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Fairseq-S2T
Commit bb6ce82c by xuchen
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modify the shell scripts

parent 71eae3fd
正在显示 包含 3347 行增加 和 54 行删除
egs/covost/asr/conf/train.yaml 0 → 100644
train-subset: train_asr
valid-subset: dev_asr
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
egs/covost/asr/conf/train_ctc.yaml 0 → 100644
train-subset: train_asr
valid-subset: dev_asr
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
\ No newline at end of file
egs/covost/asr/conf/train_ctc_conformer.yaml 0 → 100644
train-subset: train_asr
valid-subset: dev_asr
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: s2t_conformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/asr/conf/train_ctc_conformer_m.yaml 0 → 100644
train-subset: train_asr
valid-subset: dev_asr
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: s2t_conformer_m
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
#dropout: 0.1
#activation-fn: relu
#encoder-embed-dim: 256
#encoder-ffn-embed-dim: 2048
#encoder-layers: 12
#decoder-layers: 6
#encoder-attention-heads: 4
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/asr/conf/train_ctc_rpr.yaml 0 → 100644
train-subset: train_asr
valid-subset: dev_asr
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
encoder-attention-type: relative
#decoder-attention-type: relative
max-relative-length: 100
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/asr/decode.sh 0 → 100755
#! /bin/bash
gpu_num=1
data_dir=
test_subset=tst-COMMON
exp_name=
if [ "$#" -eq 1 ]; then
exp_name=$1
fi
n_average=10
beam_size=5
max_tokens=40000
cmd="./run.sh
--stage 2
--stop_stage 2
--gpu_num ${gpu_num}
--exp_name ${exp_name}
--n_average ${n_average}
--beam_size ${beam_size}
--max_tokens ${max_tokens}
"
if [[ -n ${data_dir} ]]; then
cmd="$cmd --data_dir ${data_dir}"
fi
if [[ -n ${test_subset} ]]; then
cmd="$cmd --test_subset ${test_subset}"
fi
echo $cmd
eval $cmd
egs/covost/asr/local/monitor.sh 0 → 100755
gpu_num=1
while :
do
all_devices=$(seq 0 `gpustat | sed '1,2d' | wc -l`);
count=0
for dev in ${all_devices[@]}
do
line=`expr $dev + 2`
use=`gpustat -p | head -n $line | tail -1 | cut -d '|' -f4 | wc -w`
if [[ $use -eq 0 ]]; then
device[$count]=$dev
count=`expr $count + 1`
if [[ $count -eq $gpu_num ]]; then
break
fi
fi
done
if [[ ${#device[@]} -lt $gpu_num ]]; then
sleep 60s
else
echo "Run $cmd"
eval $cmd
sleep 10s
exit
fi
done
egs/covost/asr/local/parse_options.sh 0 → 100755
#!/usr/bin/env bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey);
# Arnab Ghoshal, Karel Vesely
# 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
#
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
# MERCHANTABLITY OR NON-INFRINGEMENT.
# See the Apache 2 License for the specific language governing permissions and
# limitations under the License.
# Parse command-line options.
# To be sourced by another script (as in ". parse_options.sh").
# Option format is: --option-name arg
# and shell variable "option_name" gets set to value "arg."
# The exception is --help, which takes no arguments, but prints the
# $help_message variable (if defined).
###
### The --config file options have lower priority to command line
### options, so we need to import them first...
###
# Now import all the configs specified by command-line, in left-to-right order
for ((argpos=1; argpos<$#; argpos++)); do
if [ "${!argpos}" == "--config" ]; then
argpos_plus1=$((argpos+1))
config=${!argpos_plus1}
[ ! -r $config ] && echo "$0: missing config '$config'" && exit 1
. $config # source the config file.
fi
done
###
### Now we process the command line options
###
while true; do
[ -z "${1:-}" ] && break; # break if there are no arguments
case "$1" in
# If the enclosing script is called with --help option, print the help
# message and exit. Scripts should put help messages in $help_message
--help|-h) if [ -z "$help_message" ]; then echo "No help found." 1>&2;
else printf "$help_message\n" 1>&2 ; fi;
exit 0 ;;
--*=*) echo "$0: options to scripts must be of the form --name value, got '$1'"
exit 1 ;;
# If the first command-line argument begins with "--" (e.g. --foo-bar),
# then work out the variable name as $name, which will equal "foo_bar".
--*) name=`echo "$1" | sed s/^--// | sed s/-/_/g`;
# Next we test whether the variable in question is undefned-- if so it's
# an invalid option and we die. Note: $0 evaluates to the name of the
# enclosing script.
# The test [ -z ${foo_bar+xxx} ] will return true if the variable foo_bar
# is undefined. We then have to wrap this test inside "eval" because
# foo_bar is itself inside a variable ($name).
eval '[ -z "${'$name'+xxx}" ]' && echo "$0: invalid option $1" 1>&2 && exit 1;
oldval="`eval echo \\$$name`";
# Work out whether we seem to be expecting a Boolean argument.
if [ "$oldval" == "true" ] || [ "$oldval" == "false" ]; then
was_bool=true;
else
was_bool=false;
fi
# Set the variable to the right value-- the escaped quotes make it work if
# the option had spaces, like --cmd "queue.pl -sync y"
eval $name=\"$2\";
# Check that Boolean-valued arguments are really Boolean.
if $was_bool && [[ "$2" != "true" && "$2" != "false" ]]; then
echo "$0: expected \"true\" or \"false\": $1 $2" 1>&2
exit 1;
fi
shift 2;
;;
*) break;
esac
done
# Check for an empty argument to the --cmd option, which can easily occur as a
# result of scripting errors.
[ ! -z "${cmd+xxx}" ] && [ -z "$cmd" ] && echo "$0: empty argument to --cmd option" 1>&2 && exit 1;
true; # so this script returns exit code 0.
egs/covost/asr/local/path.sh 0 → 100755
MAIN_ROOT=$PWD/../../..
