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Merge remote-tracking branch 'dan/master' into rnnt-lstm-2022-04-21

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This commit is contained in:
Fangjun Kuang 2022-05-10 22:10:27 +08:00
commit b0d9da0ad1
71 changed files with 7275 additions and 162 deletions
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3
.flake8
View File

@ -9,6 +9,9 @@ per-file-ignores =
egs/tedlium3/ASR/*/conformer.py: E501,
egs/gigaspeech/ASR/*/conformer.py: E501,
egs/librispeech/ASR/pruned_transducer_stateless2/*.py: E501,
egs/librispeech/ASR/pruned_transducer_stateless4/*.py: E501,
egs/librispeech/ASR/*/optim.py: E501,
egs/librispeech/ASR/*/scaling.py: E501,
# invalid escape sequence (cause by tex formular), W605
icefall/utils.py: E501, W605

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

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

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

11
.github/scripts/prepare-librispeech-test-clean-and-test-other-manifests.sh vendored Executable file
View File

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

28
.github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh vendored
View File

@ -45,3 +45,31 @@ for method in modified_beam_search beam_search; do
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
done
echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
mkdir -p pruned_transducer_stateless/exp
ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless/exp/epoch-999.pt
ln -s $PWD/$repo/data/lang_bpe_500 data/
ls -lh data
ls -lh pruned_transducer_stateless/exp
log "Decoding test-clean and test-other"
# use a small value for decoding with CPU
max_duration=50
for method in greedy_search fast_beam_search; do
log "Decoding with $method"
./pruned_transducer_stateless/decode.py \
--decoding-method $method \
--epoch 999 \
--avg 1 \
--max-duration $max_duration \
--exp-dir pruned_transducer_stateless/exp
done
rm pruned_transducer_stateless/exp/*.pt
fi

79
.github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh vendored Executable file
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@ -0,0 +1,79 @@
#!/usr/bin/env bash
log() {
# This function is from espnet
local fname=${BASH_SOURCE[1]##*/}
echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
}
cd egs/librispeech/ASR
repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless2-2022-04-29
log "Downloading pre-trained model from $repo_url"
git lfs install
git clone $repo_url
repo=$(basename $repo_url)
log "Display test files"
tree $repo/
soxi $repo/test_wavs/*.wav
ls -lh $repo/test_wavs/*.wav
pushd $repo/exp
ln -s pretrained-epoch-38-avg-10.pt pretrained.pt
popd
for sym in 1 2 3; do
log "Greedy search with --max-sym-per-frame $sym"
./pruned_transducer_stateless2/pretrained.py \
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
done
for method in modified_beam_search beam_search fast_beam_search; do
log "$method"
./pruned_transducer_stateless2/pretrained.py \
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
done
echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
mkdir -p pruned_transducer_stateless2/exp
ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless2/exp/epoch-999.pt
ln -s $PWD/$repo/data/lang_bpe_500 data/
ls -lh data
ls -lh pruned_transducer_stateless2/exp
log "Decoding test-clean and test-other"
# use a small value for decoding with CPU
max_duration=50
for method in greedy_search fast_beam_search; do
log "Decoding with $method"
./pruned_transducer_stateless2/decode.py \
--decoding-method $method \
--epoch 999 \
--avg 1 \
--max-duration $max_duration \
--exp-dir pruned_transducer_stateless2/exp
done
rm pruned_transducer_stateless2/exp/*.pt
fi

79
.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh vendored Executable file
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@ -0,0 +1,79 @@
#!/usr/bin/env bash
log() {
# This function is from espnet
local fname=${BASH_SOURCE[1]##*/}
echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
}
cd egs/librispeech/ASR
repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-04-29
log "Downloading pre-trained model from $repo_url"
git lfs install
git clone $repo_url
repo=$(basename $repo_url)
log "Display test files"
tree $repo/
soxi $repo/test_wavs/*.wav
ls -lh $repo/test_wavs/*.wav
pushd $repo/exp
ln -s pretrained-epoch-25-avg-6.pt pretrained.pt
popd
for sym in 1 2 3; do
log "Greedy search with --max-sym-per-frame $sym"
./pruned_transducer_stateless3/pretrained.py \
--method greedy_search \
--max-sym-per-frame $sym \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
done
for method in modified_beam_search beam_search fast_beam_search; do
log "$method"
./pruned_transducer_stateless3/pretrained.py \
--method $method \
--beam-size 4 \
--checkpoint $repo/exp/pretrained.pt \
--bpe-model $repo/data/lang_bpe_500/bpe.model \
$repo/test_wavs/1089-134686-0001.wav \
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
done
echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
mkdir -p pruned_transducer_stateless3/exp
ln -s $PWD/$repo/exp/pretrained.pt pruned_transducer_stateless3/exp/epoch-999.pt
ln -s $PWD/$repo/data/lang_bpe_500 data/
ls -lh data
ls -lh pruned_transducer_stateless3/exp
log "Decoding test-clean and test-other"
# use a small value for decoding with CPU
max_duration=50
for method in greedy_search fast_beam_search; do
log "Decoding with $method"
./pruned_transducer_stateless3/decode.py \
--decoding-method $method \
--epoch 999 \
--avg 1 \
--max-duration $max_duration \
--exp-dir pruned_transducer_stateless3/exp
done
rm pruned_transducer_stateless3/exp/*.pt
fi

28
.github/scripts/run-librispeech-transducer-stateless2-2022-04-19.sh vendored
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@ -45,3 +45,31 @@ for method in modified_beam_search beam_search; do
$repo/test_wavs/1221-135766-0001.wav \
$repo/test_wavs/1221-135766-0002.wav
done
echo "GITHUB_EVENT_NAME: ${GITHUB_EVENT_NAME}"
if [[ x"${GITHUB_EVENT_NAME}" == x"schedule" ]]; then
mkdir -p transducer_stateless2/exp
ln -s $PWD/$repo/exp/pretrained.pt transducer_stateless2/exp/epoch-999.pt
ln -s $PWD/$repo/data/lang_bpe_500 data/
ls -lh data
ls -lh transducer_stateless2/exp
log "Decoding test-clean and test-other"
# use a small value for decoding with CPU
max_duration=50
for method in greedy_search modified_beam_search; do
log "Decoding with $method"
./transducer_stateless2/decode.py \
--decoding-method $method \
--epoch 999 \
--avg 1 \
--max-duration $max_duration \
--exp-dir transducer_stateless2/exp
done
rm transducer_stateless2/exp/*.pt
fi

85
.github/workflows/run-librispeech-2022-03-12.yml vendored
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@ -24,9 +24,18 @@ on:
pull_request:
types: [labeled]
schedule:
# minute (0-59)
# hour (0-23)
# day of the month (1-31)
# month (1-12)
# day of the week (0-6)
# nightly build at 15:50 UTC time every day
- cron: "50 15 * * *"
jobs:
run_librispeech_2022_03_12:
if: github.event.label.name == 'ready' || github.event_name == 'push'
if: github.event.label.name == 'ready' || github.event_name == 'push' || github.event_name == 'schedule'
runs-on: ${{ matrix.os }}
strategy:
matrix:
@ -63,20 +72,78 @@ jobs:
if: steps.my-cache.outputs.cache-hit != 'true'
shell: bash
run: |
mkdir -p ~/tmp
cd ~/tmp
git clone https://github.com/csukuangfj/kaldifeat
cd kaldifeat
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j2 _kaldifeat
.github/scripts/install-kaldifeat.sh
- name: Cache LibriSpeech test-clean and test-other datasets
id: libri-test-clean-and-test-other-data
uses: actions/cache@v2
with:
path: |
~/tmp/download
key: cache-libri-test-clean-and-test-other
- name: Download LibriSpeech test-clean and test-other
if: steps.libri-test-clean-and-test-other-data.outputs.cache-hit != 'true'
shell: bash
run: |
.github/scripts/download-librispeech-test-clean-and-test-other-dataset.sh
- name: Prepare manifests for LibriSpeech test-clean and test-other
shell: bash
run: |
.github/scripts/prepare-librispeech-test-clean-and-test-other-manifests.sh
- name: Cache LibriSpeech test-clean and test-other fbank features
id: libri-test-clean-and-test-other-fbank
uses: actions/cache@v2
with:
path: |
~/tmp/fbank-libri
key: cache-libri-fbank-test-clean-and-test-other
- name: Compute fbank for LibriSpeech test-clean and test-other
if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true'
shell: bash
run: |
.github/scripts/compute-fbank-librispeech-test-clean-and-test-other.sh
- name: Inference with pre-trained model
shell: bash
env:
GITHUB_EVENT_NAME: ${{ github.event_name }}
run: |
mkdir -p egs/librispeech/ASR/data
ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank
ls -lh egs/librispeech/ASR/data/*
sudo apt-get -qq install git-lfs tree sox
export PYTHONPATH=$PWD:$PYTHONPATH
export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
.github/scripts/run-librispeech-pruned-transducer-stateless-2022-03-12.sh
- name: Display decoding results
if: github.event_name == 'schedule'
shell: bash
run: |
cd egs/librispeech/ASR/
tree ./pruned_transducer_stateless/exp
cd pruned_transducer_stateless
echo "results for pruned_transducer_stateless"
echo "===greedy search==="
find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
echo "===fast_beam_search==="
find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
- name: Upload decoding results for pruned_transducer_stateless
uses: actions/upload-artifact@v2
if: github.event_name == 'schedule'
with:
name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless-2022-03-12
path: egs/librispeech/ASR/pruned_transducer_stateless/exp/

168
.github/workflows/run-librispeech-2022-04-29.yml vendored Normal file
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@ -0,0 +1,168 @@
# Copyright 2021 Fangjun Kuang (csukuangfj@gmail.com)
# See ../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
name: run-librispeech-2022-04-29
# stateless pruned transducer (reworked model) + giga speech
on:
push:
branches:
- master
pull_request:
types: [labeled]
schedule:
# minute (0-59)
# hour (0-23)
# day of the month (1-31)
# month (1-12)
# day of the week (0-6)
# nightly build at 15:50 UTC time every day
- cron: "50 15 * * *"
jobs:
run_librispeech_2022_04_29:
if: github.event.label.name == 'ready' || github.event_name == 'push' || github.event_name == 'schedule'
runs-on: ${{ matrix.os }}
strategy:
matrix:
os: [ubuntu-18.04]
python-version: [3.7, 3.8, 3.9]
fail-fast: false
steps:
- uses: actions/checkout@v2
with:
fetch-depth: 0
- name: Setup Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
cache: 'pip'
cache-dependency-path: '**/requirements-ci.txt'
- name: Install Python dependencies
run: |
grep -v '^#' ./requirements-ci.txt | xargs -n 1 -L 1 pip install
- name: Cache kaldifeat
id: my-cache
uses: actions/cache@v2
with:
path: |
~/tmp/kaldifeat
key: cache-tmp-${{ matrix.python-version }}
- name: Install kaldifeat
if: steps.my-cache.outputs.cache-hit != 'true'
shell: bash
run: |
.github/scripts/install-kaldifeat.sh
- name: Cache LibriSpeech test-clean and test-other datasets
id: libri-test-clean-and-test-other-data
uses: actions/cache@v2
with:
path: |
~/tmp/download
key: cache-libri-test-clean-and-test-other
- name: Download LibriSpeech test-clean and test-other
if: steps.libri-test-clean-and-test-other-data.outputs.cache-hit != 'true'
shell: bash
run: |
.github/scripts/download-librispeech-test-clean-and-test-other-dataset.sh
- name: Prepare manifests for LibriSpeech test-clean and test-other
shell: bash
run: |
.github/scripts/prepare-librispeech-test-clean-and-test-other-manifests.sh
- name: Cache LibriSpeech test-clean and test-other fbank features
id: libri-test-clean-and-test-other-fbank
uses: actions/cache@v2
with:
path: |
~/tmp/fbank-libri
key: cache-libri-fbank-test-clean-and-test-other
- name: Compute fbank for LibriSpeech test-clean and test-other
if: steps.libri-test-clean-and-test-other-fbank.outputs.cache-hit != 'true'
shell: bash
run: |
.github/scripts/compute-fbank-librispeech-test-clean-and-test-other.sh
- name: Inference with pre-trained model
shell: bash
env:
GITHUB_EVENT_NAME: ${{ github.event_name }}
run: |
mkdir -p egs/librispeech/ASR/data
ln -sfv ~/tmp/fbank-libri egs/librispeech/ASR/data/fbank
ls -lh egs/librispeech/ASR/data/*
sudo apt-get -qq install git-lfs tree sox
export PYTHONPATH=$PWD:$PYTHONPATH
export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
.github/scripts/run-librispeech-pruned-transducer-stateless2-2022-04-29.sh
.github/scripts/run-librispeech-pruned-transducer-stateless3-2022-04-29.sh
- name: Display decoding results
if: github.event_name == 'schedule'
shell: bash
run: |
cd egs/librispeech/ASR
tree pruned_transducer_stateless2/exp
cd pruned_transducer_stateless2
echo "results for pruned_transducer_stateless2"
echo "===greedy search==="
find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
echo "===fast_beam_search==="
find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
cd ../
tree pruned_transducer_stateless3/exp
cd pruned_transducer_stateless3
echo "results for pruned_transducer_stateless3"
echo "===greedy search==="
find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
find exp/greedy_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
echo "===fast_beam_search==="
find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-clean" {} + | sort -n -k2
find exp/fast_beam_search -name "log-*" -exec grep -n --color "best for test-other" {} + | sort -n -k2
- name: Upload decoding results for pruned_transducer_stateless2
uses: actions/upload-artifact@v2
if: github.event_name == 'schedule'
with:
name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless2-2022-04-29
path: egs/librispeech/ASR/pruned_transducer_stateless2/exp/
- name: Upload decoding results for pruned_transducer_stateless3
uses: actions/upload-artifact@v2
if: github.event_name == 'schedule'
with:
name: torch-${{ matrix.torch }}-python-${{ matrix.python-version }}-ubuntu-18.04-cpu-pruned_transducer_stateless3-2022-04-29
path: egs/librispeech/ASR/pruned_transducer_stateless3/exp/

