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

.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/gigaspeech/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/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 \
@@ -47,7 +47,8 @@ for method in modified_beam_search beam_search; do
 done
 
 echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
-if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
+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/
@@ -58,9 +59,9 @@ if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
   log "Decoding test-clean and test-other"
 
   # use a small value for decoding with CPU
-  max_duration=50
+  max_duration=100
 
-  for method in greedy_search fast_beam_search; do
+  for method in greedy_search fast_beam_search modified_beam_search; do
     log "Decoding with $method"
 
     ./pruned_transducer_stateless/decode.py \

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

@@ -51,7 +51,8 @@ for method in modified_beam_search beam_search fast_beam_search; do
 done
 
 echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
-if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
+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/
@@ -62,9 +63,9 @@ if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
   log "Decoding test-clean and test-other"
 
   # use a small value for decoding with CPU
-  max_duration=50
+  max_duration=100
 
-  for method in greedy_search fast_beam_search; do
+  for method in greedy_search fast_beam_search modified_beam_search; do
     log "Decoding with $method"
 
     ./pruned_transducer_stateless2/decode.py \

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

@@ -51,7 +51,8 @@ for method in modified_beam_search beam_search fast_beam_search; do
 done
 
 echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
-if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
+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/
@@ -62,9 +63,9 @@ if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
   log "Decoding test-clean and test-other"
 
   # use a small value for decoding with CPU
-  max_duration=50
+  max_duration=100
 
-  for method in greedy_search fast_beam_search; do
+  for method in greedy_search fast_beam_search modified_beam_search; do
     log "Decoding with $method"
 
     ./pruned_transducer_stateless3/decode.py \

.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 \
@@ -47,7 +47,8 @@ for method in modified_beam_search beam_search; do
 done
 
 echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
-if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
+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/
@@ -58,9 +59,9 @@ if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
   log "Decoding test-clean and test-other"
 
   # use a small value for decoding with CPU
-  max_duration=50
+  max_duration=100
 
-  for method in greedy_search modified_beam_search; do
+  for method in greedy_search fast_beam_search modified_beam_search; do
     log "Decoding with $method"
 
     ./transducer_stateless2/decode.py \

.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
+echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
-  log "$method"
+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 \
+  ls -lh data
-    --method $method \
+  ls -lh transducer_stateless/exp
-    --beam-size 4 \
+
-    --checkpoint $repo/exp/pretrained.pt \
+  log "Decoding test-clean and test-other"
-    --bpe-model $repo/data/lang_bpe_500/bpe.model \
+
-    $repo/test_wavs/1089-134686-0001.wav \
+  # use a small value for decoding with CPU
-    $repo/test_wavs/1221-135766-0001.wav \
+  max_duration=100
-    $repo/test_wavs/1221-135766-0002.wav
+
-done
+  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

@@ -35,7 +35,7 @@ on:
 
 jobs:
   run_librispeech_2022_03_12:
-    if: github.event.label.name == 'ready' || github.event_name == 'push' || github.event_name == 'schedule'
+    if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -107,11 +107,11 @@ jobs:
         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
@@ -124,8 +124,8 @@ jobs:
 
           .github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh
 
-      - name: Display decoding results
+      - name: Display decoding results for pruned_transducer_stateless
-        if: github.event_name == 'schedule'
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
         shell: bash
         run: |
           cd egs/librispeech/ASR/
@@ -141,9 +141,13 @@ jobs:
           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'
+        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

@@ -35,7 +35,7 @@ on:
 
 jobs:
   run_librispeech_2022_04_29:
-    if: github.event.label.name == 'ready' || github.event_name == 'push' || github.event_name == 'schedule'
+    if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -111,6 +111,7 @@ jobs:
         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
@@ -125,44 +126,54 @@ jobs:
 
           .github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh
 
-      - name: Display decoding results
+      - name: Display decoding results for pruned_transducer_stateless2
-        if: github.event_name == 'schedule'
+        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
+          cd pruned_transducer_stateless2/exp
-          echo "results for pruned_transducer_stateless2"
           echo "===greedy search==="
-          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find 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
+          find 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 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
+          find fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
 
-          cd ../
+          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
+          cd pruned_transducer_stateless3/exp
-          echo "results for pruned_transducer_stateless3"
           echo "===greedy search==="
-          find exp/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-clean" {} + | sort -n -k2
-          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+          find 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 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
+          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'
+        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'
+        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

@@ -35,7 +35,7 @@ on:
 
 jobs:
   run_librispeech_2022_04_19:
-    if: github.event.label.name == 'ready' || github.event_name == 'push' || github.event_name == 'schedule'
+    if: github.event.label.name == 'ready' || github.event.label.name == 'run-decode' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -111,6 +111,7 @@ jobs:
         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
@@ -124,7 +125,7 @@ jobs:
           .github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh
 
       - name: Display decoding results
-        if: github.event_name == 'schedule'
+        if: github.event_name == 'schedule' || github.event.label.name == 'run-decode'
         shell: bash
         run: |
           cd egs/librispeech/ASR/
@@ -136,13 +137,17 @@ jobs:
           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'
+        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-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:
@@ -64,11 +73,80 @@ jobs:
         run: |
           .github/scripts/install-kaldifeat.sh
 
-      - name: Inference with pre-trained model
+      - name: Cache LibriSpeech test-clean and test-other datasets
+        id: libri-test-clean-and-test-other-data
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/download
+          key: cache-libri-test-clean-and-test-other
+
+      - name: Download LibriSpeech test-clean and test-other
+        if: steps.libri-test-clean-and-test-other-data.outputs.cache-hit != 'true'
         shell: bash
         run: |
+          .github/scripts/download-librispeech-test-clean-and-test-other-dataset.sh
+
+      - name: Prepare manifests for LibriSpeech test-clean and test-other
+        shell: bash
+        run: |
+          .github/scripts/prepare-librispeech-test-clean-and-test-other-manifests.sh
+
+      - name: Cache LibriSpeech test-clean and test-other fbank features
+        id: libri-test-clean-and-test-other-fbank
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/fbank-libri
+          key: cache-libri-fbank-test-clean-and-test-other
+
+      - 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:
@@ -64,11 +73,80 @@ jobs:
         run: |
           .github/scripts/install-kaldifeat.sh
 
-      - name: Inference with pre-trained model
+      - name: Cache LibriSpeech test-clean and test-other datasets
+        id: libri-test-clean-and-test-other-data
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/download
+          key: cache-libri-test-clean-and-test-other
+
+      - name: Download LibriSpeech test-clean and test-other
+        if: steps.libri-test-clean-and-test-other-data.outputs.cache-hit != 'true'
         shell: bash
         run: |
+          .github/scripts/download-librispeech-test-clean-and-test-other-dataset.sh
+
+      - name: Prepare manifests for LibriSpeech test-clean and test-other
+        shell: bash
+        run: |
+          .github/scripts/prepare-librispeech-test-clean-and-test-other-manifests.sh
+
+      - name: Cache LibriSpeech test-clean and test-other fbank features
+        id: libri-test-clean-and-test-other-fbank
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/fbank-libri
+          key: cache-libri-fbank-test-clean-and-test-other
+
+      - 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.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:
@@ -64,11 +73,80 @@ jobs:
         run: |
           .github/scripts/install-kaldifeat.sh
 
-      - name: Inference with pre-trained model
+      - name: Cache LibriSpeech test-clean and test-other datasets
+        id: libri-test-clean-and-test-other-data
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/download
+          key: cache-libri-test-clean-and-test-other
+
+      - name: Download LibriSpeech test-clean and test-other
+        if: steps.libri-test-clean-and-test-other-data.outputs.cache-hit != 'true'
         shell: bash
         run: |
+          .github/scripts/download-librispeech-test-clean-and-test-other-dataset.sh
+
+      - name: Prepare manifests for LibriSpeech test-clean and test-other
+        shell: bash
+        run: |
+          .github/scripts/prepare-librispeech-test-clean-and-test-other-manifests.sh
+
+      - name: Cache LibriSpeech test-clean and test-other fbank features
+        id: libri-test-clean-and-test-other-fbank
+        uses: actions/cache@v2
+        with:
+          path: |
+            ~/tmp/fbank-libri
+          key: cache-libri-fbank-test-clean-and-test-other
+
+      - 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/

README.md

@@ -12,13 +12,14 @@ for installation.
 Please refer to <https://icefall.readthedocs.io/en/latest/recipes/index.html>
 for more information.
 