KALDI_ROOT=$MAIN_ROOT/tools/kaldi
export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PATH
[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!" && exit 1
. $KALDI_ROOT/tools/config/common_path.sh
export LC_ALL=C
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$MAIN_ROOT/src/lib
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$MAIN_ROOT/tools/chainer_ctc/ext/warp-ctc/build
. "${MAIN_ROOT}"/tools/activate_python.sh && . "${MAIN_ROOT}"/tools/extra_path.sh
export PATH=$MAIN_ROOT/utils:$MAIN_ROOT/espnet/bin:$PATH
export OMP_NUM_THREADS=1
# check extra module installation
if ! which tokenizer.perl > /dev/null; then
echo "Error: it seems that moses is not installed." >&2
echo "Error: please install moses as follows." >&2
echo "Error: cd ${MAIN_ROOT}/tools && make moses.done" >&2
return 1
fi
# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
export PYTHONIOENCODING=UTF-8
egs/covost/asr/local/utils.sh 0 → 100755
get_devices(){
gpu_num=$1
use_cpu=$2
device=()
while :
do
record=`mktemp -t temp.record.XXXXXX`
gpustat > $record
all_devices=$(seq 0 `cat $record | sed '1,2d' | wc -l`);
count=0
for dev in ${all_devices[@]}
do
line=`expr $dev + 2`
use=`cat $record | head -n $line | tail -1 | cut -d '|' -f3 | cut -d '/' -f1`
if [[ $use -lt 100 ]]; then
device[$count]=$dev
count=`expr $count + 1`
if [[ $count -eq $gpu_num ]]; then
break
fi
fi
done
if [[ ${#device[@]} -lt $gpu_num ]]; then
if [[ $use_cpu -eq 1 ]]; then
device=(-1)
else
sleep 60s
fi
else
break
fi
done
echo ${device[*]} | sed 's/ /,/g'
return $?
}
egs/covost/asr/run.sh 0 → 100755
#! /bin/bash
# Processing MuST-C Datasets
# Copyright 2021 Natural Language Processing Laboratory
# Xu Chen (xuchenneu@163.com)
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
#set -u
set -o pipefail
export PYTHONIOENCODING=UTF-8
eval=1
time=$(date "+%m%d_%H%M")
stage=0
stop_stage=0
######## hardware ########
# devices
#device=()
gpu_num=8
update_freq=1
root_dir=~/st/Fairseq-S2T
pwd_dir=$PWD
# dataset
src_lang=en
tgt_lang=de
lang=${src_lang}-${tgt_lang}
dataset=covost
task=speech_to_text
vocab_type=unigram
vocab_size=5000
speed_perturb=0
lcrm=1
use_specific_dict=1
specific_prefix=fair
specific_dir=/home/xuchen/st/data/librispeech/fair
asr_vocab_prefix=spm_unigram_10000
org_data_dir=/media/data/asr_data/${dataset}
data_dir=~/st/data/${dataset}/asr
test_subset=tst-COMMON
# exp
exp_prefix=${time}
extra_tag=
extra_parameter=
exp_tag=baseline
exp_name=
# config
train_config=train_ctc.yaml
data_config=config_asr.yaml
data_config=config_st_share.yaml
# training setting
fp16=1
max_tokens=40000
step_valid=0
# decoding setting
n_average=10
beam_size=5
if [[ ${speed_perturb} -eq 1 ]]; then
data_dir=${data_dir}_sp
exp_prefix=${exp_prefix}_sp
fi
if [[ ${lcrm} -eq 1 ]]; then
data_dir=${data_dir}_lcrm
exp_prefix=${exp_prefix}_lcrm
fi
if [[ ${use_specific_dict} -eq 1 ]]; then
data_dir=${data_dir}_${specific_prefix}
exp_prefix=${exp_prefix}_${specific_prefix}
fi
. ./local/parse_options.sh || exit 1;
# full path
train_config=$pwd_dir/conf/${train_config}
if [[ -z ${exp_name} ]]; then
exp_name=${exp_prefix}_$(basename ${train_config%.*})_${exp_tag}
if [[ -n ${extra_tag} ]]; then
exp_name=${exp_name}_${extra_tag}
fi
fi
model_dir=$root_dir/../checkpoints/$dataset/asr/${exp_name}
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
echo "stage -1: Data Download"
# pass
fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
### Task dependent. You have to make data the following preparation part by yourself.
### But you can utilize Kaldi recipes in most cases
echo "stage 0: ASR Data Preparation"
if [[ ! -e ${data_dir}/${src_lang} ]]; then
mkdir -p ${data_dir}/${src_lang}
fi
source ~/tools/audio/bin/activate
cmd="python ${root_dir}/examples/speech_to_text/prep_covost_data.py
--data-root ${org_data_dir}
--output-root ${data_dir}
--src-lang ${src_lang}
--task asr
--vocab-type ${vocab_type}
--vocab-size ${vocab_size}"
if [[ ${use_specific_dict} -eq 1 ]]; then
cp -r ${specific_dir}/${asr_vocab_prefix}.* ${data_dir}/${src_lang}
cmd="$cmd
--asr-prefix ${asr_vocab_prefix}"
fi
if [[ ${speed_perturb} -eq 1 ]]; then
cmd="$cmd
--speed-perturb"
fi
if [[ ${lcrm} -eq 1 ]]; then
cmd="$cmd
--lowercase-src
--rm-punc-src"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
deactivate
fi
data_dir=${data_dir}/${lang}
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "stage 1: ASR Network Training"
[[ ! -d ${data_dir} ]] && echo "The data dir ${data_dir} is not existing!" && exit 1;
if [[ -z ${device} || ${#device[@]} -eq 0 ]]; then
if [[ ${gpu_num} -eq 0 ]]; then
device=()
else
source ./local/utils.sh
device=$(get_devices $gpu_num 0)
fi
fi
echo -e "dev=${device} data=${data_dir} model=${model_dir}"
if [[ ! -d ${model_dir} ]]; then
mkdir -p ${model_dir}
else
echo "${model_dir} exists."