84
.github/workflows/run-librispeech-transducer-stateless2-2022-04-19.yml vendored
View File

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

9
.github/workflows/run-pretrained-conformer-ctc.yml vendored
View File

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

9
.github/workflows/run-pretrained-transducer-stateless-librispeech-100h.yml vendored
View File

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

9
.github/workflows/run-pretrained-transducer-stateless-librispeech-multi-datasets.yml vendored
View File

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

9
.github/workflows/run-pretrained-transducer-stateless-modified-2-aishell.yml vendored
View File

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

9
.github/workflows/run-pretrained-transducer-stateless-modified-aishell.yml vendored
View File

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

9
.github/workflows/run-pretrained-transducer-stateless.yml vendored
View File

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

7
.github/workflows/run-pretrained-transducer.yml vendored
View File

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

17
README.md
View File

@ -35,6 +35,9 @@ We do provide a Colab notebook for this recipe.
### LibriSpeech
Please see <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/RESULTS.md>
for the **latest** results.
We provide 4 models for this recipe:
- [conformer CTC model][LibriSpeech_conformer_ctc]
@ -92,6 +95,20 @@ in the decoding.
We provide a Colab notebook to run a pre-trained transducer conformer + stateless decoder model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1CO1bXJ-2khDckZIW8zjOPHGSKLHpTDlp?usp=sharing)
#### k2 pruned RNN-T
| | test-clean | test-other |
|-----|------------|------------|
| WER | 2.57 | 5.95 |
#### k2 pruned RNN-T + GigaSpeech
| | test-clean | test-other |
|-----|------------|------------|
| WER | 2.19 | 4.97 |
### Aishell
We provide two models for this recipe: [conformer CTC model][Aishell_conformer_ctc]

23
egs/aishell/ASR/transducer_stateless/export.py
View File

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

4
egs/gigaspeech/ASR/conformer_ctc/decode.py
View File

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

1
egs/librispeech/ASR/.gitignore vendored Normal file
View File

@ -0,0 +1 @@
log-*

19
egs/librispeech/ASR/README.md
View File

@ -1,8 +1,8 @@
# Introduction
Please refer to <https://icefall.readthedocs.io/en/latest/recipes/librispeech/index.html>
for how to run models in this recipe.
Please refer to <https://icefall.readthedocs.io/en/latest/recipes/librispeech/index.html> for how to run models in this recipe.
[./RESULTS.md](./RESULTS.md) contains the latest results.
# Transducers
@ -10,14 +10,15 @@ There are various folders containing the name `transducer` in this folder.
The following table lists the differences among them.
| | Encoder | Decoder | Comment |
|---------------------------------------|---------------------|--------------------|-------------------------------------------------------|
| `transducer` | Conformer | LSTM | |
|---------------------------------------|---------------------|--------------------|---------------------------------------------------|
| `transducer` | Conformer | LSTM | |
| `transducer_stateless` | Conformer | Embedding + Conv1d | Using optimized_transducer from computing RNN-T loss |
| `transducer_stateless2` | Conformer | Embedding + Conv1d | Using torchaudio for computing RNN-T loss |
| `transducer_lstm` | LSTM | Embedding + Conv1d | Using torchaudio for computing RNN-T loss |
| `transducer_stateless_multi_datasets` | Conformer | Embedding + Conv1d | Using data from GigaSpeech as extra training data |
| `pruned_transducer_stateless` | Conformer | Embedding + Conv1d | Using k2 pruned RNN-T loss |
| `pruned_transducer_stateless2` | Conformer(modified) | Embedding + Conv1d | Using k2 pruned RNN-T loss |
| `transducer_lstm` | LSTM | LSTM | |
| `transducer_stateless_multi_datasets` | Conformer | Embedding + Conv1d | Using data from GigaSpeech as extra training data |
| `pruned_transducer_stateless` | Conformer | Embedding + Conv1d | Using k2 pruned RNN-T loss |
| `pruned_transducer_stateless2` | Conformer(modified) | Embedding + Conv1d | Using k2 pruned RNN-T loss |
| `pruned_transducer_stateless3` | Conformer(modified) | Embedding + Conv1d | Using k2 pruned RNN-T loss + using GigaSpeech as extra training data |
The decoder in `transducer_stateless` is modified from the paper

156
egs/librispeech/ASR/RESULTS.md
View File

@ -1,5 +1,157 @@
## Results
### LibriSpeech BPE training results (Pruned Transducer 3)
[pruned_transducer_stateless3](./pruned_transducer_stateless3)
Same as `Pruned Transducer 2` but using the XL subset from
[GigaSpeech](https://github.com/SpeechColab/GigaSpeech) as extra training data.
During training, it selects either a batch from GigaSpeech with prob `giga_prob`
or a batch from LibriSpeech with prob `1 - giga_prob`. All utterances within
a batch come from the same dataset.
Using commit `ac84220de91dee10c00e8f4223287f937b1930b6`.
See <https://github.com/k2-fsa/icefall/pull/312>.
The WERs are:
| | test-clean | test-other | comment |
|-------------------------------------|------------|------------|----------------------------------------|
| greedy search (max sym per frame 1) | 2.21 | 5.09 | --epoch 27 --avg 2 --max-duration 600 |
| greedy search (max sym per frame 1) | 2.25 | 5.02 | --epoch 27 --avg 12 --max-duration 600 |
| modified beam search | 2.19 | 5.03 | --epoch 25 --avg 6 --max-duration 600 |
| modified beam search | 2.23 | 4.94 | --epoch 27 --avg 10 --max-duration 600 |
| beam search | 2.16 | 4.95 | --epoch 25 --avg 7 --max-duration 600 |
| fast beam search | 2.21 | 4.96 | --epoch 27 --avg 10 --max-duration 600 |
| fast beam search | 2.19 | 4.97 | --epoch 27 --avg 12 --max-duration 600 |
The training commands are:
```bash
./prepare.sh
./prepare_giga_speech.sh
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
./pruned_transducer_stateless3/train.py \
--world-size 8 \
--num-epochs 30 \
--start-epoch 0 \
--full-libri 1 \
--exp-dir pruned_transducer_stateless3/exp \
--max-duration 300 \
--use-fp16 1 \
--lr-epochs 4 \
--num-workers 2 \
--giga-prob 0.8
```
The tensorboard log can be found at
<https://tensorboard.dev/experiment/gaD34WeYSMCOkzoo3dZXGg/>
(Note: The training process is killed manually after saving `epoch-28.pt`.)
Pretrained models, training logs, decoding logs, and decoding results
are available at
<https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-04-29>
The decoding commands are:
```bash
# greedy search
./pruned_transducer_stateless3/decode.py \
--epoch 27 \
--avg 2 \
--exp-dir ./pruned_transducer_stateless3/exp \
--max-duration 600 \
--decoding-method greedy_search \
--max-sym-per-frame 1
# modified beam search
./pruned_transducer_stateless3/decode.py \
--epoch 25 \
--avg 6 \
--exp-dir ./pruned_transducer_stateless3/exp \
--max-duration 600 \
--decoding-method modified_beam_search \
--max-sym-per-frame 1
# beam search
./pruned_transducer_stateless3/decode.py \
--epoch 25 \
--avg 7 \
--exp-dir ./pruned_transducer_stateless3/exp \
--max-duration 600 \
--decoding-method beam_search \
--max-sym-per-frame 1
# fast beam search
for epoch in 27; do
for avg in 10 12; do
./pruned_transducer_stateless3/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./pruned_transducer_stateless3/exp \
--max-duration 600 \
--decoding-method fast_beam_search \
--max-states 32 \
--beam 8
done
done
```
The following table shows the
[Nbest oracle WER](http://kaldi-asr.org/doc/lattices.html#lattices_operations_oracle)
for fast beam search.
| epoch | avg | num_paths | nbest_scale | test-clean | test-other |
|-------|-----|-----------|-------------|------------|------------|
| 27 | 10 | 50 | 0.5 | 0.91 | 2.74 |
| 27 | 10 | 50 | 0.8 | 0.94 | 2.82 |
| 27 | 10 | 50 | 1.0 | 1.06 | 2.88 |
| 27 | 10 | 100 | 0.5 | 0.82 | 2.58 |
| 27 | 10 | 100 | 0.8 | 0.92 | 2.65 |
| 27 | 10 | 100 | 1.0 | 0.95 | 2.77 |
| 27 | 10 | 200 | 0.5 | 0.81 | 2.50 |
| 27 | 10 | 200 | 0.8 | 0.85 | 2.56 |
| 27 | 10 | 200 | 1.0 | 0.91 | 2.64 |
| 27 | 10 | 400 | 0.5 | N/A | N/A |
| 27 | 10 | 400 | 0.8 | 0.81 | 2.49 |
| 27 | 10 | 400 | 1.0 | 0.85 | 2.54 |
The Nbest oracle WER is computed using the following steps:
- 1. Use `fast_beam_search` to produce a lattice.
- 2. Extract `N` paths from the lattice using [k2.random_path](https://k2-fsa.github.io/k2/python_api/api.html#random-paths)
- 3. [Unique](https://k2-fsa.github.io/k2/python_api/api.html#unique) paths so that each path
has a distinct sequence of tokens
- 4. Compute the edit distance of each path with the ground truth
- 5. The path with the lowest edit distance is the final output and is used to
compute the WER
The command to compute the Nbest oracle WER is:
```bash
for epoch in 27; do
for avg in 10 ; do
for num_paths in 50 100 200 400; do
for nbest_scale in 0.5 0.8 1.0; do
./pruned_transducer_stateless3/decode.py \
--epoch $epoch \
--avg $avg \
--exp-dir ./pruned_transducer_stateless3/exp \
--max-duration 600 \
--decoding-method fast_beam_search_nbest_oracle \
--num-paths $num_paths \
--max-states 32 \
--beam 8 \
--nbest-scale $nbest_scale
done
done
done
done
```
### LibriSpeech BPE training results (Pruned Transducer 2)
[pruned_transducer_stateless2](./pruned_transducer_stateless2)
@ -33,6 +185,10 @@ and:
The Tensorboard log is at <https://tensorboard.dev/experiment/Xoz0oABMTWewo1slNFXkyA> (apologies, log starts
only from epoch 3).
The pretrained models, training logs, decoding logs, and decoding results
can be found at
<https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless2-2022-04-29>
#### Training on train-clean-100:

92
egs/librispeech/ASR/local/compute_fbank_gigaspeech_dev_test.py Normal file
View File

@ -0,0 +1,92 @@
#!/usr/bin/env python3
# Copyright 2021 Johns Hopkins University (Piotr Żelasko)
# Copyright 2021 Xiaomi Corp. (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.
import logging
from pathlib import Path
import torch
from lhotse import (
CutSet,
KaldifeatFbank,
KaldifeatFbankConfig,
)
# Torch's multithreaded behavior needs to be disabled or
# it wastes a lot of CPU and slow things down.
# Do this outside of main() in case it needs to take effect
# even when we are not invoking the main (e.g. when spawning subprocesses).
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
def compute_fbank_gigaspeech_dev_test():
in_out_dir = Path("data/fbank")
# number of workers in dataloader
num_workers = 20
# number of seconds in a batch
batch_duration = 600
subsets = ("DEV", "TEST")
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device))
logging.info(f"device: {device}")
for partition in subsets:
cuts_path = in_out_dir / f"cuts_{partition}.jsonl.gz"
if cuts_path.is_file():
logging.info(f"{cuts_path} exists - skipping")
continue
raw_cuts_path = in_out_dir / f"cuts_{partition}_raw.jsonl.gz"
logging.info(f"Loading {raw_cuts_path}")
cut_set = CutSet.from_file(raw_cuts_path)
logging.info("Computing features")
cut_set = cut_set.compute_and_store_features_batch(
extractor=extractor,
storage_path=f"{in_out_dir}/feats_{partition}",
num_workers=num_workers,
batch_duration=batch_duration,
)
cut_set = cut_set.trim_to_supervisions(
keep_overlapping=False, min_duration=None
)
logging.info(f"Saving to {cuts_path}")
cut_set.to_file(cuts_path)
logging.info(f"Saved to {cuts_path}")
def main():
formatter = (
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
)
logging.basicConfig(format=formatter, level=logging.INFO)
compute_fbank_gigaspeech_dev_test()
if __name__ == "__main__":
main()

169
egs/librispeech/ASR/local/compute_fbank_gigaspeech_splits.py Normal file
View File