-We provide four recipes at present:
+We provide 6 recipes at present:
 
   - [yesno][yesno]
   - [LibriSpeech][librispeech]
   - [Aishell][aishell]
   - [TIMIT][timit]
   - [TED-LIUM3][tedlium3]
+  - [GigaSpeech][gigaspeech]
 
 ### yesno
 
@@ -197,6 +198,23 @@ The best WER using modified beam search with beam size 4 is:
 
 We provide a Colab notebook to run a pre-trained Pruned Transducer Stateless model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1je_1zGrOkGVVd4WLzgkXRHxl-I27yWtz?usp=sharing)
 
+### GigaSpeech
+
+#### Conformer CTC
+
+|     |  Dev  | Test  |
+|-----|-------|-------|
+| WER | 10.47 | 10.58 |
+
+#### Pruned stateless RNN-T
+
+|                      |  Dev  | Test  |
+|----------------------|-------|-------|
+|    greedy search     | 10.59 | 10.87 |
+|   fast beam search   | 10.56 | 10.80 |
+| modified beam search | 10.52 | 10.62 |
+
+
 ## Deployment with C++
 
 Once you have trained a model in icefall, you may want to deploy it with C++,
@@ -225,4 +243,5 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad
 [aishell]: egs/aishell/ASR
 [timit]: egs/timit/ASR
 [tedlium3]: egs/tedlium3/ASR
+[gigaspeech]: egs/gigaspeech/ASR
 [k2]: https://github.com/k2-fsa/k2

egs/aishell/ASR/transducer_stateless/conformer.py

@@ -110,6 +110,8 @@ class Conformer(Transformer):
         x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
 
         # Caution: We assume the subsampling factor is 4!
+        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_modified-2/decode.py

@@ -25,8 +25,8 @@ Usage:
     --max-duration 100 \
     --decoding-method greedy_search
 
-(2) beam search
+(2) beam search (not recommended)
-./transducer_stateless_modified/decode.py \
+./transducer_stateless_modified-2/decode.py \
     --epoch 89 \
     --avg 38 \
     --exp-dir ./transducer_stateless_modified-2/exp \
@@ -38,30 +38,43 @@ Usage:
 ./transducer_stateless_modified-2/decode.py \
     --epoch 89 \
     --avg 38 \
-        --exp-dir ./transducer_stateless_modified/exp \
+    --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 beam_search import (
-from conformer import Conformer
+    beam_search,
-from decoder import Decoder
+    fast_beam_search_one_best,
-from joiner import Joiner
+    greedy_search,
-from model import Transducer
+    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 "
+        help="""An integer indicating how many candidates we will keep for each
-        "and modified_beam_search",
+        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,
+        default=1,
-        help="Maximum number of symbols per frame",
+        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:
+      token_table:
-        It contains the token symbol table and the word symbol table.
+        It maps token ID to a string.
     Returns:
       Return the decoding result. See above description for the format of
       the returned dict.
@@ -249,9 +230,36 @@ def decode_one_batch(
     encoder_out, encoder_out_lens = model.encoder(
         x=feature, x_lens=feature_lens
     )
-    hyps = []
-    batch_size = encoder_out.size(0)
 
+    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]]
@@ -264,29 +272,38 @@ def decode_one_batch(
                 )
             elif params.decoding_method == "beam_search":
                 hyp = beam_search(
-                model=model, encoder_out=encoder_out_i, beam=params.beam_size
+                    model=model,
-            )
+                    encoder_out=encoder_out_i,
-        elif params.decoding_method == "modified_beam_search":
+                    beam=params.beam_size,
-            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])
+            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

@@ -19,18 +19,18 @@
 Usage:
 (1) greedy search
 ./transducer_stateless_modified/decode.py \
-        --epoch 64 \
+    --epoch 14 \
-        --avg 33 \
+    --avg 7 \
     --exp-dir ./transducer_stateless_modified/exp \
-        --max-duration 100 \
+    --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 \
+    --max-duration 600 \
     --decoding-method beam_search \
     --beam-size 4
 
@@ -39,28 +39,43 @@ Usage:
     --epoch 14 \
     --avg 7 \
     --exp-dir ./transducer_stateless_modified/exp \
-        --max-duration 100 \
+    --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 beam_search import (
-from conformer import Conformer
+    beam_search,
-from decoder import Decoder
+    fast_beam_search_one_best,
-from joiner import Joiner
+    greedy_search,
-from model import Transducer
+    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,
+        default=1,
-        help="Maximum number of symbols per frame",
+        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:
+      token_table:
-        It contains the token symbol table and the word symbol 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.
@@ -247,9 +234,36 @@ def decode_one_batch(
     encoder_out, encoder_out_lens = model.encoder(
         x=feature, x_lens=feature_lens
     )
-    hyps = []
-    batch_size = encoder_out.size(0)
 
+    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]]
@@ -262,29 +276,38 @@ def decode_one_batch(
                 )
             elif params.decoding_method == "beam_search":
                 hyp = beam_search(
-                model=model, encoder_out=encoder_out_i, beam=params.beam_size
+                    model=model,
-            )
+                    encoder_out=encoder_out_i,
-        elif params.decoding_method == "modified_beam_search":
+                    beam=params.beam_size,
-            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])
+            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/README.md

@@ -14,7 +14,8 @@ ln -sfv /path/to/GigaSpeech download/GigaSpeech
 
 ## Performance Record
 |                                |  Dev  | Test  |
-|-----|-------|-------|
+|--------------------------------|-------|-------|
-| WER | 10.47 | 10.58 |
+|         `conformer_ctc`        | 10.47 | 10.58 |
+| `pruned_transducer_stateless2` | 10.52 | 10.62 |
 
 See [RESULTS](/egs/gigaspeech/ASR/RESULTS.md) for details.

egs/gigaspeech/ASR/RESULTS.md

@@ -1,4 +1,78 @@
 ## Results
+### GigaSpeech BPE training results (Pruned Transducer 2)
+
+#### 2022-05-12
+
+#### Conformer encoder + embedding decoder
+
+Conformer encoder + non-recurrent decoder. The encoder is a
+reworked version of the conformer encoder, with many changes. The
+decoder contains only an embedding layer, a Conv1d (with kernel
+size 2) and a linear layer (to transform tensor dim). k2 pruned
+RNN-T loss is used.
+
+Results are:
+
+|                      |  Dev  | Test  |
+|----------------------|-------|-------|
+|    greedy search     | 10.59 | 10.87 |
+|   fast beam search   | 10.56 | 10.80 |
+| modified beam search | 10.52 | 10.62 |
+
+To reproduce the above result, use the following commands for training:
+
+```bash
+cd egs/gigaspeech/ASR
+./prepare.sh
+export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
+./pruned_transducer_stateless2/train.py \
+  --max-duration 120 \
+  --num-workers 1 \
+  --world-size 8 \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --use-fp16 True
+```
+
+and the following commands for decoding:
+
+```bash
+# greedy search
+./pruned_transducer_stateless2/decode.py \
+  --epoch 29 \
+  --avg 11 \
+  --decoding-method greedy_search \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --max-duration 20 \
+  --num-workers 1
+
+# fast beam search
+./pruned_transducer_stateless2/decode.py \
+  --epoch 29 \
+  --avg 9 \
+  --decoding-method fast_beam_search \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --max-duration 20 \
+  --num-workers 1
+
+# modified beam search
+./pruned_transducer_stateless2/decode.py \
+  --epoch 29 \
+  --avg 8 \
+  --decoding-method modified_beam_search \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --max-duration 20 \
+  --num-workers 1
+```
+
+Pretrained model is available at
+<https://huggingface.co/wgb14/icefall-asr-gigaspeech-pruned-transducer-stateless2>
+
+The tensorboard log for training is available at
+<https://tensorboard.dev/experiment/zmmM0MLASnG1N2RmJ4MZBw/>
 
 ### GigaSpeech BPE training results (Conformer-CTC)
 
@@ -20,7 +94,7 @@ Scale values used in n-gram LM rescoring and attention rescoring for the best WE
 
 To reproduce the above result, use the following commands for training:
 
-```
+```bash
 cd egs/gigaspeech/ASR
 ./prepare.sh
 export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
@@ -34,7 +108,7 @@ export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
 
 and the following command for decoding:
 
-```
+```bash
 ./conformer_ctc/decode.py \
   --epoch 18 \
   --avg 6 \
@@ -59,7 +133,7 @@ Scale values used in n-gram LM rescoring and attention rescoring for the best WE
 
 To reproduce the above result, use the training commands above, and the following command for decoding:
 