fi
cp ${BASH_SOURCE[0]} ${model_dir}
cp ${PWD}/train.sh ${model_dir}
cp ${train_config} ${model_dir}
cmd="python3 -u ${root_dir}/fairseq_cli/train.py
${data_dir}
--config-yaml ${data_config}
--train-config ${train_config}
--task ${task}
--max-tokens ${max_tokens}
--update-freq ${update_freq}
--log-interval 100
--save-dir ${model_dir}
--tensorboard-logdir ${model_dir}"
if [[ -n ${extra_parameter} ]]; then
cmd="${cmd}
${extra_parameter}"
fi
if [[ ${gpu_num} -gt 0 ]]; then
cmd="${cmd}
--distributed-world-size $gpu_num
--ddp-backend no_c10d"
fi
if [[ $fp16 -eq 1 ]]; then
cmd="${cmd}
--fp16"
fi
if [[ $step_valid -eq 1 ]]; then
validate_interval=10000
save_interval=10000
no_epoch_checkpoints=1
save_interval_updates=5000
keep_interval_updates=3
else
validate_interval=1
keep_last_epochs=10
fi
if [[ -n $no_epoch_checkpoints && $no_epoch_checkpoints -eq 1 ]]; then
cmd="$cmd
--no-epoch-checkpoints"
fi
if [[ -n $validate_interval ]]; then
cmd="${cmd}
--validate-interval $validate_interval "
fi
if [[ -n $save_interval ]]; then
cmd="${cmd}
--save-interval $save_interval "
fi
if [[ -n $keep_last_epochs ]]; then
cmd="${cmd}
--keep-last-epochs $keep_last_epochs "
fi
if [[ -n $save_interval_updates ]]; then
cmd="${cmd}
--save-interval-updates $save_interval_updates"
if [[ -n $keep_interval_updates ]]; then
cmd="${cmd}
--keep-interval-updates $keep_interval_updates"
fi
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
# save info
log=./history.log
echo "${time} | ${device} | ${data_dir} | ${model_dir} " >> $log
cat $log | tail -n 50 > tmp.log
mv tmp.log $log
export CUDA_VISIBLE_DEVICES=${device}
cmd="nohup ${cmd} >> ${model_dir}/train.log 2>&1 &"
if [[ $eval -eq 1 ]]; then
eval $cmd
sleep 2s
tail -n `wc -l ${model_dir}/train.log | awk '{print $1+1}'` -f ${model_dir}/train.log
fi
fi
wait
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "stage 2: ASR Decoding"
if [[ ${n_average} -ne 1 ]]; then
# Average models
dec_model=avg_${n_average}_checkpoint.pt
cmd="python ${root_dir}/scripts/average_checkpoints.py
--inputs ${model_dir}
--num-epoch-checkpoints ${n_average}
--output ${model_dir}/${dec_model}"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval $cmd
else
dec_model=checkpoint_best.pt
fi
if [[ -z ${device} || ${#device[@]} -eq 0 ]]; then
if [[ ${gpu_num} -eq 0 ]]; then
device=()
else
source ./local/utils.sh
device=$(get_devices $gpu_num 0)
fi
fi
export CUDA_VISIBLE_DEVICES=${device}
#tmp_file=$(mktemp ${model_dir}/tmp-XXXXX)
#trap 'rm -rf ${tmp_file}' EXIT
result_file=${model_dir}/decode_result
[[ -f ${result_file} ]] && rm ${result_file}
test_subset=(${test_subset//,/ })
for subset in ${test_subset[@]}; do
subset=${subset}_asr
cmd="python ${root_dir}/fairseq_cli/generate.py
${data_dir}
--config-yaml ${data_config}
--gen-subset ${subset}
--task speech_to_text
--path ${model_dir}/${dec_model}
--results-path ${model_dir}
--max-tokens ${max_tokens}
--beam ${beam_size}
--scoring wer
--wer-tokenizer 13a
--wer-lowercase
--wer-remove-punct
"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
if [[ $eval -eq 1 ]]; then
eval $cmd
tail -n 1 ${model_dir}/generate-${subset}.txt >> ${result_file}
fi
done
cat ${result_file}
fi
egs/covost/asr/train.sh 0 → 100755
#! /bin/bash
# training the model
gpu_num=8
update_freq=2
max_tokens=20000
extra_tag=lcrm
extra_parameter=
#extra_tag="${extra_tag}"
#extra_parameter="${extra_parameter} "
exp_tag=
train_config=train_ctc.yaml
cmd="./run.sh
--stage 1
--stop_stage 1
--gpu_num ${gpu_num}
--update_freq ${update_freq}
--train_config ${train_config}
--max_tokens ${max_tokens}
"
if [[ -n ${exp_tag} ]]; then
cmd="$cmd --exp_tag ${exp_tag}"
fi
if [[ -n ${extra_tag} ]]; then
cmd="$cmd --extra_tag ${extra_tag}"
fi
if [[ -n ${extra_parameter} ]]; then
cmd="$cmd --extra_parameter \"${extra_parameter}\""
fi
echo $cmd
eval $cmd
egs/covost/mt/binary.sh 0 → 100644
set -e
eval=1
root_dir=~/st/Fairseq-S2T
data_dir=/home/xuchen/st/data/wmt/test
vocab_dir=/home/xuchen/st/data/wmt/mt/en-de/unigram32000_share
src_vocab_prefix=spm_unigram32000_share
tgt_vocab_prefix=spm_unigram32000_share
src_lang=en
tgt_lang=de
tokenize=1
splits=(newstest2014 newstest2016)
for split in ${splits[@]}; do
src_file=${data_dir}/${split}.${src_lang}
tgt_file=${data_dir}/${split}.${tgt_lang}
if [[ ${tokenize} -eq 1 ]]; then
cmd="tokenizer.perl -l ${src_lang} --threads 8 -no-escape < ${src_file} > ${src_file}.tok"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
cmd="tokenizer.perl -l ${tgt_lang} --threads 8 -no-escape < ${tgt_file} > ${tgt_file}.tok"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
src_file=${src_file}.tok
tgt_file=${tgt_file}.tok
fi
cmd="spm_encode
--model ${vocab_dir}/${src_vocab_prefix}.model
--output_format=piece
< ${src_file}
> ${src_file}.spm"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
cmd="spm_encode
--model ${vocab_dir}/${tgt_vocab_prefix}.model
--output_format=piece
< ${tgt_file}
> ${tgt_file}.spm"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
src_file=${src_file}.spm
tgt_file=${tgt_file}.spm
mkdir -p ${data_dir}/final
cmd="cp ${src_file} ${data_dir}/final/${split}.${src_lang}"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
cmd="cp ${tgt_file} ${data_dir}/final/${split}.${tgt_lang}"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