@ -0,0 +1,169 @@
#!/usr/bin/env python3
# Copyright 2021 Johns Hopkins University (Piotr Żelasko)
# Copyright 2021 Xiaomi Corp. (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.
import argparse
import logging
import os
from datetime import datetime
from pathlib import Path
import torch
from lhotse import CutSet, KaldifeatFbank, KaldifeatFbankConfig
# Torch's multithreaded behavior needs to be disabled or
# it wastes a lot of CPU and slow things down.
# Do this outside of main() in case it needs to take effect
# even when we are not invoking the main (e.g. when spawning subprocesses).
torch.set_num_threads(1)
torch.set_num_interop_threads(1)
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--num-workers",
type=int,
default=20,
help="Number of dataloading workers used for reading the audio.",
)
parser.add_argument(
"--batch-duration",
type=float,
default=600.0,
help="The maximum number of audio seconds in a batch."
"Determines batch size dynamically.",
)
parser.add_argument(
"--num-splits",
type=int,
required=True,
help="The number of splits of the XL subset",
)
parser.add_argument(
"--start",
type=int,
default=0,
help="Process pieces starting from this number (inclusive).",
)
parser.add_argument(
"--stop",
type=int,
default=-1,
help="Stop processing pieces until this number (exclusive).",
)
return parser
def compute_fbank_gigaspeech_splits(args):
num_splits = args.num_splits
output_dir = f"data/fbank/XL_split_{num_splits}"
output_dir = Path(output_dir)
assert output_dir.exists(), f"{output_dir} does not exist!"
num_digits = len(str(num_splits))
start = args.start
stop = args.stop
if stop < start:
stop = num_splits
stop = min(stop, num_splits)
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
extractor = KaldifeatFbank(KaldifeatFbankConfig(device=device))
logging.info(f"device: {device}")
num_digits = 8 # num_digits is fixed by lhotse split-lazy
for i in range(start, stop):
idx = f"{i + 1}".zfill(num_digits)
logging.info(f"Processing {idx}/{num_splits}")
cuts_path = output_dir / f"cuts_XL.{idx}.jsonl.gz"
if cuts_path.is_file():
logging.info(f"{cuts_path} exists - skipping")
continue
raw_cuts_path = output_dir / f"cuts_XL_raw.{idx}.jsonl.gz"
if not raw_cuts_path.is_file():
logging.info(f"{raw_cuts_path} does not exist - skipping it")
continue
logging.info(f"Loading {raw_cuts_path}")
cut_set = CutSet.from_file(raw_cuts_path)
logging.info("Computing features")
if (output_dir / f"feats_XL_{idx}.lca").exists():
logging.info(f"Removing {output_dir}/feats_XL_{idx}.lca")
os.remove(output_dir / f"feats_XL_{idx}.lca")
cut_set = cut_set.compute_and_store_features_batch(
extractor=extractor,
storage_path=f"{output_dir}/feats_XL_{idx}",
num_workers=args.num_workers,
batch_duration=args.batch_duration,
)
logging.info("About to split cuts into smaller chunks.")
cut_set = cut_set.trim_to_supervisions(
keep_overlapping=False, min_duration=None
)
logging.info(f"Saving to {cuts_path}")
cut_set.to_file(cuts_path)
logging.info(f"Saved to {cuts_path}")
def main():
now = datetime.now()
date_time = now.strftime("%Y-%m-%d-%H-%M-%S")
log_filename = "log-compute_fbank_gigaspeech_splits"
formatter = (
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
)
log_filename = f"{log_filename}-{date_time}"
logging.basicConfig(
filename=log_filename,
format=formatter,
level=logging.INFO,
filemode="w",
)
console = logging.StreamHandler()
console.setLevel(logging.INFO)
console.setFormatter(logging.Formatter(formatter))
logging.getLogger("").addHandler(console)
parser = get_parser()
args = parser.parse_args()
logging.info(vars(args))
compute_fbank_gigaspeech_splits(args)
if __name__ == "__main__":
main()

29
egs/librispeech/ASR/local/preprocess_gigaspeech.py
View File

@ -91,21 +91,20 @@ def preprocess_giga_speech():
)
# Run data augmentation that needs to be done in the
# time domain.
if partition not in ["DEV", "TEST"]:
logging.info(
f"Speed perturb for {partition} with factors 0.9 and 1.1 "
"(Perturbing may take 8 minutes and saving may take 20 minutes)"
)
cut_set = (
cut_set
+ cut_set.perturb_speed(0.9)
+ cut_set.perturb_speed(1.1)
)
logging.info("About to split cuts into smaller chunks.")
cut_set = cut_set.trim_to_supervisions(
keep_overlapping=False, min_duration=None
)
# if partition not in ["DEV", "TEST"]:
# logging.info(
# f"Speed perturb for {partition} with factors 0.9 and 1.1 "
# "(Perturbing may take 8 minutes and saving may"
# " take 20 minutes)"
# )
# cut_set = (
# cut_set
# + cut_set.perturb_speed(0.9)
# + cut_set.perturb_speed(1.1)
# )
#
# Note: No need to perturb the training subset as not all of the
# data is going to be used in the training.
logging.info(f"Saving to {raw_cuts_path}")
cut_set.to_file(raw_cuts_path)

51
egs/librispeech/ASR/local/test_load_XL_split.py Executable file
View File

@ -0,0 +1,51 @@
#!/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 file can be used to check if any split is corrupted.
"""
import glob
import re
import lhotse
def main():
d = "data/fbank/XL_split_2000"
filenames = list(glob.glob(f"{d}/cuts_XL.*.jsonl.gz"))
pattern = re.compile(r"cuts_XL.([0-9]+).jsonl.gz")
idx_filenames = [(int(pattern.search(c).group(1)), c) for c in filenames]
idx_filenames = sorted(idx_filenames, key=lambda x: x[0])
print(f"Loading {len(idx_filenames)} splits")
s = 0
for i, f in idx_filenames:
cuts = lhotse.load_manifest_lazy(f)
print(i, "filename", f)
for i, c in enumerate(cuts):
s += c.features.load().shape[0]
if i > 5:
break
if __name__ == "__main__":
main()

94
egs/librispeech/ASR/local/validate_manifest.py Executable file
View File

@ -0,0 +1,94 @@
#!/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 the following assumptions of the generated manifest:
- Single supervision per cut
- Supervision time bounds are within cut time bounds
We will add more checks later if needed.
Usage example:
python3 ./local/validate_manifest.py \
./data/fbank/cuts_train-clean-100.json.gz
"""
import argparse
import logging
from pathlib import Path
from lhotse import load_manifest, CutSet
from lhotse.cut import Cut
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"manifest",
type=Path,
help="Path to the manifest file",
)
return parser.parse_args()
def validate_one_supervision_per_cut(c: Cut):
if len(c.supervisions) != 1:
raise ValueError(f"{c.id} has {len(c.supervisions)} supervisions")
def validate_supervision_and_cut_time_bounds(c: Cut):
s = c.supervisions[0]
if s.start < c.start:
raise ValueError(
f"{c.id}: Supervision start time {s.start} is less "
f"than cut start time {c.start}"
)
if s.end > c.end:
raise ValueError(
f"{c.id}: Supervision end time {s.end} is larger "
f"than cut end time {c.end}"
)
def main():
args = get_args()
manifest = args.manifest
logging.info(f"Validating {manifest}")
assert manifest.is_file(), f"{manifest} does not exist"
cut_set = load_manifest(manifest)
assert isinstance(cut_set, CutSet)
for c in cut_set:
validate_one_supervision_per_cut(c)
validate_supervision_and_cut_time_bounds(c)
if __name__ == "__main__":
formatter = (
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
)
logging.basicConfig(format=formatter, level=logging.INFO)
main()

18
egs/librispeech/ASR/prepare.sh
View File

@ -118,6 +118,24 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
./local/compute_fbank_librispeech.py
touch data/fbank/.librispeech.done
fi
if [ ! -e data/fbank/.librispeech-validated.done ]; then
log "Validating data/fbank for LibriSpeech"
parts=(
train-clean-100
train-clean-360
train-other-500
test-clean
test-other
dev-clean
dev-other
)
for part in ${parts[@]}; do
python3 ./local/validate_manifest.py \
data/fbank/cuts_${part}.json.gz
done
touch data/fbank/.librispeech-validated.done
fi
fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then

33
egs/librispeech/ASR/prepare_giga_speech.sh
View File

@ -24,6 +24,15 @@ stop_stage=100
# DEV 12 hours
# Test 40 hours
# Split XL subset to this number of pieces
# This is to avoid OOM during feature extraction.
num_splits=2000
# We use lazy split from lhotse.
# The XL subset (10k hours) contains 37956 cuts without speed perturbing.
# We want to split it into 2000 splits, so each split
# contains about 37956 / 2000 = 19 cuts. As a result, there will be 1998 splits.
chunk_size=19 # number of cuts in each split. The last split may contain fewer cuts.
dl_dir=$PWD/download
. shared/parse_options.sh || exit 1
@ -107,3 +116,27 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
touch data/fbank/.preprocess_complete
fi
fi
if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
log "Stage 3: Compute features for DEV and TEST subsets of GigaSpeech (may take 2 minutes)"
python3 ./local/compute_fbank_gigaspeech_dev_test.py
fi
if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
log "Stage 4: Split XL subset into ${num_splits} pieces"
split_dir=data/fbank/XL_split_${num_splits}
if [ ! -f $split_dir/.split_completed ]; then
lhotse split-lazy ./data/fbank/cuts_XL_raw.jsonl.gz $split_dir $chunk_size
touch $split_dir/.split_completed
fi
fi
if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
log "Stage 5: Compute features for XL"
# Note: The script supports --start and --stop options.
# You can use several machines to compute the features in parallel.
python3 ./local/compute_fbank_gigaspeech_splits.py \
--num-workers $nj \
--batch-duration 600 \
--num-splits $num_splits
fi

13
egs/librispeech/ASR/pruned_transducer_stateless/beam_search.py
View File

@ -15,6 +15,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional
@ -565,8 +566,10 @@ def modified_beam_search(
for i in range(batch_size):
topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam)
topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
topk_token_indexes = (topk_indexes % vocab_size).tolist()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
topk_token_indexes = (topk_indexes % vocab_size).tolist()
for k in range(len(topk_hyp_indexes)):
hyp_idx = topk_hyp_indexes[k]
@ -679,8 +682,10 @@ def _deprecated_modified_beam_search(
topk_hyp_indexes = topk_indexes // logits.size(-1)
topk_token_indexes = topk_indexes % logits.size(-1)
topk_hyp_indexes = topk_hyp_indexes.tolist()
topk_token_indexes = topk_token_indexes.tolist()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
topk_hyp_indexes = topk_hyp_indexes.tolist()
topk_token_indexes = topk_token_indexes.tolist()
for i in range(len(topk_hyp_indexes)):
hyp = A[topk_hyp_indexes[i]]

2
egs/librispeech/ASR/pruned_transducer_stateless/decode.py
View File

@ -174,7 +174,7 @@ def get_parser():
"--beam-size",
type=int,
default=4,
help="""An interger indicating how many candidates we will keep for each
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.""",
)

191
egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py
View File

@ -14,6 +14,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional
@ -21,11 +22,11 @@ import k2
import torch
from model import Transducer
from icefall.decode import one_best_decoding
from icefall.decode import Nbest, one_best_decoding
from icefall.utils import get_texts
def fast_beam_search(
def fast_beam_search_one_best(
model: Transducer,
decoding_graph: k2.Fsa,
encoder_out: torch.Tensor,
@ -36,6 +37,9 @@ def fast_beam_search(
) -> 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`.
@ -55,6 +59,148 @@ def fast_beam_search(
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
@ -103,9 +249,7 @@ def fast_beam_search(
decoding_streams.terminate_and_flush_to_streams()
lattice = decoding_streams.format_output(encoder_out_lens.tolist())
best_path = one_best_decoding(lattice)
hyps = get_texts(best_path)
return hyps
return lattice
def greedy_search(
@ -130,8 +274,9 @@ def greedy_search(
blank_id = model.decoder.blank_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
@ -170,7 +315,7 @@ def greedy_search(
# logits is (1, 1, 1, vocab_size)
y = logits.argmax().item()
if y != blank_id:
if y not in (blank_id, unk_id):
hyp.append(y)
decoder_input = torch.tensor(
[hyp[-context_size:]], device=device
@ -205,12 +350,13 @@ def greedy_search_batch(
assert encoder_out.ndim == 3
assert encoder_out.size(0) >= 1, encoder_out.size(0)
device = model.device
device = next(model.parameters()).device
batch_size = encoder_out.size(0)
T = encoder_out.size(1)
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)]
@ -239,7 +385,7 @@ def greedy_search_batch(
y = logits.argmax(dim=1).tolist()
emitted = False
for i, v in enumerate(y):
if v != blank_id:
if v not in (blank_id, unk_id):
hyps[i].append(v)
emitted = True
if emitted:
@ -432,8 +578,9 @@ def modified_beam_search(
T = encoder_out.size(1)
blank_id = model.decoder.blank_id
unk_id = getattr(model, "unk_id", blank_id)
context_size = model.decoder.context_size
device = model.device
device = next(model.parameters()).device
B = [HypothesisList() for _ in range(batch_size)]
for i in range(batch_size):
B[i].add(
@ -503,8 +650,10 @@ def modified_beam_search(
for i in range(batch_size):
topk_log_probs, topk_indexes = ragged_log_probs[i].topk(beam)
topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
topk_token_indexes = (topk_indexes % vocab_size).tolist()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
topk_hyp_indexes = (topk_indexes // vocab_size).tolist()
topk_token_indexes = (topk_indexes % vocab_size).tolist()
for k in range(len(topk_hyp_indexes)):
hyp_idx = topk_hyp_indexes[k]
@ -512,7 +661,7 @@ def modified_beam_search(
new_ys = hyp.ys[:]
new_token = topk_token_indexes[k]
if new_token != blank_id:
if new_token not in (blank_id, unk_id):
new_ys.append(new_token)
new_log_prob = topk_log_probs[k]
@ -553,9 +702,10 @@ 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 = 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)
@ -614,14 +764,16 @@ def _deprecated_modified_beam_search(
topk_hyp_indexes = topk_indexes // logits.size(-1)
topk_token_indexes = topk_indexes % logits.size(-1)
topk_hyp_indexes = topk_hyp_indexes.tolist()
topk_token_indexes = topk_token_indexes.tolist()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
topk_hyp_indexes = topk_hyp_indexes.tolist()
topk_token_indexes = topk_token_indexes.tolist()
for i in range(len(topk_hyp_indexes)):
hyp = A[topk_hyp_indexes[i]]
new_ys = hyp.ys[:]
new_token = topk_token_indexes[i]
if new_token != blank_id:
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)
@ -658,9 +810,10 @@ 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 = 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,
@ -743,7 +896,7 @@ 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:
if i in (blank_id, unk_id):
continue
new_ys = y_star.ys + [i]
new_log_prob = y_star.log_prob + v