-```
+```bash
 ./conformer_ctc/decode.py \
   --epoch 18 \
   --avg 6 \

egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py

@@ -0,0 +1,416 @@
+# Copyright      2021  Piotr Żelasko
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import inspect
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import torch
+from lhotse import CutSet, Fbank, FbankConfig, load_manifest
+from lhotse.dataset import (
+    BucketingSampler,
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    K2SpeechRecognitionDataset,
+    PrecomputedFeatures,
+    SingleCutSampler,
+    SpecAugment,
+)
+from lhotse.dataset.input_strategies import OnTheFlyFeatures
+from lhotse.utils import fix_random_seed
+from torch.utils.data import DataLoader
+
+from icefall.utils import str2bool
+
+
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+
+class GigaSpeechAsrDataModule:
+    """
+    DataModule for k2 ASR experiments.
+    It assumes there is always one train and valid dataloader,
+    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
+    and test-other).
+
+    It contains all the common data pipeline modules used in ASR
+    experiments, e.g.:
+    - dynamic batch size,
+    - bucketing samplers,
+    - cut concatenation,
+    - augmentation,
+    - on-the-fly feature extraction
+
+    This class should be derived for specific corpora used in ASR tasks.
+    """
+
+    def __init__(self, args: argparse.Namespace):
+        self.args = args
+
+    @classmethod
+    def add_arguments(cls, parser: argparse.ArgumentParser):
+        group = parser.add_argument_group(
+            title="ASR data related options",
+            description="These options are used for the preparation of "
+            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
+            "effective batch sizes, sampling strategies, applied data "
+            "augmentations, etc.",
+        )
+        group.add_argument(
+            "--manifest-dir",
+            type=Path,
+            default=Path("data/fbank"),
+            help="Path to directory with train/valid/test cuts.",
+        )
+        group.add_argument(
+            "--max-duration",
+            type=int,
+            default=200.0,
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
+        )
+        group.add_argument(
+            "--bucketing-sampler",
+            type=str2bool,
+            default=True,
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
+        )
+        group.add_argument(
+            "--num-buckets",
+            type=int,
+            default=30,
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
+        )
+        group.add_argument(
+            "--concatenate-cuts",
+            type=str2bool,
+            default=False,
+            help="When enabled, utterances (cuts) will be concatenated "
+            "to minimize the amount of padding.",
+        )
+        group.add_argument(
+            "--duration-factor",
+            type=float,
+            default=1.0,
+            help="Determines the maximum duration of a concatenated cut "
+            "relative to the duration of the longest cut in a batch.",
+        )
+        group.add_argument(
+            "--gap",
+            type=float,
+            default=1.0,
+            help="The amount of padding (in seconds) inserted between "
+            "concatenated cuts. This padding is filled with noise when "
+            "noise augmentation is used.",
+        )
+        group.add_argument(
+            "--on-the-fly-feats",
+            type=str2bool,
+            default=False,
+            help="When enabled, use on-the-fly cut mixing and feature "
+            "extraction. Will drop existing precomputed feature manifests "
+            "if available.",
+        )
+        group.add_argument(
+            "--shuffle",
+            type=str2bool,
+            default=True,
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
+        )
+        group.add_argument(
+            "--return-cuts",
+            type=str2bool,
+            default=True,
+            help="When enabled, each batch will have the "
+            "field: batch['supervisions']['cut'] with the cuts that "
+            "were used to construct it.",
+        )
+
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=2,
+            help="The number of training dataloader workers that "
+            "collect the batches.",
+        )
+
+        group.add_argument(
+            "--enable-spec-aug",
+            type=str2bool,
+            default=True,
+            help="When enabled, use SpecAugment for training dataset.",
+        )
+
+        group.add_argument(
+            "--spec-aug-time-warp-factor",
+            type=int,
+            default=80,
+            help="Used only when --enable-spec-aug is True. "
+            "It specifies the factor for time warping in SpecAugment. "
+            "Larger values mean more warping. "
+            "A value less than 1 means to disable time warp.",
+        )
+
+        group.add_argument(
+            "--enable-musan",
+            type=str2bool,
+            default=True,
+            help="When enabled, select noise from MUSAN and mix it "
+            "with training dataset. ",
+        )
+
+        # GigaSpeech specific arguments
+        group.add_argument(
+            "--subset",
+            type=str,
+            default="XL",
+            help="Select the GigaSpeech subset (XS|S|M|L|XL)",
+        )
+        group.add_argument(
+            "--small-dev",
+            type=str2bool,
+            default=False,
+            help="Should we use only 1000 utterances for dev "
+            "(speeds up training)",
+        )
+
+    def train_dataloaders(
+        self,
+        cuts_train: CutSet,
+        sampler_state_dict: Optional[Dict[str, Any]] = None,
+    ) -> DataLoader:
+        """
+        Args:
+          cuts_train:
+            CutSet for training.
+          sampler_state_dict:
+            The state dict for the training sampler.
+        """
+
+        transforms = []
+        if self.args.enable_musan:
+            logging.info("Enable MUSAN")
+            logging.info("About to get Musan cuts")
+            cuts_musan = load_manifest(
+                self.args.manifest_dir / "cuts_musan.json.gz"
+            )
+            transforms.append(
+                CutMix(
+                    cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True
+                )
+            )
+        else:
+            logging.info("Disable MUSAN")
+
+        if self.args.concatenate_cuts:
+            logging.info(
+                f"Using cut concatenation with duration factor "
+                f"{self.args.duration_factor} and gap {self.args.gap}."
+            )
+            # Cut concatenation should be the first transform in the list,
+            # so that if we e.g. mix noise in, it will fill the gaps between
+            # different utterances.
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + transforms
+
+        input_transforms = []
+        if self.args.enable_spec_aug:
+            logging.info("Enable SpecAugment")
+            logging.info(
+                f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
+            )
+            # Set the value of num_frame_masks according to Lhotse's version.
+            # In different Lhotse's versions, the default of num_frame_masks is
+            # different.
+            num_frame_masks = 10
+            num_frame_masks_parameter = inspect.signature(
+                SpecAugment.__init__
+            ).parameters["num_frame_masks"]
+            if num_frame_masks_parameter.default == 1:
+                num_frame_masks = 2
+            logging.info(f"Num frame mask: {num_frame_masks}")
+            input_transforms.append(
+                SpecAugment(
+                    time_warp_factor=self.args.spec_aug_time_warp_factor,
+                    num_frame_masks=num_frame_masks,
+                    features_mask_size=27,
+                    num_feature_masks=2,
+                    frames_mask_size=100,
+                )
+            )
+        else:
+            logging.info("Disable SpecAugment")
+
+        logging.info("About to create train dataset")
+        train = K2SpeechRecognitionDataset(
+            cut_transforms=transforms,
+            input_transforms=input_transforms,
+            return_cuts=self.args.return_cuts,
+        )
+
+        if self.args.on_the_fly_feats:
+            # NOTE: the PerturbSpeed transform should be added only if we
+            # remove it from data prep stage.
+            # Add on-the-fly speed perturbation; since originally it would
+            # have increased epoch size by 3, we will apply prob 2/3 and use
+            # 3x more epochs.
+            # Speed perturbation probably should come first before
+            # concatenation, but in principle the transforms order doesn't have
+            # to be strict (e.g. could be randomized)
+            # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
+            # Drop feats to be on the safe side.
+            train = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
+                input_transforms=input_transforms,
+                return_cuts=self.args.return_cuts,
+            )
+
+        if self.args.bucketing_sampler:
+            logging.info("Using DynamicBucketingSampler.")
+            train_sampler = DynamicBucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                drop_last=True,
+            )
+        else:
+            logging.info("Using SingleCutSampler.")
+            train_sampler = SingleCutSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+            )
+        logging.info("About to create train dataloader")
+
+        if sampler_state_dict is not None:
+            logging.info("Loading sampler state dict")
+            train_sampler.load_state_dict(sampler_state_dict)
+
+        # 'seed' is derived from the current random state, which will have
+        # previously been set in the main process.
+        seed = torch.randint(0, 100000, ()).item()
+        worker_init_fn = _SeedWorkers(seed)
+
+        train_dl = DataLoader(
+            train,
+            sampler=train_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+            worker_init_fn=worker_init_fn,
+        )
+
+        return train_dl
+
+    def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
+        transforms = []
+        if self.args.concatenate_cuts:
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + transforms
+
+        logging.info("About to create dev dataset")
+        if self.args.on_the_fly_feats:
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=80))
+                ),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            validate = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                return_cuts=self.args.return_cuts,
+            )
+        valid_sampler = BucketingSampler(
+            cuts_valid,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.info("About to create dev dataloader")
+        valid_dl = DataLoader(
+            validate,
+            sampler=valid_sampler,
+            batch_size=None,
+            num_workers=2,
+            persistent_workers=False,
+        )
+
+        return valid_dl
+
+    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
+        logging.debug("About to create test dataset")
+        test = K2SpeechRecognitionDataset(
+            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+            if self.args.on_the_fly_feats
+            else PrecomputedFeatures(),
+            return_cuts=self.args.return_cuts,
+        )
+        sampler = BucketingSampler(
+            cuts, max_duration=self.args.max_duration, shuffle=False
+        )
+        logging.debug("About to create test dataloader")
+        test_dl = DataLoader(
+            test,
+            batch_size=None,
+            sampler=sampler,
+            num_workers=self.args.num_workers,
+        )
+        return test_dl
+
+    @lru_cache()
+    def train_cuts(self) -> CutSet:
+        logging.info(f"About to get train_{self.args.subset} cuts")
+        path = self.args.manifest_dir / f"cuts_{self.args.subset}.jsonl.gz"
+        cuts_train = CutSet.from_jsonl_lazy(path)
+        return cuts_train
+
+    @lru_cache()
+    def dev_cuts(self) -> CutSet:
+        logging.info("About to get dev cuts")
+        cuts_valid = load_manifest(self.args.manifest_dir / "cuts_DEV.jsonl.gz")
+        if self.args.small_dev:
+            return cuts_valid.subset(first=1000)
+        else:
+            return cuts_valid
+
+    @lru_cache()
+    def test_cuts(self) -> CutSet:
+        logging.info("About to get test cuts")
+        return load_manifest(self.args.manifest_dir / "cuts_TEST.jsonl.gz")

