage:
         /path/to/foo.wav \
         /path/to/bar.wav \
 
+(3) modified beam search
+./pruned_transducer_stateless/pretrained.py \
+        --checkpoint ./pruned_transducer_stateless/exp/pretrained.pt \
+        --bpe-model ./data/lang_bpe_500/bpe.model \
+        --method modified_beam_search \
+        --beam-size 4 \
+        /path/to/foo.wav \
+        /path/to/bar.wav \
+
+(4) fast beam search
+./pruned_transducer_stateless/pretrained.py \
+        --checkpoint ./pruned_transducer_stateless/exp/pretrained.pt \
+        --bpe-model ./data/lang_bpe_500/bpe.model \
+        --method fast_beam_search \
+        --beam-size 4 \
+        /path/to/foo.wav \
+        /path/to/bar.wav \
+
 You can also use `./pruned_transducer_stateless/exp/epoch-xx.pt`.
 
 Note: ./pruned_transducer_stateless/exp/pretrained.pt is generated by
@@ -46,12 +64,14 @@ import logging
 import math
 from typing import List
 
+import k2
 import kaldifeat
 import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import (
     beam_search,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -77,9 +97,7 @@ def get_parser():
     parser.add_argument(
         "--bpe-model",
         type=str,
-        help="""Path to bpe.model.
-        Used only when method is ctc-decoding.
-        """,
+        help="""Path to bpe.model.""",
     )
 
     parser.add_argument(
@@ -90,6 +108,7 @@ def get_parser():
           - greedy_search
           - beam_search
           - modified_beam_search
+          - fast_beam_search
         """,
     )
 
@@ -114,7 +133,33 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
-        help="Used only when --method is beam_search and modified_beam_search",
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --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 --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --method is fast_beam_search""",
     )
 
     parser.add_argument(
@@ -230,10 +275,25 @@ def main():
     if params.method == "beam_search":
         msg += f" with beam size {params.beam_size}"
     logging.info(msg)
-    if params.method == "modified_beam_search":
+
+    if params.method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+        hyp_tokens = fast_beam_search_one_best(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.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,
         )
 
@@ -243,6 +303,7 @@ def main():
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())

egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py

@@ -276,7 +276,7 @@ def greedy_search(
     context_size = model.decoder.context_size
     unk_id = getattr(model, "unk_id", blank_id)
 
-    device = model.device
+    device = next(model.parameters()).device
 
     decoder_input = torch.tensor(
         [blank_id] * context_size, device=device, dtype=torch.int64
@@ -335,7 +335,9 @@ def greedy_search(
 
 
 def greedy_search_batch(
-    model: Transducer, encoder_out: torch.Tensor
+    model: Transducer,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
 ) -> List[List[int]]:
     """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
     Args:
@@ -343,6 +345,9 @@ def greedy_search_batch(
         The transducer model.
       encoder_out:
         Output from the encoder. Its shape is (N, T, C), where N >= 1.
+      encoder_out_lens:
+        A 1-D tensor of shape (N,), containing number of valid frames in
+        encoder_out before padding.
     Returns:
       Return a list-of-list of token IDs containing the decoded results.
       len(ans) equals to encoder_out.size(0).
@@ -350,31 +355,49 @@ def greedy_search_batch(
     assert encoder_out.ndim == 3
     assert encoder_out.size(0) >= 1, encoder_out.size(0)
 
-    device = model.device
+    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
+        input=encoder_out,
+        lengths=encoder_out_lens.cpu(),
+        batch_first=True,
+        enforce_sorted=False,
+    )
 
-    batch_size = encoder_out.size(0)
-    T = encoder_out.size(1)
+    device = next(model.parameters()).device
 
     blank_id = model.decoder.blank_id
     unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
 
-    hyps = [[blank_id] * context_size for _ in range(batch_size)]
+    batch_size_list = packed_encoder_out.batch_sizes.tolist()
+    N = encoder_out.size(0)
+    assert torch.all(encoder_out_lens > 0), encoder_out_lens
+    assert N == batch_size_list[0], (N, batch_size_list)
+
+    hyps = [[blank_id] * context_size for _ in range(N)]
 
     decoder_input = torch.tensor(
         hyps,
         device=device,
         dtype=torch.int64,
-    )  # (batch_size, context_size)
+    )  # (N, context_size)
 
     decoder_out = model.decoder(decoder_input, need_pad=False)
     decoder_out = model.joiner.decoder_proj(decoder_out)
-    encoder_out = model.joiner.encoder_proj(encoder_out)
+    # decoder_out: (N, 1, decoder_out_dim)
 
-    # decoder_out: (batch_size, 1, decoder_out_dim)
-    for t in range(T):
-        current_encoder_out = encoder_out[:, t : t + 1, :].unsqueeze(2)  # noqa
+    encoder_out = model.joiner.encoder_proj(packed_encoder_out.data)
+
+    offset = 0
+    for batch_size in batch_size_list:
+        start = offset
+        end = offset + batch_size
+        current_encoder_out = encoder_out.data[start:end]
+        current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1)
         # current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim)
+        offset = end
+
+        decoder_out = decoder_out[:batch_size]
+
         logits = model.joiner(
             current_encoder_out, decoder_out.unsqueeze(1), project_input=False
         )
@@ -390,7 +413,7 @@ def greedy_search_batch(
                 emitted = True
         if emitted:
             # update decoder output
-            decoder_input = [h[-context_size:] for h in hyps]
+            decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
             decoder_input = torch.tensor(
                 decoder_input,
                 device=device,
@@ -399,7 +422,12 @@ def greedy_search_batch(
             decoder_out = model.decoder(decoder_input, need_pad=False)
             decoder_out = model.joiner.decoder_proj(decoder_out)
 
-    ans = [h[context_size:] for h in hyps]
+    sorted_ans = [h[context_size:] for h in hyps]
+    ans = []
+    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
+    for i in range(N):
+        ans.append(sorted_ans[unsorted_indices[i]])
+
     return ans
 
 
@@ -557,6 +585,7 @@ def _get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape:
 def modified_beam_search(
     model: Transducer,
     encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
     beam: int = 4,
 ) -> List[List[int]]:
     """Beam search in batch mode with --max-sym-per-frame=1 being hardcoded.
@@ -566,6 +595,9 @@ def modified_beam_search(
         The transducer model.
       encoder_out:
         Output from the encoder. Its shape is (N, T, C).
+      encoder_out_lens:
+        A 1-D tensor of shape (N,), containing number of valid frames in
+        encoder_out before padding.
       beam:
         Number of active paths during the beam search.
     Returns:
@@ -573,16 +605,27 @@ def modified_beam_search(
       for the i-th utterance.
     """
     assert encoder_out.ndim == 3, encoder_out.shape
+    assert encoder_out.size(0) >= 1, encoder_out.size(0)
 
-    batch_size = encoder_out.size(0)
-    T = encoder_out.size(1)
+    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
+        input=encoder_out,
+        lengths=encoder_out_lens.cpu(),
+        batch_first=True,
+        enforce_sorted=False,
+    )
 
     blank_id = model.decoder.blank_id
     unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
-    device = model.device
-    B = [HypothesisList() for _ in range(batch_size)]
-    for i in range(batch_size):
+    device = next(model.parameters()).device
+
+    batch_size_list = packed_encoder_out.batch_sizes.tolist()
+    N = encoder_out.size(0)
+    assert torch.all(encoder_out_lens > 0), encoder_out_lens
+    assert N == batch_size_list[0], (N, batch_size_list)
+
+    B = [HypothesisList() for _ in range(N)]
+    for i in range(N):
         B[i].add(
             Hypothesis(
                 ys=[blank_id] * context_size,
@@ -590,11 +633,20 @@ def modified_beam_search(
             )
         )
 
-    encoder_out = model.joiner.encoder_proj(encoder_out)
+    encoder_out = model.joiner.encoder_proj(packed_encoder_out.data)
 
-    for t in range(T):
-        current_encoder_out = encoder_out[:, t : t + 1, :].unsqueeze(2)  # noqa
+    offset = 0
+    finalized_B = []
+    for batch_size in batch_size_list:
+        start = offset
+        end = offset + batch_size
+        current_encoder_out = encoder_out.data[start:end]
+        current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1)
         # current_encoder_out's shape is (batch_size, 1, 1, encoder_out_dim)
+        offset = end
+
+        finalized_B = B[batch_size:] + finalized_B
+        B = B[:batch_size]
 
         hyps_shape = _get_hyps_shape(B).to(device)
 
@@ -668,8 +720,14 @@ def modified_beam_search(
                 new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob)
                 B[i].add(new_hyp)
 
+    B = B + finalized_B
     best_hyps = [b.get_most_probable(length_norm=True) for b in B]
-    ans = [h.ys[context_size:] for h in best_hyps]
+
+    sorted_ans = [h.ys[context_size:] for h in best_hyps]
+    ans = []
+    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
+    for i in range(N):
+        ans.append(sorted_ans[unsorted_indices[i]])
 
     return ans
 
@@ -705,7 +763,7 @@ def _deprecated_modified_beam_search(
     unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
 
-    device = model.device
+    device = next(model.parameters()).device
 
     T = encoder_out.size(1)
 
@@ -813,7 +871,7 @@ def beam_search(
     unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
 
-    device = model.device
+    device = next(model.parameters()).device
 
     decoder_input = torch.tensor(
         [blank_id] * context_size,

egs/librispeech/ASR/pruned_transducer_stateless2/decode.py

@@ -22,15 +22,15 @@ Usage:
         --epoch 28 \
         --avg 15 \
         --exp-dir ./pruned_transducer_stateless2/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method greedy_search
 