4
egs/librispeech/ASR/pruned_transducer_stateless2/decode.py
View File

@ -69,7 +69,7 @@ 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,
@ -252,7 +252,7 @@ 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,

306
egs/librispeech/ASR/pruned_transducer_stateless2/pretrained.py Executable file
View File

@ -0,0 +1,306 @@
#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Usage:
(1) greedy search
./pruned_transducer_stateless2/pretrained.py \
--checkpoint ./pruned_transducer_stateless2/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav \
(1) beam search
./pruned_transducer_stateless2/pretrained.py \
--checkpoint ./pruned_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 \
You can also use `./pruned_transducer_stateless2/exp/epoch-xx.pt`.
Note: ./pruned_transducer_stateless2/exp/pretrained.pt is generated by
./pruned_transducer_stateless2/export.py
"""
import argparse
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,
)
from torch.nn.utils.rnn import pad_sequence
from train import get_params, get_transducer_model
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--checkpoint",
type=str,
required=True,
help="Path to the checkpoint. "
"The checkpoint is assumed to be saved by "
"icefall.checkpoint.save_checkpoint().",
)
parser.add_argument(
"--bpe-model",
type=str,
help="""Path to bpe.model.
Used only when method is ctc-decoding.
""",
)
parser.add_argument(
"--method",
type=str,
default="greedy_search",
help="""Possible values are:
- greedy_search
- beam_search
- modified_beam_search
- fast_beam_search
""",
)
parser.add_argument(
"sound_files",
type=str,
nargs="+",
help="The input sound file(s) to transcribe. "
"Supported formats are those supported by torchaudio.load(). "
"For example, wav and flac are supported. "
"The sample rate has to be 16kHz.",
)
parser.add_argument(
"--sample-rate",
type=int,
default=16000,
help="The sample rate of the input sound file",
)
parser.add_argument(
"--beam-size",
type=int,
default=4,
help="Used only when --method is beam_search and modified_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
--method is greedy_search.
""",
)
return parser
def read_sound_files(
filenames: List[str], expected_sample_rate: float
) -> List[torch.Tensor]:
"""Read a list of sound files into a list 1-D float32 torch tensors.
Args:
filenames:
A list of sound filenames.
expected_sample_rate:
The expected sample rate of the sound files.
Returns:
Return a list of 1-D float32 torch tensors.
"""
ans = []
for f in filenames:
wave, sample_rate = torchaudio.load(f)
assert sample_rate == expected_sample_rate, (
f"expected sample rate: {expected_sample_rate}. "
f"Given: {sample_rate}"
)
# We use only the first channel
ans.append(wave[0])
return ans
@torch.no_grad()
def main():
parser = get_parser()
args = parser.parse_args()
params = get_params()
params.update(vars(args))
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
logging.info(f"{params}")
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
logging.info(f"device: {device}")
logging.info("Creating 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}")
checkpoint = torch.load(args.checkpoint, map_location="cpu")
model.load_state_dict(checkpoint["model"], strict=False)
model.to(device)
model.eval()
model.device = device
logging.info("Constructing Fbank computer")
opts = kaldifeat.FbankOptions()
opts.device = device
opts.frame_opts.dither = 0
opts.frame_opts.snip_edges = False
opts.frame_opts.samp_freq = params.sample_rate
opts.mel_opts.num_bins = params.feature_dim
fbank = kaldifeat.Fbank(opts)
logging.info(f"Reading sound files: {params.sound_files}")
waves = read_sound_files(
filenames=params.sound_files, expected_sample_rate=params.sample_rate
)
waves = [w.to(device) for w in waves]
logging.info("Decoding started")
features = fbank(waves)
feature_lengths = [f.size(0) for f in features]
features = pad_sequence(
features, batch_first=True, padding_value=math.log(1e-10)
)
feature_lengths = torch.tensor(feature_lengths, device=device)
encoder_out, encoder_out_lens = model.encoder(
x=features, x_lens=feature_lengths
)
num_waves = encoder_out.size(0)
hyps = []
msg = f"Using {params.method}"
if params.method == "beam_search":
msg += f" with beam size {params.beam_size}"
logging.info(msg)
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=8.0,
max_contexts=32,
max_states=8,
)
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,
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
else:
for i in range(num_waves):
# fmt: off
encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
# fmt: on
if params.method == "greedy_search":
hyp = greedy_search(
model=model,
encoder_out=encoder_out_i,
max_sym_per_frame=params.max_sym_per_frame,
)
elif params.method == "beam_search":
hyp = beam_search(
model=model,
encoder_out=encoder_out_i,
beam=params.beam_size,
)
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps.append(sp.decode(hyp).split())
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
logging.info(s)
logging.info("Decoding Done")
if __name__ == "__main__":
formatter = (
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
)
logging.basicConfig(format=formatter, level=logging.INFO)
main()

82
egs/librispeech/ASR/pruned_transducer_stateless2/train.py
View File

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

314
egs/librispeech/ASR/pruned_transducer_stateless3/asr_datamodule.py Normal file
View File

@ -0,0 +1,314 @@
# Copyright 2021 Piotr Żelasko
# 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.
import argparse
import logging
from pathlib import Path
from typing import Optional
from lhotse import CutSet, Fbank, FbankConfig
from lhotse.dataset import (
BucketingSampler,
CutMix,
DynamicBucketingSampler,
K2SpeechRecognitionDataset,
SpecAugment,
)
from lhotse.dataset.input_strategies import (
OnTheFlyFeatures,
PrecomputedFeatures,
)
from torch.utils.data import DataLoader
from icefall.utils import str2bool
class AsrDataModule:
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(
"--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 BucketingSampler "
"and DynamicBucketingSampler."
"(you might want to increase it for larger datasets).",
)
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(
"--on-the-fly-num-workers",
type=int,
default=0,
help="The number of workers for on-the-fly feature extraction",
)
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. ",
)
group.add_argument(
"--manifest-dir",
type=Path,
default=Path("data/fbank"),
help="Path to directory with train/valid/test cuts.",
)
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. Used only in dev/test CutSet",
)
def train_dataloaders(
self,
cuts_train: CutSet,
dynamic_bucketing: bool,
on_the_fly_feats: bool,
cuts_musan: Optional[CutSet] = None,
) -> DataLoader:
"""
Args:
cuts_train:
Cuts for training.
cuts_musan:
If not None, it is the cuts for mixing.
dynamic_bucketing:
True to use DynamicBucketingSampler;
False to use BucketingSampler.
on_the_fly_feats:
True to use OnTheFlyFeatures;
False to use PrecomputedFeatures.
"""
transforms = []
if cuts_musan is not None:
logging.info("Enable MUSAN")
transforms.append(
CutMix(
cuts=cuts_musan, prob=0.5, snr=(10, 20), preserve_id=True
)
)
else:
logging.info("Disable MUSAN")
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}"
)
input_transforms.append(
SpecAugment(
time_warp_factor=self.args.spec_aug_time_warp_factor,
num_frame_masks=2,
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,
)
# 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(
extractor=Fbank(FbankConfig(num_mel_bins=80)),
num_workers=self.args.on_the_fly_num_workers,
)
if on_the_fly_feats
else PrecomputedFeatures()
),
input_transforms=input_transforms,
return_cuts=self.args.return_cuts,
)
if dynamic_bucketing:
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 BucketingSampler.")
train_sampler = BucketingSampler(
cuts_train,
max_duration=self.args.max_duration,
shuffle=self.args.shuffle,
num_buckets=self.args.num_buckets,
bucket_method="equal_duration",
drop_last=True,
)
logging.info("About to create train dataloader")
train_dl = DataLoader(
train,
sampler=train_sampler,
batch_size=None,
num_workers=self.args.num_workers,
persistent_workers=False,
)
return train_dl
def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
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

1
egs/librispeech/ASR/pruned_transducer_stateless3/beam_search.py Symbolic link
View File

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

1
egs/librispeech/ASR/pruned_transducer_stateless3/conformer.py Symbolic link
View File

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

644
egs/librispeech/ASR/pruned_transducer_stateless3/decode-giga.py Executable file
View File

@ -0,0 +1,644 @@
#!/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_stateless3/decode-giga.py \
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless3/exp \
--max-duration 100 \
--decoding-method greedy_search
(2) beam search
./pruned_transducer_stateless3/decode-giga.py \
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless3/exp \
--max-duration 100 \
--decoding-method beam_search \
--beam-size 4
(3) modified beam search
./pruned_transducer_stateless3/decode-giga.py \
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless3/exp \
--max-duration 100 \
--decoding-method modified_beam_search \
--beam-size 4
(4) fast beam search
./pruned_transducer_stateless3/decode-giga.py \
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless3/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 AsrDataModule
from beam_search import (
beam_search,
fast_beam_search_nbest_oracle,
fast_beam_search_one_best,
greedy_search,
greedy_search_batch,
modified_beam_search,
)
from gigaspeech import GigaSpeech
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=28,
help="""It specifies the checkpoint to use for decoding.
Note: Epoch counts from 0.
You can specify --avg to use more checkpoints for model averaging.""",
)
parser.add_argument(
"--iter",
type=int,
default=0,
help="""If positive, --epoch is ignored and it
will use the checkpoint exp_dir/checkpoint-iter.pt.
You can specify --avg to use more checkpoints for model averaging.
""",
)
parser.add_argument(
"--avg",
type=int,
default=15,
help="Number of checkpoints to average. Automatically select "
"consecutive checkpoints before the checkpoint specified by "
"'--epoch' and '--iter'",
)
parser.add_argument(
"--exp-dir",
type=str,
default="pruned_transducer_stateless3/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
- fast_beam_search_nbest_oracle
""",
)
parser.add_argument(
"--beam-size",
type=int,
default=4,
help="""An integer indicating how many candidates we will keep for each
frame. Used only when --decoding-method is beam_search or
modified_beam_search.""",
)
parser.add_argument(
"--beam",
type=float,
default=4,
help="""A floating point value to calculate the cutoff score during beam
search (i.e., `cutoff = max-score - beam`), which is the same as the
`beam` in Kaldi.
Used only when --decoding-method is
fast_beam_search or fast_beam_search_nbest_oracle""",
)
parser.add_argument(
"--max-contexts",
type=int,
default=4,
help="""Used only when --decoding-method is
fast_beam_search or fast_beam_search_nbest_oracle""",
)
parser.add_argument(
"--max-states",
type=int,
default=8,
help="""Used only when --decoding-method is
fast_beam_search or fast_beam_search_nbest_oracle""",
)
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""",
)
parser.add_argument(
"--num-paths",
type=int,
default=100,
help="""Number of paths for computed nbest oracle WER
when the decoding method is fast_beam_search_nbest_oracle.
""",
)
parser.add_argument(
"--nbest-scale",
type=float,
default=0.5,
help="""Scale applied to lattice scores when computing nbest paths.
Used only when the decoding_method is fast_beam_search_nbest_oracle.
""",
)
return parser
def post_processing(
results: List[Tuple[List[List[str]], List[List[str]]]],
) -> List[Tuple[List[List[str]], List[List[str]]]]:
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 or fast_beam_search_nbest_oracle.
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 == "fast_beam_search_nbest_oracle":
hyp_tokens = fast_beam_search_nbest_oracle(
model=model,
decoding_graph=decoding_graph,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
beam=params.beam,
max_contexts=params.max_contexts,
max_states=params.max_states,
num_paths=params.num_paths,
ref_texts=sp.encode(supervisions["text"]),
nbest_scale=params.nbest_scale,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
elif (
params.decoding_method == "greedy_search"
and params.max_sym_per_frame == 1
):
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
elif params.decoding_method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
encoder_out=encoder_out,
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
else:
batch_size = encoder_out.size(0)
for i in range(batch_size):
# fmt: off
encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
# fmt: on
if params.decoding_method == "greedy_search":
hyp = greedy_search(
model=model,
encoder_out=encoder_out_i,
max_sym_per_frame=params.max_sym_per_frame,
)
elif params.decoding_method == "beam_search":
hyp = beam_search(
model=model,
encoder_out=encoder_out_i,
beam=params.beam_size,
)
else:
raise ValueError(
f"Unsupported decoding method: {params.decoding_method}"
)
hyps.append(sp.decode(hyp).split())
if params.decoding_method == "greedy_search":
return {"greedy_search": hyps}
elif params.decoding_method == "fast_beam_search":
return {
(
f"beam_{params.beam}_"
f"max_contexts_{params.max_contexts}_"
f"max_states_{params.max_states}"
): hyps
}
elif params.decoding_method == "fast_beam_search_nbest_oracle":
return {
(
f"beam_{params.beam}_"
f"max_contexts_{params.max_contexts}_"
f"max_states_{params.max_states}_"
f"num_paths_{params.num_paths}_"
f"nbest_scale_{params.nbest_scale}"
): hyps
}
else:
return {f"beam_size_{params.beam_size}": hyps}
def decode_dataset(
dl: torch.utils.data.DataLoader,
params: AttributeDict,
model: nn.Module,
sp: spm.SentencePieceProcessor,
decoding_graph: Optional[k2.Fsa] = None,
) -> Dict[str, List[Tuple[List[str], List[str]]]]:
"""Decode dataset.
Args:
dl:
PyTorch's dataloader containing the dataset to decode.
params:
It is returned by :func:`get_params`.
model:
The neural model.
sp:
The BPE model.
decoding_graph:
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
only when --decoding_method is fast_beam_search.
Returns:
Return a dict, whose key may be "greedy_search" if greedy search
is used, or it may be "beam_7" if beam size of 7 is used.
Its value is a list of tuples. Each tuple contains two elements:
The first is the reference transcript, and the second is the
predicted result.
"""
num_cuts = 0
try:
num_batches = len(dl)
except TypeError:
num_batches = "?"
if params.decoding_method == "greedy_search":
log_interval = 100
else:
log_interval = 2
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
hyps_dict = decode_one_batch(
params=params,
model=model,
sp=sp,
decoding_graph=decoding_graph,
batch=batch,
)
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
results[name].extend(this_batch)
num_cuts += len(texts)
if batch_idx % log_interval == 0:
batch_str = f"{batch_idx}/{num_batches}"
logging.info(
f"batch {batch_str}, cuts processed until now is {num_cuts}"
)
return results
def save_results(
params: AttributeDict,
test_set_name: str,
results_dict: Dict[str, List[Tuple[List[str], List[str]]]],
):
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()
AsrDataModule.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",
"fast_beam_search_nbest_oracle",
"modified_beam_search",
)
params.res_dir = params.exp_dir / "giga" / params.decoding_method
if params.iter > 0:
params.suffix = f"iter-{params.iter}-avg-{params.avg}"
else:
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
if params.decoding_method == "fast_beam_search":
params.suffix += f"-beam-{params.beam}"
params.suffix += f"-max-contexts-{params.max_contexts}"
params.suffix += f"-max-states-{params.max_states}"
elif params.decoding_method == "fast_beam_search_nbest_oracle":
params.suffix += f"-beam-{params.beam}"
params.suffix += f"-max-contexts-{params.max_contexts}"
params.suffix += f"-max-states-{params.max_states}"
params.suffix += f"-num-paths-{params.num_paths}"
params.suffix += f"-nbest-scale-{params.nbest_scale}"
elif "beam_search" in params.decoding_method:
params.suffix += (
f"-{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}"
setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
logging.info("Decoding started")
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
logging.info(f"Device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> and <unk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.unk_id()
params.vocab_size = sp.get_piece_size()
logging.info(params)
logging.info("About to create model")
model = get_transducer_model(params)
if params.iter > 0:
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
: params.avg
]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for"
f" --iter {params.iter}, --avg {params.avg}"
)
elif len(filenames) < params.avg:
raise ValueError(
f"Not enough checkpoints ({len(filenames)}) found for"
f" --iter {params.iter}, --avg {params.avg}"
)
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(average_checkpoints(filenames, device=device))
elif params.avg == 1:
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
else:
start = params.epoch - params.avg + 1
filenames = []
for i in range(start, params.epoch + 1):
if 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
model.unk_id = params.unk_id
# In beam_search.py, we are using model.decoder() and model.joiner(),
# so we have to switch to the branch for the GigaSpeech dataset.
model.decoder = model.decoder_giga
model.joiner = model.joiner_giga
if params.decoding_method in (
"fast_beam_search",
"fast_beam_search_nbest_oracle",
):
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}")
asr_datamodule = AsrDataModule(args)
gigaspeech = GigaSpeech(manifest_dir=args.manifest_dir)
test_cuts = gigaspeech.test_cuts()
dev_cuts = gigaspeech.dev_cuts()
test_dl = asr_datamodule.test_dataloaders(test_cuts)
dev_dl = asr_datamodule.test_dataloaders(dev_cuts)
test_sets = ["test", "dev"]
test_sets_dl = [test_dl, dev_dl]
for test_set, dl in zip(test_sets, test_sets_dl):
results_dict = decode_dataset(
dl=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()

