diff --git a/.flake8 b/.flake8 index 190387886..8c497fac3 100644 --- a/.flake8 +++ b/.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, diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh index 59e9edf41..bd816c2d6 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh +++ b/.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 \ diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh index 1b62caab8..6b5b51bd7 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh +++ b/.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 \ diff --git a/.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh b/.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh index 1177e5a86..62ea02c47 100755 --- a/.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh +++ b/.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 \ diff --git a/.github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh b/.github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh index d2a2d3c02..c22660d0a 100755 --- a/.github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh +++ b/.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 \ diff --git a/.github/scripts/run-pre-trained-transducer-stateless-librispeech-100h.sh b/.github/scripts/run-pre-trained-transducer-stateless-librispeech-100h.sh index f484bd49a..dcc99d62e 100755 --- a/.github/scripts/run-pre-trained-transducer-stateless-librispeech-100h.sh +++ b/.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 diff --git a/.github/scripts/run-pre-trained-transducer-stateless-librispeech-960h.sh b/.github/scripts/run-pre-trained-transducer-stateless-librispeech-960h.sh index 5501dcecd..9622224c9 100755 --- a/.github/scripts/run-pre-trained-transducer-stateless-librispeech-960h.sh +++ b/.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 diff --git a/.github/scripts/run-pre-trained-transducer-stateless.sh b/.github/scripts/run-pre-trained-transducer-stateless.sh index cb57602e3..4a1dc1a7e 100755 --- a/.github/scripts/run-pre-trained-transducer-stateless.sh +++ b/.github/scripts/run-pre-trained-transducer-stateless.sh @@ -33,7 +33,7 @@ for sym in 1 2 3; do $repo/test_wavs/1221-135766-0002.wav done -for method in modified_beam_search beam_search; do +for method in fast_beam_search modified_beam_search beam_search; do log "$method" ./transducer_stateless/pretrained.py \ @@ -46,15 +46,31 @@ for method in modified_beam_search beam_search; do $repo/test_wavs/1221-135766-0002.wav done -for method in modified_beam_search beam_search; do - log "$method" +echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}" +echo "GITHUB_EVENT_LABEL_NAME: ${GITHUB_EVENT_LABEL_NAME}" +if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" || x"${GITHUB_EVENT_LABEL_NAME}" == x"run-decode" ]]; then + mkdir -p transducer_stateless/exp + ln -s $PWD/$repo/exp/pretrained.pt transducer_stateless/exp/epoch-999.pt + ln -s $PWD/$repo/data/lang_bpe_500 data/ - ./transducer_stateless_multi_datasets/pretrained.py \ - --method $method \ - --beam-size 4 \ - --checkpoint $repo/exp/pretrained.pt \ - --bpe-model $repo/data/lang_bpe_500/bpe.model \ - $repo/test_wavs/1089-134686-0001.wav \ - $repo/test_wavs/1221-135766-0001.wav \ - $repo/test_wavs/1221-135766-0002.wav -done + ls -lh data + ls -lh transducer_stateless/exp + + log "Decoding test-clean and test-other" + + # use a small value for decoding with CPU + max_duration=100 + + for method in greedy_search fast_beam_search modified_beam_search; do + log "Decoding with $method" + + ./transducer_stateless/decode.py \ + --decoding-method $method \ + --epoch 999 \ + --avg 1 \ + --max-duration $max_duration \ + --exp-dir transducer_stateless/exp + done + + rm transducer_stateless/exp/*.pt +fi diff --git a/.github/workflows/run-librispeech-2022-03-12.yml b/.github/workflows/run-librispeech-2022-03-12.yml index 39c6fd24f..b18b84378 100644 --- a/.github/workflows/run-librispeech-2022-03-12.yml +++ b/.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: ${{ github.event.label.name }} run: | mkdir -p egs/librispeech/ASR/data ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank @@ -124,8 +124,8 @@ jobs: .github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh - - name: Display decoding results - if: github.event_name == 'schedule' + - name: Display decoding results for pruned_transducer_stateless + if: github.event_name == 'schedule' || github.event.label.name == 'run-decode' shell: bash run: | cd egs/librispeech/ASR/ @@ -141,9 +141,13 @@ jobs: find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2 find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2 + echo "===modified beam search===" + find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2 + find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2 + - name: Upload decoding results for pruned_transducer_stateless uses: actions/upload-artifact@v2 - if: github.event_name == 'schedule' + if: github.event_name == 'schedule' || github.event.label.name == 'run-decode' with: name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless-2022-03-12 path: egs/librispeech/ASR/pruned_transducer_stateless/exp/ diff --git a/.github/workflows/run-librispeech-2022-04-29.yml b/.github/workflows/run-librispeech-2022-04-29.yml index ffaee25f1..e3fe3b904 100644 --- a/.github/workflows/run-librispeech-2022-04-29.yml +++ b/.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 - if: github.event_name == 'schedule' + - name: Display decoding results for pruned_transducer_stateless2 + if: github.event_name == 'schedule' || github.event.label.name == 'run-decode' shell: bash run: | cd egs/librispeech/ASR tree pruned_transducer_stateless2/exp - cd pruned_transducer_stateless2 - echo "results for pruned_transducer_stateless2" + cd pruned_transducer_stateless2/exp 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 + find greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2 + find greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2 echo "===fast_beam_search===" - find 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 + find fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2 + find fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2 - 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 - echo "results for pruned_transducer_stateless3" + cd pruned_transducer_stateless3/exp 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 + find greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2 + find greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2 echo "===fast_beam_search===" - find 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 + find fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2 + find fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2 + + echo "===modified beam search===" + find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2 + find exp/modified_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2 - name: Upload decoding results for pruned_transducer_stateless2 uses: actions/upload-artifact@v2 - if: github.event_name == 'schedule' + 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/ diff --git a/.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml b/.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml index c52b543d8..3864f4aa3 100644 --- a/.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml +++ b/.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/ diff --git a/.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml b/.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml index 438f6e882..f77d9e658 100644 --- a/.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml +++ b/.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/ diff --git a/.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml b/.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml index f50ac2af7..ddfa62073 100644 --- a/.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml +++ b/.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/ diff --git a/.github/workflows/run-pretrained-transducer-stateless.yml b/.github/workflows/run-pretrained-transducer-stateless.yml index ca355e778..cdea78a88 100644 --- a/.github/workflows/run-pretrained-transducer-stateless.yml +++ b/.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/ diff --git a/README.md b/README.md index af4a22706..c4dad6aaf 100644 --- a/README.md +++ b/README.md @@ -12,13 +12,14 @@ for installation. Please refer to 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 diff --git a/egs/aishell/ASR/transducer_stateless/conformer.py b/egs/aishell/ASR/transducer_stateless/conformer.py index 81d7708f9..149df92ab 100644 --- a/egs/aishell/ASR/transducer_stateless/conformer.py +++ b/egs/aishell/ASR/transducer_stateless/conformer.py @@ -110,7 +110,9 @@ class Conformer(Transformer): x = x.permute(1, 0, 2) # (N, T, C) -> (T, N, C) # Caution: We assume the subsampling factor is 4! - lengths = ((x_lens - 1) // 2 - 1) // 2 + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + lengths = ((x_lens - 1) // 2 - 1) // 2 assert x.size(0) == lengths.max().item() mask = make_pad_mask(lengths) diff --git a/egs/aishell/ASR/transducer_stateless_modified-2/decode.py b/egs/aishell/ASR/transducer_stateless_modified-2/decode.py index 8b851bd17..47265f846 100755 --- a/egs/aishell/ASR/transducer_stateless_modified-2/decode.py +++ b/egs/aishell/ASR/transducer_stateless_modified-2/decode.py @@ -19,49 +19,62 @@ Usage: (1) greedy search ./transducer_stateless_modified-2/decode.py \ - --epoch 89 \ - --avg 38 \ - --exp-dir ./transducer_stateless_modified-2/exp \ - --max-duration 100 \ - --decoding-method greedy_search + --epoch 89 \ + --avg 38 \ + --exp-dir ./transducer_stateless_modified-2/exp \ + --max-duration 100 \ + --decoding-method greedy_search -(2) beam search -./transducer_stateless_modified/decode.py \ - --epoch 89 \ - --avg 38 \ - --exp-dir ./transducer_stateless_modified-2/exp \ - --max-duration 100 \ - --decoding-method beam_search \ - --beam-size 4 +(2) beam search (not recommended) +./transducer_stateless_modified-2/decode.py \ + --epoch 89 \ + --avg 38 \ + --exp-dir ./transducer_stateless_modified-2/exp \ + --max-duration 100 \ + --decoding-method beam_search \ + --beam-size 4 (3) modified beam search ./transducer_stateless_modified-2/decode.py \ - --epoch 89 \ - --avg 38 \ - --exp-dir ./transducer_stateless_modified/exp \ - --max-duration 100 \ - --decoding-method modified_beam_search \ - --beam-size 4 + --epoch 89 \ + --avg 38 \ + --exp-dir ./transducer_stateless_modified-2/exp \ + --max-duration 100 \ + --decoding-method modified_beam_search \ + --beam-size 4 +(4) fast beam search +./transducer_stateless_modified-2/decode.py \ + --epoch 89 \ + --avg 38 \ + --exp-dir ./transducer_stateless_modified-2/exp \ + --max-duration 100 \ + --decoding-method fast_beam_search \ + --beam-size 4 \ + --max-contexts 4 \ + --max-states 8 """ import argparse import logging from collections import defaultdict from pathlib import Path -from typing import Dict, List, Tuple +from typing import Dict, List, Optional, Tuple +import k2 import torch import torch.nn as nn from aishell import AIShell from asr_datamodule import AsrDataModule -from beam_search import beam_search, greedy_search, modified_beam_search -from conformer import Conformer -from decoder import Decoder -from joiner import Joiner -from model import Transducer +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from train import get_params, get_transducer_model from icefall.checkpoint import average_checkpoints, load_checkpoint -from icefall.env import get_env_info from icefall.lexicon import Lexicon from icefall.utils import ( AttributeDict, @@ -114,6 +127,7 @@ def get_parser(): - greedy_search - beam_search - modified_beam_search + - fast_beam_search """, ) @@ -121,8 +135,35 @@ def get_parser(): "--beam-size", type=int, default=4, - help="Used only when --decoding-method is beam_search " - "and modified_beam_search", + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", ) parser.add_argument( @@ -132,84 +173,24 @@ def get_parser(): help="The context size in the decoder. 