done
n_set=${#splits[*]}
for ((i=0;i<$n_set;i++)); do
dataset[$i]=${data_dir}/final/${splits[$i]}
done
pref=`echo ${dataset[*]} | sed 's/ /,/g'`
cmd="python ${root_dir}/fairseq_cli/preprocess.py
--source-lang ${src_lang}
--target-lang ${tgt_lang}
--testpref ${pref}
--destdir ${data_dir}/data-bin
--srcdict ${vocab_dir}/${src_vocab_prefix}.txt
--tgtdict ${vocab_dir}/${tgt_vocab_prefix}.txt
--workers 64"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
\ No newline at end of file
egs/covost/mt/conf/train.yaml 0 → 100644
train-subset: train
valid-subset: valid
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: transformer
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 8000
lr: 1e-3
adam_betas: (0.9,0.997)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
dropout: 0.1
attention-dropout: 0.1
activation-dropout: 0.1
activation-fn: relu
encoder-normalize-before: True
decoder-normalize-before: True
encoder-embed-dim: 512
encoder-ffn-embed-dim: 2048
encoder-layers: 6
decoder-layers: 6
encoder-attention-heads: 8
decoder-embed-dim: 512
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 8
egs/covost/mt/conf/train_dlcl.yaml 0 → 100644
train-subset: train
valid-subset: valid
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: dlcl_transformer
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 8000
lr: 1e-3
adam_betas: (0.9,0.997)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
dropout: 0.1
attention-dropout: 0.1
activation-dropout: 0.1
activation-fn: relu
encoder-normalize-before: True
decoder-normalize-before: True
encoder-embed-dim: 512
encoder-ffn-embed-dim: 2048
encoder-layers: 6
decoder-layers: 6
encoder-attention-heads: 8
decoder-embed-dim: 512
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 8
egs/covost/mt/conf/train_dlcl_rpr.yaml 0 → 100644
train-subset: train
valid-subset: valid
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: dlcl_transformer
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 8000
lr: 1e-3
adam_betas: (0.9,0.997)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
dropout: 0.1
attention-dropout: 0.1
activation-dropout: 0.1
activation-fn: relu
encoder-normalize-before: True
decoder-normalize-before: True
encoder-embed-dim: 512
encoder-ffn-embed-dim: 2048
encoder-layers: 6
decoder-layers: 6
encoder-attention-heads: 8
decoder-embed-dim: 512
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 8
encoder-attention-type: relative
decoder-attention-type: relative
max-relative-length: 20
\ No newline at end of file
egs/covost/mt/conf/train_rpr.yaml 0 → 100644
train-subset: train
valid-subset: valid
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: transformer
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 8000
lr: 1e-3
adam_betas: (0.9,0.997)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
dropout: 0.1
attention-dropout: 0.1
activation-dropout: 0.1
activation-fn: relu
encoder-normalize-before: True
decoder-normalize-before: True
encoder-embed-dim: 512
encoder-ffn-embed-dim: 2048
encoder-layers: 6
decoder-layers: 6
encoder-attention-heads: 8
decoder-embed-dim: 512
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 8
encoder-attention-type: relative
decoder-attention-type: relative
max-relative-length: 20
egs/covost/mt/conf/train_s.yaml 0 → 100644
train-subset: train
valid-subset: valid
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: transformer
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 8000
lr: 1e-3
adam_betas: (0.9,0.997)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
dropout: 0.1
attention-dropout: 0.1
activation-dropout: 0.1
activation-fn: relu
encoder-normalize-before: True
decoder-normalize-before: True
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 6
decoder-layers: 6
encoder-attention-heads: 4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
egs/covost/mt/decode.sh 0 → 100755
#! /bin/bash
gpu_num=1
data_dir=
test_subset=test
exp_name=
if [ "$#" -eq 1 ]; then
exp_name=$1
fi
n_average=10
beam_size=5
max_tokens=20000
cmd="./run.sh
--stage 2
--stop_stage 2
--gpu_num ${gpu_num}
--exp_name ${exp_name}
--n_average ${n_average}
--beam_size ${beam_size}
--max_tokens ${max_tokens}
"
if [[ -n ${data_dir} ]]; then
cmd="$cmd --data_dir ${data_dir}"
fi
if [[ -n ${test_subset} ]]; then
cmd="$cmd --test_subset ${test_subset}"
fi
echo $cmd
eval $cmd
egs/covost/mt/local/lower_rm.py 0 → 100644
import sys
import string
in_file = sys.argv[1]
with open(in_file, "r", encoding="utf-8") as f:
for line in f.readlines():
line = line.strip().lower()
for w in string.punctuation:
line = line.replace(w, "")
line = line.replace(" ", "")
print(line)
egs/covost/mt/local/monitor.sh 0 → 100755
gpu_num=1
while :
do
all_devices=$(seq 0 `gpustat | sed '1,2d' | wc -l`);
count=0
for dev in ${all_devices[@]}
do
line=`expr $dev + 2`
use=`gpustat -p | head -n $line | tail -1 | cut -d '|' -f4 | wc -w`
if [[ $use -eq 0 ]]; then
device[$count]=$dev
count=`expr $count + 1`
if [[ $count -eq $gpu_num ]]; then
break
fi
fi
done
if [[ ${#device[@]} -lt $gpu_num ]]; then
sleep 60s
else
echo "Run $cmd"
eval $cmd
sleep 10s
exit
fi
done
egs/covost/mt/local/parse_options.sh 0 → 100755
#!/usr/bin/env bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey);