egs/gigaspeech/ASR/pruned_transducer_stateless2/beam_search.py

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

egs/gigaspeech/ASR/pruned_transducer_stateless2/conformer.py

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

egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py

@@ -0,0 +1,559 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Usage:
+(1) greedy search
+./pruned_transducer_stateless2/decode.py \
+        --epoch 28 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --max-duration 100 \
+        --decoding-method greedy_search
+
+(2) beam search
+./pruned_transducer_stateless2/decode.py \
+        --epoch 28 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --max-duration 100 \
+        --decoding-method beam_search \
+        --beam-size 4
+
+(3) modified beam search
+./pruned_transducer_stateless2/decode.py \
+        --epoch 28 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --max-duration 100 \
+        --decoding-method modified_beam_search \
+        --beam-size 4
+
+(4) fast beam search
+./pruned_transducer_stateless2/decode.py \
+        --epoch 28 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --max-duration 1500 \
+        --decoding-method fast_beam_search \
+        --beam 4 \
+        --max-contexts 4 \
+        --max-states 8
+"""
+
+
+import argparse
+import logging
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from asr_datamodule import GigaSpeechAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from gigaspeech_scoring import asr_text_post_processing
+from train import get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+    store_transcripts,
+    write_error_stats,
+)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=29,
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
+    )
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=8,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch'. ",
+    )
+
+    parser.add_argument(
+        "--avg-last-n",
+        type=int,
+        default=0,
+        help="""If positive, --epoch and --avg are ignored and it
+        will use the last n checkpoints exp_dir/checkpoint-xxx.pt
+        where xxx is the number of processed batches while
+        saving that checkpoint.
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless2/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--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 interger indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=4,
+        help="""A floating point value to calculate the cutoff score during beam
+        search (i.e., `cutoff = max-score - beam`), which is the same as the
+        `beam` in Kaldi.
+        Used only when --decoding-method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
+    )
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame.
+        Used only when --decoding_method is greedy_search""",
+    )
+
+    return parser
+
+
+def post_processing(
+    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()
+        new_hyp = asr_text_post_processing(" ".join(hyp)).split()
+        new_results.append((new_ref, new_hyp))
+    return new_results
+
+
+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 = model.device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+    feature_lens = supervisions["num_frames"].to(device)
+
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
+    hyps = []
+
+    if params.decoding_method == "fast_beam_search":
+        hyp_tokens = fast_beam_search_one_best(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif (
+        params.decoding_method == "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 = "?"
+
+    log_interval = 20
+
+    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"
+        )
+        results = post_processing(results)
+        store_transcripts(filename=recog_path, texts=results)
+        logging.info(f"The transcripts are stored in {recog_path}")
+
+        # The following prints out WERs, per-word error statistics and aligned
+        # ref/hyp pairs.
+        errs_filename = (
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+    test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
+    errs_info = (
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
+    with open(errs_info, "w") as f:
+        print("settings\tWER", file=f)
+        for key, val in test_set_wers:
+            print("{}\t{}".format(key, val), file=f)
+
+    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key, val in test_set_wers:
+        s += "{}\t{}{}\n".format(key, val, note)
+        note = ""
+    logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    GigaSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    assert params.decoding_method in (
+        "greedy_search",
+        "beam_search",
+        "fast_beam_search",
+        "modified_beam_search",
+    )
+    params.res_dir = params.exp_dir / params.decoding_method
+
+    params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+    if "fast_beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+    elif "beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{params.beam_size}"
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
+    logging.info("Decoding started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    if params.avg_last_n > 0:
+        filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n]
+        logging.info(f"averaging {filenames}")
+        model.to(device)
+        model.load_state_dict(average_checkpoints(filenames, device=device))
+    elif params.avg == 1:
+        load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+    else:
+        start = params.epoch - params.avg + 1
+        filenames = []
+        for i in range(start, params.epoch + 1):
+            if start >= 0:
+                filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+        logging.info(f"averaging {filenames}")
+        model.to(device)
+        model.load_state_dict(average_checkpoints(filenames, device=device))
+
+    model.to(device)
+    model.eval()
+    model.device = device
+
+    if params.decoding_method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    gigaspeech = GigaSpeechAsrDataModule(args)
+
+    dev_cuts = gigaspeech.dev_cuts()
+    test_cuts = gigaspeech.test_cuts()
+
+    dev_dl = gigaspeech.test_dataloaders(dev_cuts)
+    test_dl = gigaspeech.test_dataloaders(test_cuts)
+
+    test_sets = ["dev", "test"]
+    test_dls = [dev_dl, test_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dls):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            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/gigaspeech/ASR/pruned_transducer_stateless2/decoder.py

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

egs/gigaspeech/ASR/pruned_transducer_stateless2/encoder_interface.py

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

egs/gigaspeech/ASR/pruned_transducer_stateless2/export.py

@@ -0,0 +1,182 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021 Xiaomi Corporation (Author: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# This script converts several saved checkpoints
+# to a single one using model averaging.
+"""
+Usage:
+./pruned_transducer_stateless2/export.py \
+  --exp-dir ./pruned_transducer_stateless2/exp \
+  --bpe-model data/lang_bpe_500/bpe.model \
+  --epoch 20 \
+  --avg 10
+
+It will generate a file exp_dir/pretrained.pt
+
+To use the generated file with `pruned_transducer_stateless2/decode.py`,
+you can do:
+
+    cd /path/to/exp_dir
+    ln -s pretrained.pt epoch-9999.pt
+
+    cd /path/to/egs/librispeech/ASR
+    ./pruned_transducer_stateless2/decode.py \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --epoch 9999 \
+        --avg 1 \
+        --max-duration 100 \
+        --bpe-model data/lang_bpe_500/bpe.model
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import sentencepiece as spm
+import torch
+from train import get_params, get_transducer_model
+
+from icefall.checkpoint import average_checkpoints, load_checkpoint
+from icefall.utils import str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch'. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless2/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--jit",
+        type=str2bool,
+        default=False,
+        help="""True to save a model after applying torch.jit.script.
+        """,
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
+    )
+
+    return parser
+
+
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    assert args.jit is False, "Support torchscript will be added later"
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    model.to(device)
+
+    if params.avg == 1:
+        load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+    else:
+        start = params.epoch - params.avg + 1
+        filenames = []
+        for i in range(start, params.epoch + 1):
+            if start >= 0:
+                filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+        logging.info(f"averaging {filenames}")
+        model.to(device)
+        model.load_state_dict(average_checkpoints(filenames, device=device))
+
+    model.eval()
+
+    model.to("cpu")
+    model.eval()
+
+    if params.jit:
+        logging.info("Using torch.jit.script")
+        model = torch.jit.script(model)
+        filename = params.exp_dir / "cpu_jit.pt"
+        model.save(str(filename))
+        logging.info(f"Saved to {filename}")
+    else:
+        logging.info("Not using torch.jit.script")
+        # Save it using a format so that it can be loaded
+        # by :func:`load_checkpoint`
+        filename = params.exp_dir / "pretrained.pt"
+        torch.save({"model": model.state_dict()}, str(filename))
+        logging.info(f"Saved to {filename}")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/gigaspeech/ASR/pruned_transducer_stateless2/gigaspeech_scoring.py