626
egs/librispeech/ASR/pruned_transducer_stateless3/decode.py Executable file
View File

@ -0,0 +1,626 @@
#!/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_stateless3/decode.py \
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless3/exp \
--max-duration 100 \
--decoding-method greedy_search
(2) beam search
./pruned_transducer_stateless3/decode.py \
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless3/exp \
--max-duration 100 \
--decoding-method beam_search \
--beam-size 4
(3) modified beam search
./pruned_transducer_stateless3/decode.py \
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless3/exp \
--max-duration 100 \
--decoding-method modified_beam_search \
--beam-size 4
(4) fast beam search
./pruned_transducer_stateless3/decode.py \
--epoch 28 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless3/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 AsrDataModule
from beam_search import (
beam_search,
fast_beam_search_nbest_oracle,
fast_beam_search_one_best,
greedy_search,
greedy_search_batch,
modified_beam_search,
)
from librispeech import LibriSpeech
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=28,
help="""It specifies the checkpoint to use for decoding.
Note: Epoch counts from 0.
You can specify --avg to use more checkpoints for model averaging.""",
)
parser.add_argument(
"--iter",
type=int,
default=0,
help="""If positive, --epoch is ignored and it
will use the checkpoint exp_dir/checkpoint-iter.pt.
You can specify --avg to use more checkpoints for model averaging.
""",
)
parser.add_argument(
"--avg",
type=int,
default=15,
help="Number of checkpoints to average. Automatically select "
"consecutive checkpoints before the checkpoint specified by "
"'--epoch' and '--iter'",
)
parser.add_argument(
"--exp-dir",
type=str,
default="pruned_transducer_stateless3/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
- fast_beam_search_nbest_oracle
""",
)
parser.add_argument(
"--beam-size",
type=int,
default=4,
help="""An integer indicating how many candidates we will keep for each
frame. Used only when --decoding-method is beam_search or
modified_beam_search.""",
)
parser.add_argument(
"--beam",
type=float,
default=4,
help="""A floating point value to calculate the cutoff score during beam
search (i.e., `cutoff = max-score - beam`), which is the same as the
`beam` in Kaldi.
Used only when --decoding-method is
fast_beam_search or fast_beam_search_nbest_oracle""",
)
parser.add_argument(
"--max-contexts",
type=int,
default=4,
help="""Used only when --decoding-method is
fast_beam_search or fast_beam_search_nbest_oracle""",
)
parser.add_argument(
"--max-states",
type=int,
default=8,
help="""Used only when --decoding-method is
fast_beam_search or fast_beam_search_nbest_oracle""",
)
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""",
)
parser.add_argument(
"--num-paths",
type=int,
default=100,
help="""Number of paths for computed nbest oracle WER
when the decoding method is fast_beam_search_nbest_oracle.
""",
)
parser.add_argument(
"--nbest-scale",
type=float,
default=0.5,
help="""Scale applied to lattice scores when computing nbest paths.
Used only when the decoding_method is fast_beam_search_nbest_oracle.
""",
)
return parser
def 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 or fast_beam_search_nbest_oracle.
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 == "fast_beam_search_nbest_oracle":
hyp_tokens = fast_beam_search_nbest_oracle(
model=model,
decoding_graph=decoding_graph,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
beam=params.beam,
max_contexts=params.max_contexts,
max_states=params.max_states,
num_paths=params.num_paths,
ref_texts=sp.encode(supervisions["text"]),
nbest_scale=params.nbest_scale,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
elif (
params.decoding_method == "greedy_search"
and params.max_sym_per_frame == 1
):
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
elif params.decoding_method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
encoder_out=encoder_out,
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
else:
batch_size = encoder_out.size(0)
for i in range(batch_size):
# fmt: off
encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
# fmt: on
if params.decoding_method == "greedy_search":
hyp = greedy_search(
model=model,
encoder_out=encoder_out_i,
max_sym_per_frame=params.max_sym_per_frame,
)
elif params.decoding_method == "beam_search":
hyp = beam_search(
model=model,
encoder_out=encoder_out_i,
beam=params.beam_size,
)
else:
raise ValueError(
f"Unsupported decoding method: {params.decoding_method}"
)
hyps.append(sp.decode(hyp).split())
if params.decoding_method == "greedy_search":
return {"greedy_search": hyps}
elif params.decoding_method == "fast_beam_search":
return {
(
f"beam_{params.beam}_"
f"max_contexts_{params.max_contexts}_"
f"max_states_{params.max_states}"
): hyps
}
elif params.decoding_method == "fast_beam_search_nbest_oracle":
return {
(
f"beam_{params.beam}_"
f"max_contexts_{params.max_contexts}_"
f"max_states_{params.max_states}_"
f"num_paths_{params.num_paths}_"
f"nbest_scale_{params.nbest_scale}"
): hyps
}
else:
return {f"beam_size_{params.beam_size}": hyps}
def decode_dataset(
dl: torch.utils.data.DataLoader,
params: AttributeDict,
model: nn.Module,
sp: spm.SentencePieceProcessor,
decoding_graph: Optional[k2.Fsa] = None,
) -> Dict[str, List[Tuple[List[str], List[str]]]]:
"""Decode dataset.
Args:
dl:
PyTorch's dataloader containing the dataset to decode.
params:
It is returned by :func:`get_params`.
model:
The neural model.
sp:
The BPE model.
decoding_graph:
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
only when --decoding_method is fast_beam_search.
Returns:
Return a dict, whose key may be "greedy_search" if greedy search
is used, or it may be "beam_7" if beam size of 7 is used.
Its value is a list of tuples. Each tuple contains two elements:
The first is the reference transcript, and the second is the
predicted result.
"""
num_cuts = 0
try:
num_batches = len(dl)
except TypeError:
num_batches = "?"
if params.decoding_method == "greedy_search":
log_interval = 100
else:
log_interval = 2
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
hyps_dict = decode_one_batch(
params=params,
model=model,
sp=sp,
decoding_graph=decoding_graph,
batch=batch,
)
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
results[name].extend(this_batch)
num_cuts += len(texts)
if batch_idx % log_interval == 0:
batch_str = f"{batch_idx}/{num_batches}"
logging.info(
f"batch {batch_str}, cuts processed until now is {num_cuts}"
)
return results
def save_results(
params: AttributeDict,
test_set_name: str,
results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
):
test_set_wers = dict()
for key, results in results_dict.items():
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
# The following prints out WERs, per-word error statistics and aligned
# ref/hyp pairs.
errs_filename = (
params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
)
with open(errs_filename, "w") as f:
wer = write_error_stats(
f, f"{test_set_name}-{key}", results, enable_log=True
)
test_set_wers[key] = wer
logging.info("Wrote detailed error stats to {}".format(errs_filename))
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
errs_info = (
params.res_dir
/ f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
)
with open(errs_info, "w") as f:
print("settings\tWER", file=f)
for key, val in test_set_wers:
print("{}\t{}".format(key, val), file=f)
s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
note = "\tbest for {}".format(test_set_name)
for key, val in test_set_wers:
s += "{}\t{}{}\n".format(key, val, note)
note = ""
logging.info(s)
@torch.no_grad()
def main():
parser = get_parser()
AsrDataModule.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",
"fast_beam_search_nbest_oracle",
"modified_beam_search",
)
params.res_dir = params.exp_dir / params.decoding_method
if params.iter > 0:
params.suffix = f"iter-{params.iter}-avg-{params.avg}"
else:
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
if params.decoding_method == "fast_beam_search":
params.suffix += f"-beam-{params.beam}"
params.suffix += f"-max-contexts-{params.max_contexts}"
params.suffix += f"-max-states-{params.max_states}"
elif params.decoding_method == "fast_beam_search_nbest_oracle":
params.suffix += f"-beam-{params.beam}"
params.suffix += f"-max-contexts-{params.max_contexts}"
params.suffix += f"-max-states-{params.max_states}"
params.suffix += f"-num-paths-{params.num_paths}"
params.suffix += f"-nbest-scale-{params.nbest_scale}"
elif "beam_search" in params.decoding_method:
params.suffix += (
f"-{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}"
setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
logging.info("Decoding started")
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
logging.info(f"Device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> and <unk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.unk_id()
params.vocab_size = sp.get_piece_size()
logging.info(params)
logging.info("About to create model")
model = get_transducer_model(params)
if params.iter > 0:
filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
: params.avg
]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for"
f" --iter {params.iter}, --avg {params.avg}"
)
elif len(filenames) < params.avg:
raise ValueError(
f"Not enough checkpoints ({len(filenames)}) found for"
f" --iter {params.iter}, --avg {params.avg}"
)
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(average_checkpoints(filenames, device=device))
elif params.avg == 1:
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
else:
start = params.epoch - params.avg + 1
filenames = []
for i in range(start, params.epoch + 1):
if 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
model.unk_id = params.unk_id
if params.decoding_method in (
"fast_beam_search",
"fast_beam_search_nbest_oracle",
):
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
else:
decoding_graph = None
num_param = sum([p.numel() for p in model.parameters()])
logging.info(f"Number of model parameters: {num_param}")
asr_datamodule = AsrDataModule(args)
librispeech = LibriSpeech(manifest_dir=args.manifest_dir)
test_clean_cuts = librispeech.test_clean_cuts()
test_other_cuts = librispeech.test_other_cuts()
test_clean_dl = asr_datamodule.test_dataloaders(test_clean_cuts)
test_other_dl = asr_datamodule.test_dataloaders(test_other_cuts)
test_sets = ["test-clean", "test-other"]
test_dl = [test_clean_dl, test_other_dl]
for test_set, test_dl in zip(test_sets, test_dl):
results_dict = decode_dataset(
dl=test_dl,
params=params,
model=model,
sp=sp,
decoding_graph=decoding_graph,
)
save_results(
params=params,
test_set_name=test_set,
results_dict=results_dict,
)
logging.info("Done!")
if __name__ == "__main__":
main()

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egs/librispeech/ASR/pruned_transducer_stateless3/decoder.py Symbolic link
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../pruned_transducer_stateless2/decoder.py

