Merge remote-tracking branch 'upstream/master' into gigaspeech_rnnt

.flake8

@@ -10,6 +10,7 @@ per-file-ignores =
     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/compute-fbank-librispeech-test-clean-and-test-other.sh

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

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

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

.github/scripts/install-kaldifeat.sh

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

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

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

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

@@ -45,3 +45,31 @@ 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: ${GITHUB_EVENT_NAME}"
+if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
+  mkdir -p pruned_transducer_stateless/exp
+  ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh pruned_transducer_stateless/exp
+
+  log "Decoding test-clean and test-other"
+
+  # use a small value for decoding with CPU
+  max_duration=50
+
+  for method in greedy_search fast_beam_search; do
+    log "Decoding with $method"
+
+    ./pruned_transducer_stateless/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir pruned_transducer_stateless/exp
+  done
+
+  rm pruned_transducer_stateless/exp/*.pt
+fi

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

@@ -49,3 +49,31 @@ for method in modified_beam_search beam_search fast_beam_search; do
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav
 done
+
+echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
+if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
+  mkdir -p pruned_transducer_stateless2/exp
+  ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless2/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh pruned_transducer_stateless2/exp
+
+  log "Decoding test-clean and test-other"
+
+  # use a small value for decoding with CPU
+  max_duration=50
+
+  for method in greedy_search fast_beam_search; do
+    log "Decoding with $method"
+
+    ./pruned_transducer_stateless2/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir pruned_transducer_stateless2/exp
+  done
+
+  rm pruned_transducer_stateless2/exp/*.pt
+fi

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

@@ -49,3 +49,31 @@ for method in modified_beam_search beam_search fast_beam_search; do
     $repo/test_wavs/1221-135766-0001.wav \
     $repo/test_wavs/1221-135766-0002.wav
 done
+
+echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
+if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
+  mkdir -p pruned_transducer_stateless3/exp
+  ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless3/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh pruned_transducer_stateless3/exp
+
+  log "Decoding test-clean and test-other"
+
+  # use a small value for decoding with CPU
+  max_duration=50
+
+  for method in greedy_search fast_beam_search; do
+    log "Decoding with $method"
+
+    ./pruned_transducer_stateless3/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir pruned_transducer_stateless3/exp
+  done
+
+  rm pruned_transducer_stateless3/exp/*.pt
+fi

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

@@ -45,3 +45,31 @@ 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: ${GITHUB_EVENT_NAME}"
+if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
+  mkdir -p transducer_stateless2/exp
+  ln -s $PWD/$repo/exp/pretrained.pt transducer_stateless2/exp/epoch-999.pt
+  ln -s $PWD/$repo/data/lang_bpe_500 data/
+
+  ls -lh data
+  ls -lh transducer_stateless2/exp
+
+  log "Decoding test-clean and test-other"
+
+  # use a small value for decoding with CPU
+  max_duration=50
+
+  for method in greedy_search modified_beam_search; do
+    log "Decoding with $method"
+
+    ./transducer_stateless2/decode.py \
+      --decoding-method $method \
+      --epoch 999 \
+      --avg 1 \
+      --max-duration $max_duration \
+      --exp-dir transducer_stateless2/exp
+  done
+
+  rm transducer_stateless2/exp/*.pt
+fi

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

@@ -24,9 +24,18 @@ on:
   pull_request:
     types: [labeled]
 