@@ -0,0 +1 @@
+../conformer_ctc/gigaspeech_scoring.py

egs/gigaspeech/ASR/pruned_transducer_stateless2/joiner.py

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

egs/gigaspeech/ASR/pruned_transducer_stateless2/model.py

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

egs/gigaspeech/ASR/pruned_transducer_stateless2/optim.py

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

egs/gigaspeech/ASR/pruned_transducer_stateless2/scaling.py

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

egs/gigaspeech/ASR/pruned_transducer_stateless2/train.py

@@ -0,0 +1,977 @@
+#!/usr/bin/env python3
+# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                  Wei Kang
+#                                                  Mingshuang Luo)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Usage:
+
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+
+./pruned_transducer_stateless2/train.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 0 \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --full-libri 1 \
+  --max-duration 300
+
+# For mix precision training:
+
+./pruned_transducer_stateless2/train.py \
+  --world-size 4 \
+  --num-epochs 30 \
+  --start-epoch 0 \
+  --use_fp16 1 \
+  --exp-dir pruned_transducer_stateless2/exp \
+  --full-libri 1 \
+  --max-duration 550
+
+"""
+
+
+import argparse
+import logging
+import warnings
+from pathlib import Path
+from shutil import copyfile
+from typing import Any, Dict, Optional, Tuple, Union
+
+import k2
+import optim
+import sentencepiece as spm
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from asr_datamodule import GigaSpeechAsrDataModule
+from conformer import Conformer
+from decoder import Decoder
+from joiner import Joiner
+from lhotse.dataset.sampling.base import CutSampler
+from lhotse.utils import fix_random_seed
+from model import Transducer
+from optim import Eden, Eve
+from torch import Tensor
+from torch.cuda.amp import GradScaler
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.utils.tensorboard import SummaryWriter
+
+from icefall import diagnostics
+from icefall.checkpoint import load_checkpoint, remove_checkpoints
+from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
+from icefall.checkpoint import save_checkpoint_with_global_batch_idx
+from icefall.dist import cleanup_dist, setup_dist
+from icefall.env import get_env_info
+from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+
+LRSchedulerType = Union[
+    torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler
+]
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--world-size",
+        type=int,
+        default=1,
+        help="Number of GPUs for DDP training.",
+    )
+
+    parser.add_argument(
+        "--master-port",
+        type=int,
+        default=12354,
+        help="Master port to use for DDP training.",
+    )
+
+    parser.add_argument(
+        "--tensorboard",
+        type=str2bool,
+        default=True,
+        help="Should various information be logged in tensorboard.",
+    )
+
+    parser.add_argument(
+        "--num-epochs",
+        type=int,
+        default=30,
+        help="Number of epochs to train.",
+    )
+
+    parser.add_argument(
+        "--start-epoch",
+        type=int,
+        default=0,
+        help="""Resume training from from this epoch.
+        If it is positive, it will load checkpoint from
+        transducer_stateless2/exp/epoch-{start_epoch-1}.pt
+        """,
+    )
+
+    parser.add_argument(
+        "--start-batch",
+        type=int,
+        default=0,
+        help="""If positive, --start-epoch is ignored and
+        it loads the checkpoint from exp-dir/checkpoint-{start_batch}.pt
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless2/exp",
+        help="""The experiment dir.
+        It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--initial-lr",
+        type=float,
+        default=0.003,
+        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.""",
+    )
+
+    parser.add_argument(
+        "--lr-epochs",
+        type=float,
+        default=6,
+        help="""Number of epochs that affects how rapidly the learning rate decreases.
+        """,
+    )
+
+    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(
+        "--prune-range",
+        type=int,
+        default=5,
+        help="The prune range for rnnt loss, it means how many symbols(context)"
+        "we are using to compute the loss",
+    )
+
+    parser.add_argument(
+        "--lm-scale",
+        type=float,
+        default=0.25,
+        help="The scale to smooth the loss with lm "
+        "(output of prediction network) part.",
+    )
+
+    parser.add_argument(
+        "--am-scale",
+        type=float,
+        default=0.0,
+        help="The scale to smooth the loss with am (output of encoder network)"
+        "part.",
+    )
+
+    parser.add_argument(
+        "--simple-loss-scale",
+        type=float,
+        default=0.5,
+        help="To get pruning ranges, we will calculate a simple version"
+        "loss(joiner is just addition), this simple loss also uses for"
+        "training (as a regularization item). We will scale the simple loss"
+        "with this parameter before adding to the final loss.",
+    )
+
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="The seed for random generators intended for reproducibility",
+    )
+
+    parser.add_argument(
+        "--print-diagnostics",
+        type=str2bool,
+        default=False,
+        help="Accumulate stats on activations, print them and exit.",
+    )
+
+    parser.add_argument(
+        "--save-every-n",
+        type=int,
+        default=8000,
+        help="""Save checkpoint after processing this number of batches"
+        periodically. We save checkpoint to exp-dir/ whenever
+        params.batch_idx_train % save_every_n == 0. The checkpoint filename
+        has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt'
+        Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the
+        end of each epoch where `xxx` is the epoch number counting from 0.
+        """,
+    )
+
+    parser.add_argument(
+        "--keep-last-k",
+        type=int,
+        default=20,
+        help="""Only keep this number of checkpoints on disk.
+        For instance, if it is 3, there are only 3 checkpoints
+        in the exp-dir with filenames `checkpoint-xxx.pt`.
+        It does not affect checkpoints with name `epoch-xxx.pt`.
+        """,
+    )
+
+    parser.add_argument(
+        "--use-fp16",
+        type=str2bool,
+        default=False,
+        help="Whether to use half precision training.",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    """Return a dict containing training parameters.
+
+    All training related parameters that are not passed from the commandline
+    are saved in the variable `params`.
+
+    Commandline options are merged into `params` after they are parsed, so
+    you can also access them via `params`.
+
+    Explanation of options saved in `params`:
+
+        - best_train_loss: Best training loss so far. It is used to select
+                           the model that has the lowest training loss. It is
+                           updated during the training.
+
+        - best_valid_loss: Best validation loss so far. It is used to select
+                           the model that has the lowest validation loss. It is
+                           updated during the training.
+
+        - best_train_epoch: It is the epoch that has the best training loss.
+
+        - best_valid_epoch: It is the epoch that has the best validation loss.
+
+        - batch_idx_train: Used to writing statistics to tensorboard. It
+                           contains number of batches trained so far across
+                           epochs.
+
+        - log_interval:  Print training loss if batch_idx % log_interval` is 0
+
+        - reset_interval: Reset statistics if batch_idx % reset_interval is 0
+
+        - valid_interval:  Run validation if batch_idx % valid_interval is 0
+
+        - feature_dim: The model input dim. It has to match the one used
+                       in computing features.
+
+        - subsampling_factor:  The subsampling factor for the model.
+
+        - encoder_dim: Hidden dim for multi-head attention model.
+
+        - num_decoder_layers: Number of decoder layer of transformer decoder.
+
+        - warm_step: The warm_step for Noam optimizer.
+    """
+    params = AttributeDict(
+        {
+            "best_train_loss": float("inf"),
+            "best_valid_loss": float("inf"),
+            "best_train_epoch": -1,
+            "best_valid_epoch": -1,
+            "batch_idx_train": 0,
+            "log_interval": 500,
+            "reset_interval": 2000,
+            "valid_interval": 20000,
+            # parameters for conformer
+            "feature_dim": 80,
+            "subsampling_factor": 4,
+            "encoder_dim": 512,
+            "nhead": 8,
+            "dim_feedforward": 2048,
+            "num_encoder_layers": 12,
+            # parameters for decoder
+            "decoder_dim": 512,
+            # parameters for joiner
+            "joiner_dim": 512,
+            # parameters for Noam
+            "model_warm_step": 20000,  # arg given to model, not for lrate
+            "env_info": get_env_info(),
+        }
+    )
+
+    return params
+
+
+def get_encoder_model(params: AttributeDict) -> nn.Module:
+    # TODO: We can add an option to switch between Conformer and Transformer
+    encoder = Conformer(
+        num_features=params.feature_dim,
+        subsampling_factor=params.subsampling_factor,
+        d_model=params.encoder_dim,
+        nhead=params.nhead,
+        dim_feedforward=params.dim_feedforward,
+        num_encoder_layers=params.num_encoder_layers,
+    )
+    return encoder
+
+
+def get_decoder_model(params: AttributeDict) -> nn.Module:
+    decoder = Decoder(
+        vocab_size=params.vocab_size,
+        decoder_dim=params.decoder_dim,
+        blank_id=params.blank_id,
+        context_size=params.context_size,
+    )
+    return decoder
+
+
+def get_joiner_model(params: AttributeDict) -> nn.Module:
+    joiner = Joiner(
+        encoder_dim=params.encoder_dim,
+        decoder_dim=params.decoder_dim,
+        joiner_dim=params.joiner_dim,
+        vocab_size=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,
+        encoder_dim=params.encoder_dim,
+        decoder_dim=params.decoder_dim,
+        joiner_dim=params.joiner_dim,
+        vocab_size=params.vocab_size,
+    )
+    return model
+
+
+def load_checkpoint_if_available(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: Optional[torch.optim.Optimizer] = None,
+    scheduler: Optional[LRSchedulerType] = None,
+) -> Optional[Dict[str, Any]]:
+    """Load checkpoint from file.
+
+    If params.start_batch is positive, it will load the checkpoint from
+    `params.exp_dir/checkpoint-{params.start_batch}.pt`. Otherwise, if
+    params.start_epoch is positive, it will load the checkpoint from
+    `params.start_epoch - 1`.
+
+    Apart from loading state dict for `model` and `optimizer` it also updates
+    `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
+    and `best_valid_loss` in `params`.
+
+    Args:
+      params:
+        The return value of :func:`get_params`.
+      model:
+        The training model.
+      optimizer:
+        The optimizer that we are using.
+      scheduler:
+        The scheduler that we are using.
+    Returns:
+      Return a dict containing previously saved training info.
+    """
+    if params.start_batch > 0:
+        filename = params.exp_dir / f"checkpoint-{params.start_batch}.pt"
+    elif params.start_epoch > 0:
+        filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
+    else:
+        return None
+
+    assert filename.is_file(), f"{filename} does not exist!"
+
+    saved_params = load_checkpoint(
+        filename,
+        model=model,
+        optimizer=optimizer,
+        scheduler=scheduler,
+    )
+
+    keys = [
+        "best_train_epoch",
+        "best_valid_epoch",
+        "batch_idx_train",
+        "best_train_loss",
+        "best_valid_loss",
+    ]
+    for k in keys:
+        params[k] = saved_params[k]
+
+    if params.start_batch > 0:
+        if "cur_epoch" in saved_params:
+            params["start_epoch"] = saved_params["cur_epoch"]
+
+        if "cur_batch_idx" in saved_params:
+            params["cur_batch_idx"] = saved_params["cur_batch_idx"]
+
+    return saved_params
+
+
+def save_checkpoint(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: Optional[torch.optim.Optimizer] = None,
+    scheduler: Optional[LRSchedulerType] = None,
+    sampler: Optional[CutSampler] = None,
+    scaler: Optional[GradScaler] = None,
+    rank: int = 0,
+) -> None:
+    """Save model, optimizer, scheduler and training stats to file.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The training model.
+      optimizer:
+        The optimizer used in the training.
+      sampler:
+       The sampler for the training dataset.
+      scaler:
+        The scaler used for mix precision training.
+    """
+    if rank != 0:
+        return
+    filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
+    save_checkpoint_impl(
+        filename=filename,
+        model=model,
+        params=params,
+        optimizer=optimizer,
+        scheduler=scheduler,
+        sampler=sampler,
+        scaler=scaler,
+        rank=rank,
+    )
+
+    if params.best_train_epoch == params.cur_epoch:
+        best_train_filename = params.exp_dir / "best-train-loss.pt"
+        copyfile(src=filename, dst=best_train_filename)
+
+    if params.best_valid_epoch == params.cur_epoch:
+        best_valid_filename = params.exp_dir / "best-valid-loss.pt"
+        copyfile(src=filename, dst=best_valid_filename)
+
+