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egs/librispeech/ASR/pruned_transducer_stateless3/encoder_interface.py Symbolic link
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../pruned_transducer_stateless2/encoder_interface.py

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#!/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_stateless3/export.py \
--exp-dir ./pruned_transducer_stateless3/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_stateless3/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_stateless3/decode.py \
--exp-dir ./pruned_transducer_stateless3/exp \
--epoch 9999 \
--avg 1 \
--max-duration 600 \
--decoding-method greedy_search \
--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_stateless3/exp",
help="""It specifies the directory where all training related
files, e.g., checkpoints, log, etc, are saved
""",
)
parser.add_argument(
"--bpe-model",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
)
parser.add_argument(
"--jit",
type=str2bool,
default=False,
help="""True to save a model after applying torch.jit.script.
""",
)
parser.add_argument(
"--context-size",
type=int,
default=2,
help="The context size in the decoder. 1 means bigram; "
"2 means tri-gram",
)
return parser
def main():
args = get_parser().parse_args()
args.exp_dir = Path(args.exp_dir)
assert args.jit is False, "Support torchscript will be added later"
params = get_params()
params.update(vars(args))
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
logging.info(f"device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.vocab_size = sp.get_piece_size()
logging.info(params)
logging.info("About to create model")
model = get_transducer_model(params)
model.to(device)
if params.avg == 1:
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
else:
start = params.epoch - params.avg + 1
filenames = []
for i in range(start, params.epoch + 1):
if start >= 0:
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(average_checkpoints(filenames, device=device))
model.eval()
model.to("cpu")
model.eval()
if params.jit:
logging.info("Using torch.jit.script")
model = torch.jit.script(model)
filename = params.exp_dir / "cpu_jit.pt"
model.save(str(filename))
logging.info(f"Saved to {filename}")
else:
logging.info("Not using torch.jit.script")
# Save it using a format so that it can be loaded
# by :func:`load_checkpoint`
filename = params.exp_dir / "pretrained.pt"
torch.save({"model": model.state_dict()}, str(filename))
logging.info(f"Saved to {filename}")
if __name__ == "__main__":
formatter = (
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
)
logging.basicConfig(format=formatter, level=logging.INFO)
main()

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# Copyright 2021 Piotr Żelasko
# 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.
import glob
import logging
import re
from pathlib import Path
import lhotse
from lhotse import CutSet, load_manifest
class GigaSpeech:
def __init__(self, manifest_dir: str):
"""
Args:
manifest_dir:
It is expected to contain the following files::
- XL_split_2000/cuts_XL.*.jsonl.gz
- cuts_L_raw.jsonl.gz
- cuts_M_raw.jsonl.gz
- cuts_S_raw.jsonl.gz
- cuts_XS_raw.jsonl.gz
- cuts_DEV_raw.jsonl.gz
- cuts_TEST_raw.jsonl.gz
"""
self.manifest_dir = Path(manifest_dir)
def train_XL_cuts(self) -> CutSet:
logging.info("About to get train-XL cuts")
filenames = list(
glob.glob(f"{self.manifest_dir}/XL_split_2000/cuts_XL.*.jsonl.gz")
)
pattern = re.compile(r"cuts_XL.([0-9]+).jsonl.gz")
idx_filenames = [
(int(pattern.search(f).group(1)), f) for f in filenames
]
idx_filenames = sorted(idx_filenames, key=lambda x: x[0])
sorted_filenames = [f[1] for f in idx_filenames]
logging.info(f"Loading {len(sorted_filenames)} splits")
return lhotse.combine(
lhotse.load_manifest_lazy(p) for p in sorted_filenames
)
def train_L_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_L_raw.jsonl.gz"
logging.info(f"About to get train-L cuts from {f}")
return CutSet.from_jsonl_lazy(f)
def train_M_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_M_raw.jsonl.gz"
logging.info(f"About to get train-M cuts from {f}")
return CutSet.from_jsonl_lazy(f)
def train_S_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_S_raw.jsonl.gz"
logging.info(f"About to get train-S cuts from {f}")
return CutSet.from_jsonl_lazy(f)
def train_XS_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_XS_raw.jsonl.gz"
logging.info(f"About to get train-XS cuts from {f}")
return CutSet.from_jsonl_lazy(f)
def test_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_TEST.jsonl.gz"
logging.info(f"About to get TEST cuts from {f}")
return load_manifest(f)
def dev_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_DEV.jsonl.gz"
logging.info(f"About to get DEV cuts from {f}")
return load_manifest(f)

1
egs/librispeech/ASR/pruned_transducer_stateless3/gigaspeech_scoring.py Symbolic link
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../../../gigaspeech/ASR/conformer_ctc/gigaspeech_scoring.py

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egs/librispeech/ASR/pruned_transducer_stateless3/joiner.py Symbolic link
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../pruned_transducer_stateless2/joiner.py

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# Copyright 2021 Piotr Żelasko
# 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.
import logging
from pathlib import Path
from lhotse import CutSet, load_manifest
class LibriSpeech:
def __init__(self, manifest_dir: str):
"""
Args:
manifest_dir:
It is expected to contain the following files::
- cuts_dev-clean.json.gz
- cuts_dev-other.json.gz
- cuts_test-clean.json.gz
- cuts_test-other.json.gz
- cuts_train-clean-100.json.gz
- cuts_train-clean-360.json.gz
- cuts_train-other-500.json.gz
"""
self.manifest_dir = Path(manifest_dir)
def train_clean_100_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_train-clean-100.json.gz"
logging.info(f"About to get train-clean-100 cuts from {f}")
return load_manifest(f)
def train_clean_360_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_train-clean-360.json.gz"
logging.info(f"About to get train-clean-360 cuts from {f}")
return load_manifest(f)
def train_other_500_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_train-other-500.json.gz"
logging.info(f"About to get train-other-500 cuts from {f}")
return load_manifest(f)
def test_clean_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_test-clean.json.gz"
logging.info(f"About to get test-clean cuts from {f}")
return load_manifest(f)
def test_other_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_test-other.json.gz"
logging.info(f"About to get test-other cuts from {f}")
return load_manifest(f)
def dev_clean_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_dev-clean.json.gz"
logging.info(f"About to get dev-clean cuts from {f}")
return load_manifest(f)
def dev_other_cuts(self) -> CutSet:
f = self.manifest_dir / "cuts_dev-other.json.gz"
logging.info(f"About to get dev-other cuts from {f}")
return load_manifest(f)

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# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang, Wei Kang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import k2
import torch
import torch.nn as nn
from encoder_interface import EncoderInterface
from scaling import ScaledLinear
from icefall.utils import add_sos
class Transducer(nn.Module):
"""It implements https://arxiv.org/pdf/1211.3711.pdf
"Sequence Transduction with Recurrent Neural Networks"
"""
def __init__(
self,
encoder: EncoderInterface,
decoder: nn.Module,
joiner: nn.Module,
encoder_dim: int,
decoder_dim: int,
joiner_dim: int,
vocab_size: int,
decoder_giga: Optional[nn.Module] = None,
joiner_giga: Optional[nn.Module] = None,
):
"""
Args:
encoder:
It is the transcription network in the paper. Its accepts
two inputs: `x` of (N, T, encoder_dim) and `x_lens` of shape (N,).
It returns two tensors: `logits` of shape (N, T, encoder_dm) and
`logit_lens` of shape (N,).
decoder:
It is the prediction network in the paper. Its input shape
is (N, U) and its output shape is (N, U, decoder_dim).
It should contain one attribute: `blank_id`.
joiner:
It has two inputs with shapes: (N, T, encoder_dim) and
(N, U, decoder_dim). Its output shape is (N, T, U, vocab_size).
Note that its output contains
unnormalized probs, i.e., not processed by log-softmax.
encoder_dim:
Output dimension of the encoder network.
decoder_dim:
Output dimension of the decoder network.
joiner_dim:
Input dimension of the joiner network.
vocab_size:
Output dimension of the joiner network.
decoder_giga:
Optional. The decoder network for the GigaSpeech dataset.
joiner_giga:
Optional. The joiner network for the GigaSpeech dataset.
"""
super().__init__()
assert isinstance(encoder, EncoderInterface), type(encoder)
assert hasattr(decoder, "blank_id")
self.encoder = encoder
self.decoder = decoder
self.joiner = joiner
self.decoder_giga = decoder_giga
self.joiner_giga = joiner_giga
self.simple_am_proj = ScaledLinear(
encoder_dim, vocab_size, initial_speed=0.5
)
self.simple_lm_proj = ScaledLinear(decoder_dim, vocab_size)
if decoder_giga is not None:
self.simple_am_proj_giga = ScaledLinear(
encoder_dim, vocab_size, initial_speed=0.5
)
self.simple_lm_proj_giga = ScaledLinear(decoder_dim, vocab_size)
def forward(
self,
x: torch.Tensor,
x_lens: torch.Tensor,
y: k2.RaggedTensor,
libri: bool = True,
prune_range: int = 5,
am_scale: float = 0.0,
lm_scale: float = 0.0,
warmup: float = 1.0,
) -> torch.Tensor:
"""
Args:
x:
A 3-D tensor of shape (N, T, C).
x_lens:
A 1-D tensor of shape (N,). It contains the number of frames in `x`
before padding.
y:
A ragged tensor with 2 axes [utt][label]. It contains labels of each
utterance.
libri:
True to use the decoder and joiner for the LibriSpeech dataset.
False to use the decoder and joiner for the GigaSpeech dataset.
prune_range:
The prune range for rnnt loss, it means how many symbols(context)
we are considering for each frame to compute the loss.
am_scale:
The scale to smooth the loss with am (output of encoder network)
part
lm_scale:
The scale to smooth the loss with lm (output of predictor network)
part
warmup:
A value warmup >= 0 that determines which modules are active, values
warmup > 1 "are fully warmed up" and all modules will be active.
Returns:
Return the transducer loss.
Note:
Regarding am_scale & lm_scale, it will make the loss-function one of
the form:
lm_scale * lm_probs + am_scale * am_probs +
(1-lm_scale-am_scale) * combined_probs
"""
assert x.ndim == 3, x.shape
assert x_lens.ndim == 1, x_lens.shape
assert y.num_axes == 2, y.num_axes
assert x.size(0) == x_lens.size(0) == y.dim0
encoder_out, encoder_out_lens = self.encoder(x, x_lens, warmup=warmup)
assert torch.all(encoder_out_lens > 0)
if libri:
decoder = self.decoder
simple_lm_proj = self.simple_lm_proj
simple_am_proj = self.simple_am_proj
joiner = self.joiner
else:
decoder = self.decoder_giga
simple_lm_proj = self.simple_lm_proj_giga
simple_am_proj = self.simple_am_proj_giga
joiner = self.joiner_giga
# Now for the decoder, i.e., the prediction network
row_splits = y.shape.row_splits(1)
y_lens = row_splits[1:] - row_splits[:-1]
blank_id = decoder.blank_id
sos_y = add_sos(y, sos_id=blank_id)
# sos_y_padded: [B, S + 1], start with SOS.
sos_y_padded = sos_y.pad(mode="constant", padding_value=blank_id)
# decoder_out: [B, S + 1, decoder_dim]
decoder_out = decoder(sos_y_padded)
# Note: y does not start with SOS
# y_padded : [B, S]
y_padded = y.pad(mode="constant", padding_value=0)
y_padded = y_padded.to(torch.int64)
boundary = torch.zeros(
(x.size(0), 4), dtype=torch.int64, device=x.device
)
boundary[:, 2] = y_lens
boundary[:, 3] = encoder_out_lens
lm = simple_lm_proj(decoder_out)
am = simple_am_proj(encoder_out)
with torch.cuda.amp.autocast(enabled=False):
simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
lm=lm.float(),
am=am.float(),
symbols=y_padded,
termination_symbol=blank_id,
lm_only_scale=lm_scale,
am_only_scale=am_scale,
boundary=boundary,
reduction="sum",
return_grad=True,
)
# ranges : [B, T, prune_range]
ranges = k2.get_rnnt_prune_ranges(
px_grad=px_grad,
py_grad=py_grad,
boundary=boundary,
s_range=prune_range,
)
# am_pruned : [B, T, prune_range, encoder_dim]
# lm_pruned : [B, T, prune_range, decoder_dim]
am_pruned, lm_pruned = k2.do_rnnt_pruning(
am=joiner.encoder_proj(encoder_out),
lm=joiner.decoder_proj(decoder_out),
ranges=ranges,
)
# logits : [B, T, prune_range, vocab_size]
# project_input=False since we applied the decoder's input projections
# prior to do_rnnt_pruning (this is an optimization for speed).
logits = joiner(am_pruned, lm_pruned, project_input=False)
with torch.cuda.amp.autocast(enabled=False):
pruned_loss = k2.rnnt_loss_pruned(
logits=logits.float(),
symbols=y_padded,
ranges=ranges,
termination_symbol=blank_id,
boundary=boundary,
reduction="sum",
)
return (simple_loss, pruned_loss)