+  schedule:
+    # minute (0-59)
+    # hour (0-23)
+    # day of the month (1-31)
+    # month (1-12)
+    # day of the week (0-6)
+    # nightly build at 15:50 UTC time every day
+    - cron: "50 15 * * *"
+
 jobs:
   run_librispeech_2022_03_12:
-    if: github.event.label.name == 'ready' || github.event_name == 'push'
+    if: github.event.label.name == 'ready' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -63,20 +72,78 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          mkdir -p ~/tmp
+          .github/scripts/install-kaldifeat.sh
-          cd ~/tmp
+
-          git clone https://github.com/csukuangfj/kaldifeat
+      - name: Cache LibriSpeech test-clean and test-other datasets
-          cd kaldifeat
+        id: libri-test-clean-and-test-other-data
-          mkdir build
+        uses: actions/cache@v2
-          cd build
+        with:
-          cmake -DCMAKE_BUILD_TYPE=Release ..
+          path: |
-          make -j2 _kaldifeat
+            ~/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: ${{ github.event_name }}
         run: |
+          mkdir -p egs/librispeech/ASR/data
+          ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank
+          ls -lh egs/librispeech/ASR/data/*
+
           sudo apt-get -qq install git-lfs tree sox
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
+
           .github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh
+
+      - name: Display decoding results
+        if: github.event_name == 'schedule'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR/
+          tree ./pruned_transducer_stateless/exp
+
+          cd pruned_transducer_stateless
+          echo "results for pruned_transducer_stateless"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for pruned_transducer_stateless
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless-2022-03-12
+          path: egs/librispeech/ASR/pruned_transducer_stateless/exp/

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

@@ -24,9 +24,18 @@ on:
   pull_request:
     types: [labeled]
 
+  schedule:
+    # minute (0-59)
+    # hour (0-23)
+    # day of the month (1-31)
+    # month (1-12)
+    # day of the week (0-6)
+    # nightly build at 15:50 UTC time every day
+    - cron: "50 15 * * *"
+
 jobs:
   run_librispeech_2022_04_29:
-    if: github.event.label.name == 'ready' || github.event_name == 'push'
+    if: github.event.label.name == 'ready' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -63,18 +72,50 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          mkdir -p ~/tmp
+          .github/scripts/install-kaldifeat.sh
-          cd ~/tmp
+
-          git clone https://github.com/csukuangfj/kaldifeat
+      - name: Cache LibriSpeech test-clean and test-other datasets
-          cd kaldifeat
+        id: libri-test-clean-and-test-other-data
-          mkdir build
+        uses: actions/cache@v2
-          cd build
+        with:
-          cmake -DCMAKE_BUILD_TYPE=Release ..
+          path: |
-          make -j2 _kaldifeat
+            ~/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: ${{ github.event_name }}
         run: |
+          mkdir -p egs/librispeech/ASR/data
+          ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank
+          ls -lh egs/librispeech/ASR/data/*
+
           sudo apt-get -qq install git-lfs tree sox
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
@@ -83,3 +124,45 @@ jobs:
           .github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh
 
           .github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh
+
+      - name: Display decoding results
+        if: github.event_name == 'schedule'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR
+          tree pruned_transducer_stateless2/exp
+          cd pruned_transducer_stateless2
+          echo "results for pruned_transducer_stateless2"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===fast_beam_search==="
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          cd ../
+          tree pruned_transducer_stateless3/exp
+          cd pruned_transducer_stateless3
+          echo "results for pruned_transducer_stateless3"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-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
+
+      - name: Upload decoding results for pruned_transducer_stateless2
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule'
+        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'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless3-2022-04-29
+          path: egs/librispeech/ASR/pruned_transducer_stateless3/exp/

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

@@ -24,9 +24,18 @@ on:
   pull_request:
     types: [labeled]
 