+def compute_loss(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    is_training: bool,
+    warmup: float = 1.0,
+) -> Tuple[Tensor, MetricsTracker]:
+    """
+    Compute CTC loss given the model and its inputs.
+
+    Args:
+      params:
+        Parameters for training. See :func:`get_params`.
+      model:
+        The model for training. It is an instance of Conformer in our case.
+      batch:
+        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
+        for the content in it.
+      is_training:
+        True for training. False for validation. When it is True, this
+        function enables autograd during computation; when it is False, it
+        disables autograd.
+     warmup: a floating point value which increases throughout training;
+        values >= 1.0 are fully warmed up and have all modules present.
+    """
+    device = model.device
+    feature = batch["inputs"]
+    # at entry, feature is (N, T, C)
+    assert feature.ndim == 3
+    feature = feature.to(device)
+
+    supervisions = batch["supervisions"]
+    feature_lens = supervisions["num_frames"].to(device)
+
+    texts = batch["supervisions"]["text"]
+    y = sp.encode(texts, out_type=int)
+    y = k2.RaggedTensor(y).to(device)
+
+    with torch.set_grad_enabled(is_training):
+        simple_loss, pruned_loss = model(
+            x=feature,
+            x_lens=feature_lens,
+            y=y,
+            prune_range=params.prune_range,
+            am_scale=params.am_scale,
+            lm_scale=params.lm_scale,
+            warmup=warmup,
+        )
+        # after the main warmup step, we keep pruned_loss_scale small
+        # for the same amount of time (model_warm_step), to avoid
+        # overwhelming the simple_loss and causing it to diverge,
+        # in case it had not fully learned the alignment yet.
+        pruned_loss_scale = (
+            0.0
+            if warmup < 1.0
+            else (0.1 if warmup > 1.0 and warmup < 2.0 else 1.0)
+        )
+        loss = (
+            params.simple_loss_scale * simple_loss
+            + pruned_loss_scale * pruned_loss
+        )
+
+    assert loss.requires_grad == is_training
+
+    info = MetricsTracker()
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        info["frames"] = (
+            (feature_lens // params.subsampling_factor).sum().item()
+        )
+
+    # Note: We use reduction=sum while computing the loss.
+    info["loss"] = loss.detach().cpu().item()
+    info["simple_loss"] = simple_loss.detach().cpu().item()
+    info["pruned_loss"] = pruned_loss.detach().cpu().item()
+
+    return loss, info
+
+
+def compute_validation_loss(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    valid_dl: torch.utils.data.DataLoader,
+    world_size: int = 1,
+) -> MetricsTracker:
+    """Run the validation process."""
+    model.eval()
+
+    tot_loss = MetricsTracker()
+
+    for batch_idx, batch in enumerate(valid_dl):
+        loss, loss_info = compute_loss(
+            params=params,
+            model=model,
+            sp=sp,
+            batch=batch,
+            is_training=False,
+        )
+        assert loss.requires_grad is False
+        tot_loss = tot_loss + loss_info
+
+    if world_size > 1:
+        tot_loss.reduce(loss.device)
+
+    loss_value = tot_loss["loss"] / tot_loss["frames"]
+    if loss_value < params.best_valid_loss:
+        params.best_valid_epoch = params.cur_epoch
+        params.best_valid_loss = loss_value
+
+    return tot_loss
+
+
+def train_one_epoch(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: torch.optim.Optimizer,
+    scheduler: LRSchedulerType,
+    sp: spm.SentencePieceProcessor,
+    train_dl: torch.utils.data.DataLoader,
+    valid_dl: torch.utils.data.DataLoader,
+    scaler: GradScaler,
+    tb_writer: Optional[SummaryWriter] = None,
+    world_size: int = 1,
+    rank: int = 0,
+) -> None:
+    """Train the model for one epoch.
+
+    The training loss from the mean of all frames is saved in
+    `params.train_loss`. It runs the validation process every
+    `params.valid_interval` batches.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The model for training.
+      optimizer:
+        The optimizer we are using.
+      scheduler:
+        The learning rate scheduler, we call step() every step.
+      train_dl:
+        Dataloader for the training dataset.
+      valid_dl:
+        Dataloader for the validation dataset.
+      scaler:
+        The scaler used for mix precision training.
+      tb_writer:
+        Writer to write log messages to tensorboard.
+      world_size:
+        Number of nodes in DDP training. If it is 1, DDP is disabled.
+      rank:
+        The rank of the node in DDP training. If no DDP is used, it should
+        be set to 0.
+    """
+    model.train()
+
+    tot_loss = MetricsTracker()
+
+    cur_batch_idx = params.get("cur_batch_idx", 0)
+
+    for batch_idx, batch in enumerate(train_dl):
+        if batch_idx < cur_batch_idx:
+            continue
+        cur_batch_idx = batch_idx
+
+        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
+
+        # 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()
+
+        if params.print_diagnostics and batch_idx == 5:
+            return
+
+        if (
+            params.batch_idx_train > 0
+            and params.batch_idx_train % params.save_every_n == 0
+        ):
+            params.cur_batch_idx = batch_idx
+            save_checkpoint_with_global_batch_idx(
+                out_dir=params.exp_dir,
+                global_batch_idx=params.batch_idx_train,
+                model=model,
+                params=params,
+                optimizer=optimizer,
+                scheduler=scheduler,
+                sampler=train_dl.sampler,
+                scaler=scaler,
+                rank=rank,
+            )
+            del params.cur_batch_idx
+            remove_checkpoints(
+                out_dir=params.exp_dir,
+                topk=params.keep_last_k,
+                rank=rank,
+            )
+
+        if batch_idx % params.log_interval == 0:
+            cur_lr = scheduler.get_last_lr()[0]
+            logging.info(
+                f"Epoch {params.cur_epoch}, "
+                f"batch {batch_idx}, loss[{loss_info}], "
+                f"tot_loss[{tot_loss}], batch size: {batch_size}, "
+                f"lr: {cur_lr:.2e}"
+            )
+
+            if tb_writer is not None:
+                tb_writer.add_scalar(
+                    "train/learning_rate", cur_lr, params.batch_idx_train
+                )
+
+                loss_info.write_summary(
+                    tb_writer, "train/current_", params.batch_idx_train
+                )
+                tot_loss.write_summary(
+                    tb_writer, "train/tot_", params.batch_idx_train
+                )
+
+        if batch_idx > 0 and batch_idx % params.valid_interval == 0:
+            logging.info("Computing validation loss")
+            valid_info = compute_validation_loss(
+                params=params,
+                model=model,
+                sp=sp,
+                valid_dl=valid_dl,
+                world_size=world_size,
+            )
+            model.train()
+            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
+            if tb_writer is not None:
+                valid_info.write_summary(
+                    tb_writer, "train/valid_", params.batch_idx_train
+                )
+
+    loss_value = tot_loss["loss"] / tot_loss["frames"]
+    params.train_loss = loss_value
+    if params.train_loss < params.best_train_loss:
+        params.best_train_epoch = params.cur_epoch
+        params.best_train_loss = params.train_loss
+
+
+def run(rank, world_size, args):
+    """
+    Args:
+      rank:
+        It is a value between 0 and `world_size-1`, which is
+        passed automatically by `mp.spawn()` in :func:`main`.
+        The node with rank 0 is responsible for saving checkpoint.
+      world_size:
+        Number of GPUs for DDP training.
+      args:
+        The return value of get_parser().parse_args()
+    """
+    params = get_params()
+    params.update(vars(args))
+
+    fix_random_seed(params.seed)
+    if world_size > 1:
+        setup_dist(rank, world_size, params.master_port)
+
+    setup_logger(f"{params.exp_dir}/log/log-train")
+    logging.info("Training started")
+
+    if args.tensorboard and rank == 0:
+        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
+    else:
+        tb_writer = None
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", rank)
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    checkpoints = load_checkpoint_if_available(params=params, model=model)
+
+    model.to(device)
+    if world_size > 1:
+        logging.info("Using DDP")
+        model = DDP(model, device_ids=[rank])
+    model.device = device
+
+    optimizer = Eve(model.parameters(), lr=params.initial_lr)
+
+    scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs)
+
+    if checkpoints and "optimizer" in checkpoints:
+        logging.info("Loading optimizer state dict")
+        optimizer.load_state_dict(checkpoints["optimizer"])
+
+    if (
+        checkpoints
+        and "scheduler" in checkpoints
+        and checkpoints["scheduler"] is not None
+    ):
+        logging.info("Loading scheduler state dict")
+        scheduler.load_state_dict(checkpoints["scheduler"])
+
+    if params.print_diagnostics:
+        opts = diagnostics.TensorDiagnosticOptions(
+            2 ** 22
+        )  # allow 4 megabytes per sub-module
+        diagnostic = diagnostics.attach_diagnostics(model, opts)
+
+    gigaspeech = GigaSpeechAsrDataModule(args)
+
+    train_cuts = gigaspeech.train_cuts()
+
+    if params.start_batch > 0 and checkpoints and "sampler" in checkpoints:
+        # We only load the sampler's state dict when it loads a checkpoint
+        # saved in the middle of an epoch
+        sampler_state_dict = checkpoints["sampler"]
+    else:
+        sampler_state_dict = None
+
+    train_dl = gigaspeech.train_dataloaders(
+        train_cuts, sampler_state_dict=sampler_state_dict
+    )
+
+    valid_cuts = gigaspeech.dev_cuts()
+    valid_dl = gigaspeech.valid_dataloaders(valid_cuts)
+
+    if not params.print_diagnostics:
+        scan_pessimistic_batches_for_oom(
+            model=model,
+            train_dl=train_dl,
+            optimizer=optimizer,
+            sp=sp,
+            params=params,
+        )
+
+    scaler = GradScaler(enabled=params.use_fp16)
+    if checkpoints and "grad_scaler" in checkpoints:
+        logging.info("Loading grad scaler state dict")
+        scaler.load_state_dict(checkpoints["grad_scaler"])
+
+    for epoch in range(params.start_epoch, params.num_epochs):
+        scheduler.step_epoch(epoch)
+        fix_random_seed(params.seed + epoch)
+        train_dl.sampler.set_epoch(epoch)
+
+        if tb_writer is not None:
+            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
+
+        params.cur_epoch = epoch
+
+        train_one_epoch(
+            params=params,
+            model=model,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            sp=sp,
+            train_dl=train_dl,
+            valid_dl=valid_dl,
+            scaler=scaler,
+            tb_writer=tb_writer,
+            world_size=world_size,
+            rank=rank,
+        )
+
+        if params.print_diagnostics:
+            diagnostic.print_diagnostics()
+            break
+
+        save_checkpoint(
+            params=params,
+            model=model,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            sampler=train_dl.sampler,
+            scaler=scaler,
+            rank=rank,
+        )
+
+    logging.info("Done!")
+
+    if world_size > 1:
+        torch.distributed.barrier()
+        cleanup_dist()
+
+
+def scan_pessimistic_batches_for_oom(
+    model: nn.Module,
+    train_dl: torch.utils.data.DataLoader,
+    optimizer: torch.optim.Optimizer,
+    sp: spm.SentencePieceProcessor,
+    params: AttributeDict,
+):
+    from lhotse.dataset import find_pessimistic_batches
+
+    logging.info(
+        "Sanity check -- see if any of the batches in epoch 0 would cause OOM."
+    )
+    batches, crit_values = find_pessimistic_batches(train_dl.sampler)
+    for criterion, cuts in batches.items():
+        batch = train_dl.dataset[cuts]
+        try:
+            # warmup = 0.0 is so that the derivs for the pruned loss stay zero
+            # (i.e. are not remembered by the decaying-average in adam), because
+            # we want to avoid these params being subject to shrinkage in adam.
+            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+                loss, _ = compute_loss(
+                    params=params,
+                    model=model,
+                    sp=sp,
+                    batch=batch,
+                    is_training=True,
+                    warmup=0.0,
+                )
+            loss.backward()
+            optimizer.step()
+            optimizer.zero_grad()
+        except RuntimeError as e:
+            if "CUDA out of memory" in str(e):
+                logging.error(
+                    "Your GPU ran out of memory with the current "
+                    "max_duration setting. We recommend decreasing "
+                    "max_duration and trying again.\n"
+                    f"Failing criterion: {criterion} "
+                    f"(={crit_values[criterion]}) ..."
+                )
+            raise
+
+
+def main():
+    parser = get_parser()
+    GigaSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    world_size = args.world_size
+    assert world_size >= 1
+    if world_size > 1:
+        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
+    else:
+        run(rank=0, world_size=1, args=args)
+
+
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+if __name__ == "__main__":
+    main()