-(2) beam search
+(2) beam search (not recommended)
 ./pruned_transducer_stateless2/decode.py \
         --epoch 28 \
         --avg 15 \
         --exp-dir ./pruned_transducer_stateless2/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method beam_search \
         --beam-size 4
 
@@ -39,7 +39,7 @@ Usage:
         --epoch 28 \
         --avg 15 \
         --exp-dir ./pruned_transducer_stateless2/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method modified_beam_search \
         --beam-size 4
 
@@ -48,7 +48,7 @@ Usage:
         --epoch 28 \
         --avg 15 \
         --exp-dir ./pruned_transducer_stateless2/exp \
-        --max-duration 1500 \
+        --max-duration 600 \
         --decoding-method fast_beam_search \
         --beam 4 \
         --max-contexts 4 \
@@ -270,6 +270,7 @@ def decode_one_batch(
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
@@ -277,6 +278,7 @@ def decode_one_batch(
         hyp_tokens = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
         for hyp in sp.decode(hyp_tokens):
@@ -356,9 +358,9 @@ def decode_dataset(
         num_batches = "?"
 
     if params.decoding_method == "greedy_search":
-        log_interval = 100
+        log_interval = 50
     else:
-        log_interval = 2
+        log_interval = 10
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):

egs/librispeech/ASR/pruned_transducer_stateless2/train.py

@@ -156,15 +156,16 @@ def get_parser():
         "--initial-lr",
         type=float,
         default=0.003,
-        help="The initial learning rate.  This value should not need to be changed.",
+        help="The initial learning rate.  This value should not need to "
+        "be changed.",
     )
 
     parser.add_argument(
         "--lr-batches",
         type=float,
         default=5000,
-        help="""Number of steps that affects how rapidly the learning rate decreases.
-        We suggest not to change this.""",
+        help="""Number of steps that affects how rapidly the learning rate
+        decreases. We suggest not to change this.""",
     )
 
     parser.add_argument(
@@ -670,25 +671,29 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
-            loss, loss_info = compute_loss(
-                params=params,
-                model=model,
-                sp=sp,
-                batch=batch,
-                is_training=True,
-                warmup=(params.batch_idx_train / params.model_warm_step),
-            )
-        # summary stats
-        tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
+        try:
+            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+                loss, loss_info = compute_loss(
+                    params=params,
+                    model=model,
+                    sp=sp,
+                    batch=batch,
+                    is_training=True,
+                    warmup=(params.batch_idx_train / params.model_warm_step),
+                )
+            # summary stats
+            tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
 
-        # NOTE: We use reduction==sum and loss is computed over utterances
-        # in the batch and there is no normalization to it so far.
-        scaler.scale(loss).backward()
-        scheduler.step_batch(params.batch_idx_train)
-        scaler.step(optimizer)
-        scaler.update()
-        optimizer.zero_grad()
+            # NOTE: We use reduction==sum and loss is computed over utterances
+            # in the batch and there is no normalization to it so far.
+            scaler.scale(loss).backward()
+            scheduler.step_batch(params.batch_idx_train)
+            scaler.step(optimizer)
+            scaler.update()
+            optimizer.zero_grad()
+        except:  # noqa
+            display_and_save_batch(batch, params=params, sp=sp)
+            raise
 
         if params.print_diagnostics and batch_idx == 5:
             return
@@ -933,6 +938,38 @@ def run(rank, world_size, args):
         cleanup_dist()
 
 
+def display_and_save_batch(
+    batch: dict,
+    params: AttributeDict,
+    sp: spm.SentencePieceProcessor,
+) -> None:
+    """Display the batch statistics and save the batch into disk.
+
+    Args:
+      batch:
+        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
+        for the content in it.
+      params:
+        Parameters for training. See :func:`get_params`.
+      sp:
+        The BPE model.
+    """
+    from lhotse.utils import uuid4
+
+    filename = f"{params.exp_dir}/batch-{uuid4()}.pt"
+    logging.info(f"Saving batch to {filename}")
+    torch.save(batch, filename)
+
+    supervisions = batch["supervisions"]
+    features = batch["inputs"]
+
+    logging.info(f"features shape: {features.shape}")
+
+    y = sp.encode(supervisions["text"], out_type=int)
+    num_tokens = sum(len(i) for i in y)
+    logging.info(f"num tokens: {num_tokens}")
+
+
 def scan_pessimistic_batches_for_oom(
     model: nn.Module,
     train_dl: torch.utils.data.DataLoader,
@@ -964,7 +1001,7 @@ def scan_pessimistic_batches_for_oom(
             loss.backward()
             optimizer.step()
             optimizer.zero_grad()
-        except RuntimeError as e:
+        except Exception as e:
             if "CUDA out of memory" in str(e):
                 logging.error(
                     "Your GPU ran out of memory with the current "
@@ -973,6 +1010,7 @@ def scan_pessimistic_batches_for_oom(
                     f"Failing criterion: {criterion} "
                     f"(={crit_values[criterion]}) ..."
                 )
+            display_and_save_batch(batch, params=params, sp=sp)
             raise
 
 

egs/librispeech/ASR/pruned_transducer_stateless3/decode-giga.py

@@ -22,15 +22,15 @@ Usage:
         --epoch 28 \
         --avg 15 \
         --exp-dir ./pruned_transducer_stateless3/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method greedy_search
 
-(2) beam search
+(2) beam search (not recommended)
 ./pruned_transducer_stateless3/decode-giga.py \
         --epoch 28 \
         --avg 15 \
         --exp-dir ./pruned_transducer_stateless3/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method beam_search \
         --beam-size 4
 
@@ -39,7 +39,7 @@ Usage:
         --epoch 28 \
         --avg 15 \
         --exp-dir ./pruned_transducer_stateless3/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method modified_beam_search \
         --beam-size 4
 
@@ -48,7 +48,7 @@ Usage:
         --epoch 28 \
         --avg 15 \
         --exp-dir ./pruned_transducer_stateless3/exp \
-        --max-duration 1500 \
+        --max-duration 600 \
         --decoding-method fast_beam_search \
         --beam 4 \
         --max-contexts 4 \
@@ -69,7 +69,8 @@ import torch.nn as nn
 from asr_datamodule import AsrDataModule
 from beam_search import (
     beam_search,
-    fast_beam_search,
+    fast_beam_search_nbest_oracle,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -100,27 +101,28 @@ def get_parser():
         "--epoch",
         type=int,
         default=28,
-        help="It specifies the checkpoint to use for decoding."
-        "Note: Epoch counts from 0.",
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 0.
+        You can specify --avg to use more checkpoints for model averaging.""",
     )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
     parser.add_argument(
         "--avg",
         type=int,
         default=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.
-        """,
+        "'--epoch' and '--iter'",
     )
 
     parser.add_argument(
@@ -146,6 +148,7 @@ def get_parser():
           - beam_search
           - modified_beam_search
           - fast_beam_search
+          - fast_beam_search_nbest_oracle
         """,
     )
 
@@ -165,7 +168,8 @@ def get_parser():
         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""",
+        Used only when --decoding-method is
+        fast_beam_search or fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -173,7 +177,7 @@ def get_parser():
         type=int,
         default=4,
         help="""Used only when --decoding-method is
-        fast_beam_search""",
+        fast_beam_search or fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -181,7 +185,7 @@ def get_parser():
         type=int,
         default=8,
         help="""Used only when --decoding-method is
-        fast_beam_search""",
+        fast_beam_search or fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -199,12 +203,29 @@ def get_parser():
         Used only when --decoding_method is greedy_search""",
     )
 
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=100,
+        help="""Number of paths for computed nbest oracle WER
+        when the decoding method is fast_beam_search_nbest_oracle.
+        """,
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""Scale applied to lattice scores when computing nbest paths.
+        Used only when the decoding_method is fast_beam_search_nbest_oracle.
+        """,
+    )
     return parser
 
 
 def post_processing(
-    results: List[Tuple[List[List[str]], List[List[str]]]],
-) -> List[Tuple[List[List[str]], List[List[str]]]]:
+    results: List[Tuple[List[str], List[str]]],
+) -> List[Tuple[List[str], List[str]]]:
     new_results = []
     for ref, hyp in results:
         new_ref = asr_text_post_processing(" ".join(ref)).split()
@@ -243,7 +264,8 @@ def decode_one_batch(
         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.
+        only when --decoding_method is
+        fast_beam_search or fast_beam_search_nbest_oracle.
     Returns:
       Return the decoding result. See above description for the format of
       the returned dict.
@@ -264,7 +286,7 @@ def decode_one_batch(
     hyps = []
 
     if params.decoding_method == "fast_beam_search":
-        hyp_tokens = fast_beam_search(
+        hyp_tokens = fast_beam_search_one_best(
             model=model,
             decoding_graph=decoding_graph,
             encoder_out=encoder_out,
@@ -275,6 +297,21 @@ def decode_one_batch(
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        hyp_tokens = fast_beam_search_nbest_oracle(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            ref_texts=sp.encode(supervisions["text"]),
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     elif (
         params.decoding_method == "greedy_search"
         and params.max_sym_per_frame == 1
@@ -328,6 +365,16 @@ def decode_one_batch(
                 f"max_states_{params.max_states}"
             ): hyps
         }
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        return {
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}_"
+                f"num_paths_{params.num_paths}_"
+                f"nbest_scale_{params.nbest_scale}"
+            ): hyps
+        }
     else:
         return {f"beam_size_{params.beam_size}": hyps}
 