1 means bigram; " "2 means tri-gram", ) + parser.add_argument( "--max-sym-per-frame", type=int, - default=3, - help="Maximum number of symbols per frame", + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", ) + return parser -def get_params() -> AttributeDict: - params = AttributeDict( - { - # parameters for conformer - "feature_dim": 80, - "encoder_out_dim": 512, - "subsampling_factor": 4, - "attention_dim": 512, - "nhead": 8, - "dim_feedforward": 2048, - "num_encoder_layers": 12, - "vgg_frontend": False, - "env_info": get_env_info(), - } - ) - return params - - -def get_encoder_model(params: AttributeDict): - # TODO: We can add an option to switch between Conformer and Transformer - encoder = Conformer( - num_features=params.feature_dim, - output_dim=params.encoder_out_dim, - subsampling_factor=params.subsampling_factor, - d_model=params.attention_dim, - nhead=params.nhead, - dim_feedforward=params.dim_feedforward, - num_encoder_layers=params.num_encoder_layers, - vgg_frontend=params.vgg_frontend, - ) - return encoder - - -def get_decoder_model(params: AttributeDict): - decoder = Decoder( - vocab_size=params.vocab_size, - embedding_dim=params.encoder_out_dim, - blank_id=params.blank_id, - context_size=params.context_size, - ) - return decoder - - -def get_joiner_model(params: AttributeDict): - joiner = Joiner( - input_dim=params.encoder_out_dim, - output_dim=params.vocab_size, - ) - return joiner - - -def get_transducer_model(params: AttributeDict): - encoder = get_encoder_model(params) - decoder = get_decoder_model(params) - joiner = get_joiner_model(params) - - model = Transducer( - encoder=encoder, - decoder=decoder, - joiner=joiner, - ) - return model - - def decode_one_batch( params: AttributeDict, model: nn.Module, - lexicon: Lexicon, + token_table: k2.SymbolTable, batch: dict, + decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[List[str]]]: """Decode one batch and return the result in a dict. The dict has the following format: @@ -230,8 +211,8 @@ def decode_one_batch( It is the return value from iterating `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation for the format of the `batch`. - lexicon: - It contains the token symbol table and the word symbol table. + token_table: + It maps token ID to a string. Returns: Return the decoding result. See above description for the format of the returned dict. @@ -249,44 +230,80 @@ def decode_one_batch( encoder_out, encoder_out_lens = model.encoder( x=feature, x_lens=feature_lens ) - hyps = [] - batch_size = encoder_out.size(0) - for i in range(batch_size): - # fmt: off - encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] - # fmt: on - if params.decoding_method == "greedy_search": - hyp = greedy_search( - model=model, - encoder_out=encoder_out_i, - max_sym_per_frame=params.max_sym_per_frame, - ) - elif params.decoding_method == "beam_search": - hyp = beam_search( - model=model, encoder_out=encoder_out_i, beam=params.beam_size - ) - elif params.decoding_method == "modified_beam_search": - hyp = modified_beam_search( - model=model, encoder_out=encoder_out_i, beam=params.beam_size - ) - else: - raise ValueError( - f"Unsupported decoding method: {params.decoding_method}" - ) - hyps.append([lexicon.token_table[i] for i in hyp]) + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + else: + hyp_tokens = [] + batch_size = encoder_out.size(0) + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyp_tokens.append(hyp) + + hyps = [[token_table[t] for t in tokens] for tokens in hyp_tokens] if params.decoding_method == "greedy_search": return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } else: - return {f"beam_{params.beam_size}": hyps} + return {f"beam_size_{params.beam_size}": hyps} def decode_dataset( dl: torch.utils.data.DataLoader, params: AttributeDict, model: nn.Module, - lexicon: Lexicon, + token_table: k2.SymbolTable, + decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[Tuple[List[str], List[str]]]]: """Decode dataset. @@ -297,6 +314,11 @@ def decode_dataset( It is returned by :func:`get_params`. model: The neural model. + token_table: + It maps a token ID to a string. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. Returns: Return a dict, whose key may be "greedy_search" if greedy search is used, or it may be "beam_7" if beam size of 7 is used. @@ -312,9 +334,9 @@ def decode_dataset( num_batches = "?" if params.decoding_method == "greedy_search": - log_interval = 100 + log_interval = 50 else: - log_interval = 2 + log_interval = 10 results = defaultdict(list) for batch_idx, batch in enumerate(dl): @@ -323,7 +345,8 @@ def decode_dataset( hyps_dict = decode_one_batch( params=params, model=model, - lexicon=lexicon, + token_table=token_table, + decoding_graph=decoding_graph, batch=batch, ) @@ -358,6 +381,7 @@ def save_results( params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" ) store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") # The following prints out WERs, per-word error statistics and aligned # ref/hyp pairs. @@ -408,13 +432,21 @@ def main(): assert params.decoding_method in ( "greedy_search", "beam_search", + "fast_beam_search", "modified_beam_search", ) params.res_dir = params.exp_dir / params.decoding_method params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" - if "beam_search" in params.decoding_method: - params.suffix += f"-beam-{params.beam_size}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += ( + f"-{params.decoding_method}-beam-size-{params.beam_size}" + ) else: params.suffix += f"-context-{params.context_size}" params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" @@ -456,6 +488,11 @@ def main(): model.eval() model.device = device + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") @@ -472,7 +509,8 @@ def main(): dl=test_dl, params=params, model=model, - lexicon=lexicon, + token_table=lexicon.token_table, + decoding_graph=decoding_graph, ) save_results( @@ -484,8 +522,5 @@ def main(): logging.info("Done!") -torch.set_num_threads(1) -torch.set_num_interop_threads(1) - if __name__ == "__main__": main() diff --git a/egs/aishell/ASR/transducer_stateless_modified-2/pretrained.py b/egs/aishell/ASR/transducer_stateless_modified-2/pretrained.py index 9e6ed96b1..a95a4bc52 100755 --- a/egs/aishell/ASR/transducer_stateless_modified-2/pretrained.py +++ b/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 diff --git a/egs/aishell/ASR/transducer_stateless_modified/decode.py b/egs/aishell/ASR/transducer_stateless_modified/decode.py index 5b5fe6ffa..4773ebc7d 100755 --- a/egs/aishell/ASR/transducer_stateless_modified/decode.py +++ b/egs/aishell/ASR/transducer_stateless_modified/decode.py @@ -19,48 +19,63 @@ Usage: (1) greedy search ./transducer_stateless_modified/decode.py \ - --epoch 64 \ - --avg 33 \ - --exp-dir ./transducer_stateless_modified/exp \ - --max-duration 100 \ - --decoding-method greedy_search + --epoch 14 \ + --avg 7 \ + --exp-dir ./transducer_stateless_modified/exp \ + --max-duration 600 \ + --decoding-method greedy_search -(2) beam search +(2) beam search (not recommended) ./transducer_stateless_modified/decode.py \ - --epoch 14 \ - --avg 7 \ - --exp-dir ./transducer_stateless_modified/exp \ - --max-duration 100 \ - --decoding-method beam_search \ - --beam-size 4 + --epoch 14 \ + --avg 7 \ + --exp-dir ./transducer_stateless_modified/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 (3) modified beam search ./transducer_stateless_modified/decode.py \ - --epoch 14 \ - --avg 7 \ - --exp-dir ./transducer_stateless_modified/exp \ - --max-duration 100 \ - --decoding-method modified_beam_search \ - --beam-size 4 + --epoch 14 \ + --avg 7 \ + --exp-dir ./transducer_stateless_modified/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search +./transducer_stateless_modified/decode.py \ + --epoch 14 \ + --avg 7 \ + --exp-dir ./transducer_stateless_modified/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 """ + import argparse import logging from collections import defaultdict from pathlib import Path -from typing import Dict, List, Tuple +from typing import Dict, List, Optional, Tuple +import k2 import torch import torch.nn as nn from asr_datamodule import AishellAsrDataModule -from beam_search import beam_search, greedy_search, modified_beam_search -from conformer import Conformer -from decoder import Decoder -from joiner import Joiner -from model import Transducer +from beam_search import ( + beam_search, + fast_beam_search_one_best, + greedy_search, + greedy_search_batch, + modified_beam_search, +) +from train import get_params, get_transducer_model from icefall.checkpoint import average_checkpoints, load_checkpoint -from icefall.env import get_env_info from icefall.lexicon import Lexicon from icefall.utils import ( AttributeDict, @@ -113,6 +128,7 @@ def get_parser(): - greedy_search - beam_search - modified_beam_search + - fast_beam_search """, ) @@ -120,7 +136,35 @@ def get_parser(): "--beam-size", type=int, default=4, - help="Used only when --decoding-method is beam_search", + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --decoding-method is + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", ) parser.add_argument( @@ -130,84 +174,24 @@ def get_parser(): help="The context size in the decoder. 1 means bigram; " "2 means tri-gram", ) + parser.add_argument( "--max-sym-per-frame", type=int, - default=3, - help="Maximum number of symbols per frame", + default=1, + help="""Maximum number of symbols per frame. + Used only when --decoding_method is greedy_search""", ) + return parser -def get_params() -> AttributeDict: - params = AttributeDict( - { - # parameters for conformer - "feature_dim": 80, - "encoder_out_dim": 512, - "subsampling_factor": 4, - "attention_dim": 512, - "nhead": 8, - "dim_feedforward": 2048, - "num_encoder_layers": 12, - "vgg_frontend": False, - "env_info": get_env_info(), - } - ) - return params - - -def get_encoder_model(params: AttributeDict): - # TODO: We can add an option to switch between Conformer and Transformer - encoder = Conformer( - num_features=params.feature_dim, - output_dim=params.encoder_out_dim, - subsampling_factor=params.subsampling_factor, - d_model=params.attention_dim, - nhead=params.nhead, - dim_feedforward=params.dim_feedforward, - num_encoder_layers=params.num_encoder_layers, - vgg_frontend=params.vgg_frontend, - ) - return encoder - - -def get_decoder_model(params: AttributeDict): - decoder = Decoder( - vocab_size=params.vocab_size, - embedding_dim=params.encoder_out_dim, - blank_id=params.blank_id, - context_size=params.context_size, - ) - return decoder - - -def get_joiner_model(params: AttributeDict): - joiner = Joiner( - input_dim=params.encoder_out_dim, - output_dim=params.vocab_size, - ) - return joiner - - -def get_transducer_model(params: AttributeDict): - encoder = get_encoder_model(params) - decoder = get_decoder_model(params) - joiner = get_joiner_model(params) - - model = Transducer( - encoder=encoder, - decoder=decoder, - joiner=joiner, - ) - return model - - def decode_one_batch( params: AttributeDict, model: nn.Module, - lexicon: Lexicon, + token_table: k2.SymbolTable, batch: dict, + decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[List[str]]]: """Decode one batch and return the result in a dict. The dict has the following format: @@ -228,8 +212,11 @@ def decode_one_batch( It is the return value from iterating `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation for the format of the `batch`. - lexicon: - It contains the token symbol table and the word symbol table. + token_table: + It maps token ID to a string. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. Returns: Return the decoding result. See above description for the format of the returned dict. @@ -247,44 +234,80 @@ def decode_one_batch( encoder_out, encoder_out_lens = model.encoder( x=feature, x_lens=feature_lens ) - hyps = [] - batch_size = encoder_out.size(0) - for i in range(batch_size): - # fmt: off - encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] - # fmt: on - if params.decoding_method == "greedy_search": - hyp = greedy_search( - model=model, - encoder_out=encoder_out_i, - max_sym_per_frame=params.max_sym_per_frame, - ) - elif params.decoding_method == "beam_search": - hyp = beam_search( - model=model, encoder_out=encoder_out_i, beam=params.beam_size - ) - elif params.decoding_method == "modified_beam_search": - hyp = modified_beam_search( - model=model, encoder_out=encoder_out_i, beam=params.beam_size - ) - else: - raise ValueError( - f"Unsupported decoding method: {params.decoding_method}" - ) - hyps.append([lexicon.token_table[i] for i in hyp]) + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + elif ( + params.decoding_method == "greedy_search" + and params.max_sym_per_frame == 1 + ): + hyp_tokens = greedy_search_batch( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + ) + elif params.decoding_method == "modified_beam_search": + hyp_tokens = modified_beam_search( + model=model, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam_size, + ) + else: + hyp_tokens = [] + batch_size = encoder_out.size(0) + for i in range(batch_size): + # fmt: off + encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] + # fmt: on + if params.decoding_method == "greedy_search": + hyp = greedy_search( + model=model, + encoder_out=encoder_out_i, + max_sym_per_frame=params.max_sym_per_frame, + ) + elif params.decoding_method == "beam_search": + hyp = beam_search( + model=model, + encoder_out=encoder_out_i, + beam=params.beam_size, + ) + else: + raise ValueError( + f"Unsupported decoding method: {params.decoding_method}" + ) + hyp_tokens.append(hyp) + + hyps = [[token_table[t] for t in tokens] for tokens in hyp_tokens] if params.decoding_method == "greedy_search": return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } else: - return {f"beam_{params.beam_size}": hyps} + return {f"beam_size_{params.beam_size}": hyps} def decode_dataset( dl: torch.utils.data.DataLoader, params: AttributeDict, model: nn.Module, - lexicon: Lexicon, + token_table: k2.SymbolTable, + decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[Tuple[List[str], List[str]]]]: """Decode dataset. @@ -295,6 +318,11 @@ def decode_dataset( It is returned by :func:`get_params`. model: The neural model. + token_table: + It maps a token ID to a string. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. Returns: Return a dict, whose key may be "greedy_search" if greedy search is used, or it may be "beam_7" if beam size of 7 is used. @@ -310,9 +338,9 @@ def decode_dataset( num_batches = "?" if params.decoding_method == "greedy_search": - log_interval = 100 + log_interval = 50 else: - log_interval = 2 + log_interval = 10 results = defaultdict(list) for batch_idx, batch in enumerate(dl): @@ -321,7 +349,8 @@ def decode_dataset( hyps_dict = decode_one_batch( params=params, model=model, - lexicon=lexicon, + token_table=token_table, + decoding_graph=decoding_graph, batch=batch, ) @@ -356,6 +385,7 @@ def save_results( params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt" ) store_transcripts(filename=recog_path, texts=results) + logging.info(f"The transcripts are stored in {recog_path}") # The following prints out WERs, per-word error statistics and aligned # ref/hyp pairs. @@ -406,13 +436,21 @@ def main(): assert params.decoding_method in ( "greedy_search", "beam_search", + "fast_beam_search", "modified_beam_search", ) params.res_dir = params.exp_dir / params.decoding_method params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" - if "beam_search" in params.decoding_method: - params.suffix += f"-beam-{params.beam_size}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += ( + f"-{params.decoding_method}-beam-size-{params.beam_size}" + ) else: params.suffix += f"-context-{params.context_size}" params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" @@ -452,6 +490,11 @@ def main(): model.eval() model.device = device + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") @@ -467,7 +510,8 @@ def main(): dl=test_dl, params=params, model=model, - lexicon=lexicon, + token_table=lexicon.token_table, + decoding_graph=decoding_graph, ) save_results( @@ -479,8 +523,5 @@ def main(): logging.info("Done!") -torch.set_num_threads(1) -torch.set_num_interop_threads(1) - if __name__ == "__main__": main() diff --git a/egs/aishell/ASR/transducer_stateless_modified/pretrained.py b/egs/aishell/ASR/transducer_stateless_modified/pretrained.py index f7c5b24ba..262e822c2 100755 --- a/egs/aishell/ASR/transducer_stateless_modified/pretrained.py +++ b/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 diff --git a/egs/gigaspeech/ASR/README.md b/egs/gigaspeech/ASR/README.md index 7796ef2a0..1fca69e8b 100644 --- a/egs/gigaspeech/ASR/README.md +++ b/egs/gigaspeech/ASR/README.md @@ -13,8 +13,9 @@ ln -sfv /path/to/GigaSpeech download/GigaSpeech ``` ## Performance Record -| | Dev | Test | -|-----|-------|-------| -| WER | 10.47 | 10.58 | +| | Dev | Test | +|--------------------------------|-------|-------| +| `conformer_ctc` | 10.47 | 10.58 | +| `pruned_transducer_stateless2` | 10.52 | 10.62 | See [RESULTS](/egs/gigaspeech/ASR/RESULTS.md) for details. diff --git a/egs/gigaspeech/ASR/RESULTS.md b/egs/gigaspeech/ASR/RESULTS.md index b29e893da..de7b84202 100644 --- a/egs/gigaspeech/ASR/RESULTS.md +++ b/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 + + +The tensorboard log for training is available at + ### 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 \ diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/__init__.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py new file mode 100644 index 000000000..ff3d3b07a --- /dev/null +++ b/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") diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/beam_search.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/beam_search.py new file mode 120000 index 000000000..e24eca39f --- /dev/null +++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/beam_search.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/beam_search.py \ No newline at end of file diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/conformer.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/conformer.py new file mode 120000 index 000000000..a65957180 --- /dev/null +++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/conformer.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/conformer.py \ No newline at end of file diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/decode.py new file mode 100755 index 000000000..92a5b0b28 --- /dev/null +++ b/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) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + 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() diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/decoder.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/decoder.py new file mode 120000 index 000000000..722e1c894 --- /dev/null +++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/decoder.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/decoder.py \ No newline at end of file diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/encoder_interface.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/encoder_interface.py new file mode 120000 index 000000000..f58253127 --- /dev/null +++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/encoder_interface.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/encoder_interface.py \ No newline at end of file diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/export.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/export.py new file mode 100755 index 000000000..b5757ee8c --- /dev/null +++ b/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) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + 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() diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/gigaspeech_scoring.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/gigaspeech_scoring.py new file mode 120000 index 000000000..a6a4d12b1 --- /dev/null +++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/gigaspeech_scoring.py @@ -0,0 +1 @@ +../conformer_ctc/gigaspeech_scoring.py \ No newline at end of file diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/joiner.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/joiner.py new file mode 120000 index 000000000..9052f3cbb --- /dev/null +++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/joiner.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/joiner.py \ No newline at end of file diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/model.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/model.py new file mode 120000 index 000000000..a99e74334 --- /dev/null +++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/model.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/model.py \ No newline at end of file diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/optim.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/optim.py new file mode 120000 index 000000000..0a2f285aa --- /dev/null +++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/optim.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/optim.py \ No newline at end of file diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/scaling.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/scaling.py new file mode 120000 index 000000000..c10cdfe12 --- /dev/null +++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/scaling.py @@ -0,0 +1 @@ +../../../librispeech/ASR/pruned_transducer_stateless2/scaling.py \ No newline at end of file diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/train.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/train.py new file mode 100755 index 000000000..4421ce2aa --- /dev/null +++ b/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) + + # is defined in local/train_bpe_model.py + params.blank_id = sp.piece_to_id("") + 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() diff --git a/egs/librispeech/ASR/local/prepare_lang_bpe.py b/egs/librispeech/ASR/local/prepare_lang_bpe.py index cf32f308d..dec8a7442 100755 --- a/egs/librispeech/ASR/local/prepare_lang_bpe.py +++ b/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): diff --git a/egs/librispeech/ASR/local/validate_bpe_lexicon.py b/egs/librispeech/ASR/local/validate_bpe_lexicon.py new file mode 100755 index 000000000..c542f2fab --- /dev/null +++ b/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() diff --git a/egs/librispeech/ASR/prepare.sh b/egs/librispeech/ASR/prepare.sh index 33d298a7a..8cfb046c8 100755 --- a/egs/librispeech/ASR/prepare.sh +++ b/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 - ./local/train_bpe_model.py \ - --lang-dir $lang_dir \ - --vocab-size $vocab_size \ - --transcript $lang_dir/transcript_words.txt + 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 diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py b/egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py index 5d1e9b471..db23fd993 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py +++ b/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, -) -> List[List[int]]: +) -> k2.Fsa: """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: - best_path = one_best_decoding(lattice) - hyps = get_texts(best_path) - return hyps - else: - num_paths = 200 - use_double_scores = True - nbest_scale = 0.8 - - nbest = Nbest.from_lattice( - lattice=lattice, - num_paths=num_paths, - use_double_scores=use_double_scores, - nbest_scale=nbest_scale, - ) - # The following code is modified from nbest.intersect() - word_fsa = k2.invert(nbest.fsa) - if hasattr(lattice, "aux_labels"): - # delete token IDs as it is not needed - del word_fsa.aux_labels - word_fsa.scores.zero_() - - word_fsa_with_epsilon_loops = k2.linear_fsa_with_self_loops(word_fsa) - path_to_utt_map = nbest.shape.row_ids(1) - - if hasattr(lattice, "aux_labels"): - # lattice has token IDs as labels and word IDs as aux_labels. - # inv_lattice has word IDs as labels and token IDs as aux_labels - inv_lattice = k2.invert(lattice) - inv_lattice = k2.arc_sort(inv_lattice) - else: - inv_lattice = k2.arc_sort(lattice) - - if inv_lattice.shape[0] == 1: - path_lattice = k2.intersect_device( - inv_lattice, - word_fsa_with_epsilon_loops, - b_to_a_map=torch.zeros_like(path_to_utt_map), - sorted_match_a=True, - ) - else: - path_lattice = k2.intersect_device( - inv_lattice, - word_fsa_with_epsilon_loops, - b_to_a_map=path_to_utt_map, - sorted_match_a=True, - ) - - # path_lattice has word IDs as labels and token IDs as aux_labels - path_lattice = k2.top_sort(k2.connect(path_lattice)) - - tot_scores = path_lattice.get_tot_scores( - use_double_scores=use_double_scores, log_semiring=True - ) - - ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores) - best_hyp_indexes = ragged_tot_scores.argmax() - - best_path = k2.index_fsa(nbest.fsa, best_hyp_indexes) - hyps = get_texts(best_path) - return hyps + return lattice def greedy_search( @@ -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) - T = encoder_out.size(1) + device = next(model.parameters()).device blank_id = model.decoder.blank_id - unk_id = model.decoder.unk_id + unk_id = getattr(model, "unk_id", blank_id) context_size = model.decoder.context_size - hyps = [[blank_id] * context_size for _ in range(batch_size)] + batch_size_list = packed_encoder_out.batch_sizes.tolist() + N = encoder_out.size(0) + assert torch.all(encoder_out_lens > 0), encoder_out_lens + assert N == batch_size_list[0], (N, batch_size_list) + + hyps = [[blank_id] * context_size for _ in range(N)] decoder_input = torch.tensor( hyps, device=device, dtype=torch.int64, - ) # (batch_size, context_size) + ) # (N, context_size) decoder_out = model.decoder(decoder_input, need_pad=False) - # decoder_out: (batch_size, 1, decoder_out_dim) - for t in range(T): - current_encoder_out = encoder_out[:, t : t + 1, :].unsqueeze(2) # noqa + # decoder_out: (N, 1, decoder_out_dim) + + encoder_out = packed_encoder_out.data + + offset = 0 + for batch_size in batch_size_list: + start = offset + end = offset + batch_size + current_encoder_out = encoder_out.data[start:end] + current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1) # current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim) + offset = end + + decoder_out = decoder_out[:batch_size] + logits = model.joiner(current_encoder_out, decoder_out.unsqueeze(1)) # logits'shape (batch_size, 1, 1, vocab_size) @@ -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) - T = encoder_out.size(1) + packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence( + input=encoder_out, + lengths=encoder_out_lens.cpu(), + batch_first=True, + enforce_sorted=False, + ) blank_id = model.decoder.blank_id - unk_id = model.decoder.unk_id + unk_id = getattr(model, "unk_id", blank_id) context_size = model.decoder.context_size - device = model.device - B = [HypothesisList() for _ in range(batch_size)] - for i in range(batch_size): + device = next(model.parameters()).device + + batch_size_list = packed_encoder_out.batch_sizes.tolist() + N = encoder_out.size(0) + assert torch.all(encoder_out_lens > 0), encoder_out_lens + assert N == batch_size_list[0], (N, batch_size_list) + + B = [HypothesisList() for _ in range(N)] + for i in range(N): B[i].add( Hypothesis( ys=[blank_id] * context_size, @@ -510,9 +636,20 @@ def modified_beam_search( use_max=use_max, ) - for t in range(T): - current_encoder_out = encoder_out[:, t : t + 1, :].unsqueeze(2) # noqa + encoder_out = packed_encoder_out.data + + offset = 0 + finalized_B = [] + for batch_size in batch_size_list: + start = offset + end = offset + batch_size + current_encoder_out = encoder_out.data[start:end] + current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1) # current_encoder_out's shape is (batch_size, 1, 1, encoder_out_dim) + offset = end + + finalized_B = B[batch_size:] + finalized_B + B = B[:batch_size] hyps_shape = _get_hyps_shape(B).to(device) @@ -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 diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless/decode.py index 349e4c281..ea43836bd 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless/decode.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/decode.py @@ -19,53 +19,53 @@ Usage: (1) greedy search ./pruned_transducer_stateless/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./pruned_transducer_stateless/exp \ - --max-duration 100 \ - --decoding-method greedy_search + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless/exp \ + --max-duration 600 \ + --decoding-method greedy_search -(2) beam search +(2) beam search (not recommended) ./pruned_transducer_stateless/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./pruned_transducer_stateless/exp \ - --max-duration 100 \ - --decoding-method beam_search \ - --beam-size 4 + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 (3) modified beam search ./pruned_transducer_stateless/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./pruned_transducer_stateless/exp \ - --max-duration 100 \ - --decoding-method modified_beam_search \ - --beam-size 4 + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 (4) fast beam search ./pruned_transducer_stateless/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./pruned_transducer_stateless/exp \ - --max-duration 1500 \ - --decoding-method fast_beam_search \ - --beam 4 \ - --max-contexts 4 \ - --max-states 8 + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 (5) fast beam search using LG ./pruned_transducer_stateless/decode.py \ - --epoch 28 \ - --avg 15 \ - --exp-dir ./pruned_transducer_stateless/exp \ - --use-LG True \ - --use-max False \ - --max-duration 1500 \ - --decoding-method fast_beam_search \ - --beam 8 \ - --max-contexts 8 \ - --max-states 64 + --epoch 28 \ + --avg 15 \ + --exp-dir ./pruned_transducer_stateless/exp \ + --use-LG True \ + --use-max False \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 8 \ + --max-contexts 8 \ + --max-states 64 """ @@ -82,7 +82,7 @@ import torch.nn as nn from asr_datamodule import LibriSpeechAsrDataModule from beam_search import ( beam_search, - fast_beam_search, + fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, @@ -307,7 +307,7 @@ def decode_one_batch( hyps = [] if params.decoding_method == "fast_beam_search": - hyp_tokens = fast_beam_search( + hyp_tokens = fast_beam_search_one_best( model=model, decoding_graph=decoding_graph, encoder_out=encoder_out, @@ -315,7 +315,6 @@ def decode_one_batch( beam=params.beam, max_contexts=params.max_contexts, max_states=params.max_states, - use_max=params.use_max, ) if params.use_LG: for hyp in hyp_tokens: @@ -330,6 +329,7 @@ def decode_one_batch( hyp_tokens = greedy_search_batch( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, ) for hyp in sp.decode(hyp_tokens): hyps.append(hyp.split()) @@ -337,6 +337,7 @@ def decode_one_batch( hyp_tokens = modified_beam_search( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, beam=params.beam_size, use_max=params.use_max, ) @@ -421,9 +422,9 @@ def decode_dataset( num_batches = "?" if params.decoding_method == "greedy_search": - log_interval = 100 + log_interval = 50 else: - log_interval = 2 + log_interval = 10 results = defaultdict(list) for batch_idx, batch in enumerate(dl): diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless/pretrained.py index 3cc472974..148bf7b02 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless/pretrained.py +++ b/egs/librispeech/ASR/pruned_transducer_stateless/pretrained.py @@ -25,7 +25,7 @@ Usage: /path/to/foo.wav \ /path/to/bar.wav \ -(1) beam search +(2) beam search ./pruned_transducer_stateless/pretrained.py \ --checkpoint ./pruned_transducer_stateless/exp/pretrained.pt \ --bpe-model ./data/lang_bpe_500/bpe.model \ @@ -34,6 +34,24 @@ Usage: /path/to/foo.wav \ /path/to/bar.wav \ +(3) modified beam search +./pruned_transducer_stateless/pretrained.py \ + --checkpoint ./pruned_transducer_stateless/exp/pretrained.pt \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --method modified_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav \ + +(4) fast beam search +./pruned_transducer_stateless/pretrained.py \ + --checkpoint ./pruned_transducer_stateless/exp/pretrained.pt \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --method fast_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav \ + You can also use `./pruned_transducer_stateless/exp/epoch-xx.pt`. Note: ./pruned_transducer_stateless/exp/pretrained.pt is generated by @@ -46,12 +64,14 @@ import logging import math from typing import List +import k2 import kaldifeat import sentencepiece as spm import torch import torchaudio from beam_search import ( beam_search, + fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, @@ -77,9 +97,7 @@ def get_parser(): parser.add_argument( "--bpe-model", type=str, - help="""Path to bpe.model. - Used only when method is ctc-decoding. - """, + help="""Path to bpe.model.""", ) parser.add_argument( @@ -90,6 +108,7 @@ def get_parser(): - greedy_search - beam_search - modified_beam_search + - fast_beam_search """, ) @@ -114,7 +133,33 @@ def get_parser(): "--beam-size", type=int, default=4, - help="Used only when --method is beam_search and modified_beam_search", + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --method is fast_beam_search""", ) parser.add_argument( @@ -230,10 +275,25 @@ def main(): if params.method == "beam_search": msg += f" with beam size {params.beam_size}" logging.info(msg) - if params.method == "modified_beam_search": + + if params.method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif params.method == "modified_beam_search": hyp_tokens = modified_beam_search( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, beam=params.beam_size, ) @@ -243,6 +303,7 @@ def main(): hyp_tokens = greedy_search_batch( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, ) for hyp in sp.decode(hyp_tokens): hyps.append(hyp.split()) diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py b/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py index fc1285dc7..ce8b04afd 100644 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py +++ b/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) - T = encoder_out.size(1) + device = next(model.parameters()).device blank_id = model.decoder.blank_id unk_id = getattr(model, "unk_id", blank_id) context_size = model.decoder.context_size - hyps = [[blank_id] * context_size for _ in range(batch_size)] + batch_size_list = packed_encoder_out.batch_sizes.tolist() + N = encoder_out.size(0) + assert torch.all(encoder_out_lens > 0), encoder_out_lens + assert N == batch_size_list[0], (N, batch_size_list) + + hyps = [[blank_id] * context_size for _ in range(N)] decoder_input = torch.tensor( hyps, device=device, dtype=torch.int64, - ) # (batch_size, context_size) + ) # (N, context_size) decoder_out = model.decoder(decoder_input, need_pad=False) decoder_out = model.joiner.decoder_proj(decoder_out) - encoder_out = model.joiner.encoder_proj(encoder_out) + # decoder_out: (N, 1, decoder_out_dim) - # decoder_out: (batch_size, 1, decoder_out_dim) - for t in range(T): - current_encoder_out = encoder_out[:, t : t + 1, :].unsqueeze(2) # noqa + encoder_out = model.joiner.encoder_proj(packed_encoder_out.data) + + offset = 0 + for batch_size in batch_size_list: + start = offset + end = offset + batch_size + current_encoder_out = encoder_out.data[start:end] + current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1) # current_encoder_out's shape: (batch_size, 1, 1, encoder_out_dim) + offset = end + + decoder_out = decoder_out[:batch_size] + logits = model.joiner( current_encoder_out, decoder_out.unsqueeze(1), project_input=False ) @@ -390,7 +413,7 @@ def greedy_search_batch( emitted = True if emitted: # update decoder output - decoder_input = [h[-context_size:] for h in hyps] + decoder_input = [h[-context_size:] for h in hyps[:batch_size]] decoder_input = torch.tensor( decoder_input, device=device, @@ -399,7 +422,12 @@ def greedy_search_batch( decoder_out = model.decoder(decoder_input, need_pad=False) decoder_out = model.joiner.decoder_proj(decoder_out) - ans = [h[context_size:] for h in hyps] + sorted_ans = [h[context_size:] for h in hyps] + ans = [] + unsorted_indices = packed_encoder_out.unsorted_indices.tolist() + for i in range(N): + ans.append(sorted_ans[unsorted_indices[i]]) + return ans @@ -557,6 +585,7 @@ def _get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape: def modified_beam_search( model: Transducer, encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, beam: int = 4, ) -> List[List[int]]: """Beam search in batch mode with --max-sym-per-frame=1 being hardcoded. @@ -566,6 +595,9 @@ def modified_beam_search( The transducer model. encoder_out: Output from the encoder. Its shape is (N, T, C). + encoder_out_lens: + A 1-D tensor of shape (N,), containing number of valid frames in + encoder_out before padding. beam: Number of active paths during the beam