# Arnab Ghoshal, Karel Vesely
# 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
#
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
# MERCHANTABLITY OR NON-INFRINGEMENT.
# See the Apache 2 License for the specific language governing permissions and
# limitations under the License.
# Parse command-line options.
# To be sourced by another script (as in ". parse_options.sh").
# Option format is: --option-name arg
# and shell variable "option_name" gets set to value "arg."
# The exception is --help, which takes no arguments, but prints the
# $help_message variable (if defined).
###
### The --config file options have lower priority to command line
### options, so we need to import them first...
###
# Now import all the configs specified by command-line, in left-to-right order
for ((argpos=1; argpos<$#; argpos++)); do
if [ "${!argpos}" == "--config" ]; then
argpos_plus1=$((argpos+1))
config=${!argpos_plus1}
[ ! -r $config ] && echo "$0: missing config '$config'" && exit 1
. $config # source the config file.
fi
done
###
### Now we process the command line options
###
while true; do
[ -z "${1:-}" ] && break; # break if there are no arguments
case "$1" in
# If the enclosing script is called with --help option, print the help
# message and exit. Scripts should put help messages in $help_message
--help|-h) if [ -z "$help_message" ]; then echo "No help found." 1>&2;
else printf "$help_message\n" 1>&2 ; fi;
exit 0 ;;
--*=*) echo "$0: options to scripts must be of the form --name value, got '$1'"
exit 1 ;;
# If the first command-line argument begins with "--" (e.g. --foo-bar),
# then work out the variable name as $name, which will equal "foo_bar".
--*) name=`echo "$1" | sed s/^--// | sed s/-/_/g`;
# Next we test whether the variable in question is undefned-- if so it's
# an invalid option and we die. Note: $0 evaluates to the name of the
# enclosing script.
# The test [ -z ${foo_bar+xxx} ] will return true if the variable foo_bar
# is undefined. We then have to wrap this test inside "eval" because
# foo_bar is itself inside a variable ($name).
eval '[ -z "${'$name'+xxx}" ]' && echo "$0: invalid option $1" 1>&2 && exit 1;
oldval="`eval echo \\$$name`";
# Work out whether we seem to be expecting a Boolean argument.
if [ "$oldval" == "true" ] || [ "$oldval" == "false" ]; then
was_bool=true;
else
was_bool=false;
fi
# Set the variable to the right value-- the escaped quotes make it work if
# the option had spaces, like --cmd "queue.pl -sync y"
eval $name=\"$2\";
# Check that Boolean-valued arguments are really Boolean.
if $was_bool && [[ "$2" != "true" && "$2" != "false" ]]; then
echo "$0: expected \"true\" or \"false\": $1 $2" 1>&2
exit 1;
fi
shift 2;
;;
*) break;
esac
done
# Check for an empty argument to the --cmd option, which can easily occur as a
# result of scripting errors.
[ ! -z "${cmd+xxx}" ] && [ -z "$cmd" ] && echo "$0: empty argument to --cmd option" 1>&2 && exit 1;
true; # so this script returns exit code 0.
egs/covost/mt/local/path.sh 0 → 100755
MAIN_ROOT=$PWD/../../..
KALDI_ROOT=$MAIN_ROOT/tools/kaldi
export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PATH
[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!" && exit 1
. $KALDI_ROOT/tools/config/common_path.sh
export LC_ALL=C
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$MAIN_ROOT/src/lib
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$MAIN_ROOT/tools/chainer_ctc/ext/warp-ctc/build
. "${MAIN_ROOT}"/tools/activate_python.sh && . "${MAIN_ROOT}"/tools/extra_path.sh
export PATH=$MAIN_ROOT/utils:$MAIN_ROOT/espnet/bin:$PATH
export OMP_NUM_THREADS=1
# check extra module installation
if ! which tokenizer.perl > /dev/null; then
echo "Error: it seems that moses is not installed." >&2
echo "Error: please install moses as follows." >&2
echo "Error: cd ${MAIN_ROOT}/tools && make moses.done" >&2
return 1
fi
# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
export PYTHONIOENCODING=UTF-8
egs/covost/mt/local/utils.sh 0 → 100755
get_devices(){
gpu_num=$1
use_cpu=$2
device=()
while :
do
record=`mktemp -t temp.record.XXXXXX`
gpustat > $record
all_devices=$(seq 0 `cat $record | sed '1,2d' | wc -l`);
count=0
for dev in ${all_devices[@]}
do
line=`expr $dev + 2`
use=`cat $record | head -n $line | tail -1 | cut -d '|' -f3 | cut -d '/' -f1`
if [[ $use -lt 100 ]]; then
device[$count]=$dev
count=`expr $count + 1`
if [[ $count -eq $gpu_num ]]; then
break
fi
fi
done
if [[ ${#device[@]} -lt $gpu_num ]]; then
if [[ $use_cpu -eq 1 ]]; then
device=(-1)
else
sleep 60s
fi
else
break
fi
done
echo ${device[*]} | sed 's/ /,/g'
return $?