@@ -368,9 +415,9 @@ def decode_dataset(
         num_batches = "?"
 
     if params.decoding_method == "greedy_search":
-        log_interval = 100
+        log_interval = 50
     else:
-        log_interval = 2
+        log_interval = 10
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -463,17 +510,30 @@ def main():
         "greedy_search",
         "beam_search",
         "fast_beam_search",
+        "fast_beam_search_nbest_oracle",
         "modified_beam_search",
     )
     params.res_dir = params.exp_dir / "giga" / params.decoding_method
 
-    params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
-    if "fast_beam_search" in params.decoding_method:
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    if params.decoding_method == "fast_beam_search":
         params.suffix += f"-beam-{params.beam}"
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+        params.suffix += f"-num-paths-{params.num_paths}"
+        params.suffix += f"-nbest-scale-{params.nbest_scale}"
     elif "beam_search" in params.decoding_method:
-        params.suffix += f"-beam-{params.beam_size}"
+        params.suffix += (
+            f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -490,8 +550,9 @@ def main():
     sp = spm.SentencePieceProcessor()
     sp.load(params.bpe_model)
 
-    # <blk> is defined in local/train_bpe_model.py
+    # <blk> and <unk> is defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.unk_id()
     params.vocab_size = sp.get_piece_size()
 
     logging.info(params)
@@ -499,8 +560,20 @@ def main():
     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]
+    if params.iter > 0:
+        filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+            : params.avg
+        ]
+        if len(filenames) == 0:
+            raise ValueError(
+                f"No checkpoints found for"
+                f" --iter {params.iter}, --avg {params.avg}"
+            )
+        elif len(filenames) < params.avg:
+            raise ValueError(
+                f"Not enough checkpoints ({len(filenames)}) found for"
+                f" --iter {params.iter}, --avg {params.avg}"
+            )
         logging.info(f"averaging {filenames}")
         model.to(device)
         model.load_state_dict(average_checkpoints(filenames, device=device))
@@ -519,13 +592,17 @@ def main():
     model.to(device)
     model.eval()
     model.device = device
+    model.unk_id = params.unk_id
 
     # In beam_search.py, we are using model.decoder() and model.joiner(),
     # so we have to switch to the branch for the GigaSpeech dataset.
     model.decoder = model.decoder_giga
     model.joiner = model.joiner_giga
 
-    if params.decoding_method == "fast_beam_search":
+    if params.decoding_method in (
+        "fast_beam_search",
+        "fast_beam_search_nbest_oracle",
+    ):
         decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
     else:
         decoding_graph = None

egs/librispeech/ASR/pruned_transducer_stateless3/decode.py

@@ -22,15 +22,15 @@ Usage:
         --epoch 28 \
         --avg 15 \
         --exp-dir ./pruned_transducer_stateless3/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method greedy_search
 
-(2) beam search
+(2) beam search (not recommended)
 ./pruned_transducer_stateless3/decode.py \
         --epoch 28 \
         --avg 15 \
         --exp-dir ./pruned_transducer_stateless3/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method beam_search \
         --beam-size 4
 
@@ -39,7 +39,7 @@ Usage:
         --epoch 28 \
         --avg 15 \
         --exp-dir ./pruned_transducer_stateless3/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method modified_beam_search \
         --beam-size 4
 
@@ -48,7 +48,7 @@ Usage:
         --epoch 28 \
         --avg 15 \
         --exp-dir ./pruned_transducer_stateless3/exp \
-        --max-duration 1500 \
+        --max-duration 600 \
         --decoding-method fast_beam_search \
         --beam 4 \
         --max-contexts 4 \
@@ -307,6 +307,7 @@ def decode_one_batch(
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
@@ -314,6 +315,7 @@ def decode_one_batch(
         hyp_tokens = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
         for hyp in sp.decode(hyp_tokens):
@@ -403,9 +405,9 @@ def decode_dataset(
         num_batches = "?"
 
     if params.decoding_method == "greedy_search":
-        log_interval = 100
+        log_interval = 50
     else:
-        log_interval = 2
+        log_interval = 10
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):

egs/librispeech/ASR/pruned_transducer_stateless4/__init__.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/asr_datamodule.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/beam_search.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/conformer.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/decode.py

@@ -0,0 +1,633 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Zengwei Yao)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Usage:
+(1) greedy search
+./pruned_transducer_stateless4/decode.py \
+        --epoch 30 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless4/exp \
+        --max-duration 600 \
+        --decoding-method greedy_search
+
+(2) beam search (not recommended)
+./pruned_transducer_stateless4/decode.py \
+        --epoch 30 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless4/exp \
+        --max-duration 600 \
+        --decoding-method beam_search \
+        --beam-size 4
+
+(3) modified beam search
+./pruned_transducer_stateless4/decode.py \
+        --epoch 30 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless4/exp \
+        --max-duration 600 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+
+(4) fast beam search
+./pruned_transducer_stateless4/decode.py \
+        --epoch 30 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless4/exp \
+        --max-duration 600 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
+"""
+
+
+import argparse
+import logging
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from asr_datamodule import LibriSpeechAsrDataModule
+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,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=30,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=False,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless4/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--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 integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=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,
+    sp: spm.SentencePieceProcessor,
+    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.
+      sp:
+        The BPE model.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict.
+    """
+    device = next(model.parameters()).device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+    feature_lens = supervisions["num_frames"].to(device)
+
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
+    hyps = []
+
+    if params.decoding_method == "fast_beam_search":
+        hyp_tokens = fast_beam_search_one_best(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif (
+        params.decoding_method == "greedy_search"
+        and params.max_sym_per_frame == 1
+    ):
+        hyp_tokens = greedy_search_batch(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "modified_beam_search":
+        hyp_tokens = modified_beam_search(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    else:
+        batch_size = encoder_out.size(0)
+
+        for i in range(batch_size):
+            # fmt: off
+            encoder_out_i = encoder_out[i:i + 1, :encoder_out_lens[i]]
+            # fmt: on
+            if params.decoding_method == "greedy_search":
+                hyp = greedy_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    max_sym_per_frame=params.max_sym_per_frame,
+                )
+            elif params.decoding_method == "beam_search":
+                hyp = beam_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
+                )
+            else:
+                raise ValueError(
+                    f"Unsupported decoding method: {params.decoding_method}"
+                )
+            hyps.append(sp.decode(hyp).split())
+
+    if params.decoding_method == "greedy_search":
+        return {"greedy_search": hyps}
+    elif params.decoding_method == "fast_beam_search":
+        return {
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
+        }
+    else:
+        return {f"beam_size_{params.beam_size}": hyps}
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[Tuple[List[str], List[str]]]]:
+    """Decode dataset.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
+    Returns:
+      Return a dict, whose key may be "greedy_search" if greedy search
+      is used, or it may be "beam_7" if beam size of 7 is used.
+      Its value is a list of tuples. Each tuple contains two elements:
+      The first is the reference transcript, and the second is the
+      predicted result.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    if params.decoding_method == "greedy_search":
+        log_interval = 50
+    else:
+        log_interval = 10
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            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):
+                ref_words = ref_text.split()
+                this_batch.append((ref_words, hyp_words))
+
+            results[name].extend(this_batch)
+
+        num_cuts += len(texts)
+
+        if batch_idx % log_interval == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
+    return results
+
+
+def save_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
+):
+    test_set_wers = dict()
+    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()
+    LibriSpeechAsrDataModule.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
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    if "fast_beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+    elif "beam_search" in params.decoding_method:
+        params.suffix += (
+            f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    if params.use_averaged_model:
+        params.suffix += "-use-averaged-model"
+
+    setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
+    logging.info("Decoding started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> and <unk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg + 1]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0
+            start = params.epoch - params.avg
+            assert start >= 1
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to(device)
+    model.eval()
+
+    if 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}")
+
+    librispeech = LibriSpeechAsrDataModule(args)
+
+    test_clean_cuts = librispeech.test_clean_cuts()
+    test_other_cuts = librispeech.test_other_cuts()
+
+    test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
+    test_other_dl = librispeech.test_dataloaders(test_other_cuts)
+
+    test_sets = ["test-clean", "test-other"]
+    test_dl = [test_clean_dl, test_other_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            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/librispeech/ASR/pruned_transducer_stateless4/decoder.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/encoder_interface.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/export.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/joiner.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/model.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/optim.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/scaling.py