egs/librispeech/ASR/local/prepare_lang_bpe.py

@@ -145,7 +145,14 @@ def generate_lexicon(
     sp = spm.SentencePieceProcessor()
     sp.load(str(model_file))
 
-    words_pieces: List[List[str]] = sp.encode(words, out_type=str)
+    # Convert word to word piece IDs instead of word piece strings
+    # to avoid OOV tokens.
+    words_pieces_ids: List[List[int]] = sp.encode(words, out_type=int)
+
+    # Now convert word piece IDs back to word piece strings.
+    words_pieces: List[List[str]] = [
+        sp.id_to_piece(ids) for ids in words_pieces_ids
+    ]
 
     lexicon = []
     for word, pieces in zip(words, words_pieces):

egs/librispeech/ASR/local/validate_bpe_lexicon.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python3
+# Copyright    2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This script checks that there are no OOV tokens in the BPE-based lexicon.
+
+Usage example:
+
+    python3 ./local/validate_bpe_lexicon.py \
+            --lexicon /path/to/lexicon.txt \
+            --bpe-model /path/to/bpe.model
+"""
+
+import argparse
+from pathlib import Path
+from typing import List, Tuple
+
+import sentencepiece as spm
+
+from icefall.lexicon import read_lexicon
+
+# Map word to word pieces
+Lexicon = List[Tuple[str, List[str]]]
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--lexicon",
+        required=True,
+        type=Path,
+        help="Path to lexicon.txt",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        required=True,
+        type=Path,
+        help="Path to bpe.model",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    assert args.lexicon.is_file(), args.lexicon
+    assert args.bpe_model.is_file(), args.bpe_model
+
+    lexicon = read_lexicon(args.lexicon)
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(str(args.bpe_model))
+
+    word_pieces = set(sp.id_to_piece(list(range(sp.vocab_size()))))
+    for word, pieces in lexicon:
+        for p in pieces:
+            if p not in word_pieces:
+                raise ValueError(f"The word {word} contains an OOV token {p}")
+
+
+if __name__ == "__main__":
+    main()

egs/librispeech/ASR/prepare.sh

@@ -184,13 +184,20 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
       done > $lang_dir/transcript_words.txt
     fi
 
+    if [ ! -f $lang_dir/bpe.model ]; then
       ./local/train_bpe_model.py \
         --lang-dir $lang_dir \
         --vocab-size $vocab_size \
         --transcript $lang_dir/transcript_words.txt
+    fi
 
     if [ ! -f $lang_dir/L_disambig.pt ]; then
       ./local/prepare_lang_bpe.py --lang-dir $lang_dir
+
+      log "Validating $lang_dir/lexicon.txt"
+      ./local/validate_bpe_lexicon.py \
+        --lexicon $lang_dir/lexicon.txt \
+        --bpe-model $lang_dir/bpe.model
     fi
   done
 fi

egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py

@@ -27,6 +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,
@@ -35,8 +178,7 @@ def fast_beam_search(
     beam: float,
     max_states: int,
     max_contexts: int,
-    use_max: bool = False,
+) -> k2.Fsa:
-) -> List[List[int]]:
     """It limits the maximum number of symbols per frame to 1.
 
     Args:
@@ -55,11 +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
 
@@ -92,7 +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
@@ -109,67 +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:
+    return lattice
-        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
 
 
 def greedy_search(
@@ -193,10 +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
@@ -230,7 +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
@@ -249,7 +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:
@@ -257,6 +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).
@@ -264,28 +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)
+    device = next(model.parameters()).device
-    T = encoder_out.size(1)
 
     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)
+    # decoder_out: (N, 1, decoder_out_dim)
-    for t in range(T):
+
-        current_encoder_out = encoder_out[:, t : t + 1, :].unsqueeze(2)  # 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).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)
 
@@ -294,12 +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,
@@ -307,7 +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
 
 
@@ -472,6 +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]]:
@@ -482,6 +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:
@@ -492,16 +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)
+    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
-    T = encoder_out.size(1)
+        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
+    device = next(model.parameters()).device
-    B = [HypothesisList() for _ in range(batch_size)]
+
-    for i 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)
+
+    B = [HypothesisList() for _ in range(N)]
+    for i in range(N):
         B[i].add(
             Hypothesis(
                 ys=[blank_id] * context_size,
@@ -510,9 +636,20 @@ def modified_beam_search(
             use_max=use_max,
         )
 
-    for t in range(T):
+    encoder_out = packed_encoder_out.data
-        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)
 
@@ -577,15 +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
 
@@ -622,10 +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)
 
@@ -691,7 +834,7 @@ def _deprecated_modified_beam_search(
             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)
@@ -732,10 +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,
@@ -818,7 +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