search. Returns: @@ -573,16 +605,27 @@ def modified_beam_search( for the i-th utterance. """ assert encoder_out.ndim == 3, encoder_out.shape + assert encoder_out.size(0) >= 1, encoder_out.size(0) - batch_size = encoder_out.size(0) - T = encoder_out.size(1) + packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence( + input=encoder_out, + lengths=encoder_out_lens.cpu(), + batch_first=True, + enforce_sorted=False, + ) blank_id = model.decoder.blank_id unk_id = getattr(model, "unk_id", blank_id) context_size = model.decoder.context_size device = 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): - current_encoder_out = encoder_out[:, t : t + 1, :].unsqueeze(2) # noqa + offset = 0 + finalized_B = [] + for batch_size in batch_size_list: + start = offset + end = offset + batch_size + current_encoder_out = encoder_out.data[start:end] + current_encoder_out = current_encoder_out.unsqueeze(1).unsqueeze(1) # current_encoder_out's shape is (batch_size, 1, 1, encoder_out_dim) + offset = end + + finalized_B = B[batch_size:] + finalized_B + B = B[:batch_size] hyps_shape = _get_hyps_shape(B).to(device) @@ -668,8 +720,14 @@ def modified_beam_search( new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob) B[i].add(new_hyp) + B = B + finalized_B best_hyps = [b.get_most_probable(length_norm=True) for b in B] - ans = [h.ys[context_size:] for h in best_hyps] + + sorted_ans = [h.ys[context_size:] for h in best_hyps] + ans = [] + unsorted_indices = packed_encoder_out.unsorted_indices.tolist() + for i in range(N): + ans.append(sorted_ans[unsorted_indices[i]]) return ans diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py index 5d946003a..05a4cdca5 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py +++ b/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): diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/export.py b/egs/librispeech/ASR/pruned_transducer_stateless2/export.py index b5757ee8c..6b3a7a9ff 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless2/export.py +++ b/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." - "Note: Epoch counts from 0.", + help="""It specifies the checkpoint to use for averaging. + Note: Epoch counts from 0. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, ) parser.add_argument( @@ -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 diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/decode-giga.py b/egs/librispeech/ASR/pruned_transducer_stateless3/decode-giga.py index a715a2a5c..8d6e33e9d 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/decode-giga.py +++ b/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]]]], -) -> List[Tuple[List[List[str]], List[List[str]]]]: + results: List[Tuple[List[str], List[str]]], +) -> List[Tuple[List[str], List[str]]]: new_results = [] for ref, hyp in results: new_ref = asr_text_post_processing(" ".join(ref)).split() @@ -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): diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py index 9a6b5a117..5b3dce853 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py +++ b/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): diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/export.py b/egs/librispeech/ASR/pruned_transducer_stateless3/export.py index 29acc7181..0cdb0b957 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless3/export.py +++ b/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." - "Note: Epoch counts from 0.", + help="""It specifies the checkpoint to use for averaging. + Note: Epoch counts from 0. + You can specify --avg to use more checkpoints for model averaging.""", + ) + + parser.add_argument( + "--iter", + type=int, + default=0, + help="""If positive, --epoch is ignored and it + will use the checkpoint exp_dir/checkpoint-iter.pt. + You can specify --avg to use more checkpoints for model averaging. + """, ) parser.add_argument( @@ -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 diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py index 1f4a22213..9982cc530 100755 --- a/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py +++ b/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 \ - --max-duration 100 \ + --exp-dir ./pruned_transducer_stateless4/exp \ + --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 \ - --max-duration 100 \ + --exp-dir ./pruned_transducer_stateless4/exp \ + --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 \ - --max-duration 100 \ + --exp-dir ./pruned_transducer_stateless4/exp \ + --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 \ - --max-duration 1500 \ + --exp-dir ./pruned_transducer_stateless4/exp \ + --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): diff --git a/egs/librispeech/ASR/transducer_stateless/beam_search.py b/egs/librispeech/ASR/transducer_stateless/beam_search.py index 388a8d67a..ea985f30d 100644 --- a/egs/librispeech/ASR/transducer_stateless/beam_search.py +++ b/egs/librispeech/ASR/transducer_stateless/beam_search.py @@ -22,6 +22,235 @@ import k2 import torch from model import Transducer +from icefall.decode import Nbest, one_best_decoding +from icefall.utils import get_texts + + +def fast_beam_search_one_best( + model: Transducer, + decoding_graph: k2.Fsa, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + beam: float, + max_states: int, + max_contexts: int, +) -> List[List[int]]: + """It limits the maximum number of symbols per frame to 1. + + A lattice is first obtained using modified beam search, and then + the shortest path within the lattice is used as the final output. + + Args: + model: + An instance of `Transducer`. + decoding_graph: + Decoding graph used for decoding, may be a TrivialGraph or a HLG. + encoder_out: + A tensor of shape (N, T, C) from the encoder. + encoder_out_lens: + A tensor of shape (N,) containing the number of frames in `encoder_out` + before padding. + beam: + Beam value, similar to the beam used in Kaldi.. + max_states: + Max states per stream per frame. + max_contexts: + Max contexts pre stream per frame. + Returns: + Return the decoded result. + """ + lattice = fast_beam_search( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=beam, + max_states=max_states, + max_contexts=max_contexts, + ) + + best_path = one_best_decoding(lattice) + hyps = get_texts(best_path) + return hyps + + +def fast_beam_search_nbest_oracle( + model: Transducer, + decoding_graph: k2.Fsa, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + beam: float, + max_states: int, + max_contexts: int, + num_paths: int, + ref_texts: List[List[int]], + use_double_scores: bool = True, + nbest_scale: float = 0.5, +) -> List[List[int]]: + """It limits the maximum number of symbols per frame to 1. + + A lattice is first obtained using modified beam search, and then + we select `num_paths` linear paths from the lattice. The path + that has the minimum edit distance with the given reference transcript + is used as the output. + + This is the best result we can achieve for any nbest based rescoring + methods. + + Args: + model: + An instance of `Transducer`. + decoding_graph: + Decoding graph used for decoding, may be a TrivialGraph or a HLG. + encoder_out: + A tensor of shape (N, T, C) from the encoder. + encoder_out_lens: + A tensor of shape (N,) containing the number of frames in `encoder_out` + before padding. + beam: + Beam value, similar to the beam used in Kaldi.. + max_states: + Max states per stream per frame. + max_contexts: + Max contexts pre stream per frame. + num_paths: + Number of paths to extract from the decoded lattice. + ref_texts: + A list-of-list of integers containing the reference transcripts. + If the decoding_graph is a trivial_graph, the integer ID is the + BPE token ID. + use_double_scores: + True to use double precision for computation. False to use + single precision. + nbest_scale: + It's the scale applied to the lattice.scores. A smaller value + yields more unique paths. + + Returns: + Return the decoded result. + """ + lattice = fast_beam_search( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=beam, + max_states=max_states, + max_contexts=max_contexts, + ) + + nbest = Nbest.from_lattice( + lattice=lattice, + num_paths=num_paths, + use_double_scores=use_double_scores, + nbest_scale=nbest_scale, + ) + + hyps = nbest.build_levenshtein_graphs() + refs = k2.levenshtein_graph(ref_texts, device=hyps.device) + + levenshtein_alignment = k2.levenshtein_alignment( + refs=refs, + hyps=hyps, + hyp_to_ref_map=nbest.shape.row_ids(1), + sorted_match_ref=True, + ) + + tot_scores = levenshtein_alignment.get_tot_scores( + use_double_scores=False, log_semiring=False + ) + ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores) + + max_indexes = ragged_tot_scores.argmax() + + best_path = k2.index_fsa(nbest.fsa, max_indexes) + + hyps = get_texts(best_path) + return hyps + + +def fast_beam_search( + model: Transducer, + decoding_graph: k2.Fsa, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, + beam: float, + max_states: int, + max_contexts: int, +) -> k2.Fsa: + """It limits the maximum number of symbols per frame to 1. + + Args: + model: + An instance of `Transducer`. + decoding_graph: + Decoding graph used for decoding, may be a TrivialGraph or a HLG. + encoder_out: + A tensor of shape (N, T, C) from the encoder. + encoder_out_lens: + A tensor of shape (N,) containing the number of frames in `encoder_out` + before padding. + beam: + Beam value, similar to the beam used in Kaldi.. + max_states: + Max states per stream per frame. + max_contexts: + Max contexts pre stream per frame. + Returns: + Return an FsaVec with axes [utt][state][arc] containing the decoded + lattice. Note: When the input graph is a TrivialGraph, the returned + lattice is actually an acceptor. + """ + assert encoder_out.ndim == 3 + + context_size = model.decoder.context_size + vocab_size = model.decoder.vocab_size + + B, T, C = encoder_out.shape + + config = k2.RnntDecodingConfig( + vocab_size=vocab_size, + decoder_history_len=context_size, + beam=beam, + max_contexts=max_contexts, + max_states=max_states, + ) + individual_streams = [] + for i in range(B): + individual_streams.append(k2.RnntDecodingStream(decoding_graph)) + decoding_streams = k2.RnntDecodingStreams(individual_streams, config) + + encoder_out_len = torch.ones(1, dtype=torch.int32) + decoder_out_len = torch.ones(1, dtype=torch.int32) + + for t in range(T): + # shape is a RaggedShape of shape (B, context) + # contexts is a Tensor of shape (shape.NumElements(), context_size) + shape, contexts = decoding_streams.get_contexts() + # `nn.Embedding()` in torch below v1.7.1 supports only torch.int64 + contexts = contexts.to(torch.int64) + # decoder_out is of shape (shape.NumElements(), 1, decoder_out_dim) + decoder_out = model.decoder(contexts, need_pad=False) + # current_encoder_out is of shape + # (shape.NumElements(), 1, joiner_dim) + # fmt: off + current_encoder_out = torch.index_select( + encoder_out[:, t:t + 1, :], 0, shape.row_ids(1).to(torch.int64) + ) + # fmt: on + logits = model.joiner( + current_encoder_out, + decoder_out, + encoder_out_len.expand(decoder_out.size(0)), + decoder_out_len.expand(decoder_out.size(0)), + ) # (N, vocab_size) + log_probs = logits.log_softmax(dim=-1) + decoding_streams.advance(log_probs) + decoding_streams.terminate_and_flush_to_streams() + lattice = decoding_streams.format_output(encoder_out_lens.tolist()) + + return lattice + def greedy_search( model: Transducer, encoder_out: torch.Tensor, max_sym_per_frame: int @@ -104,7 +333,9 @@ def greedy_search( def greedy_search_batch( - model: Transducer, encoder_out: torch.Tensor + model: Transducer, + encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, ) -> List[List[int]]: """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1. Args: @@ -112,6 +343,9 @@ def greedy_search_batch( The transducer model. encoder_out: Output from the encoder. Its shape is (N, T, C), where N >= 1. + encoder_out_lens: + A 1-D tensor of shape (N,), containing number of valid frames in + encoder_out before padding. Returns: Return a list-of-list of token IDs containing the decoded results. len(ans) equals to encoder_out.size(0). @@ -119,32 +353,54 @@ def greedy_search_batch( assert encoder_out.ndim == 3 assert encoder_out.size(0) >= 1, encoder_out.size(0) - device = model.device + packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence( + input=encoder_out, + lengths=encoder_out_lens.cpu(), + batch_first=True, + enforce_sorted=False, + ) - batch_size = encoder_out.size(0) - T = encoder_out.size(1) + device = next(model.parameters()).device blank_id = model.decoder.blank_id context_size = model.decoder.context_size - hyps = [[blank_id] * context_size for _ in range(batch_size)] + batch_size_list = packed_encoder_out.batch_sizes.tolist() + N = encoder_out.size(0) + assert torch.all(encoder_out_lens > 0), encoder_out_lens + assert N == batch_size_list[0], (N, batch_size_list) + + hyps = [[blank_id] * context_size for _ in range(N)] decoder_input = torch.tensor( hyps, device=device, dtype=torch.int64, - ) # (batch_size, context_size) + ) # (N, context_size) decoder_out = model.decoder(decoder_input, need_pad=False) - # decoder_out: (batch_size, 1, decoder_out_dim) + # decoder_out: (N, 1, decoder_out_dim) - encoder_out_len = torch.ones(batch_size, dtype=torch.int32) - decoder_out_len = torch.ones(batch_size, dtype=torch.int32) + encoder_out_len = torch.ones(1, dtype=torch.int32) + decoder_out_len = torch.ones(1, dtype=torch.int32) - for t in range(T): - current_encoder_out = encoder_out[:, t : t + 1, :] # noqa + encoder_out = packed_encoder_out.data + + offset = 0 + for batch_size in batch_size_list: + start = offset + end = offset + batch_size + current_encoder_out = encoder_out.data[start:end] + current_encoder_out = current_encoder_out.unsqueeze(1) # current_encoder_out's shape: (batch_size, 1, encoder_out_dim) + offset = end + + decoder_out = decoder_out[:batch_size] + logits = model.joiner( - current_encoder_out, decoder_out, encoder_out_len, decoder_out_len + current_encoder_out, + decoder_out, + encoder_out_len.expand(batch_size), + decoder_out_len.expand(batch_size), ) # (batch_size, vocab_size) assert logits.ndim == 2, logits.shape @@ -157,7 +413,7 @@ def greedy_search_batch( if emitted: # update decoder output - decoder_input = [h[-context_size:] for h in hyps] + decoder_input = [h[-context_size:] for h in hyps[:batch_size]] decoder_input = torch.tensor( decoder_input, device=device, @@ -168,7 +424,12 @@ def greedy_search_batch( need_pad=False, ) # (batch_size, 1, decoder_out_dim) - ans = [h[context_size:] for h in hyps] + sorted_ans = [h[context_size:] for h in hyps] + ans = [] + unsorted_indices = packed_encoder_out.unsorted_indices.tolist() + for i in range(N): + ans.append(sorted_ans[unsorted_indices[i]]) + return ans @@ -415,6 +676,7 @@ def _get_hyps_shape(hyps: List[HypothesisList]) -> k2.RaggedShape: def modified_beam_search( model: Transducer, encoder_out: torch.Tensor, + encoder_out_lens: torch.Tensor, beam: int = 4, ) -> List[List[int]]: """Beam search in batch mode with --max-sym-per-frame=1 being hardcodded. @@ -424,6 +686,9 @@ def modified_beam_search( The transducer model. encoder_out: Output from the encoder. Its shape is (N, T, C). + encoder_out_lens: + A 1-D tensor of shape (N,), containing number of valid frames in + encoder_out before padding. beam: Number of active paths during the beam search. Returns: @@ -431,15 +696,26 @@ def modified_beam_search( for the i-th utterance. """ assert encoder_out.ndim == 3, encoder_out.shape + assert encoder_out.size(0) >= 1, encoder_out.size(0) - batch_size = encoder_out.size(0) - T = encoder_out.size(1) + packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence( + input=encoder_out, + lengths=encoder_out_lens.cpu(), + batch_first=True, + enforce_sorted=False, + ) blank_id = model.decoder.blank_id context_size = model.decoder.context_size - device = model.device - B = [HypothesisList() for _ in range(batch_size)] - for i in range(batch_size): + device = next(model.parameters()).device + + batch_size_list = packed_encoder_out.batch_sizes.tolist() + N = encoder_out.size(0) + assert torch.all(encoder_out_lens > 0), encoder_out_lens + assert N == batch_size_list[0], (N, batch_size_list) + + B = [HypothesisList() for _ in range(N)] + for i in range(N): B[i].add( Hypothesis( ys=[blank_id] * context_size, @@ -449,9 +725,20 @@ def modified_beam_search( encoder_out_len = torch.tensor([1]) decoder_out_len = torch.tensor([1]) - for t in range(T): - current_encoder_out = encoder_out[:, t : t + 1, :] # noqa + + encoder_out = packed_encoder_out.data + offset = 0 + finalized_B = [] + for batch_size in batch_size_list: + start = offset + end = offset + batch_size + current_encoder_out = encoder_out.data[start:end] + current_encoder_out = current_encoder_out.unsqueeze(1) # current_encoder_out's shape is: (batch_size, 1, encoder_out_dim) + offset = end + + finalized_B = B[batch_size:] + finalized_B + B = B[:batch_size] hyps_shape = _get_hyps_shape(B).to(device) @@ -524,8 +811,14 @@ def modified_beam_search( new_hyp = Hypothesis(ys=new_ys, log_prob=new_log_prob) B[i].add(new_hyp) + B = B + finalized_B best_hyps = [b.get_most_probable(length_norm=True) for b in B] - ans = [h.ys[context_size:] for h in best_hyps] + + sorted_ans = [h.ys[context_size:] for h in best_hyps] + ans = [] + unsorted_indices = packed_encoder_out.unsorted_indices.tolist() + for i in range(N): + ans.append(sorted_ans[unsorted_indices[i]]) return ans diff --git a/egs/librispeech/ASR/transducer_stateless/decode.py b/egs/librispeech/ASR/transducer_stateless/decode.py index ac66c9b49..5ea17b173 100755 --- a/egs/librispeech/ASR/transducer_stateless/decode.py +++ b/egs/librispeech/ASR/transducer_stateless/decode.py @@ -19,29 +19,40 @@ Usage: (1) greedy search ./transducer_stateless/decode.py \ - --epoch 14 \ - --avg 7 \ - --exp-dir ./transducer_stateless/exp \ - --max-duration 100 \ - --decoding-method greedy_search + --epoch 14 \ + --avg 7 \ + --exp-dir ./transducer_stateless/exp \ + --max-duration 600 \ + --decoding-method greedy_search -(2) beam search +(2) beam search (not recommended) ./transducer_stateless/decode.py \ - --epoch 14 \ - --avg 7 \ - --exp-dir ./transducer_stateless/exp \ - --max-duration 100 \ - --decoding-method beam_search \ - --beam-size 4 + --epoch 14 \ + --avg 7 \ + --exp-dir ./transducer_stateless/exp \ + --max-duration 600 \ + --decoding-method beam_search \ + --beam-size 4 (3) modified beam search ./transducer_stateless/decode.py \ - --epoch 14 \ - --avg 7 \ - --exp-dir ./transducer_stateless/exp \ - --max-duration 100 \ - --decoding-method modified_beam_search \ - --beam-size 4 + --epoch 14 \ + --avg 7 \ + --exp-dir ./transducer_stateless/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search +./transducer_stateless/decode.py \ + --epoch 14 \ + --avg 7 \ + --exp-dir ./transducer_stateless/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 """ @@ -49,14 +60,16 @@ import argparse import logging from collections import defaultdict from pathlib import Path -from typing import Dict, List, Tuple +from typing import Dict, List, Optional, Tuple +import k2 import sentencepiece as spm import torch import torch.nn as nn from asr_datamodule import LibriSpeechAsrDataModule from beam_search import ( beam_search, + fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, @@ -115,6 +128,7 @@ def get_parser(): - greedy_search - beam_search - modified_beam_search + - fast_beam_search """, ) @@ -122,8 +136,35 @@ def get_parser(): "--beam-size", type=int, default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, help="""Used only when --decoding-method is - beam_search or modified_beam_search""", + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", ) parser.add_argument( @@ -149,6 +190,7 @@ def decode_one_batch( model: nn.Module, sp: spm.SentencePieceProcessor, batch: dict, + decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[List[str]]]: """Decode one batch and return the result in a dict. The dict has the following format: @@ -171,6 +213,9 @@ def decode_one_batch( It is the return value from iterating `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. Returns: Return the decoding result. See above description for the format of the returned dict. @@ -188,24 +233,44 @@ def decode_one_batch( encoder_out, encoder_out_lens = model.encoder( x=feature, x_lens=feature_lens ) - hyp_list: List[List[int]] = [] - if ( + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif ( params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1 ): - hyp_list = greedy_search_batch( + hyp_tokens = greedy_search_batch( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) elif params.decoding_method == "modified_beam_search": - hyp_list = modified_beam_search( + hyp_tokens = modified_beam_search( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, beam=params.beam_size, ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) else: batch_size = encoder_out.size(0) + for i in range(batch_size): # fmt: off encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] @@ -226,14 +291,20 @@ def decode_one_batch( raise ValueError( f"Unsupported decoding method: {params.decoding_method}" ) - hyp_list.append(hyp) - - hyps = [sp.decode(hyp).split() for hyp in hyp_list] + hyps.append(sp.decode(hyp).split()) if params.decoding_method == "greedy_search": return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } else: - return {f"beam_{params.beam_size}": hyps} + return {f"beam_size_{params.beam_size}": hyps} def decode_dataset( @@ -241,6 +312,7 @@ def decode_dataset( params: AttributeDict, model: nn.Module, sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[Tuple[List[str], List[str]]]]: """Decode dataset. @@ -253,6 +325,9 @@ def decode_dataset( The neural model. sp: The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. Returns: Return a dict, whose key may be "greedy_search" if greedy search is used, or it may be "beam_7" if beam size of 7 is used. @@ -268,9 +343,9 @@ def decode_dataset( num_batches = "?" if params.decoding_method == "greedy_search": - log_interval = 100 + log_interval = 50 else: - log_interval = 2 + log_interval = 10 results = defaultdict(list) for batch_idx, batch in enumerate(dl): @@ -280,6 +355,7 @@ def decode_dataset( params=params, model=model, sp=sp, + decoding_graph=decoding_graph, batch=batch, ) @@ -360,13 +436,21 @@ def main(): assert params.decoding_method in ( "greedy_search", "beam_search", + "fast_beam_search", "modified_beam_search", ) params.res_dir = params.exp_dir / params.decoding_method params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" - if "beam_search" in params.decoding_method: - params.suffix += f"-beam-{params.beam_size}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += ( + f"-{params.decoding_method}-beam-size-{params.beam_size}" + ) else: params.suffix += f"-context-{params.context_size}" params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" @@ -408,6 +492,11 @@ def main(): model.eval() model.device = device + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") @@ -428,6 +517,7 @@ def main(): params=params, model=model, sp=sp, + decoding_graph=decoding_graph, ) save_results( diff --git a/egs/librispeech/ASR/transducer_stateless/decoder.py b/egs/librispeech/ASR/transducer_stateless/decoder.py index b82fed37b..fbc2373a9 100644 --- a/egs/librispeech/ASR/transducer_stateless/decoder.py +++ b/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 diff --git a/egs/librispeech/ASR/transducer_stateless/pretrained.py b/egs/librispeech/ASR/transducer_stateless/pretrained.py index 4fb5d92c5..b64521801 100755 --- a/egs/librispeech/ASR/transducer_stateless/pretrained.py +++ b/egs/librispeech/ASR/transducer_stateless/pretrained.py @@ -19,30 +19,39 @@ Usage: (1) greedy search ./transducer_stateless/pretrained.py \ - --checkpoint ./transducer_stateless/exp/pretrained.pt \ - --bpe-model ./data/lang_bpe_500/bpe.model \ - --method greedy_search \ - --max-sym-per-frame 1 \ - /path/to/foo.wav \ - /path/to/bar.wav \ + --checkpoint ./transducer_stateless/exp/pretrained.pt \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --method greedy_search \ + --max-sym-per-frame 1 \ + /path/to/foo.wav \ + /path/to/bar.wav (2) beam search ./transducer_stateless/pretrained.py \ - --checkpoint ./transducer_stateless/exp/pretrained.pt \ - --bpe-model ./data/lang_bpe_500/bpe.model \ - --method beam_search \ - --beam-size 4 \ - /path/to/foo.wav \ - /path/to/bar.wav \ + --checkpoint ./transducer_stateless/exp/pretrained.pt \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --method beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav (3) modified beam search ./transducer_stateless/pretrained.py \ - --checkpoint ./transducer_stateless/exp/pretrained.pt \ - --bpe-model ./data/lang_bpe_500/bpe.model \ - --method modified_beam_search \ - --beam-size 4 \ - /path/to/foo.wav \ - /path/to/bar.wav \ + --checkpoint ./transducer_stateless/exp/pretrained.pt \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --method modified_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav + +(4) fast beam search +./transducer_stateless/pretrained.py \ + --checkpoint ./transducer_stateless/exp/pretrained.pt \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --method fast_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav You can also use `./transducer_stateless/exp/epoch-xx.pt`. @@ -56,12 +65,14 @@ import logging import math from typing import List +import k2 import kaldifeat import sentencepiece as spm import torch import torchaudio from beam_search import ( beam_search, + fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, @@ -87,9 +98,7 @@ def get_parser(): parser.add_argument( "--bpe-model", type=str, - help="""Path to bpe.model. - Used only when method is ctc-decoding. - """, + help="""Path to bpe.model.""", ) parser.add_argument( @@ -100,6 +109,7 @@ def get_parser(): - greedy_search - beam_search - modified_beam_search + - fast_beam_search """, ) @@ -124,7 +134,33 @@ def get_parser(): "--beam-size", type=int, default=4, - help="Used only when --method is beam_search and modified_beam_search ", + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --method is fast_beam_search""", ) parser.add_argument( @@ -241,15 +277,28 @@ def main(): msg += f" with beam size {params.beam_size}" logging.info(msg) - if params.method == "greedy_search" and params.max_sym_per_frame == 1: + if params.method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + hyp_list = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + elif params.method == "greedy_search" and params.max_sym_per_frame == 1: hyp_list = greedy_search_batch( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, ) elif params.method == "modified_beam_search": hyp_list = modified_beam_search( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, beam=params.beam_size, ) else: diff --git a/egs/librispeech/ASR/transducer_stateless2/decode.py b/egs/librispeech/ASR/transducer_stateless2/decode.py index 08c61c2be..4cf1e559c 100755 --- a/egs/librispeech/ASR/transducer_stateless2/decode.py +++ b/egs/librispeech/ASR/transducer_stateless2/decode.py @@ -22,15 +22,15 @@ Usage: --epoch 14 \ --avg 7 \ --exp-dir ./transducer_stateless2/exp \ - --max-duration 100 \ + --max-duration 600 \ --decoding-method greedy_search -(2) beam search +(2) beam search (not recommended) ./transducer_stateless2/decode.py \ --epoch 14 \ --avg 7 \ --exp-dir ./transducer_stateless2/exp \ - --max-duration 100 \ + --max-duration 600 \ --decoding-method beam_search \ --beam-size 4 @@ -39,9 +39,20 @@ Usage: --epoch 14 \ --avg 7 \ --exp-dir ./transducer_stateless2/exp \ - --max-duration 100 \ + --max-duration 600 \ --decoding-method modified_beam_search \ --beam-size 4 + +(4) fast beam search +./transducer_stateless2/decode.py \ + --epoch 14 \ + --avg 7 \ + --exp-dir ./transducer_stateless2/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 """ @@ -49,14 +60,16 @@ import argparse import logging from collections import defaultdict from pathlib import Path -from typing import Dict, List, Tuple +from typing import Dict, List, Optional, Tuple +import k2 import sentencepiece as spm import torch import torch.nn as nn from asr_datamodule import LibriSpeechAsrDataModule from beam_search import ( beam_search, + fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, @@ -115,6 +128,7 @@ def get_parser(): - greedy_search - beam_search - modified_beam_search + - fast_beam_search """, ) @@ -122,8 +136,35 @@ def get_parser(): "--beam-size", type=int, default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, help="""Used only when --decoding-method is - beam_search or modified_beam_search""", + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", ) parser.add_argument( @@ -149,6 +190,7 @@ def decode_one_batch( model: nn.Module, sp: spm.SentencePieceProcessor, batch: dict, + decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[List[str]]]: """Decode one batch and return the result in a dict. The dict has the following format: @@ -171,6 +213,9 @@ def decode_one_batch( It is the return value from iterating `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. Returns: Return the decoding result. See above description for the format of the returned dict. @@ -188,24 +233,44 @@ def decode_one_batch( encoder_out, encoder_out_lens = model.encoder( x=feature, x_lens=feature_lens ) - hyp_list: List[List[int]] = [] - if ( + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif ( params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1 ): - hyp_list = greedy_search_batch( + hyp_tokens = greedy_search_batch( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) elif params.decoding_method == "modified_beam_search": - hyp_list = modified_beam_search( + hyp_tokens = modified_beam_search( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, beam=params.beam_size, ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) else: batch_size = encoder_out.size(0) + for i in range(batch_size): # fmt: off encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] @@ -226,14 +291,20 @@ def decode_one_batch( raise ValueError( f"Unsupported decoding method: {params.decoding_method}" ) - hyp_list.append(hyp) - - hyps = [sp.decode(hyp).split() for hyp in hyp_list] + hyps.append(sp.decode(hyp).split()) if params.decoding_method == "greedy_search": return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } else: - return {f"beam_{params.beam_size}": hyps} + return {f"beam_size_{params.beam_size}": hyps} def decode_dataset( @@ -241,6 +312,7 @@ def decode_dataset( params: AttributeDict, model: nn.Module, sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[Tuple[List[str], List[str]]]]: """Decode dataset. @@ -253,6 +325,9 @@ def decode_dataset( The neural model. sp: The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. Returns: Return a dict, whose key may be "greedy_search" if greedy search is used, or it may be "beam_7" if beam size of 7 is used. @@ -268,9 +343,9 @@ def decode_dataset( num_batches = "?" if params.decoding_method == "greedy_search": - log_interval = 100 + log_interval = 50 else: - log_interval = 2 + log_interval = 10 results = defaultdict(list) for batch_idx, batch in enumerate(dl): @@ -280,6 +355,7 @@ def decode_dataset( params=params, model=model, sp=sp, + decoding_graph=decoding_graph, batch=batch, ) @@ -360,13 +436,21 @@ def main(): assert params.decoding_method in ( "greedy_search", "beam_search", + "fast_beam_search", "modified_beam_search", ) params.res_dir = params.exp_dir / params.decoding_method params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" - if "beam_search" in params.decoding_method: - params.suffix += f"-beam-{params.beam_size}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += ( + f"-{params.decoding_method}-beam-size-{params.beam_size}" + ) else: params.suffix += f"-context-{params.context_size}" params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" @@ -408,6 +492,11 @@ def main(): model.eval() model.device = device + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") @@ -428,6 +517,7 @@ def main(): params=params, model=model, sp=sp, + decoding_graph=decoding_graph, ) save_results( diff --git a/egs/librispeech/ASR/transducer_stateless2/pretrained.py b/egs/librispeech/ASR/transducer_stateless2/pretrained.py index 2f0604893..292f77f03 100755 --- a/egs/librispeech/ASR/transducer_stateless2/pretrained.py +++ b/egs/librispeech/ASR/transducer_stateless2/pretrained.py @@ -19,30 +19,39 @@ Usage: (1) greedy search ./transducer_stateless2/pretrained.py \ - --checkpoint ./transducer_stateless2/exp/pretrained.pt \ - --bpe-model ./data/lang_bpe_500/bpe.model \ - --method greedy_search \ - --max-sym-per-frame 1 \ - /path/to/foo.wav \ - /path/to/bar.wav \ + --checkpoint ./transducer_stateless2/exp/pretrained.pt \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --method greedy_search \ + --max-sym-per-frame 1 \ + /path/to/foo.wav \ + /path/to/bar.wav (2) beam search ./transducer_stateless2/pretrained.py \ - --checkpoint ./transducer_stateless2/exp/pretrained.pt \ - --bpe-model ./data/lang_bpe_500/bpe.model \ - --method beam_search \ - --beam-size 4 \ - /path/to/foo.wav \ - /path/to/bar.wav \ + --checkpoint ./transducer_stateless2/exp/pretrained.pt \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --method beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav (3) modified beam search ./transducer_stateless2/pretrained.py \ - --checkpoint ./transducer_stateless2/exp/pretrained.pt \ - --bpe-model ./data/lang_bpe_500/bpe.model \ - --method modified_beam_search \ - --beam-size 4 \ - /path/to/foo.wav \ - /path/to/bar.wav \ + --checkpoint ./transducer_stateless2/exp/pretrained.pt \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --method modified_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav + +(4) fast beam search +./transducer_stateless2/pretrained.py \ + --checkpoint ./transducer_stateless2/exp/pretrained.pt \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --method fast_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav You can also use `./transducer_stateless2/exp/epoch-xx.pt`. @@ -56,12 +65,14 @@ import logging import math from typing import List +import k2 import kaldifeat import sentencepiece as spm import torch import torchaudio from beam_search import ( beam_search, + fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, @@ -87,9 +98,7 @@ def get_parser(): parser.add_argument( "--bpe-model", type=str, - help="""Path to bpe.model. - Used only when method is ctc-decoding. - """, + help="""Path to bpe.model.""", ) parser.add_argument( @@ -100,6 +109,7 @@ def get_parser(): - greedy_search - beam_search - modified_beam_search + - fast_beam_search """, ) @@ -124,7 +134,33 @@ def get_parser(): "--beam-size", type=int, default=4, - help="Used only when --method is beam_search and modified_beam_search ", + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --method is fast_beam_search""", ) parser.add_argument( @@ -241,15 +277,28 @@ def main(): msg += f" with beam size {params.beam_size}" logging.info(msg) - if params.method == "greedy_search" and params.max_sym_per_frame == 1: + if params.method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + hyp_list = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + elif params.method == "greedy_search" and params.max_sym_per_frame == 1: hyp_list = greedy_search_batch( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, ) elif params.method == "modified_beam_search": hyp_list = modified_beam_search( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, beam=params.beam_size, ) else: diff --git a/egs/librispeech/ASR/transducer_stateless_multi_datasets/decode.py b/egs/librispeech/ASR/transducer_stateless_multi_datasets/decode.py index 22f137d36..955366970 100755 --- a/egs/librispeech/ASR/transducer_stateless_multi_datasets/decode.py +++ b/egs/librispeech/ASR/transducer_stateless_multi_datasets/decode.py @@ -22,17 +22,37 @@ Usage: --epoch 14 \ --avg 7 \ --exp-dir ./transducer_stateless_multi_datasets/exp \ - --max-duration 100 \ + --max-duration 600 \ --decoding-method greedy_search -(2) beam search +(2) beam search (not recommended) ./transducer_stateless_multi_datasets/decode.py \ --epoch 14 \ --avg 7 \ --exp-dir ./transducer_stateless_multi_datasets/exp \ - --max-duration 100 \ + --max-duration 600 \ --decoding-method beam_search \ --beam-size 4 + +(3) modified beam search +./transducer_stateless_multi_datasets/decode.py \ + --epoch 14 \ + --avg 7 \ + --exp-dir ./transducer_stateless_multi_datasets/exp \ + --max-duration 600 \ + --decoding-method modified_beam_search \ + --beam-size 4 + +(4) fast beam search +./transducer_stateless_multi_datasets/decode.py \ + --epoch 14 \ + --avg 7 \ + --exp-dir ./transducer_stateless_multi_datasets/exp \ + --max-duration 600 \ + --decoding-method fast_beam_search \ + --beam 4 \ + --max-contexts 4 \ + --max-states 8 """ @@ -40,14 +60,16 @@ import argparse import logging from collections import defaultdict from pathlib import Path -from typing import Dict, List, Tuple +from typing import Dict, List, Optional, Tuple +import k2 import sentencepiece as spm import torch import torch.nn as nn from asr_datamodule import AsrDataModule from beam_search import ( beam_search, + fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, @@ -107,6 +129,7 @@ def get_parser(): - greedy_search - beam_search - modified_beam_search + - fast_beam_search """, ) @@ -114,8 +137,35 @@ def get_parser(): "--beam-size", type=int, default=4, + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --decoding-method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --decoding-method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, help="""Used only when --decoding-method is - beam_search or modified_beam_search""", + fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --decoding-method is + fast_beam_search""", ) parser.add_argument( @@ -141,6 +191,7 @@ def decode_one_batch( model: nn.Module, sp: spm.SentencePieceProcessor, batch: dict, + decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[List[str]]]: """Decode one batch and return the result in a dict. The dict has the following format: @@ -163,6 +214,9 @@ def decode_one_batch( It is the return value from iterating `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation for the format of the `batch`. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. Returns: Return the decoding result. See above description for the format of the returned dict. @@ -180,24 +234,44 @@ def decode_one_batch( encoder_out, encoder_out_lens = model.encoder( x=feature, x_lens=feature_lens ) - hyp_list = [] - batch_size = encoder_out.size(0) - if ( + hyps = [] + + if params.decoding_method == "fast_beam_search": + hyp_tokens = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) + elif ( params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1 ): - hyp_list = greedy_search_batch( + hyp_tokens = greedy_search_batch( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) elif params.decoding_method == "modified_beam_search": - hyp_list = modified_beam_search( + hyp_tokens = modified_beam_search( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, beam=params.beam_size, ) + for hyp in sp.decode(hyp_tokens): + hyps.append(hyp.split()) else: + batch_size = encoder_out.size(0) + for i in range(batch_size): # fmt: off encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]] @@ -218,14 +292,20 @@ def decode_one_batch( raise ValueError( f"Unsupported decoding method: {params.decoding_method}" ) - hyp_list.append(sp.decode(hyp).split()) - - hyps = [sp.decode(hyp).split() for hyp in hyp_list] + hyps.append(sp.decode(hyp).split()) if params.decoding_method == "greedy_search": return {"greedy_search": hyps} + elif params.decoding_method == "fast_beam_search": + return { + ( + f"beam_{params.beam}_" + f"max_contexts_{params.max_contexts}_" + f"max_states_{params.max_states}" + ): hyps + } else: - return {f"beam_{params.beam_size}": hyps} + return {f"beam_size_{params.beam_size}": hyps} def decode_dataset( @@ -233,6 +313,7 @@ def decode_dataset( params: AttributeDict, model: nn.Module, sp: spm.SentencePieceProcessor, + decoding_graph: Optional[k2.Fsa] = None, ) -> Dict[str, List[Tuple[List[str], List[str]]]]: """Decode dataset. @@ -245,6 +326,9 @@ def decode_dataset( The neural model. sp: The BPE model. + decoding_graph: + The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used + only when --decoding_method is fast_beam_search. Returns: Return a dict, whose key may be "greedy_search" if greedy search is used, or it may be "beam_7" if beam size of 7 is used. @@ -260,9 +344,9 @@ def decode_dataset( num_batches = "?" if params.decoding_method == "greedy_search": - log_interval = 100 + log_interval = 50 else: - log_interval = 2 + log_interval = 10 results = defaultdict(list) for batch_idx, batch in enumerate(dl): @@ -272,6 +356,7 @@ def decode_dataset( params=params, model=model, sp=sp, + decoding_graph=decoding_graph, batch=batch, ) @@ -352,13 +437,21 @@ def main(): assert params.decoding_method in ( "greedy_search", "beam_search", + "fast_beam_search", "modified_beam_search", ) params.res_dir = params.exp_dir / params.decoding_method params.suffix = f"epoch-{params.epoch}-avg-{params.avg}" - if "beam_search" in params.decoding_method: - params.suffix += f"-beam-{params.beam_size}" + + if "fast_beam_search" in params.decoding_method: + params.suffix += f"-beam-{params.beam}" + params.suffix += f"-max-contexts-{params.max_contexts}" + params.suffix += f"-max-states-{params.max_states}" + elif "beam_search" in params.decoding_method: + params.suffix += ( + f"-{params.decoding_method}-beam-size-{params.beam_size}" + ) else: params.suffix += f"-context-{params.context_size}" params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}" @@ -402,6 +495,11 @@ def main(): model.eval() model.device = device + if params.decoding_method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + else: + decoding_graph = None + num_param = sum([p.numel() for p in model.parameters()]) logging.info(f"Number of model parameters: {num_param}") @@ -423,6 +521,7 @@ def main(): params=params, model=model, sp=sp, + decoding_graph=decoding_graph, ) save_results( diff --git a/egs/librispeech/ASR/transducer_stateless_multi_datasets/pretrained.py b/egs/librispeech/ASR/transducer_stateless_multi_datasets/pretrained.py index df9c3186f..f297fa2b2 100755 --- a/egs/librispeech/ASR/transducer_stateless_multi_datasets/pretrained.py +++ b/egs/librispeech/ASR/transducer_stateless_multi_datasets/pretrained.py @@ -44,6 +44,15 @@ Usage: /path/to/foo.wav \ /path/to/bar.wav +(4) fast beam search +./transducer_stateless_multi_datasets/pretrained.py \ + --checkpoint ./transducer_stateless_multi_datasets/exp/pretrained.pt \ + --bpe-model ./data/lang_bpe_500/bpe.model \ + --method fast_beam_search \ + --beam-size 4 \ + /path/to/foo.wav \ + /path/to/bar.wav + You can also use `./transducer_stateless_multi_datasets/exp/epoch-xx.pt`. Note: ./transducer_stateless_multi_datasets/exp/pretrained.pt is generated by @@ -56,12 +65,14 @@ import logging import math from typing import List +import k2 import kaldifeat import sentencepiece as spm import torch import torchaudio from beam_search import ( beam_search, + fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, @@ -87,9 +98,7 @@ def get_parser(): parser.add_argument( "--bpe-model", type=str, - help="""Path to bpe.model. - Used only when method is ctc-decoding. - """, + help="""Path to bpe.model.""", ) parser.add_argument( @@ -100,6 +109,7 @@ def get_parser(): - greedy_search - beam_search - modified_beam_search + - fast_beam_search """, ) @@ -124,7 +134,33 @@ def get_parser(): "--beam-size", type=int, default=4, - help="Used only when --method is beam_search and modified_beam_search ", + help="""An integer indicating how many candidates we will keep for each + frame. Used only when --method is beam_search or + modified_beam_search.""", + ) + + parser.add_argument( + "--beam", + type=float, + default=4, + help="""A floating point value to calculate the cutoff score during beam + search (i.e., `cutoff = max-score - beam`), which is the same as the + `beam` in Kaldi. + Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-contexts", + type=int, + default=4, + help="""Used only when --method is fast_beam_search""", + ) + + parser.add_argument( + "--max-states", + type=int, + default=8, + help="""Used only when --method is fast_beam_search""", ) parser.add_argument( @@ -241,18 +277,30 @@ def main(): msg += f" with beam size {params.beam_size}" logging.info(msg) - if params.method == "greedy_search" and params.max_sym_per_frame == 1: + if params.method == "fast_beam_search": + decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device) + hyp_list = fast_beam_search_one_best( + model=model, + decoding_graph=decoding_graph, + encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, + beam=params.beam, + max_contexts=params.max_contexts, + max_states=params.max_states, + ) + elif params.method == "greedy_search" and params.max_sym_per_frame == 1: hyp_list = greedy_search_batch( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, ) elif params.method == "modified_beam_search": hyp_list = modified_beam_search( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, beam=params.beam_size, ) - else: for i in range(num_waves): # fmt: off diff --git a/egs/tedlium3/ASR/pruned_transducer_stateless/decode.py b/egs/tedlium3/ASR/pruned_transducer_stateless/decode.py index fd8d2dd0e..4d9d3c3cf 100755 --- a/egs/tedlium3/ASR/pruned_transducer_stateless/decode.py +++ b/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): diff --git a/egs/tedlium3/ASR/pruned_transducer_stateless/pretrained.py b/egs/tedlium3/ASR/pruned_transducer_stateless/pretrained.py index 08e4962e2..8480ac029 100644 --- a/egs/tedlium3/ASR/pruned_transducer_stateless/pretrained.py +++ b/egs/tedlium3/ASR/pruned_transducer_stateless/pretrained.py @@ -72,23 +72,16 @@ import k2 import kaldifeat import sentencepiece as spm import torch -import torch.nn as nn import torchaudio from beam_search import ( beam_search, - fast_beam_search, + fast_beam_search_one_best, greedy_search, greedy_search_batch, modified_beam_search, ) -from conformer import Conformer -from decoder import Decoder -from joiner import Joiner -from model import Transducer from torch.nn.utils.rnn import pad_sequence - -from icefall.env import get_env_info -from icefall.utils import AttributeDict +from train import get_params, get_transducer_model def get_parser(): @@ -185,76 +178,16 @@ def get_parser(): """, ) + parser.add_argument( + "--sample-rate", + type=int, + default=16000, + help="The sample rate of the input sound file", + ) + return parser -def get_params() -> AttributeDict: - params = AttributeDict( - { - "sample_rate": 16000, - # parameters for conformer - "feature_dim": 80, - "subsampling_factor": 4, - "attention_dim": 512, - "nhead": 8, - "dim_feedforward": 2048, - "num_encoder_layers": 12, - "vgg_frontend": False, - # parameters for decoder - "embedding_dim": 512, - "env_info": get_env_info(), - } - ) - return params - - -def get_encoder_model(params: AttributeDict) -> nn.Module: - encoder = Conformer( - num_features=params.feature_dim, - output_dim=params.vocab_size, - subsampling_factor=params.subsampling_factor, - d_model=params.attention_dim, - nhead=params.nhead, - dim_feedforward=params.dim_feedforward, - num_encoder_layers=params.num_encoder_layers, - vgg_frontend=params.vgg_frontend, - ) - return encoder - - -def get_decoder_model(params: AttributeDict) -> nn.Module: - decoder = Decoder( - vocab_size=params.vocab_size, - embedding_dim=params.embedding_dim, - blank_id=params.blank_id, - unk_id=params.unk_id, - context_size=params.context_size, - ) - return decoder - - -def get_joiner_model(params: AttributeDict) -> nn.Module: - joiner = Joiner( - input_dim=params.vocab_size, - inner_dim=params.embedding_dim, - output_dim=params.vocab_size, - ) - return joiner - - -def get_transducer_model(params: AttributeDict) -> nn.Module: - encoder = get_encoder_model(params) - decoder = get_decoder_model(params) - joiner = get_joiner_model(params) - - model = Transducer( - encoder=encoder, - decoder=decoder, - joiner=joiner, - ) - return model - - def read_sound_files( filenames: List[str], expected_sample_rate: float ) -> List[torch.Tensor]: @@ -354,7 +287,7 @@ def main(): logging.info(msg) if params.decoding_method == "fast_beam_search": - hyp_tokens = fast_beam_search( + hyp_tokens = fast_beam_search_one_best( model=model, decoding_graph=decoding_graph, encoder_out=encoder_out, @@ -372,6 +305,7 @@ def main(): hyp_tokens = greedy_search_batch( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, ) for hyp in sp.decode(hyp_tokens): hyps.append(hyp.split()) @@ -379,6 +313,7 @@ def main(): hyp_tokens = modified_beam_search( model=model, encoder_out=encoder_out, + encoder_out_lens=encoder_out_lens, beam=params.beam_size, ) for hyp in sp.decode(hyp_tokens):