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

egs/librispeech/ASR/transducer_stateless/beam_search.py

@@ -22,6 +22,235 @@ import k2
 import torch
 from model import Transducer
 
+from icefall.decode import Nbest, one_best_decoding
+from icefall.utils import get_texts
+
+
+def fast_beam_search_one_best(
+    model: Transducer,
+    decoding_graph: k2.Fsa,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+) -> List[List[int]]:
+    """It limits the maximum number of symbols per frame to 1.
+
+    A lattice is first obtained using modified beam search, and then
+    the shortest path within the lattice is used as the final output.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      decoding_graph:
+        Decoding graph used for decoding, may be a TrivialGraph or a HLG.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      encoder_out_lens:
+        A tensor of shape (N,) containing the number of frames in `encoder_out`
+        before padding.
+      beam:
+        Beam value, similar to the beam used in Kaldi..
+      max_states:
+        Max states per stream per frame.
+      max_contexts:
+        Max contexts pre stream per frame.
+    Returns:
+      Return the decoded result.
+    """
+    lattice = fast_beam_search(
+        model=model,
+        decoding_graph=decoding_graph,
+        encoder_out=encoder_out,
+        encoder_out_lens=encoder_out_lens,
+        beam=beam,
+        max_states=max_states,
+        max_contexts=max_contexts,
+    )
+
+    best_path = one_best_decoding(lattice)
+    hyps = get_texts(best_path)
+    return hyps
+
+
+def fast_beam_search_nbest_oracle(
+    model: Transducer,
+    decoding_graph: k2.Fsa,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+    num_paths: int,
+    ref_texts: List[List[int]],
+    use_double_scores: bool = True,
+    nbest_scale: float = 0.5,
+) -> List[List[int]]:
+    """It limits the maximum number of symbols per frame to 1.
+
+    A lattice is first obtained using modified beam search, and then
+    we select `num_paths` linear paths from the lattice. The path
+    that has the minimum edit distance with the given reference transcript
+    is used as the output.
+
+    This is the best result we can achieve for any nbest based rescoring
+    methods.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      decoding_graph:
+        Decoding graph used for decoding, may be a TrivialGraph or a HLG.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      encoder_out_lens:
+        A tensor of shape (N,) containing the number of frames in `encoder_out`
+        before padding.
+      beam:
+        Beam value, similar to the beam used in Kaldi..
+      max_states:
+        Max states per stream per frame.
+      max_contexts:
+        Max contexts pre stream per frame.
+      num_paths:
+        Number of paths to extract from the decoded lattice.
+      ref_texts:
+        A list-of-list of integers containing the reference transcripts.
+        If the decoding_graph is a trivial_graph, the integer ID is the
+        BPE token ID.
+      use_double_scores:
+        True to use double precision for computation. False to use
+        single precision.
+      nbest_scale:
+        It's the scale applied to the lattice.scores. A smaller value
+        yields more unique paths.
+
+    Returns:
+      Return the decoded result.
+    """
+    lattice = fast_beam_search(
+        model=model,
+        decoding_graph=decoding_graph,
+        encoder_out=encoder_out,
+        encoder_out_lens=encoder_out_lens,
+        beam=beam,
+        max_states=max_states,
+        max_contexts=max_contexts,
+    )
+
+    nbest = Nbest.from_lattice(
+        lattice=lattice,
+        num_paths=num_paths,
+        use_double_scores=use_double_scores,
+        nbest_scale=nbest_scale,
+    )
+
+    hyps = nbest.build_levenshtein_graphs()
+    refs = k2.levenshtein_graph(ref_texts, device=hyps.device)
+
+    levenshtein_alignment = k2.levenshtein_alignment(
+        refs=refs,
+        hyps=hyps,
+        hyp_to_ref_map=nbest.shape.row_ids(1),
+        sorted_match_ref=True,
+    )
+
+    tot_scores = levenshtein_alignment.get_tot_scores(
+        use_double_scores=False, log_semiring=False
+    )
+    ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores)
+
+    max_indexes = ragged_tot_scores.argmax()
+
+    best_path = k2.index_fsa(nbest.fsa, max_indexes)
+
+    hyps = get_texts(best_path)
+    return hyps
+
+
+def fast_beam_search(
+    model: Transducer,
+    decoding_graph: k2.Fsa,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+    beam: float,
+    max_states: int,
+    max_contexts: int,
+) -> k2.Fsa:
+    """It limits the maximum number of symbols per frame to 1.
+
+    Args:
+      model:
+        An instance of `Transducer`.
+      decoding_graph:
+        Decoding graph used for decoding, may be a TrivialGraph or a HLG.
+      encoder_out:
+        A tensor of shape (N, T, C) from the encoder.
+      encoder_out_lens:
+        A tensor of shape (N,) containing the number of frames in `encoder_out`
+        before padding.
+      beam:
+        Beam value, similar to the beam used in Kaldi..
+      max_states:
+        Max states per stream per frame.
+      max_contexts:
+        Max contexts pre stream per frame.
+    Returns:
+      Return an FsaVec with axes [utt][state][arc] containing the decoded
+      lattice. Note: When the input graph is a TrivialGraph, the returned
+      lattice is actually an acceptor.
+    """
+    assert encoder_out.ndim == 3
+
+    context_size = model.decoder.context_size
+    vocab_size = model.decoder.vocab_size
+
+    B, T, C = encoder_out.shape
+
+    config = k2.RnntDecodingConfig(
+        vocab_size=vocab_size,
+        decoder_history_len=context_size,
+        beam=beam,
+        max_contexts=max_contexts,
+        max_states=max_states,
+    )
+    individual_streams = []
+    for i in range(B):
+        individual_streams.append(k2.RnntDecodingStream(decoding_graph))
+    decoding_streams = k2.RnntDecodingStreams(individual_streams, config)
+
+    encoder_out_len = torch.ones(1, dtype=torch.int32)
+    decoder_out_len = torch.ones(1, dtype=torch.int32)
+
+    for t in range(T):
+        # shape is a RaggedShape of shape (B, context)
+        # contexts is a Tensor of shape (shape.NumElements(), context_size)
+        shape, contexts = decoding_streams.get_contexts()
+        # `nn.Embedding()` in torch below v1.7.1 supports only torch.int64
+        contexts = contexts.to(torch.int64)
+        # decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim)
+        decoder_out = model.decoder(contexts, need_pad=False)
+        # current_encoder_out is of shape
+        # (shape.NumElements(), 1, joiner_dim)
+        # fmt: off
+        current_encoder_out = torch.index_select(
+            encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64)
+        )
+        # fmt: on
+        logits = model.joiner(
+            current_encoder_out,
+            decoder_out,
+            encoder_out_len.expand(decoder_out.size(0)),
+            decoder_out_len.expand(decoder_out.size(0)),
+        )  # (N, vocab_size)
+        log_probs = logits.log_softmax(dim=-1)
+        decoding_streams.advance(log_probs)
+    decoding_streams.terminate_and_flush_to_streams()
+    lattice = decoding_streams.format_output(encoder_out_lens.tolist())
+
+    return lattice
+
 
 def greedy_search(
     model: Transducer, encoder_out: torch.Tensor, max_sym_per_frame: int
@@ -104,7 +333,9 @@ def greedy_search(
 
 
 def greedy_search_batch(
-    model: Transducer, encoder_out: torch.Tensor
+    model: Transducer,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
 ) -> List[List[int]]:
     """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
     Args:
@@ -112,6 +343,9 @@ def greedy_search_batch(
         The transducer model.
       encoder_out:
         Output from the encoder. Its shape is (N, T, C), where N >= 1.
+      encoder_out_lens:
+        A 1-D tensor of shape (N,), containing number of valid frames in
+        encoder_out before padding.
     Returns:
       Return a list-of-list of token IDs containing the decoded results.
       len(ans) equals to encoder_out.size(0).
@@ -119,32 +353,54 @@ def greedy_search_batch(
     assert encoder_out.ndim == 3
     assert encoder_out.size(0) >= 1, encoder_out.size(0)
 
-    device = model.device
+    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
+        input=encoder_out,
+        lengths=encoder_out_lens.cpu(),
+        batch_first=True,
+        enforce_sorted=False,
+    )
 
-    batch_size = encoder_out.size(0)
-    T = encoder_out.size(1)
+    device = next(model.parameters()).device
 
     blank_id = model.decoder.blank_id
     context_size = model.decoder.context_size
 
-    hyps = [[blank_id] * context_size for _ in range(batch_size)]
+    batch_size_list = packed_encoder_out.batch_sizes.tolist()
+    N = encoder_out.size(0)
+    assert torch.all(encoder_out_lens > 0), encoder_out_lens
+    assert N == batch_size_list[0], (N, batch_size_list)
+
+    hyps = [[blank_id] * context_size for _ in range(N)]
 
     decoder_input = torch.tensor(
         hyps,
         device=device,
         dtype=torch.int64,
-    )  # (batch_size, context_size)
+    )  # (N, context_size)
     decoder_out = model.decoder(decoder_input, need_pad=False)
-    # decoder_out: (batch_size, 1, decoder_out_dim)
+    # decoder_out: (N, 1, decoder_out_dim)
 
-    encoder_out_len = torch.ones(batch_size, dtype=torch.int32)
-    decoder_out_len = torch.ones(batch_size, dtype=torch.int32)
+    encoder_out_len = torch.ones(1, dtype=torch.int32)
+    decoder_out_len = torch.ones(1, dtype=torch.int32)
 
-    for t in range(T):
-        current_encoder_out = encoder_out[:, t : t + 1, :]  # noqa
+    encoder_out = packed_encoder_out.data
+
+    offset = 0
+    for batch_size in batch_size_list:
+        start = offset
+        end = offset + batch_size
+        current_encoder_out = encoder_out.data[start:end]
+        current_encoder_out = current_encoder_out.unsqueeze(1)
         # current_encoder_out's shape: (batch_size, 1, encoder_out_dim)
+        offset = end
+
+        decoder_out = decoder_out[:batch_size]
+
         logits = model.joiner(
-            current_encoder_out, decoder_out, encoder_out_len, decoder_out_len
+            current_encoder_out,
+            decoder_out,
+            encoder_out_len.expand(batch_size),
+            decoder_out_len.expand(batch_size),
         )  # (batch_size, vocab_size)
 
         assert logits.ndim == 2, logits.shape
@@ -157,7 +413,7 @@ def greedy_search_batch(
 
         if emitted:
             # update decoder output
-            decoder_input = [h[-context_size:] for h in hyps]
+            decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
             decoder_input = torch.tensor(
                 decoder_input,
                 device=device,
@@ -168,7 +424,12 @@ def greedy_search_batch(
                 need_pad=False,
             )  # (batch_size, 1, decoder_out_dim)
 