+  schedule:
+    # minute (0-59)
+    # hour (0-23)
+    # day of the month (1-31)
+    # month (1-12)
+    # day of the week (0-6)
+    # nightly build at 15:50 UTC time every day
+    - cron: "50 15 * * *"
+
 jobs:
   run_librispeech_2022_04_19:
-    if: github.event.label.name == 'ready' || github.event_name == 'push'
+    if: github.event.label.name == 'ready' || github.event_name == 'push' || github.event_name == 'schedule'
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -63,20 +72,77 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          mkdir -p ~/tmp
+          .github/scripts/install-kaldifeat.sh
-          cd ~/tmp
+
-          git clone https://github.com/csukuangfj/kaldifeat
+      - name: Cache LibriSpeech test-clean and test-other datasets
-          cd kaldifeat
+        id: libri-test-clean-and-test-other-data
-          mkdir build
+        uses: actions/cache@v2
-          cd build
+        with:
-          cmake -DCMAKE_BUILD_TYPE=Release ..
+          path: |
-          make -j2 _kaldifeat
+            ~/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: ${{ github.event_name }}
         run: |
+          mkdir -p egs/librispeech/ASR/data
+          ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank
+          ls -lh egs/librispeech/ASR/data/*
+
           sudo apt-get -qq install git-lfs tree sox
           export PYTHONPATH=$PWD:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
           export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
+
           .github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh
+
+      - name: Display decoding results
+        if: github.event_name == 'schedule'
+        shell: bash
+        run: |
+          cd egs/librispeech/ASR/
+          tree ./transducer_stateless2/exp
+
+          cd transducer_stateless2
+          echo "results for transducer_stateless2"
+          echo "===greedy search==="
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+          echo "===modified_beam_search==="
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
+          find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
+
+      - name: Upload decoding results for transducer_stateless2
+        uses: actions/upload-artifact@v2
+        if: github.event_name == 'schedule'
+        with:
+          name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-transducer_stateless2-2022-04-19
+          path: egs/librispeech/ASR/transducer_stateless2/exp/

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

@@ -62,14 +62,7 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          mkdir -p ~/tmp
+          .github/scripts/install-kaldifeat.sh
-          cd ~/tmp
-          git clone https://github.com/csukuangfj/kaldifeat
-          cd kaldifeat
-          mkdir build
-          cd build
-          cmake -DCMAKE_BUILD_TYPE=Release ..
-          make -j2 _kaldifeat
 
       - name: Inference with pre-trained model
         shell: bash

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

@@ -62,14 +62,7 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          mkdir -p ~/tmp
+          .github/scripts/install-kaldifeat.sh
-          cd ~/tmp
-          git clone https://github.com/csukuangfj/kaldifeat
-          cd kaldifeat
-          mkdir build
-          cd build
-          cmake -DCMAKE_BUILD_TYPE=Release ..
-          make -j2 _kaldifeat
 
       - name: Inference with pre-trained model
         shell: bash

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

@@ -62,14 +62,7 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          mkdir -p ~/tmp
+          .github/scripts/install-kaldifeat.sh
-          cd ~/tmp
-          git clone https://github.com/csukuangfj/kaldifeat
-          cd kaldifeat
-          mkdir build
-          cd build
-          cmake -DCMAKE_BUILD_TYPE=Release ..
-          make -j2 _kaldifeat
 
       - name: Inference with pre-trained model
         shell: bash

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

@@ -62,14 +62,7 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          mkdir -p ~/tmp
+          .github/scripts/install-kaldifeat.sh
-          cd ~/tmp
-          git clone https://github.com/csukuangfj/kaldifeat
-          cd kaldifeat
-          mkdir build
-          cd build
-          cmake -DCMAKE_BUILD_TYPE=Release ..
-          make -j2 _kaldifeat
 
       - name: Inference with pre-trained model
         shell: bash

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

@@ -62,14 +62,7 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          mkdir -p ~/tmp
+          .github/scripts/install-kaldifeat.sh
-          cd ~/tmp
-          git clone https://github.com/csukuangfj/kaldifeat
-          cd kaldifeat
-          mkdir build
-          cd build
-          cmake -DCMAKE_BUILD_TYPE=Release ..
-          make -j2 _kaldifeat
 