@@ -22,15 +22,15 @@ Usage:
     --epoch 28 \
     --avg 15 \
     --exp-dir ./pruned_transducer_stateless/exp \
-        --max-duration 100 \
+    --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 \
+    --max-duration 600 \
     --decoding-method beam_search \
     --beam-size 4
 
@@ -39,7 +39,7 @@ Usage:
     --epoch 28 \
     --avg 15 \
     --exp-dir ./pruned_transducer_stateless/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_stateless/exp \
-        --max-duration 1500 \
+    --max-duration 600 \
     --decoding-method fast_beam_search \
     --beam 4 \
     --max-contexts 4 \
@@ -61,7 +61,7 @@ Usage:
     --exp-dir ./pruned_transducer_stateless/exp \
     --use-LG True \
     --use-max False \
-        --max-duration 1500 \
+    --max-duration 600 \
     --decoding-method fast_beam_search \
     --beam 8 \
     --max-contexts 8 \
@@ -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.
+        help="""Path to bpe.model.""",
-        Used only when method is ctc-decoding.
-        """,
     )
 
     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

@@ -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 = next(model.parameters()).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)
+    device = next(model.parameters()).device
-    T = encoder_out.size(1)
 
     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)
+    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
+    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)
+    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
-    T = encoder_out.size(1)
+        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 = next(model.parameters()).device
-    B = [HypothesisList() for _ in range(batch_size)]
+
-    for i 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)
+
+    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):
+    offset = 0
-        current_encoder_out = encoder_out[:, t : t + 1, :].unsqueeze(2)  # noqa
+    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
 

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/export.py

@@ -51,7 +51,11 @@ import sentencepiece as spm
 import torch
 from train import get_params, get_transducer_model
 
-from icefall.checkpoint import average_checkpoints, load_checkpoint
+from icefall.checkpoint import (
+    average_checkpoints,
+    find_checkpoints,
+    load_checkpoint,
+)
 from icefall.utils import str2bool
 
 
@@ -64,8 +68,19 @@ def get_parser():
         "--epoch",
         type=int,
         default=28,
-        help="It specifies the checkpoint to use for decoding."
+        help="""It specifies the checkpoint to use for averaging.
-        "Note: Epoch counts from 0.",
+        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(
@@ -74,7 +89,7 @@ def get_parser():
         default=15,
         help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-        "'--epoch'. ",
+        "'--epoch' and '--iter'",
     )
 
     parser.add_argument(
@@ -141,7 +156,24 @@ def main():
 
     model.to(device)
 
-    if params.avg == 1:
+    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

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 \
@@ -224,8 +224,8 @@ def get_parser():
 
 
 def post_processing(
-    results: List[Tuple[List[List[str]], List[List[str]]]],
+    results: List[Tuple[List[str], List[str]]],
-) -> List[Tuple[List[List[str]], List[List[str]]]]:
+) -> List[Tuple[List[str], List[str]]]:
     new_results = []
     for ref, hyp in results:
         new_ref = asr_text_post_processing(" ".join(ref)).split()
@@ -415,9 +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):

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_stateless3/export.py

@@ -52,7 +52,11 @@ import sentencepiece as spm
 import torch
 from train import get_params, get_transducer_model
 
-from icefall.checkpoint import average_checkpoints, load_checkpoint
+from icefall.checkpoint import (
+    average_checkpoints,
+    find_checkpoints,
+    load_checkpoint,
+)
 from icefall.utils import str2bool
 
 
@@ -65,8 +69,19 @@ def get_parser():
         "--epoch",
         type=int,
         default=28,
-        help="It specifies the checkpoint to use for decoding."
+        help="""It specifies the checkpoint to use for averaging.
-        "Note: Epoch counts from 0.",
+        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(
@@ -75,7 +90,7 @@ def get_parser():
         default=15,
         help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-        "'--epoch'. ",
+        "'--epoch' and '--iter'",
     )
 
     parser.add_argument(
@@ -142,7 +157,24 @@ def main():
 
     model.to(device)
 
-    if params.avg == 1:
+    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

egs/librispeech/ASR/pruned_transducer_stateless4/decode.py

@@ -22,16 +22,16 @@ Usage:
 ./pruned_transducer_stateless4/decode.py \
         --epoch 30 \
         --avg 15 \
-        --exp-dir ./pruned_transducer_stateless2/exp \
+        --exp-dir ./pruned_transducer_stateless4/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method greedy_search
 
-(2) beam search
+(2) beam search (not recommended)
 ./pruned_transducer_stateless4/decode.py \
         --epoch 30 \
         --avg 15 \
-        --exp-dir ./pruned_transducer_stateless2/exp \
+        --exp-dir ./pruned_transducer_stateless4/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method beam_search \
         --beam-size 4
 
@@ -39,8 +39,8 @@ Usage:
 ./pruned_transducer_stateless4/decode.py \
         --epoch 30 \
         --avg 15 \
-        --exp-dir ./pruned_transducer_stateless2/exp \
+        --exp-dir ./pruned_transducer_stateless4/exp \
-        --max-duration 100 \
+        --max-duration 600 \
         --decoding-method modified_beam_search \
         --beam-size 4
 
@@ -48,8 +48,8 @@ Usage:
 ./pruned_transducer_stateless4/decode.py \
         --epoch 30 \
         --avg 15 \
-        --exp-dir ./pruned_transducer_stateless2/exp \
+        --exp-dir ./pruned_transducer_stateless4/exp \
-        --max-duration 1500 \
+        --max-duration 600 \
         --decoding-method fast_beam_search \
         --beam 4 \
         --max-contexts 4 \
@@ -70,7 +70,7 @@ 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,
@@ -266,7 +266,7 @@ 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,
@@ -284,6 +284,7 @@ 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())
@@ -291,6 +292,7 @@ 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):
@@ -370,9 +372,9 @@ 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/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)
+    device = next(model.parameters()).device
-    T = encoder_out.size(1)
 
     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)
+    encoder_out_len = torch.ones(1, dtype=torch.int32)
-    decoder_out_len = torch.ones(batch_size, dtype=torch.int32)
+    decoder_out_len = torch.ones(1, dtype=torch.int32)
 
-    for t in range(T):
+    encoder_out = packed_encoder_out.data
-        current_encoder_out = encoder_out[:, t : t + 1, :]  # noqa
+
+    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)
+    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
-    T = encoder_out.size(1)
+        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
+    device = next(model.parameters()).device
-    B = [HypothesisList() for _ in range(batch_size)]
+
-    for i 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)
+
+    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

@@ -22,15 +22,15 @@ Usage:
     --epoch 14 \
     --avg 7 \
     --exp-dir ./transducer_stateless/exp \
-        --max-duration 100 \
+    --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 \
+    --max-duration 600 \
     --decoding-method beam_search \
     --beam-size 4
 
@@ -39,9 +39,20 @@ Usage:
     --epoch 14 \
     --avg 7 \
     --exp-dir ./transducer_stateless/exp \
-        --max-duration 100 \
+    --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.append(sp.decode(hyp).split())
-
-    hyps = [sp.decode(hyp).split() for hyp in hyp_list]
 
     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

@@ -24,7 +24,7 @@ Usage:
     --method greedy_search \
     --max-sym-per-frame 1 \
     /path/to/foo.wav \
-        /path/to/bar.wav \
+    /path/to/bar.wav
 
 (2) beam search
 ./transducer_stateless/pretrained.py \
@@ -33,7 +33,7 @@ Usage:
     --method beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
-        /path/to/bar.wav \
+    /path/to/bar.wav
 
 (3) modified beam search
 ./transducer_stateless/pretrained.py \
@@ -42,7 +42,16 @@ Usage:
     --method modified_beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
-        /path/to/bar.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.
+        help="""Path to bpe.model.""",
-        Used only when method is ctc-decoding.
-        """,
     )
 
     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.append(sp.decode(hyp).split())
-
-    hyps = [sp.decode(hyp).split() for hyp in hyp_list]
 
     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

@@ -24,7 +24,7 @@ Usage:
     --method greedy_search \
     --max-sym-per-frame 1 \
     /path/to/foo.wav \
-        /path/to/bar.wav \
+    /path/to/bar.wav
 
 (2) beam search
 ./transducer_stateless2/pretrained.py \
@@ -33,7 +33,7 @@ Usage:
     --method beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
-        /path/to/bar.wav \
+    /path/to/bar.wav
 
 (3) modified beam search
 ./transducer_stateless2/pretrained.py \
@@ -42,7 +42,16 @@ Usage:
     --method modified_beam_search \
     --beam-size 4 \
     /path/to/foo.wav \
-        /path/to/bar.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.
+        help="""Path to bpe.model.""",
-        Used only when method is ctc-decoding.
-        """,
     )
 
     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.append(sp.decode(hyp).split())
-
-    hyps = [sp.decode(hyp).split() for hyp in hyp_list]
 
     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.
+        help="""Path to bpe.model.""",
-        Used only when method is ctc-decoding.
-        """,
     )
 
     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 train import get_params, get_transducer_model
-from icefall.env import get_env_info
-from icefall.utils import AttributeDict
 
 
 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):