}
egs/covost/mt/run.sh 0 → 100755
#! /bin/bash
# Processing MuST-C Datasets
# Copyright 2021 Natural Language Processing Laboratory
# Xu Chen (xuchenneu@163.com)
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
#set -u
set -o pipefail
export PYTHONIOENCODING=UTF-8
eval=1
time=$(date "+%m%d_%H%M")
stage=0
stop_stage=0
######## hardware ########
# devices
#device=()
gpu_num=8
update_freq=1
root_dir=~/st/Fairseq-S2T
pwd_dir=$PWD
# dataset
src_lang=en
tgt_lang=de
lang=${src_lang}-${tgt_lang}
dataset=mustc
task=translation
vocab_type=unigram
vocab_size=10000
share_dict=1
lc_rm=1
use_specific_dict=1
specific_prefix=st_share10k_lcrm
specific_dir=/home/xuchen/st/data/mustc/st_lcrm/en-de
src_vocab_prefix=spm_unigram10000_st_share
tgt_vocab_prefix=spm_unigram10000_st_share
org_data_dir=/media/data/${dataset}
data_dir=~/st/data/${dataset}/mt/${lang}
train_subset=train
valid_subset=dev
test_subset=tst-COMMON
trans_set=test
# exp
extra_tag=
extra_parameter=
exp_tag=baseline
exp_name=
# config
train_config=train.yaml
# training setting
fp16=1
max_tokens=4096
step_valid=0
bleu_valid=0
# decoding setting
n_average=10
beam_size=5
if [[ ${use_specific_dict} -eq 1 ]]; then
exp_tag=${specific_prefix}_${exp_tag}
data_dir=${data_dir}/${specific_prefix}
mkdir -p ${data_dir}
else
data_dir=${data_dir}/${vocab_type}${vocab_size}
src_vocab_prefix=spm_${vocab_type}${vocab_size}_${src_lang}
tgt_vocab_prefix=spm_${vocab_type}${vocab_size}_${tgt_lang}
if [[ $share_dict -eq 1 ]]; then
data_dir=${data_dir}_share
src_vocab_prefix=spm_${vocab_type}${vocab_size}_share
tgt_vocab_prefix=spm_${vocab_type}${vocab_size}_share
fi
fi
. ./local/parse_options.sh || exit 1;
# full path
train_config=$pwd_dir/conf/${train_config}
if [[ -z ${exp_name} ]]; then
exp_name=$(basename ${train_config%.*})_${exp_tag}
if [[ -n ${extra_tag} ]]; then
exp_name=${exp_name}_${extra_tag}
fi
fi
model_dir=$root_dir/../checkpoints/$dataset/mt/${exp_name}
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
echo "stage -1: Data Download"
# pass
fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
### Task dependent. You have to make data the following preparation part by yourself.
echo "stage 0: MT Data Preparation"
if [[ ! -e ${data_dir} ]]; then
mkdir -p ${data_dir}
fi
if [[ ! -f ${data_dir}/${src_vocab_prefix}.txt || ! -f ${data_dir}/${tgt_vocab_prefix}.txt ]]; then
if [[ ${use_specific_dict} -eq 0 ]]; then
cmd="python ${root_dir}/examples/speech_to_text/prep_mt_data.py
--data-root ${org_data_dir}
--output-root ${data_dir}
--splits ${train_subset},${valid_subset},${test_subset}
--src-lang ${src_lang}
--tgt-lang ${tgt_lang}
--vocab-type ${vocab_type}
--vocab-size ${vocab_size}"
if [[ $share_dict -eq 1 ]]; then
cmd="$cmd
--share"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
else
cp -r ${specific_dir}/${src_vocab_prefix}.* ${data_dir}
cp ${specific_dir}/${tgt_vocab_prefix}.* ${data_dir}
fi
fi
mkdir -p ${data_dir}/data
for split in ${train_subset} ${valid_subset} ${test_subset}; do
{
cmd="cat ${org_data_dir}/${lang}/data/${split}.${src_lang}"
if [[ ${lc_rm} -eq 1 ]]; then
cmd="python local/lower_rm.py ${org_data_dir}/${lang}/data/${split}.${src_lang}"
fi
cmd="${cmd}
| spm_encode --model ${data_dir}/${src_vocab_prefix}.model
--output_format=piece
> ${data_dir}/data/${split}.${src_lang}"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
cmd="spm_encode
--model ${data_dir}/${tgt_vocab_prefix}.model
--output_format=piece
< ${org_data_dir}/${lang}/data/${split}.${tgt_lang}
> ${data_dir}/data/${split}.${tgt_lang}"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
}&
done
wait
cmd="python ${root_dir}/fairseq_cli/preprocess.py
--source-lang ${src_lang} --target-lang ${tgt_lang}
--trainpref ${data_dir}/data/${train_subset}
--validpref ${data_dir}/data/${valid_subset}
--testpref ${data_dir}/data/${test_subset}
--destdir ${data_dir}/data-bin
--srcdict ${data_dir}/${src_vocab_prefix}.txt
--tgtdict ${data_dir}/${tgt_vocab_prefix}.txt
--workers 64"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
fi
data_dir=${data_dir}/data-bin
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "stage 1: MT Network Training"
[[ ! -d ${data_dir} ]] && echo "The data dir ${data_dir} is not existing!" && exit 1;
if [[ -z ${device} || ${#device[@]} -eq 0 ]]; then
if [[ ${gpu_num} -eq 0 ]]; then
device=()
else
source ./local/utils.sh
device=$(get_devices $gpu_num 0)
fi
fi
echo -e "dev=${device} data=${data_dir} model=${model_dir}"
if [[ ! -d ${model_dir} ]]; then
mkdir -p ${model_dir}
else
echo "${model_dir} exists."