       - name: Inference with pre-trained model
         shell: bash

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

@@ -62,14 +62,7 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          mkdir -p ~/tmp
+          .github/scripts/install-kaldifeat.sh
-          cd ~/tmp
-          git clone https://github.com/csukuangfj/kaldifeat
-          cd kaldifeat
-          mkdir build
-          cd build
-          cmake -DCMAKE_BUILD_TYPE=Release ..
-          make -j2 _kaldifeat
 
       - name: Inference with pre-trained model
         shell: bash

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

@@ -62,13 +62,6 @@ jobs:
         if: steps.my-cache.outputs.cache-hit != 'true'
         shell: bash
         run: |
-          mkdir -p ~/tmp
-          cd ~/tmp
-          git clone https://github.com/csukuangfj/kaldifeat
-          cd kaldifeat
-          mkdir build
-          cd build
-          cmake -DCMAKE_BUILD_TYPE=Release ..
           make -j2 _kaldifeat
 
       - name: Inference with pre-trained model

egs/aishell/ASR/transducer_stateless/export.py

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

egs/gigaspeech/ASR/conformer_ctc/decode.py

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

egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py

@@ -15,6 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import warnings
 from dataclasses import dataclass
 from typing import Dict, List, Optional
 
@@ -565,6 +566,8 @@ def modified_beam_search(
         for i in range(batch_size):
             topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam)
 
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
                 topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
                 topk_token_indexes = (topk_indexes % vocab_size).tolist()
 
@@ -679,6 +682,8 @@ def _deprecated_modified_beam_search(
         topk_hyp_indexes = topk_indexes // logits.size(-1)
         topk_token_indexes = topk_indexes % logits.size(-1)
 
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
             topk_hyp_indexes = topk_hyp_indexes.tolist()
             topk_token_indexes = topk_token_indexes.tolist()
 

egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py

@@ -276,7 +276,7 @@ def greedy_search(
     context_size = model.decoder.context_size
     unk_id = getattr(model, "unk_id", blank_id)
 
-    device = model.device
+    device = next(model.parameters()).device
 
     decoder_input = torch.tensor(
         [blank_id] * context_size, device=device, dtype=torch.int64
@@ -350,7 +350,7 @@ def greedy_search_batch(
     assert encoder_out.ndim == 3
     assert encoder_out.size(0) >= 1, encoder_out.size(0)
 
-    device = model.device
+    device = next(model.parameters()).device
 
     batch_size = encoder_out.size(0)
     T = encoder_out.size(1)
@@ -580,7 +580,7 @@ def modified_beam_search(
     blank_id = model.decoder.blank_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):
         B[i].add(
@@ -705,7 +705,7 @@ def _deprecated_modified_beam_search(
     unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
 
-    device = model.device
+    device = next(model.parameters()).device
 
     T = encoder_out.size(1)
 
@@ -813,7 +813,7 @@ def beam_search(
     unk_id = getattr(model, "unk_id", blank_id)
     context_size = model.decoder.context_size
 
-    device = model.device
+    device = next(model.parameters()).device
 
     decoder_input = torch.tensor(
         [blank_id] * context_size,

egs/librispeech/ASR/pruned_transducer_stateless2/train.py

@@ -156,15 +156,16 @@ def get_parser():
         "--initial-lr",
         type=float,
         default=0.003,
-        help="The initial learning rate.  This value should not need to be changed.",
+        help="The initial learning rate.  This value should not need to "
+        "be changed.",
     )
 
     parser.add_argument(
         "--lr-batches",
         type=float,
         default=5000,
-        help="""Number of steps that affects how rapidly the learning rate decreases.
+        help="""Number of steps that affects how rapidly the learning rate
-        We suggest not to change this.""",
+        decreases. We suggest not to change this.""",
     )
 
     parser.add_argument(
@@ -670,6 +671,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
+        try:
             with torch.cuda.amp.autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
@@ -689,6 +691,9 @@ def train_one_epoch(
             scaler.step(optimizer)
             scaler.update()
             optimizer.zero_grad()
+        except:  # noqa
+            display_and_save_batch(batch, params=params, sp=sp)
+            raise
 
         if params.print_diagnostics and batch_idx == 5:
             return
@@ -933,6 +938,38 @@ def run(rank, world_size, args):
         cleanup_dist()
 