-    ans = [h[context_size:] for h in hyps]
+    sorted_ans = [h[context_size:] for h in hyps]
+    ans = []
+    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
+    for i in range(N):
+        ans.append(sorted_ans[unsorted_indices[i]])
+
     return ans
 
 
@@ -415,6 +676,7 @@ def _get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape:
 def modified_beam_search(
     model: Transducer,
     encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
     beam: int = 4,
 ) -> List[List[int]]:
     """Beam search in batch mode with --max-sym-per-frame=1 being hardcodded.
@@ -424,6 +686,9 @@ def modified_beam_search(
         The transducer model.
       encoder_out:
         Output from the encoder. Its shape is (N, T, C).
+      encoder_out_lens:
+        A 1-D tensor of shape (N,), containing number of valid frames in
+        encoder_out before padding.
       beam:
         Number of active paths during the beam search.
     Returns:
@@ -431,15 +696,26 @@ def modified_beam_search(
       for the i-th utterance.
     """
     assert encoder_out.ndim == 3, encoder_out.shape
+    assert encoder_out.size(0) >= 1, encoder_out.size(0)
 
-    batch_size = encoder_out.size(0)
-    T = encoder_out.size(1)
+    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
+        input=encoder_out,
+        lengths=encoder_out_lens.cpu(),
+        batch_first=True,
+        enforce_sorted=False,
+    )
 
     blank_id = model.decoder.blank_id
     context_size = model.decoder.context_size
-    device = model.device
-    B = [HypothesisList() for _ in range(batch_size)]
-    for i in range(batch_size):
+    device = next(model.parameters()).device
+
+    batch_size_list = packed_encoder_out.batch_sizes.tolist()
+    N = encoder_out.size(0)
+    assert torch.all(encoder_out_lens > 0), encoder_out_lens
+    assert N == batch_size_list[0], (N, batch_size_list)
+
+    B = [HypothesisList() for _ in range(N)]
+    for i in range(N):
         B[i].add(
             Hypothesis(
                 ys=[blank_id] * context_size,
@@ -449,9 +725,20 @@ def modified_beam_search(
 
     encoder_out_len = torch.tensor([1])
     decoder_out_len = torch.tensor([1])
-    for t in range(T):
-        current_encoder_out = encoder_out[:, t : t + 1, :]  # noqa
+
+    encoder_out = packed_encoder_out.data
+    offset = 0
+    finalized_B = []
+    for batch_size in batch_size_list:
+        start = offset
+        end = offset + batch_size
+        current_encoder_out = encoder_out.data[start:end]
+        current_encoder_out = current_encoder_out.unsqueeze(1)
         # current_encoder_out's shape is: (batch_size, 1, encoder_out_dim)
+        offset = end
+
+        finalized_B = B[batch_size:] + finalized_B
+        B = B[:batch_size]
 
         hyps_shape = _get_hyps_shape(B).to(device)
 
@@ -524,8 +811,14 @@ def modified_beam_search(
                 new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob)
                 B[i].add(new_hyp)
 
+    B = B + finalized_B
     best_hyps = [b.get_most_probable(length_norm=True) for b in B]
-    ans = [h.ys[context_size:] for h in best_hyps]
+
+    sorted_ans = [h.ys[context_size:] for h in best_hyps]
+    ans = []
+    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
+    for i in range(N):
+        ans.append(sorted_ans[unsorted_indices[i]])
 
     return ans
 

egs/librispeech/ASR/transducer_stateless/decode.py

@@ -19,29 +19,40 @@
 Usage:
 (1) greedy search
 ./transducer_stateless/decode.py \
-        --epoch 14 \
-        --avg 7 \
-        --exp-dir ./transducer_stateless/exp \
-        --max-duration 100 \
-        --decoding-method greedy_search
+    --epoch 14 \
+    --avg 7 \
+    --exp-dir ./transducer_stateless/exp \
+    --max-duration 600 \
+    --decoding-method greedy_search
 
-(2) beam search
+(2) beam search (not recommended)
 ./transducer_stateless/decode.py \
-        --epoch 14 \
-        --avg 7 \
-        --exp-dir ./transducer_stateless/exp \
-        --max-duration 100 \
-        --decoding-method beam_search \
-        --beam-size 4
+    --epoch 14 \
+    --avg 7 \
+    --exp-dir ./transducer_stateless/exp \
+    --max-duration 600 \
+    --decoding-method beam_search \
+    --beam-size 4
 
 (3) modified beam search
 ./transducer_stateless/decode.py \
-        --epoch 14 \
-        --avg 7 \
-        --exp-dir ./transducer_stateless/exp \
-        --max-duration 100 \
-        --decoding-method modified_beam_search \
-        --beam-size 4
+    --epoch 14 \
+    --avg 7 \
+    --exp-dir ./transducer_stateless/exp \
+    --max-duration 600 \
+    --decoding-method modified_beam_search \
+    --beam-size 4
+
+(4) fast beam search
+./transducer_stateless/decode.py \
+    --epoch 14 \
+    --avg 7 \
+    --exp-dir ./transducer_stateless/exp \
+    --max-duration 600 \
+    --decoding-method fast_beam_search \
+    --beam 4 \
+    --max-contexts 4 \
+    --max-states 8
 """
 
 
@@ -49,14 +60,16 @@ import argparse
 import logging
 from collections import defaultdict
 from pathlib import Path
-from typing import Dict, List, Tuple
+from typing import Dict, List, Optional, Tuple
 
+import k2
 import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
 from beam_search import (
     beam_search,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -115,6 +128,7 @@ def get_parser():
           - greedy_search
           - beam_search
           - modified_beam_search
+          - fast_beam_search
         """,
     )
 
@@ -122,8 +136,35 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=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
-        beam_search or modified_beam_search""",
+        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(
@@ -149,6 +190,7 @@ def decode_one_batch(
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
     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:
@@ -171,6 +213,9 @@ def decode_one_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.
@@ -188,24 +233,44 @@ def decode_one_batch(
     encoder_out, encoder_out_lens = model.encoder(
         x=feature, x_lens=feature_lens
     )
-    hyp_list: List[List[int]] = []
 
-    if (
+    hyps = []
+
+    if params.decoding_method == "fast_beam_search":
+        hyp_tokens = fast_beam_search_one_best(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif (
         params.decoding_method == "greedy_search"
         and params.max_sym_per_frame == 1
     ):
-        hyp_list = greedy_search_batch(
+        hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     elif params.decoding_method == "modified_beam_search":
-        hyp_list = modified_beam_search(
+        hyp_tokens = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     else:
         batch_size = encoder_out.size(0)
+
         for i in range(batch_size):
             # fmt: off
             encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
@@ -226,14 +291,20 @@ def decode_one_batch(
                 raise ValueError(
                     f"Unsupported decoding method: {params.decoding_method}"
                 )
-            hyp_list.append(hyp)
-
-    hyps = [sp.decode(hyp).split() for hyp in hyp_list]
+            hyps.append(sp.decode(hyp).split())
 
     if params.decoding_method == "greedy_search":
         return {"greedy_search": hyps}
+    elif params.decoding_method == "fast_beam_search":
+        return {
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
+        }
     else:
-        return {f"beam_{params.beam_size}": hyps}
+        return {f"beam_size_{params.beam_size}": hyps}
 
 
 def decode_dataset(
@@ -241,6 +312,7 @@ def decode_dataset(
     params: AttributeDict,
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
+    decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
 
@@ -253,6 +325,9 @@ def decode_dataset(
         The neural model.
       sp:
         The BPE model.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
     Returns:
       Return a dict, whose key may be "greedy_search" if greedy search
       is used, or it may be "beam_7" if beam size of 7 is used.
@@ -268,9 +343,9 @@ def decode_dataset(
         num_batches = "?"
 
     if params.decoding_method == "greedy_search":
-        log_interval = 100
+        log_interval = 50
     else:
-        log_interval = 2
+        log_interval = 10
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -280,6 +355,7 @@ def decode_dataset(
             params=params,
             model=model,
             sp=sp,
+            decoding_graph=decoding_graph,
             batch=batch,
         )
 
@@ -360,13 +436,21 @@ def main():
     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 "beam_search" in params.decoding_method:
-        params.suffix += f"-beam-{params.beam_size}"
+
+    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"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -408,6 +492,11 @@ def main():
     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}")
 
@@ -428,6 +517,7 @@ def main():
             params=params,
             model=model,
             sp=sp,
+            decoding_graph=decoding_graph,
         )
 
         save_results(

egs/librispeech/ASR/transducer_stateless/decoder.py

@@ -58,6 +58,7 @@ class Decoder(nn.Module):
             padding_idx=blank_id,
         )
         self.blank_id = blank_id
+        self.vocab_size = vocab_size
 
         assert context_size >= 1, context_size
         self.context_size = context_size

egs/librispeech/ASR/transducer_stateless/pretrained.py

@@ -19,30 +19,39 @@ Usage:
 
 (1) greedy search
 ./transducer_stateless/pretrained.py \
-        --checkpoint ./transducer_stateless/exp/pretrained.pt \
-        --bpe-model ./data/lang_bpe_500/bpe.model \
-        --method greedy_search \
-        --max-sym-per-frame 1 \
-        /path/to/foo.wav \
-        /path/to/bar.wav \
+    --checkpoint ./transducer_stateless/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method greedy_search \
+    --max-sym-per-frame 1 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
 
 (2) beam search
 ./transducer_stateless/pretrained.py \
-        --checkpoint ./transducer_stateless/exp/pretrained.pt \
-        --bpe-model ./data/lang_bpe_500/bpe.model \
-        --method beam_search \
-        --beam-size 4 \
-        /path/to/foo.wav \
-        /path/to/bar.wav \
+    --checkpoint ./transducer_stateless/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method beam_search \
+    --beam-size 4 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
 
 (3) modified beam search
 ./transducer_stateless/pretrained.py \
-        --checkpoint ./transducer_stateless/exp/pretrained.pt \
-        --bpe-model ./data/lang_bpe_500/bpe.model \
-        --method modified_beam_search \
-        --beam-size 4 \
-        /path/to/foo.wav \
-        /path/to/bar.wav \
+    --checkpoint ./transducer_stateless/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method modified_beam_search \
+    --beam-size 4 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
+
+(4) fast beam search
+./transducer_stateless/pretrained.py \
+    --checkpoint ./transducer_stateless/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method fast_beam_search \
+    --beam-size 4 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
 
 You can also use `./transducer_stateless/exp/epoch-xx.pt`.
 