fi
cp ${BASH_SOURCE[0]} ${model_dir}
cp ${PWD}/train.sh ${model_dir}
cp ${train_config} ${model_dir}
cmd="python3 -u ${root_dir}/fairseq_cli/train.py
${data_dir}
--source-lang ${src_lang}
--target-lang ${tgt_lang}
--train-config ${train_config}
--task ${task}
--max-tokens ${max_tokens}
--update-freq ${update_freq}
--log-interval 100
--save-dir ${model_dir}
--tensorboard-logdir ${model_dir}"
if [[ -n ${extra_parameter} ]]; then
cmd="${cmd}
${extra_parameter}"
fi
if [[ ${gpu_num} -gt 0 ]]; then
cmd="${cmd}
--distributed-world-size $gpu_num
--ddp-backend no_c10d"
fi
if [[ $fp16 -eq 1 ]]; then
cmd="${cmd}
--fp16"
fi
if [[ $step_valid -eq 1 ]]; then
validate_interval=1
save_interval=1
keep_last_epochs=10
no_epoch_checkpoints=0
save_interval_updates=10000
keep_interval_updates=10
else
validate_interval=1
keep_last_epochs=10
fi
if [[ $bleu_valid -eq 1 ]]; then
cmd="$cmd
--eval-bleu
--eval-bleu-args '{\"beam\": 1}'
--eval-tokenized-bleu
--eval-bleu-remove-bpe
--best-checkpoint-metric bleu
--maximize-best-checkpoint-metric"
fi
if [[ -n $no_epoch_checkpoints && $no_epoch_checkpoints -eq 1 ]]; then
cmd="$cmd
--no-epoch-checkpoints"
fi
if [[ -n $validate_interval ]]; then
cmd="${cmd}
--validate-interval $validate_interval "
fi
if [[ -n $save_interval ]]; then
cmd="${cmd}
--save-interval $save_interval "
fi
if [[ -n $keep_last_epochs ]]; then
cmd="${cmd}
--keep-last-epochs $keep_last_epochs "
fi
if [[ -n $save_interval_updates ]]; then
cmd="${cmd}
--save-interval-updates $save_interval_updates"
if [[ -n $keep_interval_updates ]]; then
cmd="${cmd}
--keep-interval-updates $keep_interval_updates"
fi
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
# save info
log=./history.log
echo "${time} | ${device} | ${data_dir} | ${model_dir} " >> $log
cat $log | tail -n 50 > tmp.log
mv tmp.log $log
export CUDA_VISIBLE_DEVICES=${device}
cmd="nohup ${cmd} >> ${model_dir}/train.log 2>&1 &"
if [[ $eval -eq 1 ]]; then
eval $cmd
sleep 2s
tail -n `wc -l ${model_dir}/train.log | awk '{print $1+1}'` -f ${model_dir}/train.log
fi
fi
wait
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "stage 2: MT Decoding"
if [[ ${n_average} -ne 1 ]]; then
# Average models
dec_model=avg_${n_average}_checkpoint.pt
cmd="python ${root_dir}/scripts/average_checkpoints.py
--inputs ${model_dir}
--num-epoch-checkpoints ${n_average}
--output ${model_dir}/${dec_model}"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval $cmd
else
dec_model=checkpoint_best.pt
fi
if [[ -z ${device} || ${#device[@]} -eq 0 ]]; then
if [[ ${gpu_num} -eq 0 ]]; then
device=()
else
source ./local/utils.sh
device=$(get_devices $gpu_num 0)
fi
fi
export CUDA_VISIBLE_DEVICES=${device}
#tmp_file=$(mktemp ${model_dir}/tmp-XXXXX)
#trap 'rm -rf ${tmp_file}' EXIT
result_file=${model_dir}/decode_result
[[ -f ${result_file} ]] && rm ${result_file}
trans_set=(${trans_set//,/ })
for subset in ${trans_set[@]}; do
cmd="python ${root_dir}/fairseq_cli/generate.py
${data_dir}
--source-lang ${src_lang}
--target-lang ${tgt_lang}
--gen-subset ${subset}
--task ${task}
--path ${model_dir}/${dec_model}
--results-path ${model_dir}
--max-tokens ${max_tokens}
--beam ${beam_size}
--post-process sentencepiece
--tokenizer moses
--moses-source-lang ${src_lang}
--moses-target-lang ${tgt_lang}
--scoring sacrebleu"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
if [[ $eval -eq 1 ]]; then
eval $cmd
tail -n 1 ${model_dir}/generate-${subset}.txt >> ${result_file}
fi
done
cat ${result_file}
fi
egs/covost/mt/train.sh 0 → 100755
#! /bin/bash
# training the model
gpu_num=1
update_freq=1
max_tokens=4096
extra_tag=
extra_parameter=
#extra_tag="${extra_tag}"
#extra_parameter="${extra_parameter} "
exp_tag=baseline
train_config=train.yaml
cmd="./run.sh
--stage 1
--stop_stage 1
--gpu_num ${gpu_num}
--update_freq ${update_freq}
--train_config ${train_config}
--max_tokens ${max_tokens}
"
if [[ -n ${exp_tag} ]]; then
cmd="$cmd --exp_tag ${exp_tag}"
fi
if [[ -n ${extra_tag} ]]; then
cmd="$cmd --extra_tag ${extra_tag}"
fi
if [[ -n ${extra_parameter} ]]; then
cmd="$cmd --extra_parameter \"${extra_parameter}\""
fi
echo $cmd
eval $cmd
egs/covost/st/binary.sh 0 → 100644
set -e
eval=1
root_dir=~/st/Fairseq-S2T
data_dir=/home/xuchen/st/data/test
vocab_dir=/home/xuchen/st/data/mustc/st_lcrm/en-de
asr_vocab_prefix=spm_unigram10000_st_share
st_vocab_prefix=spm_unigram10000_st_share
src_lang=en
tgt_lang=de
splits=(2019)
source ~/tools/audio/bin/activate
splits=`echo ${splits[*]} | sed 's/ /,/g'`
cp -r ${vocab_dir}/${asr_vocab_prefix}.* ${data_dir}
cp -r ${vocab_dir}/${st_vocab_prefix}.* ${data_dir}
cmd="python ${root_dir}/examples/speech_to_text/prep_st_data.py
--data-root ${data_dir}
--output-root ${data_dir}
--splits ${splits}
--task st
--src-lang ${src_lang}
--tgt-lang ${tgt_lang}
--add-src
--lowercase-src
--rm-punc-src
--asr-prefix ${asr_vocab_prefix}
--tgt-prefix ${st_vocab_prefix}
--cmvn-type utterance"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
deactivate
egs/covost/st/conf/train.yaml 0 → 100644
train-subset: train_st
valid-subset: dev_st
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/st/conf/train_ctc.yaml 0 → 100644
train-subset: train_st
valid-subset: dev_st
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/st/conf/train_ctc_conformer.yaml 0 → 100644
train-subset: train_st
valid-subset: dev_st
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-params:
#load-pretrained-encoder-from:
arch: s2t_conformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/st/conf/train_ctc_conformer_m.yaml 0 → 100644
train-subset: train_st
valid-subset: dev_st
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: s2t_conformer_m
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
#dropout: 0.1
#activation-fn: relu
#encoder-embed-dim: 256
#encoder-ffn-embed-dim: 2048
#encoder-layers: 12
#decoder-layers: 6
#encoder-attention-heads: 4
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/st/conf/train_ctc_debug.yaml 0 → 100644
train-subset: train_st,train_v2
valid-subset: dev_st
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_baseline/avg_10_checkpoint.pt