 
+def display_and_save_batch(
+    batch: dict,
+    params: AttributeDict,
+    sp: spm.SentencePieceProcessor,
+) -> None:
+    """Display the batch statistics and save the batch into disk.
+
+    Args:
+      batch:
+        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
+        for the content in it.
+      params:
+        Parameters for training. See :func:`get_params`.
+      sp:
+        The BPE model.
+    """
+    from lhotse.utils import uuid4
+
+    filename = f"{params.exp_dir}/batch-{uuid4()}.pt"
+    logging.info(f"Saving batch to {filename}")
+    torch.save(batch, filename)
+
+    supervisions = batch["supervisions"]
+    features = batch["inputs"]
+
+    logging.info(f"features shape: {features.shape}")
+
+    y = sp.encode(supervisions["text"], out_type=int)
+    num_tokens = sum(len(i) for i in y)
+    logging.info(f"num tokens: {num_tokens}")
+
+
 def scan_pessimistic_batches_for_oom(
     model: nn.Module,
     train_dl: torch.utils.data.DataLoader,
@@ -964,7 +1001,7 @@ def scan_pessimistic_batches_for_oom(
             loss.backward()
             optimizer.step()
             optimizer.zero_grad()
-        except RuntimeError as e:
+        except Exception as e:
             if "CUDA out of memory" in str(e):
                 logging.error(
                     "Your GPU ran out of memory with the current "
@@ -973,6 +1010,7 @@ def scan_pessimistic_batches_for_oom(
                     f"Failing criterion: {criterion} "
                     f"(={crit_values[criterion]}) ..."
                 )
+            display_and_save_batch(batch, params=params, sp=sp)
             raise
 
 

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

@@ -69,7 +69,8 @@ import torch.nn as nn
 from asr_datamodule import AsrDataModule
 from beam_search import (
     beam_search,
-    fast_beam_search,
+    fast_beam_search_nbest_oracle,
+    fast_beam_search_one_best,
     greedy_search,
     greedy_search_batch,
     modified_beam_search,
@@ -100,27 +101,28 @@ def get_parser():
         "--epoch",
         type=int,
         default=28,
-        help="It specifies the checkpoint to use for decoding."
+        help="""It specifies the checkpoint to use for decoding.
-        "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(
         "--avg",
         type=int,
         default=15,
         help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
-        "'--epoch'. ",
+        "'--epoch' and '--iter'",
-    )
-
-    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(
@@ -146,6 +148,7 @@ def get_parser():
           - beam_search
           - modified_beam_search
           - fast_beam_search
+          - fast_beam_search_nbest_oracle
         """,
     )
 
@@ -165,7 +168,8 @@ def get_parser():
         help="""A floating point value to calculate the cutoff score during beam
         search (i.e., `cutoff = max-score - beam`), which is the same as the
         `beam` in Kaldi.
-        Used only when --decoding-method is fast_beam_search""",
+        Used only when --decoding-method is
+        fast_beam_search or fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -173,7 +177,7 @@ def get_parser():
         type=int,
         default=4,
         help="""Used only when --decoding-method is
-        fast_beam_search""",
+        fast_beam_search or fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -181,7 +185,7 @@ def get_parser():
         type=int,
         default=8,
         help="""Used only when --decoding-method is
-        fast_beam_search""",
+        fast_beam_search or fast_beam_search_nbest_oracle""",
     )
 
     parser.add_argument(
@@ -199,6 +203,23 @@ def get_parser():
         Used only when --decoding_method is greedy_search""",
     )
 