@@ -56,12 +65,14 @@ import logging
 import math
 from typing import List
 
+import k2
 import kaldifeat
 import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import (
     beam_search,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -87,9 +98,7 @@ def get_parser():
     parser.add_argument(
         "--bpe-model",
         type=str,
-        help="""Path to bpe.model.
-        Used only when method is ctc-decoding.
-        """,
+        help="""Path to bpe.model.""",
     )
 
     parser.add_argument(
@@ -100,6 +109,7 @@ def get_parser():
           - greedy_search
           - beam_search
           - modified_beam_search
+          - fast_beam_search
         """,
     )
 
@@ -124,7 +134,33 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
-        help="Used only when --method is beam_search and modified_beam_search ",
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --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 --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --method is fast_beam_search""",
     )
 
     parser.add_argument(
@@ -241,15 +277,28 @@ def main():
         msg += f" with beam size {params.beam_size}"
     logging.info(msg)
 
-    if params.method == "greedy_search" and params.max_sym_per_frame == 1:
+    if params.method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+        hyp_list = 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,
+        )
+    elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
         hyp_list = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
     elif params.method == "modified_beam_search":
         hyp_list = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
     else:

egs/librispeech/ASR/transducer_stateless2/decode.py

@@ -22,15 +22,15 @@ Usage:
         --epoch 14 \
         --avg 7 \
         --exp-dir ./transducer_stateless2/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method greedy_search
 
-(2) beam search
+(2) beam search (not recommended)
 ./transducer_stateless2/decode.py \
         --epoch 14 \
         --avg 7 \
         --exp-dir ./transducer_stateless2/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method beam_search \
         --beam-size 4
 
@@ -39,9 +39,20 @@ Usage:
         --epoch 14 \
         --avg 7 \
         --exp-dir ./transducer_stateless2/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method modified_beam_search \
         --beam-size 4
+
+(4) fast beam search
+./transducer_stateless2/decode.py \
+        --epoch 14 \
+        --avg 7 \
+        --exp-dir ./transducer_stateless2/exp \
+        --max-duration 600 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
 """
 
 
@@ -49,14 +60,16 @@ import argparse
 import logging
 from collections import defaultdict
 from pathlib import Path
-from typing import Dict, List, Tuple
+from typing import Dict, List, Optional, Tuple
 
+import k2
 import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
 from beam_search import (
     beam_search,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -115,6 +128,7 @@ def get_parser():
           - greedy_search
           - beam_search
           - modified_beam_search
+          - fast_beam_search
         """,
     )
 
@@ -122,8 +136,35 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=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
-        beam_search or modified_beam_search""",
+        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(
@@ -149,6 +190,7 @@ def decode_one_batch(
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
     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:
@@ -171,6 +213,9 @@ def decode_one_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.
@@ -188,24 +233,44 @@ def decode_one_batch(
     encoder_out, encoder_out_lens = model.encoder(
         x=feature, x_lens=feature_lens
     )
-    hyp_list: List[List[int]] = []
 
-    if (
+    hyps = []
+
+    if params.decoding_method == "fast_beam_search":
+        hyp_tokens = fast_beam_search_one_best(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif (
         params.decoding_method == "greedy_search"
         and params.max_sym_per_frame == 1
     ):
-        hyp_list = greedy_search_batch(
+        hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     elif params.decoding_method == "modified_beam_search":
-        hyp_list = modified_beam_search(
+        hyp_tokens = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     else:
         batch_size = encoder_out.size(0)
+
         for i in range(batch_size):
             # fmt: off
             encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
@@ -226,14 +291,20 @@ def decode_one_batch(
                 raise ValueError(
                     f"Unsupported decoding method: {params.decoding_method}"
                 )
-            hyp_list.append(hyp)
-
-    hyps = [sp.decode(hyp).split() for hyp in hyp_list]
+            hyps.append(sp.decode(hyp).split())
 
     if params.decoding_method == "greedy_search":
         return {"greedy_search": hyps}
+    elif params.decoding_method == "fast_beam_search":
+        return {
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
+        }
     else:
-        return {f"beam_{params.beam_size}": hyps}
+        return {f"beam_size_{params.beam_size}": hyps}
 
 
 def decode_dataset(
@@ -241,6 +312,7 @@ def decode_dataset(
     params: AttributeDict,
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
+    decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
 
@@ -253,6 +325,9 @@ def decode_dataset(
         The neural model.
       sp:
         The BPE model.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
     Returns:
       Return a dict, whose key may be "greedy_search" if greedy search
       is used, or it may be "beam_7" if beam size of 7 is used.
@@ -268,9 +343,9 @@ def decode_dataset(
         num_batches = "?"
 
     if params.decoding_method == "greedy_search":
-        log_interval = 100
+        log_interval = 50
     else:
-        log_interval = 2
+        log_interval = 10
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -280,6 +355,7 @@ def decode_dataset(
             params=params,
             model=model,
             sp=sp,
+            decoding_graph=decoding_graph,
             batch=batch,
         )
 
@@ -360,13 +436,21 @@ def main():
     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 "beam_search" in params.decoding_method:
-        params.suffix += f"-beam-{params.beam_size}"
+
+    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"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -408,6 +492,11 @@ def main():
     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}")
 
@@ -428,6 +517,7 @@ def main():
             params=params,
             model=model,
             sp=sp,
+            decoding_graph=decoding_graph,
         )
 
         save_results(

egs/librispeech/ASR/transducer_stateless2/pretrained.py

@@ -19,30 +19,39 @@ Usage:
 
 (1) greedy search
 ./transducer_stateless2/pretrained.py \
-        --checkpoint ./transducer_stateless2/exp/pretrained.pt \
-        --bpe-model ./data/lang_bpe_500/bpe.model \
-        --method greedy_search \
-        --max-sym-per-frame 1 \
-        /path/to/foo.wav \
-        /path/to/bar.wav \
+    --checkpoint ./transducer_stateless2/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method greedy_search \
+    --max-sym-per-frame 1 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
 
 (2) beam search
 ./transducer_stateless2/pretrained.py \
-        --checkpoint ./transducer_stateless2/exp/pretrained.pt \
-        --bpe-model ./data/lang_bpe_500/bpe.model \
-        --method beam_search \
-        --beam-size 4 \
-        /path/to/foo.wav \
-        /path/to/bar.wav \
+    --checkpoint ./transducer_stateless2/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method beam_search \
+    --beam-size 4 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
 
 (3) modified beam search
 ./transducer_stateless2/pretrained.py \
-        --checkpoint ./transducer_stateless2/exp/pretrained.pt \
-        --bpe-model ./data/lang_bpe_500/bpe.model \
-        --method modified_beam_search \
-        --beam-size 4 \
-        /path/to/foo.wav \
-        /path/to/bar.wav \
+    --checkpoint ./transducer_stateless2/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method modified_beam_search \
+    --beam-size 4 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
+
+(4) fast beam search
+./transducer_stateless2/pretrained.py \
+    --checkpoint ./transducer_stateless2/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method fast_beam_search \
+    --beam-size 4 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
 
 You can also use `./transducer_stateless2/exp/epoch-xx.pt`.
 