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#!/usr/bin/env python3
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Usage:
(1) greedy search
./pruned_transducer_stateless3/pretrained.py \
--checkpoint ./pruned_transducer_stateless3/exp/pretrained.pt \
--bpe-model ./data/lang_bpe_500/bpe.model \
--method greedy_search \
/path/to/foo.wav \
/path/to/bar.wav \
(1) beam search
./pruned_transducer_stateless3/pretrained.py \
--checkpoint ./pruned_transducer_stateless3/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 \
You can also use `./pruned_transducer_stateless3/exp/epoch-xx.pt`.
Note: ./pruned_transducer_stateless3/exp/pretrained.pt is generated by
./pruned_transducer_stateless3/export.py
"""
import argparse
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,
)
from torch.nn.utils.rnn import pad_sequence
from train import get_params, get_transducer_model
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--checkpoint",
type=str,
required=True,
help="Path to the checkpoint. "
"The checkpoint is assumed to be saved by "
"icefall.checkpoint.save_checkpoint().",
)
parser.add_argument(
"--bpe-model",
type=str,
help="""Path to bpe.model.
Used only when method is ctc-decoding.
""",
)
parser.add_argument(
"--method",
type=str,
default="greedy_search",
help="""Possible values are:
- greedy_search
- beam_search
- modified_beam_search
- fast_beam_search
""",
)
parser.add_argument(
"sound_files",
type=str,
nargs="+",
help="The input sound file(s) to transcribe. "
"Supported formats are those supported by torchaudio.load(). "
"For example, wav and flac are supported. "
"The sample rate has to be 16kHz.",
)
parser.add_argument(
"--sample-rate",
type=int,
default=16000,
help="The sample rate of the input sound file",
)
parser.add_argument(
"--beam-size",
type=int,
default=4,
help="Used only when --method is beam_search and modified_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
--method is greedy_search.
""",
)
return parser
def read_sound_files(
filenames: List[str], expected_sample_rate: float
) -> List[torch.Tensor]:
"""Read a list of sound files into a list 1-D float32 torch tensors.
Args:
filenames:
A list of sound filenames.
expected_sample_rate:
The expected sample rate of the sound files.
Returns:
Return a list of 1-D float32 torch tensors.
"""
ans = []
for f in filenames:
wave, sample_rate = torchaudio.load(f)
assert sample_rate == expected_sample_rate, (
f"expected sample rate: {expected_sample_rate}. "
f"Given: {sample_rate}"
)
# We use only the first channel
ans.append(wave[0])
return ans
@torch.no_grad()
def main():
parser = get_parser()
args = parser.parse_args()
params = get_params()
params.update(vars(args))
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
logging.info(f"{params}")
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
logging.info(f"device: {device}")
logging.info("Creating 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}")
checkpoint = torch.load(args.checkpoint, map_location="cpu")
model.load_state_dict(checkpoint["model"], strict=False)
model.to(device)
model.eval()
model.device = device
logging.info("Constructing Fbank computer")
opts = kaldifeat.FbankOptions()
opts.device = device
opts.frame_opts.dither = 0
opts.frame_opts.snip_edges = False
opts.frame_opts.samp_freq = params.sample_rate
opts.mel_opts.num_bins = params.feature_dim
fbank = kaldifeat.Fbank(opts)
logging.info(f"Reading sound files: {params.sound_files}")
waves = read_sound_files(
filenames=params.sound_files, expected_sample_rate=params.sample_rate
)
waves = [w.to(device) for w in waves]
logging.info("Decoding started")
features = fbank(waves)
feature_lengths = [f.size(0) for f in features]
features = pad_sequence(
features, batch_first=True, padding_value=math.log(1e-10)
)
feature_lengths = torch.tensor(feature_lengths, device=device)
encoder_out, encoder_out_lens = model.encoder(
x=features, x_lens=feature_lengths
)
num_waves = encoder_out.size(0)
hyps = []
msg = f"Using {params.method}"
if params.method == "beam_search":
msg += f" with beam size {params.beam_size}"
logging.info(msg)
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=8.0,
max_contexts=32,
max_states=8,
)
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,
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
elif params.method == "greedy_search" and params.max_sym_per_frame == 1:
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
else:
for i in range(num_waves):
# fmt: off
encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
# fmt: on
if params.method == "greedy_search":
hyp = greedy_search(
model=model,
encoder_out=encoder_out_i,
max_sym_per_frame=params.max_sym_per_frame,
)
elif params.method == "beam_search":
hyp = beam_search(
model=model,
encoder_out=encoder_out_i,
beam=params.beam_size,
)
else:
raise ValueError(f"Unsupported method: {params.method}")
hyps.append(sp.decode(hyp).split())
s = "\n"
for filename, hyp in zip(params.sound_files, hyps):
words = " ".join(hyp)
s += f"{filename}:\n{words}\n\n"
logging.info(s)
logging.info("Decoding Done")
if __name__ == "__main__":
formatter = (
"%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
)
logging.basicConfig(format=formatter, level=logging.INFO)
main()

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#!/usr/bin/env python3
#
# Copyright 2021-2022 Xiaomi Corporation (Author: Fangjun Kuang,
# Zengwei Yao)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Usage:
(1) greedy search
./pruned_transducer_stateless4/decode.py \
--epoch 30 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless2/exp \
--max-duration 100 \
--decoding-method greedy_search
(2) beam search
./pruned_transducer_stateless4/decode.py \
--epoch 30 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless2/exp \
--max-duration 100 \
--decoding-method beam_search \
--beam-size 4
(3) modified beam search
./pruned_transducer_stateless4/decode.py \
--epoch 30 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless2/exp \
--max-duration 100 \
--decoding-method modified_beam_search \
--beam-size 4
(4) fast beam search
./pruned_transducer_stateless4/decode.py \
--epoch 30 \
--avg 15 \
--exp-dir ./pruned_transducer_stateless2/exp \
--max-duration 1500 \
--decoding-method fast_beam_search \
--beam 4 \
--max-contexts 4 \
--max-states 8
"""
import argparse
import logging
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import k2
import sentencepiece as spm
import torch
import torch.nn as nn
from asr_datamodule import LibriSpeechAsrDataModule
from beam_search import (
beam_search,
fast_beam_search,
greedy_search,
greedy_search_batch,
modified_beam_search,
)
from train import get_params, get_transducer_model
from icefall.checkpoint import (
average_checkpoints,
average_checkpoints_with_averaged_model,
find_checkpoints,
load_checkpoint,
)
from icefall.utils import (
AttributeDict,
setup_logger,
store_transcripts,
str2bool,
write_error_stats,
)
def get_parser():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
"--epoch",
type=int,
default=30,
help="""It specifies the checkpoint to use for decoding.
Note: Epoch counts from 1.
You can specify --avg to use more checkpoints for model averaging.""",
)
parser.add_argument(
"--iter",
type=int,
default=0,
help="""If positive, --epoch is ignored and it
will use the checkpoint exp_dir/checkpoint-iter.pt.
You can specify --avg to use more checkpoints for model averaging.
""",
)
parser.add_argument(
"--avg",
type=int,
default=15,
help="Number of checkpoints to average. Automatically select "
"consecutive checkpoints before the checkpoint specified by "
"'--epoch' and '--iter'",
)
parser.add_argument(
"--use-averaged-model",
type=str2bool,
default=False,
help="Whether to load averaged model. Currently it only supports "
"using --epoch. If True, it would decode with the averaged model "
"over the epoch range from `epoch-avg` (excluded) to `epoch`."
"Actually only the models with epoch number of `epoch-avg` and "
"`epoch` are loaded for averaging. ",
)
parser.add_argument(
"--exp-dir",
type=str,
default="pruned_transducer_stateless4/exp",
help="The experiment dir",
)
parser.add_argument(
"--bpe-model",
type=str,
default="data/lang_bpe_500/bpe.model",
help="Path to the BPE model",
)
parser.add_argument(
"--decoding-method",
type=str,
default="greedy_search",
help="""Possible values are:
- greedy_search
- beam_search
- modified_beam_search
- fast_beam_search
""",
)
parser.add_argument(
"--beam-size",
type=int,
default=4,
help="""An integer indicating how many candidates we will keep for each
frame. Used only when --decoding-method is beam_search or
modified_beam_search.""",
)
parser.add_argument(
"--beam",
type=float,
default=4,
help="""A floating point value to calculate the cutoff score during beam
search (i.e., `cutoff = max-score - beam`), which is the same as the
`beam` in Kaldi.
Used only when --decoding-method is fast_beam_search""",
)
parser.add_argument(
"--max-contexts",
type=int,
default=4,
help="""Used only when --decoding-method is
fast_beam_search""",
)
parser.add_argument(
"--max-states",
type=int,
default=8,
help="""Used only when --decoding-method is
fast_beam_search""",
)
parser.add_argument(
"--context-size",
type=int,
default=2,
help="The context size in the decoder. 1 means bigram; "
"2 means tri-gram",
)
parser.add_argument(
"--max-sym-per-frame",
type=int,
default=1,
help="""Maximum number of symbols per frame.
Used only when --decoding_method is greedy_search""",
)
return parser
def decode_one_batch(
params: AttributeDict,
model: nn.Module,
sp: spm.SentencePieceProcessor,
batch: dict,
decoding_graph: Optional[k2.Fsa] = None,
) -> Dict[str, List[List[str]]]:
"""Decode one batch and return the result in a dict. The dict has the
following format:
- key: It indicates the setting used for decoding. For example,
if greedy_search is used, it would be "greedy_search"
If beam search with a beam size of 7 is used, it would be
"beam_7"
- value: It contains the decoding result. `len(value)` equals to
batch size. `value[i]` is the decoding result for the i-th
utterance in the given batch.
Args:
params:
It's the return value of :func:`get_params`.
model:
The neural model.
sp:
The BPE model.
batch:
It is the return value from iterating
`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
for the format of the `batch`.
decoding_graph:
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
only when --decoding_method is fast_beam_search.
Returns:
Return the decoding result. See above description for the format of
the returned dict.
"""
device = next(model.parameters()).device
feature = batch["inputs"]
assert feature.ndim == 3
feature = feature.to(device)
# at entry, feature is (N, T, C)
supervisions = batch["supervisions"]
feature_lens = supervisions["num_frames"].to(device)
encoder_out, encoder_out_lens = model.encoder(
x=feature, x_lens=feature_lens
)
hyps = []
if params.decoding_method == "fast_beam_search":
hyp_tokens = fast_beam_search(
model=model,
decoding_graph=decoding_graph,
encoder_out=encoder_out,
encoder_out_lens=encoder_out_lens,
beam=params.beam,
max_contexts=params.max_contexts,
max_states=params.max_states,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
elif (
params.decoding_method == "greedy_search"
and params.max_sym_per_frame == 1
):
hyp_tokens = greedy_search_batch(
model=model,
encoder_out=encoder_out,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
elif params.decoding_method == "modified_beam_search":
hyp_tokens = modified_beam_search(
model=model,
encoder_out=encoder_out,
beam=params.beam_size,
)
for hyp in sp.decode(hyp_tokens):
hyps.append(hyp.split())
else:
batch_size = encoder_out.size(0)
for i in range(batch_size):
# fmt: off
encoder_out_i = encoder_out[i:i + 1, :encoder_out_lens[i]]
# fmt: on
if params.decoding_method == "greedy_search":
hyp = greedy_search(
model=model,
encoder_out=encoder_out_i,
max_sym_per_frame=params.max_sym_per_frame,
)
elif params.decoding_method == "beam_search":
hyp = beam_search(
model=model,
encoder_out=encoder_out_i,
beam=params.beam_size,
)
else:
raise ValueError(
f"Unsupported decoding method: {params.decoding_method}"
)
hyps.append(sp.decode(hyp).split())
if params.decoding_method == "greedy_search":
return {"greedy_search": hyps}
elif params.decoding_method == "fast_beam_search":
return {
(
f"beam_{params.beam}_"
f"max_contexts_{params.max_contexts}_"
f"max_states_{params.max_states}"
): hyps
}
else:
return {f"beam_size_{params.beam_size}": hyps}
def decode_dataset(
dl: torch.utils.data.DataLoader,
params: AttributeDict,
model: nn.Module,
sp: spm.SentencePieceProcessor,
decoding_graph: Optional[k2.Fsa] = None,
) -> Dict[str, List[Tuple[List[str], List[str]]]]:
"""Decode dataset.
Args:
dl:
PyTorch's dataloader containing the dataset to decode.
params:
It is returned by :func:`get_params`.
model:
The neural model.
sp:
The BPE model.
decoding_graph:
The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
only when --decoding_method is fast_beam_search.
Returns:
Return a dict, whose key may be "greedy_search" if greedy search
is used, or it may be "beam_7" if beam size of 7 is used.
Its value is a list of tuples. Each tuple contains two elements:
The first is the reference transcript, and the second is the
predicted result.
"""
num_cuts = 0
try:
num_batches = len(dl)
except TypeError:
num_batches = "?"
if params.decoding_method == "greedy_search":
log_interval = 100
else:
log_interval = 2
results = defaultdict(list)
for batch_idx, batch in enumerate(dl):
texts = batch["supervisions"]["text"]
hyps_dict = decode_one_batch(
params=params,
model=model,
sp=sp,
decoding_graph=decoding_graph,
batch=batch,
)
for name, hyps in hyps_dict.items():
this_batch = []
assert len(hyps) == len(texts)
for hyp_words, ref_text in zip(hyps, texts):
ref_words = ref_text.split()
this_batch.append((ref_words, hyp_words))
results[name].extend(this_batch)
num_cuts += len(texts)
if batch_idx % log_interval == 0:
batch_str = f"{batch_idx}/{num_batches}"
logging.info(
f"batch {batch_str}, cuts processed until now is {num_cuts}"
)
return results
def save_results(
params: AttributeDict,
test_set_name: str,
results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
):
test_set_wers = dict()
for key, results in results_dict.items():
recog_path = (
params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
)
store_transcripts(filename=recog_path, texts=results)
logging.info(f"The transcripts are stored in {recog_path}")
# The following prints out WERs, per-word error statistics and aligned
# ref/hyp pairs.
errs_filename = (
params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
)
with open(errs_filename, "w") as f:
wer = write_error_stats(
f, f"{test_set_name}-{key}", results, enable_log=True
)
test_set_wers[key] = wer
logging.info("Wrote detailed error stats to {}".format(errs_filename))
test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
errs_info = (
params.res_dir
/ f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
)
with open(errs_info, "w") as f:
print("settings\tWER", file=f)
for key, val in test_set_wers:
print("{}\t{}".format(key, val), file=f)
s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
note = "\tbest for {}".format(test_set_name)
for key, val in test_set_wers:
s += "{}\t{}{}\n".format(key, val, note)
note = ""
logging.info(s)
@torch.no_grad()
def main():
parser = get_parser()
LibriSpeechAsrDataModule.add_arguments(parser)
args = parser.parse_args()
args.exp_dir = Path(args.exp_dir)
params = get_params()
params.update(vars(args))
assert params.decoding_method in (
"greedy_search",
"beam_search",
"fast_beam_search",
"modified_beam_search",
)
params.res_dir = params.exp_dir / params.decoding_method
if params.iter > 0:
params.suffix = f"iter-{params.iter}-avg-{params.avg}"
else:
params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
if "fast_beam_search" in params.decoding_method:
params.suffix += f"-beam-{params.beam}"
params.suffix += f"-max-contexts-{params.max_contexts}"
params.suffix += f"-max-states-{params.max_states}"
elif "beam_search" in params.decoding_method:
params.suffix += (
f"-{params.decoding_method}-beam-size-{params.beam_size}"
)
else:
params.suffix += f"-context-{params.context_size}"
params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
if params.use_averaged_model:
params.suffix += "-use-averaged-model"
setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
logging.info("Decoding started")
device = torch.device("cpu")
if torch.cuda.is_available():
device = torch.device("cuda", 0)
logging.info(f"Device: {device}")
sp = spm.SentencePieceProcessor()
sp.load(params.bpe_model)
# <blk> and <unk> is defined in local/train_bpe_model.py
params.blank_id = sp.piece_to_id("<blk>")
params.unk_id = sp.piece_to_id("<unk>")
params.vocab_size = sp.get_piece_size()
logging.info(params)
logging.info("About to create model")
model = get_transducer_model(params)
if not params.use_averaged_model:
if params.iter > 0:
filenames = find_checkpoints(
params.exp_dir, iteration=-params.iter
)[: params.avg]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for"
f" --iter {params.iter}, --avg {params.avg}"
)
elif len(filenames) < params.avg:
raise ValueError(
f"Not enough checkpoints ({len(filenames)}) found for"
f" --iter {params.iter}, --avg {params.avg}"
)
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(average_checkpoints(filenames, device=device))
elif params.avg == 1:
load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
else:
start = params.epoch - params.avg + 1
filenames = []
for i in range(start, params.epoch + 1):
if i >= 1:
filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
logging.info(f"averaging {filenames}")
model.to(device)
model.load_state_dict(average_checkpoints(filenames, device=device))
else:
if params.iter > 0:
filenames = find_checkpoints(
params.exp_dir, iteration=-params.iter
)[: params.avg + 1]
if len(filenames) == 0:
raise ValueError(
f"No checkpoints found for"
f" --iter {params.iter}, --avg {params.avg}"
)
elif len(filenames) < params.avg + 1:
raise ValueError(
f"Not enough checkpoints ({len(filenames)}) found for"
f" --iter {params.iter}, --avg {params.avg}"
)
filename_start = filenames[-1]
filename_end = filenames[0]
logging.info(
"Calculating the averaged model over iteration checkpoints"
f" from {filename_start} (excluded) to {filename_end}"
)
model.to(device)
model.load_state_dict(
average_checkpoints_with_averaged_model(
filename_start=filename_start,
filename_end=filename_end,
device=device,
)
)
else:
assert params.avg > 0
start = params.epoch - params.avg
assert start >= 1
filename_start = f"{params.exp_dir}/epoch-{start}.pt"
filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
logging.info(
f"Calculating the averaged model over epoch range from "
f"{start} (excluded) to {params.epoch}"
)
model.to(device)
model.load_state_dict(
average_checkpoints_with_averaged_model(
filename_start=filename_start,
filename_end=filename_end,
device=device,
)
)
model.to(device)
model.eval()
if params.decoding_method == "fast_beam_search":
decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
else:
decoding_graph = None
num_param = sum([p.numel() for p in model.parameters()])
logging.info(f"Number of model parameters: {num_param}")
librispeech = LibriSpeechAsrDataModule(args)
test_clean_cuts = librispeech.test_clean_cuts()
test_other_cuts = librispeech.test_other_cuts()
test_clean_dl = librispeech.test_dataloaders(test_clean_cuts)
test_other_dl = librispeech.test_dataloaders(test_other_cuts)
test_sets = ["test-clean", "test-other"]
test_dl = [test_clean_dl, test_other_dl]
for test_set, test_dl in zip(test_sets, test_dl):
results_dict = decode_dataset(
dl=test_dl,
params=params,
model=model,
sp=sp,
decoding_graph=decoding_graph,
)
save_results(
params=params,
test_set_name=test_set,
results_dict=results_dict,
)
logging.info("Done!")
if __name__ == "__main__":
main()