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=100,
+        help="""Number of paths for computed nbest oracle WER
+        when the decoding method is fast_beam_search_nbest_oracle.
+        """,
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""Scale applied to lattice scores when computing nbest paths.
+        Used only when the decoding_method is fast_beam_search_nbest_oracle.
+        """,
+    )
     return parser
 
 
@@ -243,7 +264,8 @@ def decode_one_batch(
         for the format of the `batch`.
       decoding_graph:
         The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
-        only when --decoding_method is fast_beam_search.
+        only when --decoding_method is
+        fast_beam_search or fast_beam_search_nbest_oracle.
     Returns:
       Return the decoding result. See above description for the format of
       the returned dict.
@@ -264,7 +286,7 @@ def decode_one_batch(
     hyps = []
 
     if params.decoding_method == "fast_beam_search":
-        hyp_tokens = fast_beam_search(
+        hyp_tokens = fast_beam_search_one_best(
             model=model,
             decoding_graph=decoding_graph,
             encoder_out=encoder_out,
@@ -275,6 +297,21 @@ def decode_one_batch(
         )
         for hyp in sp.decode(hyp_tokens):
             hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        hyp_tokens = fast_beam_search_nbest_oracle(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            ref_texts=sp.encode(supervisions["text"]),
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
     elif (
         params.decoding_method == "greedy_search"
         and params.max_sym_per_frame == 1
@@ -328,6 +365,16 @@ def decode_one_batch(
                 f"max_states_{params.max_states}"
             ): hyps
         }
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        return {
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}_"
+                f"num_paths_{params.num_paths}_"
+                f"nbest_scale_{params.nbest_scale}"
+            ): hyps
+        }
     else:
         return {f"beam_size_{params.beam_size}": hyps}
 
@@ -463,17 +510,30 @@ def main():
         "greedy_search",
         "beam_search",
         "fast_beam_search",
+        "fast_beam_search_nbest_oracle",
         "modified_beam_search",
     )
     params.res_dir = params.exp_dir / "giga" / params.decoding_method
 
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
         params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
-    if "fast_beam_search" in params.decoding_method:
+
+    if params.decoding_method == "fast_beam_search":
         params.suffix += f"-beam-{params.beam}"
         params.suffix += f"-max-contexts-{params.max_contexts}"
         params.suffix += f"-max-states-{params.max_states}"
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+        params.suffix += f"-num-paths-{params.num_paths}"
+        params.suffix += f"-nbest-scale-{params.nbest_scale}"
     elif "beam_search" in params.decoding_method:
-        params.suffix += f"-beam-{params.beam_size}"
+        params.suffix += (
+            f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
     else:
         params.suffix += f"-context-{params.context_size}"
         params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
@@ -490,8 +550,9 @@ def main():
     sp = spm.SentencePieceProcessor()
     sp.load(params.bpe_model)
 
-    # <blk> is defined in local/train_bpe_model.py
+    # <blk> and <unk> is defined in local/train_bpe_model.py
     params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.unk_id()
     params.vocab_size = sp.get_piece_size()
 
     logging.info(params)
@@ -499,8 +560,20 @@ def main():
     logging.info("About to create model")
     model = get_transducer_model(params)
 
-    if params.avg_last_n > 0:
+    if params.iter > 0:
-        filenames = find_checkpoints(params.exp_dir)[: params.avg_last_n]
+        filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+            : params.avg
+        ]
+        if len(filenames) == 0:
+            raise ValueError(
+                f"No checkpoints found for"
+                f" --iter {params.iter}, --avg {params.avg}"
+            )
+        elif len(filenames) < params.avg:
+            raise ValueError(
+                f"Not enough checkpoints ({len(filenames)}) found for"
+                f" --iter {params.iter}, --avg {params.avg}"
+            )
         logging.info(f"averaging {filenames}")
         model.to(device)
         model.load_state_dict(average_checkpoints(filenames, device=device))
@@ -519,13 +592,17 @@ def main():
     model.to(device)
     model.eval()
     model.device = device
+    model.unk_id = params.unk_id
 