@@ -56,12 +65,14 @@ import logging
 import math
 from typing import List
 
+import k2
 import kaldifeat
 import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import (
     beam_search,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -87,9 +98,7 @@ def get_parser():
     parser.add_argument(
         "--bpe-model",
         type=str,
-        help="""Path to bpe.model.
-        Used only when method is ctc-decoding.
-        """,
+        help="""Path to bpe.model.""",
     )
 
     parser.add_argument(
@@ -100,6 +109,7 @@ def get_parser():
           - greedy_search
           - beam_search
           - modified_beam_search
+          - fast_beam_search
         """,
     )
 
@@ -124,7 +134,33 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
-        help="Used only when --method is beam_search and modified_beam_search ",
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --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 --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --method is fast_beam_search""",
     )
 
     parser.add_argument(
@@ -241,15 +277,28 @@ def main():
         msg += f" with beam size {params.beam_size}"
     logging.info(msg)
 
-    if params.method == "greedy_search" and params.max_sym_per_frame == 1:
+    if params.method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+        hyp_list = 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,
+        )
+    elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
         hyp_list = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
     elif params.method == "modified_beam_search":
         hyp_list = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
     else:

egs/librispeech/ASR/transducer_stateless_multi_datasets/decode.py

@@ -22,17 +22,37 @@ Usage:
         --epoch 14 \
         --avg 7 \
         --exp-dir ./transducer_stateless_multi_datasets/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method greedy_search
 
-(2) beam search
+(2) beam search (not recommended)
 ./transducer_stateless_multi_datasets/decode.py \
         --epoch 14 \
         --avg 7 \
         --exp-dir ./transducer_stateless_multi_datasets/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method beam_search \
         --beam-size 4
+
+(3) modified beam search
+./transducer_stateless_multi_datasets/decode.py \
+        --epoch 14 \
+        --avg 7 \
+        --exp-dir ./transducer_stateless_multi_datasets/exp \
+        --max-duration 600 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+
+(4) fast beam search
+./transducer_stateless_multi_datasets/decode.py \
+        --epoch 14 \
+        --avg 7 \
+        --exp-dir ./transducer_stateless_multi_datasets/exp \
+        --max-duration 600 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
 """
 
 
@@ -40,14 +60,16 @@ import argparse
 import logging
 from collections import defaultdict
 from pathlib import Path
-from typing import Dict, List, Tuple
+from typing import Dict, List, Optional, Tuple
 
+import k2
 import sentencepiece as spm
 import torch
 import torch.nn as nn
 from asr_datamodule import AsrDataModule
 from beam_search import (
     beam_search,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -107,6 +129,7 @@ def get_parser():
           - greedy_search
           - beam_search
           - modified_beam_search
+          - fast_beam_search
         """,
     )
 
@@ -114,8 +137,35 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=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
-        beam_search or modified_beam_search""",
+        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(
@@ -141,6 +191,7 @@ def decode_one_batch(
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
     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:
@@ -163,6 +214,9 @@ def decode_one_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.
@@ -180,24 +234,44 @@ def decode_one_batch(
     encoder_out, encoder_out_lens = model.encoder(
         x=feature, x_lens=feature_lens
     )
-    hyp_list = []
-    batch_size = encoder_out.size(0)
 
-    if (
+    hyps = []
+
+    if params.decoding_method == "fast_beam_search":
+        hyp_tokens = fast_beam_search_one_best(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif (
         params.decoding_method == "greedy_search"
         and params.max_sym_per_frame == 1
     ):
-        hyp_list = greedy_search_batch(
+        hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     elif params.decoding_method == "modified_beam_search":
-        hyp_list = modified_beam_search(
+        hyp_tokens = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     else:
+        batch_size = encoder_out.size(0)
+
         for i in range(batch_size):
             # fmt: off
             encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
@@ -218,14 +292,20 @@ def decode_one_batch(
                 raise ValueError(
                     f"Unsupported decoding method: {params.decoding_method}"
                 )
-            hyp_list.append(sp.decode(hyp).split())
-
-    hyps = [sp.decode(hyp).split() for hyp in hyp_list]
+            hyps.append(sp.decode(hyp).split())
 
     if params.decoding_method == "greedy_search":
         return {"greedy_search": hyps}
+    elif params.decoding_method == "fast_beam_search":
+        return {
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
+        }
     else:
-        return {f"beam_{params.beam_size}": hyps}
+        return {f"beam_size_{params.beam_size}": hyps}
 
 
 def decode_dataset(
@@ -233,6 +313,7 @@ def decode_dataset(
     params: AttributeDict,
     model: nn.Module,
     sp: spm.SentencePieceProcessor,
+    decoding_graph: Optional[k2.Fsa] = None,
 ) -> Dict[str, List[Tuple[List[str], List[str]]]]:
     """Decode dataset.
 
@@ -245,6 +326,9 @@ def decode_dataset(
         The neural model.
       sp:
         The BPE model.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
     Returns:
       Return a dict, whose key may be "greedy_search" if greedy search
       is used, or it may be "beam_7" if beam size of 7 is used.
@@ -260,9 +344,9 @@ def decode_dataset(
         num_batches = "?"
 
     if params.decoding_method == "greedy_search":
-        log_interval = 100
+        log_interval = 50
     else:
-        log_interval = 2
+        log_interval = 10
 
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
@@ -272,6 +356,7 @@ def decode_dataset(
             params=params,
             model=model,
             sp=sp,
+            decoding_graph=decoding_graph,
             batch=batch,
         )
 
@@ -352,13 +437,21 @@ def main():
     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 "beam_search" in params.decoding_method:
-        params.suffix += f"-beam-{params.beam_size}"
+
+    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"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -402,6 +495,11 @@ def main():
     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}")
 
@@ -423,6 +521,7 @@ def main():
             params=params,
             model=model,
             sp=sp,
+            decoding_graph=decoding_graph,
         )
 
         save_results(

egs/librispeech/ASR/transducer_stateless_multi_datasets/pretrained.py

@@ -44,6 +44,15 @@ Usage:
     /path/to/foo.wav \
     /path/to/bar.wav
 
+(4) fast beam search
+./transducer_stateless_multi_datasets/pretrained.py \
+    --checkpoint ./transducer_stateless_multi_datasets/exp/pretrained.pt \
+    --bpe-model ./data/lang_bpe_500/bpe.model \
+    --method fast_beam_search \
+    --beam-size 4 \
+    /path/to/foo.wav \
+    /path/to/bar.wav
+
 You can also use `./transducer_stateless_multi_datasets/exp/epoch-xx.pt`.
 
 Note: ./transducer_stateless_multi_datasets/exp/pretrained.pt is generated by
@@ -56,12 +65,14 @@ import logging
 import math
 from typing import List
 
+import k2
 import kaldifeat
 import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import (
     beam_search,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -87,9 +98,7 @@ def get_parser():
     parser.add_argument(
         "--bpe-model",
         type=str,
-        help="""Path to bpe.model.
-        Used only when method is ctc-decoding.
-        """,
+        help="""Path to bpe.model.""",
     )
 
     parser.add_argument(
@@ -100,6 +109,7 @@ def get_parser():
           - greedy_search
           - beam_search
           - modified_beam_search
+          - fast_beam_search
         """,
     )
 
@@ -124,7 +134,33 @@ def get_parser():
         "--beam-size",
         type=int,
         default=4,
-        help="Used only when --method is beam_search and modified_beam_search ",
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --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 --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --method is fast_beam_search""",
     )
 
     parser.add_argument(
@@ -241,18 +277,30 @@ def main():
         msg += f" with beam size {params.beam_size}"
     logging.info(msg)
 
-    if params.method == "greedy_search" and params.max_sym_per_frame == 1:
+    if params.method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+        hyp_list = 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,
+        )
+    elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
         hyp_list = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
     elif params.method == "modified_beam_search":
         hyp_list = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
-
     else:
         for i in range(num_waves):
             # fmt: off

egs/tedlium3/ASR/pruned_transducer_stateless/decode.py

@@ -69,7 +69,7 @@ import torch.nn as nn
 from asr_datamodule import TedLiumAsrDataModule
 from beam_search import (
     beam_search,
-    fast_beam_search,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -237,7 +237,7 @@ def decode_one_batch(
     hyps = []
 
     if params.decoding_method == "fast_beam_search":
-        hyp_tokens = fast_beam_search(
+        hyp_tokens = fast_beam_search_one_best(
             model=model,
             decoding_graph=decoding_graph,
             encoder_out=encoder_out,
@@ -255,6 +255,7 @@ def decode_one_batch(
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
@@ -262,6 +263,7 @@ def decode_one_batch(
         hyp_tokens = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
         for hyp in sp.decode(hyp_tokens):

egs/tedlium3/ASR/pruned_transducer_stateless/pretrained.py

@@ -72,23 +72,16 @@ import k2
 import kaldifeat
 import sentencepiece as spm
 import torch
-import torch.nn as nn
 import torchaudio
 from beam_search import (
     beam_search,
-    fast_beam_search,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
 )
-from conformer import Conformer
-from decoder import Decoder
-from joiner import Joiner
-from model import Transducer
 from torch.nn.utils.rnn import pad_sequence
-
-from icefall.env import get_env_info
-from icefall.utils import AttributeDict
+from train import get_params, get_transducer_model
 
 
 def get_parser():
@@ -185,76 +178,16 @@ def get_parser():
         """,
     )
 
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="The sample rate of the input sound file",
+    )
+
     return parser
 