1
egs/librispeech/ASR/pruned_transducer_stateless4/decoder.py Symbolic link
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@ -0,0 +1 @@
../pruned_transducer_stateless2/decoder.py

1
egs/librispeech/ASR/pruned_transducer_stateless4/encoder_interface.py Symbolic link
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@ -0,0 +1 @@
../pruned_transducer_stateless2/encoder_interface.py

1
egs/librispeech/ASR/pruned_transducer_stateless4/export.py Symbolic link
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@ -0,0 +1 @@
../pruned_transducer_stateless2/export.py

1
egs/librispeech/ASR/pruned_transducer_stateless4/joiner.py Symbolic link
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@ -0,0 +1 @@
../pruned_transducer_stateless2/joiner.py

1
egs/librispeech/ASR/pruned_transducer_stateless4/model.py Symbolic link
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@ -0,0 +1 @@
../pruned_transducer_stateless2/model.py

1
egs/librispeech/ASR/pruned_transducer_stateless4/optim.py Symbolic link
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@ -0,0 +1 @@
../pruned_transducer_stateless2/optim.py

1
egs/librispeech/ASR/pruned_transducer_stateless4/scaling.py Symbolic link
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@ -0,0 +1 @@
../pruned_transducer_stateless2/scaling.py

1053
egs/librispeech/ASR/pruned_transducer_stateless4/train.py Executable file
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146
icefall/checkpoint.py
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@ -1,4 +1,5 @@
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
# Copyright 2021-2022 Xiaomi Corporation (authors: Fangjun Kuang,
# Zengwei Yao)
#
# See ../../LICENSE for clarification regarding multiple authors
#
@ -25,6 +26,7 @@ from typing import Any, Dict, List, Optional, Union
import torch
import torch.nn as nn
from lhotse.dataset.sampling.base import CutSampler
from torch import Tensor
from torch.cuda.amp import GradScaler
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import Optimizer
@ -37,6 +39,7 @@ LRSchedulerType = object
def save_checkpoint(
filename: Path,
model: Union[nn.Module, DDP],
model_avg: Optional[nn.Module] = None,
params: Optional[Dict[str, Any]] = None,
optimizer: Optional[Optimizer] = None,
scheduler: Optional[LRSchedulerType] = None,
@ -51,6 +54,8 @@ def save_checkpoint(
The checkpoint filename.
model:
The model to be saved. We only save its `state_dict()`.
model_avg:
The stored model averaged from the start of training.
params:
User defined parameters, e.g., epoch, loss.
optimizer:
@ -80,6 +85,9 @@ def save_checkpoint(
"sampler": sampler.state_dict() if sampler is not None else None,
}
if model_avg is not None:
checkpoint["model_avg"] = model_avg.state_dict()
if params:
for k, v in params.items():
assert k not in checkpoint
@ -91,6 +99,7 @@ def save_checkpoint(
def load_checkpoint(
filename: Path,
model: nn.Module,
model_avg: Optional[nn.Module] = None,
optimizer: Optional[Optimizer] = None,
scheduler: Optional[LRSchedulerType] = None,
scaler: Optional[GradScaler] = None,
@ -118,6 +127,11 @@ def load_checkpoint(
checkpoint.pop("model")
if model_avg is not None and "model_avg" in checkpoint:
logging.info("Loading averaged model")
model_avg.load_state_dict(checkpoint["model_avg"], strict=strict)
checkpoint.pop("model_avg")
def load(name, obj):
s = checkpoint.get(name, None)
if obj and s:
@ -181,6 +195,7 @@ def save_checkpoint_with_global_batch_idx(
out_dir: Path,
global_batch_idx: int,
model: Union[nn.Module, DDP],
model_avg: Optional[nn.Module] = None,
params: Optional[Dict[str, Any]] = None,
optimizer: Optional[Optimizer] = None,
scheduler: Optional[LRSchedulerType] = None,
@ -201,6 +216,8 @@ def save_checkpoint_with_global_batch_idx(
model:
The neural network model whose `state_dict` will be saved in the
checkpoint.
model_avg:
The stored model averaged from the start of training.
params:
A dict of training configurations to be saved.
optimizer:
@ -223,6 +240,7 @@ def save_checkpoint_with_global_batch_idx(
save_checkpoint(
filename=filename,
model=model,
model_avg=model_avg,
params=params,
optimizer=optimizer,
scheduler=scheduler,
@ -327,3 +345,129 @@ def remove_checkpoints(
to_remove = checkpoints[topk:]
for c in to_remove:
os.remove(c)
def update_averaged_model(
params: Dict[str, Tensor],
model_cur: Union[nn.Module, DDP],
model_avg: nn.Module,
) -> None:
"""Update the averaged model:
model_avg = model_cur * (average_period / batch_idx_train)
+ model_avg * ((batch_idx_train - average_period) / batch_idx_train)
Args:
params:
User defined parameters, e.g., epoch, loss.
model_cur:
The current model.
model_avg:
The averaged model to be updated.
"""
weight_cur = params.average_period / params.batch_idx_train
weight_avg = 1 - weight_cur
if isinstance(model_cur, DDP):
model_cur = model_cur.module
cur = model_cur.state_dict()
avg = model_avg.state_dict()
average_state_dict(
state_dict_1=avg,
state_dict_2=cur,
weight_1=weight_avg,
weight_2=weight_cur,
)
def average_checkpoints_with_averaged_model(
filename_start: str,
filename_end: str,
device: torch.device = torch.device("cpu"),
) -> Dict[str, Tensor]:
"""Average model parameters over the range with given
start model (excluded) and end model.
Let start = batch_idx_train of model-start;
end = batch_idx_train of model-end;
interval = end - start.
Then the average model over range from start (excluded) to end is
(1) avg = (model_end * end - model_start * start) / interval.
It can be written as
(2) avg = model_end * weight_end + model_start * weight_start,
where weight_end = end / interval,
weight_start = -start / interval = 1 - weight_end.
Since the terms `weight_end` and `weight_start` would be large
if the model has been trained for lots of batches, which would cause
overflow when multiplying the model parameters.
To avoid this, we rewrite (2) as:
(3) avg = (model_end + model_start * (weight_start / weight_end))
* weight_end
The model index could be epoch number or iteration number.
Args:
filename_start:
Checkpoint filename of the start model. We assume it
is saved by :func:`save_checkpoint`.
filename_end:
Checkpoint filename of the end model. We assume it
is saved by :func:`save_checkpoint`.
device:
Move checkpoints to this device before averaging.
"""
state_dict_start = torch.load(filename_start, map_location=device)
state_dict_end = torch.load(filename_end, map_location=device)
batch_idx_train_start = state_dict_start["batch_idx_train"]
batch_idx_train_end = state_dict_end["batch_idx_train"]
interval = batch_idx_train_end - batch_idx_train_start
assert interval > 0, interval
weight_end = batch_idx_train_end / interval
weight_start = 1 - weight_end
model_end = state_dict_end["model_avg"]
model_start = state_dict_start["model_avg"]
avg = model_end
# scale the weight to avoid overflow
average_state_dict(
state_dict_1=avg,
state_dict_2=model_start,
weight_1=1.0,
weight_2=weight_start / weight_end,
scaling_factor=weight_end,
)
return avg
def average_state_dict(
state_dict_1: Dict[str, Tensor],
state_dict_2: Dict[str, Tensor],
weight_1: float,
weight_2: float,
scaling_factor: float = 1.0,
) -> Dict[str, Tensor]:
"""Average two state_dict with given weights:
state_dict_1 = (state_dict_1 * weight_1 + state_dict_2 * weight_2)
* scaling_factor
It is an in-place operation on state_dict_1 itself.
"""
# Identify shared parameters. Two parameters are said to be shared
# if they have the same data_ptr
uniqued: Dict[int, str] = dict()
for k, v in state_dict_1.items():
v_data_ptr = v.data_ptr()
if v_data_ptr in uniqued:
continue
uniqued[v_data_ptr] = k
uniqued_names = list(uniqued.values())
for k in uniqued_names:
state_dict_1[k] *= weight_1
state_dict_1[k] += (
state_dict_2[k].to(device=state_dict_1[k].device) * weight_2
)
state_dict_1[k] *= scaling_factor

2
icefall/env.py
View File

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

2
requirements-ci.txt
View File

@ -11,7 +11,7 @@ graphviz==0.19.1
-f https://download.pytorch.org/whl/cpu/torch_stable.html torch==1.10.0+cpu
-f https://download.pytorch.org/whl/cpu/torch_stable.html torchaudio==0.10.0+cpu
-f https://k2-fsa.org/nightly/ k2==1.14.dev20220316+cpu.torch1.10.0
-f https://k2-fsa.org/nightly/ k2==1.15.1.dev20220426+cpu.torch1.10.0
git+https://github.com/lhotse-speech/lhotse
kaldilm==1.11