     # In beam_search.py, we are using model.decoder() and model.joiner(),
     # so we have to switch to the branch for the GigaSpeech dataset.
     model.decoder = model.decoder_giga
     model.joiner = model.joiner_giga
 
-    if params.decoding_method == "fast_beam_search":
+    if params.decoding_method in (
+        "fast_beam_search",
+        "fast_beam_search_nbest_oracle",
+    ):
         decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
     else:
         decoding_graph = None

egs/librispeech/ASR/pruned_transducer_stateless4/__init__.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/asr_datamodule.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/beam_search.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/conformer.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/decode.py

@@ -0,0 +1,631 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Zengwei Yao)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Usage:
+(1) greedy search
+./pruned_transducer_stateless4/decode.py \
+        --epoch 30 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --max-duration 100 \
+        --decoding-method greedy_search
+
+(2) beam search
+./pruned_transducer_stateless4/decode.py \
+        --epoch 30 \
+        --avg 15 \
+        --exp-dir ./pruned_transducer_stateless2/exp \
+        --max-duration 100 \
+        --decoding-method beam_search \
+        --beam-size 4
+
+(3) modified beam search
+./pruned_transducer_stateless4/decode.py \
+        --epoch 30 \
+        --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_stateless4/decode.py \
+        --epoch 30 \
+        --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 LibriSpeechAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+from train import get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=30,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=False,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless4/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_beam_search
+          - fast_beam_search
+        """,
+    )
+
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=4,
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=4,
+        help="""A floating point value to calculate the cutoff score during beam
+        search (i.e., `cutoff = max-score - beam`), which is the same as the
+        `beam` in Kaldi.
+        Used only when --decoding-method is fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=4,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search""",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; "
+        "2 means tri-gram",
+    )
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame.
+        Used only when --decoding_method is greedy_search""",
+    )
+
+    return parser
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[List[str]]]:
+    """Decode one batch and return the result in a dict. The dict has the
+    following format:
+
+        - key: It indicates the setting used for decoding. For example,
+               if greedy_search is used, it would be "greedy_search"
+               If beam search with a beam size of 7 is used, it would be
+               "beam_7"
+        - value: It contains the decoding result. `len(value)` equals to
+                 batch size. `value[i]` is the decoding result for the i-th
+                 utterance in the given batch.
+    Args:
+      params:
+        It's the return value of :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict.
+    """
+    device = next(model.parameters()).device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+    feature_lens = supervisions["num_frames"].to(device)
+
+    encoder_out, encoder_out_lens = model.encoder(
+        x=feature, x_lens=feature_lens
+    )
+    hyps = []
+
+    if params.decoding_method == "fast_beam_search":
+        hyp_tokens = fast_beam_search(
+            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,
+        )
+        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,
+            beam=params.beam_size,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    else:
+        batch_size = encoder_out.size(0)
+
+        for i in range(batch_size):
+            # fmt: off
+            encoder_out_i = encoder_out[i:i + 1, :encoder_out_lens[i]]
+            # fmt: on
+            if params.decoding_method == "greedy_search":
+                hyp = greedy_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    max_sym_per_frame=params.max_sym_per_frame,
+                )
+            elif params.decoding_method == "beam_search":
+                hyp = beam_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
+                )
+            else:
+                raise ValueError(
+                    f"Unsupported decoding method: {params.decoding_method}"
+                )
+            hyps.append(sp.decode(hyp).split())
+
+    if params.decoding_method == "greedy_search":
+        return {"greedy_search": hyps}
+    elif params.decoding_method == "fast_beam_search":
+        return {
+            (
+                f"beam_{params.beam}_"
+                f"max_contexts_{params.max_contexts}_"
+                f"max_states_{params.max_states}"
+            ): hyps
+        }
+    else:
+        return {f"beam_size_{params.beam_size}": hyps}
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[Tuple[List[str], List[str]]]]:
+    """Decode dataset.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search.
+    Returns:
+      Return a dict, whose key may be "greedy_search" if greedy search