 
-def get_params() -> AttributeDict:
-    params = AttributeDict(
-        {
-            "sample_rate": 16000,
-            # parameters for conformer
-            "feature_dim": 80,
-            "subsampling_factor": 4,
-            "attention_dim": 512,
-            "nhead": 8,
-            "dim_feedforward": 2048,
-            "num_encoder_layers": 12,
-            "vgg_frontend": False,
-            # parameters for decoder
-            "embedding_dim": 512,
-            "env_info": get_env_info(),
-        }
-    )
-    return params
-
-
-def get_encoder_model(params: AttributeDict) -> nn.Module:
-    encoder = Conformer(
-        num_features=params.feature_dim,
-        output_dim=params.vocab_size,
-        subsampling_factor=params.subsampling_factor,
-        d_model=params.attention_dim,
-        nhead=params.nhead,
-        dim_feedforward=params.dim_feedforward,
-        num_encoder_layers=params.num_encoder_layers,
-        vgg_frontend=params.vgg_frontend,
-    )
-    return encoder
-
-
-def get_decoder_model(params: AttributeDict) -> nn.Module:
-    decoder = Decoder(
-        vocab_size=params.vocab_size,
-        embedding_dim=params.embedding_dim,
-        blank_id=params.blank_id,
-        unk_id=params.unk_id,
-        context_size=params.context_size,
-    )
-    return decoder
-
-
-def get_joiner_model(params: AttributeDict) -> nn.Module:
-    joiner = Joiner(
-        input_dim=params.vocab_size,
-        inner_dim=params.embedding_dim,
-        output_dim=params.vocab_size,
-    )
-    return joiner
-
-
-def get_transducer_model(params: AttributeDict) -> nn.Module:
-    encoder = get_encoder_model(params)
-    decoder = get_decoder_model(params)
-    joiner = get_joiner_model(params)
-
-    model = Transducer(
-        encoder=encoder,
-        decoder=decoder,
-        joiner=joiner,
-    )
-    return model
-
-
 def read_sound_files(
     filenames: List[str], expected_sample_rate: float
 ) -> List[torch.Tensor]:
@@ -354,7 +287,7 @@ def main():
     logging.info(msg)
 
     if params.decoding_method == "fast_beam_search":
-        hyp_tokens = fast_beam_search(
+        hyp_tokens = fast_beam_search_one_best(
             model=model,
             decoding_graph=decoding_graph,
             encoder_out=encoder_out,
@@ -372,6 +305,7 @@ def main():
         hyp_tokens = greedy_search_batch(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
@@ -379,6 +313,7 @@ def main():
         hyp_tokens = modified_beam_search(
             model=model,
             encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
             beam=params.beam_size,
         )
         for hyp in sp.decode(hyp_tokens):

icefall/checkpoint.py

@@ -1,4 +1,5 @@
-# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+# Copyright  2021-2022  Xiaomi Corporation  (authors: Fangjun Kuang,
+#                                                     Zengwei Yao)
 #
 # See ../../LICENSE for clarification regarding multiple authors
 #
@@ -25,6 +26,7 @@ from typing import Any, Dict, List, Optional, Union
 import torch
 import torch.nn as nn
 from lhotse.dataset.sampling.base import CutSampler
+from torch import Tensor
 from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import Optimizer
@@ -37,6 +39,7 @@ LRSchedulerType = object
 def save_checkpoint(
     filename: Path,
     model: Union[nn.Module, DDP],
+    model_avg: Optional[nn.Module] = None,
     params: Optional[Dict[str, Any]] = None,
     optimizer: Optional[Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
@@ -51,6 +54,8 @@ def save_checkpoint(
         The checkpoint filename.
       model:
         The model to be saved. We only save its `state_dict()`.
+      model_avg:
+        The stored model averaged from the start of training.
       params:
         User defined parameters, e.g., epoch, loss.
       optimizer:
@@ -80,6 +85,9 @@ def save_checkpoint(
         "sampler": sampler.state_dict() if sampler is not None else None,
     }
 
+    if model_avg is not None:
+        checkpoint["model_avg"] = model_avg.state_dict()
+
     if params:
         for k, v in params.items():
             assert k not in checkpoint
@@ -91,6 +99,7 @@ def save_checkpoint(
 def load_checkpoint(
     filename: Path,
     model: nn.Module,
+    model_avg: Optional[nn.Module] = None,
     optimizer: Optional[Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     scaler: Optional[GradScaler] = None,
@@ -118,6 +127,11 @@ def load_checkpoint(
 
     checkpoint.pop("model")
 
+    if model_avg is not None and "model_avg" in checkpoint:
+        logging.info("Loading averaged model")
+        model_avg.load_state_dict(checkpoint["model_avg"], strict=strict)
+        checkpoint.pop("model_avg")
+
     def load(name, obj):
         s = checkpoint.get(name, None)
         if obj and s:
@@ -181,6 +195,7 @@ def save_checkpoint_with_global_batch_idx(
     out_dir: Path,
     global_batch_idx: int,
     model: Union[nn.Module, DDP],
+    model_avg: Optional[nn.Module] = None,
     params: Optional[Dict[str, Any]] = None,
     optimizer: Optional[Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
@@ -201,6 +216,8 @@ def save_checkpoint_with_global_batch_idx(
       model:
         The neural network model whose `state_dict` will be saved in the
         checkpoint.
+      model_avg:
+        The stored model averaged from the start of training.
       params:
         A dict of training configurations to be saved.
       optimizer:
@@ -223,6 +240,7 @@ def save_checkpoint_with_global_batch_idx(
     save_checkpoint(
         filename=filename,
         model=model,
+        model_avg=model_avg,
         params=params,
         optimizer=optimizer,
         scheduler=scheduler,
@@ -327,3 +345,129 @@ def remove_checkpoints(
     to_remove = checkpoints[topk:]
     for c in to_remove:
         os.remove(c)
+
+
+def update_averaged_model(
+    params: Dict[str, Tensor],
+    model_cur: Union[nn.Module, DDP],
+    model_avg: nn.Module,
+) -> None:
+    """Update the averaged model:
+    model_avg = model_cur * (average_period / batch_idx_train)
+      + model_avg * ((batch_idx_train - average_period) / batch_idx_train)
+
+    Args:
+      params:
+        User defined parameters, e.g., epoch, loss.
+      model_cur:
+        The current model.
+      model_avg:
+        The averaged model to be updated.
+    """
+    weight_cur = params.average_period / params.batch_idx_train
+    weight_avg = 1 - weight_cur
+
+    if isinstance(model_cur, DDP):
+        model_cur = model_cur.module
+
+    cur = model_cur.state_dict()
+    avg = model_avg.state_dict()
+
+    average_state_dict(
+        state_dict_1=avg,
+        state_dict_2=cur,
+        weight_1=weight_avg,
+        weight_2=weight_cur,
+    )
+
+
+def average_checkpoints_with_averaged_model(
+    filename_start: str,
+    filename_end: str,
+    device: torch.device = torch.device("cpu"),
+) -> Dict[str, Tensor]:
+    """Average model parameters over the range with given
+    start model (excluded) and end model.
+
+    Let start = batch_idx_train of model-start;
+        end = batch_idx_train of model-end;
+        interval = end - start.
+    Then the average model over range from start (excluded) to end is
+    (1) avg = (model_end * end - model_start * start) / interval.
+    It can be written as
+    (2) avg = model_end * weight_end + model_start * weight_start,
+        where weight_end = end / interval,
+              weight_start = -start / interval = 1 - weight_end.
+    Since the terms `weight_end` and `weight_start` would be large
+    if the model has been trained for lots of batches, which would cause
+    overflow when multiplying the model parameters.
+    To avoid this, we rewrite (2) as:
+    (3) avg = (model_end + model_start * (weight_start / weight_end))
+              * weight_end
+
+    The model index could be epoch number or iteration number.
+
+    Args:
+      filename_start:
+        Checkpoint filename of the start model. We assume it
+        is saved by :func:`save_checkpoint`.
+      filename_end:
+        Checkpoint filename of the end model. We assume it
+        is saved by :func:`save_checkpoint`.
+      device:
+        Move checkpoints to this device before averaging.
+    """
+    state_dict_start = torch.load(filename_start, map_location=device)
+    state_dict_end = torch.load(filename_end, map_location=device)
+
+    batch_idx_train_start = state_dict_start["batch_idx_train"]
+    batch_idx_train_end = state_dict_end["batch_idx_train"]
+    interval = batch_idx_train_end - batch_idx_train_start
+    assert interval > 0, interval
+    weight_end = batch_idx_train_end / interval
+    weight_start = 1 - weight_end
+
+    model_end = state_dict_end["model_avg"]
+    model_start = state_dict_start["model_avg"]
+    avg = model_end
+
+    # scale the weight to avoid overflow
+    average_state_dict(
+        state_dict_1=avg,
+        state_dict_2=model_start,
+        weight_1=1.0,
+        weight_2=weight_start / weight_end,
+        scaling_factor=weight_end,
+    )
+
+    return avg
+
+
+def average_state_dict(
+    state_dict_1: Dict[str, Tensor],
+    state_dict_2: Dict[str, Tensor],
+    weight_1: float,
+    weight_2: float,
+    scaling_factor: float = 1.0,
+) -> Dict[str, Tensor]:
+    """Average two state_dict with given weights:
+    state_dict_1 = (state_dict_1 * weight_1 + state_dict_2 * weight_2)
+      * scaling_factor
+    It is an in-place operation on state_dict_1 itself.
+    """
+    # Identify shared parameters. Two parameters are said to be shared
+    # if they have the same data_ptr
+    uniqued: Dict[int, str] = dict()
+    for k, v in state_dict_1.items():
+        v_data_ptr = v.data_ptr()
+        if v_data_ptr in uniqued:
+            continue
+        uniqued[v_data_ptr] = k
+
+    uniqued_names = list(uniqued.values())
+    for k in uniqued_names:
+        state_dict_1[k] *= weight_1
+        state_dict_1[k] += (
+            state_dict_2[k].to(device=state_dict_1[k].device) * weight_2
+        )
+        state_dict_1[k] *= scaling_factor

icefall/env.py

@@ -95,7 +95,7 @@ def get_env_info() -> Dict[str, Any]:
         "k2-git-sha1": k2.version.__git_sha1__,
         "k2-git-date": k2.version.__git_date__,
         "lhotse-version": lhotse.__version__,
-        "torch-version": torch.__version__,
+        "torch-version": str(torch.__version__),
         "torch-cuda-available": torch.cuda.is_available(),
         "torch-cuda-version": torch.version.cuda,
         "python-version": sys.version[:3],