+      is used, or it may be "beam_7" if beam size of 7 is used.
+      Its value is a list of tuples. Each tuple contains two elements:
+      The first is the reference transcript, and the second is the
+      predicted result.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    if params.decoding_method == "greedy_search":
+        log_interval = 100
+    else:
+        log_interval = 2
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+            batch=batch,
+        )
+
+        for name, hyps in hyps_dict.items():
+            this_batch = []
+            assert len(hyps) == len(texts)
+            for hyp_words, ref_text in zip(hyps, texts):
+                ref_words = ref_text.split()
+                this_batch.append((ref_words, hyp_words))
+
+            results[name].extend(this_batch)
+
+        num_cuts += len(texts)
+
+        if batch_idx % log_interval == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
+    return results
+
+
+def save_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
+):
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        store_transcripts(filename=recog_path, texts=results)
+        logging.info(f"The transcripts are stored in {recog_path}")
+
+        # The following prints out WERs, per-word error statistics and aligned
+        # ref/hyp pairs.
+        errs_filename = (
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+    test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
+    errs_info = (
+        params.res_dir
+        / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
+    with open(errs_info, "w") as f:
+        print("settings\tWER", file=f)
+        for key, val in test_set_wers:
+            print("{}\t{}".format(key, val), file=f)
+
+    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key, val in test_set_wers:
+        s += "{}\t{}{}\n".format(key, val, note)
+        note = ""
+    logging.info(s)
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    LibriSpeechAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    assert params.decoding_method in (
+        "greedy_search",
+        "beam_search",
+        "fast_beam_search",
+        "modified_beam_search",
+    )
+    params.res_dir = params.exp_dir / params.decoding_method
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    if "fast_beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+    elif "beam_search" in params.decoding_method:
+        params.suffix += (
+            f"-{params.decoding_method}-beam-size-{params.beam_size}"
+        )
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    if params.use_averaged_model:
+        params.suffix += "-use-averaged-model"
+
+    setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
+    logging.info("Decoding started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> and <unk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    else:
+        if params.iter > 0:
+            filenames = find_checkpoints(
+                params.exp_dir, iteration=-params.iter
+            )[: params.avg + 1]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg + 1:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            filename_start = filenames[-1]
+            filename_end = filenames[0]
+            logging.info(
+                "Calculating the averaged model over iteration checkpoints"
+                f" from {filename_start} (excluded) to {filename_end}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        else:
+            assert params.avg > 0
+            start = params.epoch - params.avg
+            assert start >= 1
+            filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+            filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+            logging.info(
+                f"Calculating the averaged model over epoch range from "
+                f"{start} (excluded) to {params.epoch}"
+            )
+            model.to(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to(device)
+    model.eval()
+
+    if params.decoding_method == "fast_beam_search":
+        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    librispeech = LibriSpeechAsrDataModule(args)
+
+    test_clean_cuts = librispeech.test_clean_cuts()
+    test_other_cuts = librispeech.test_other_cuts()
+
+    test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
+    test_other_dl = librispeech.test_dataloaders(test_other_cuts)
+
+    test_sets = ["test-clean", "test-other"]
+    test_dl = [test_clean_dl, test_other_dl]
+
+    for test_set, test_dl in zip(test_sets, test_dl):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+        )
+
+        save_results(
+            params=params,
+            test_set_name=test_set,
+            results_dict=results_dict,
+        )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/librispeech/ASR/pruned_transducer_stateless4/decoder.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/encoder_interface.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/export.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/joiner.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/model.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/optim.py

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

egs/librispeech/ASR/pruned_transducer_stateless4/scaling.py

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

icefall/checkpoint.py

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

icefall/env.py

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