#load-pretrained-decoder-from: /home/xuchen/st/checkpoints/mustc/mt/train_baseline/avg_10_checkpoint.pt
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/st/conf/train_ctc_rpr.yaml 0 → 100644
train-subset: train_st
valid-subset: dev_st
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
encoder-attention-type: relative
#decoder-attention-type: relative
max-relative-length: 100
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/st/conf/train_ctc_sate.yaml 0 → 100644
train-subset: train_st
valid-subset: dev_st
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-acoustic-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_st_vocab/avg_10_checkpoint.pt
#load-pretrained-text-encoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
#load-pretrained-decoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
#load-pretrained-acoustic-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_baseline_lcrm/avg_10_checkpoint.pt
#load-pretrained-text-encoder-from: /home/xuchen/st/Fairseq-S2T/../checkpoints/mustc/mt/train_st_share10k_lcrm_baseline/avg_10_checkpoint.pt
#load-pretrained-decoder-from: /home/xuchen/st/Fairseq-S2T/../checkpoints/mustc/mt/train_st_share10k_lcrm_baseline/avg_10_checkpoint.pt
arch: s2t_sate
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
text-encoder-layers: 6
decoder-layers: 6
encoder-attention-heads: 4
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
acoustic-encoder: transformer
adapter: league
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/st/conf/train_ctc_sate_conformer.yaml 0 → 100644
train-subset: train_st
valid-subset: dev_st
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-acoustic-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_baseline/avg_10_checkpoint.pt
#load-pretrained-text-encoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
#load-pretrained-decoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
arch: s2t_sate
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
text-encoder-layers: 6
decoder-layers: 6
encoder-attention-heads: 4
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
acoustic-encoder: conformer
adapter: league
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/st/conf/train_ctc_sate_rpr.yaml 0 → 100644
train-subset: train_st
valid-subset: dev_st
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-acoustic-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_st_vocab/avg_10_checkpoint.pt
#load-pretrained-text-encoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
#load-pretrained-decoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
arch: s2t_sate
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
text-encoder-layers: 6
decoder-layers: 6
encoder-attention-heads: 4
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
acoustic-encoder: transformer
adapter: league
encoder-attention-type: relative
#decoder-attention-type: relative
max-relative-length: 100
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/st/conf/train_sate.yaml 0 → 100644
train-subset: train_st
valid-subset: dev_st
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-acoustic-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_st_vocab/avg_10_checkpoint.pt
#load-pretrained-text-encoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
#load-pretrained-decoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
load-pretrained-acoustic-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_baseline_lcrm/avg_10_checkpoint.pt
load-pretrained-text-encoder-from: /home/xuchen/st/Fairseq-S2T/../checkpoints/mustc/mt/train_st_share10k_lcrm_baseline/avg_10_checkpoint.pt
load-pretrained-decoder-from: /home/xuchen/st/Fairseq-S2T/../checkpoints/mustc/mt/train_st_share10k_lcrm_baseline/avg_10_checkpoint.pt
arch: s2t_sate
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
text-encoder-layers: 6
decoder-layers: 6
encoder-attention-heads: 4
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
acoustic-encoder: transformer
adapter: league
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/st/conf/train_sate_rpr.yaml 0 → 100644
train-subset: train_st,train_covost
valid-subset: dev_st
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
load-pretrained-acoustic-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_baseline_lcrm/avg_10_checkpoint.pt
load-pretrained-text-encoder-from: /home/xuchen/st/Fairseq-S2T/../checkpoints/mustc/mt/train_st_share10k_lcrm_baseline/avg_10_checkpoint.pt
load-pretrained-decoder-from: /home/xuchen/st/Fairseq-S2T/../checkpoints/mustc/mt/train_st_share10k_lcrm_baseline/avg_10_checkpoint.pt
arch: s2t_sate
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
text-encoder-layers: 6
decoder-layers: 6
encoder-attention-heads: 4
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
acoustic-encoder: transformer
adapter: league
encoder-attention-type: relative
#decoder-attention-type: relative
max-relative-length: 100
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/covost/st/decode.sh 0 → 100755
#! /bin/bash
gpu_num=1
data_dir=
test_subset=tst-COMMON
exp_name=
if [ "$#" -eq 1 ]; then
exp_name=$1
fi
n_average=10
beam_size=5
max_tokens=40000
cmd="./run.sh
--stage 2
--stop_stage 2
--gpu_num ${gpu_num}
--exp_name ${exp_name}
--n_average ${n_average}
--beam_size ${beam_size}
--max_tokens ${max_tokens}
"
if [[ -n ${data_dir} ]]; then
cmd="$cmd --data_dir ${data_dir}"
fi
if [[ -n ${test_subset} ]]; then
cmd="$cmd --test_subset ${test_subset}"
fi
echo $cmd
eval $cmd
egs/covost/st/local/monitor.sh 0 → 100755
gpu_num=1
while :
do
all_devices=$(seq 0 `gpustat | sed '1,2d' | wc -l`);
count=0
for dev in ${all_devices[@]}
do
line=`expr $dev + 2`
use=`gpustat -p | head -n $line | tail -1 | cut -d '|' -f4 | wc -w`
if [[ $use -eq 0 ]]; then
device[$count]=$dev
count=`expr $count + 1`
if [[ $count -eq $gpu_num ]]; then
break
fi
fi
done
if [[ ${#device[@]} -lt $gpu_num ]]; then
sleep 60s
else
echo "Run $cmd"
eval $cmd
sleep 10s
exit
fi
done
egs/covost/st/local/parse_options.sh 0 → 100755
#!/usr/bin/env bash
# Copyright 2012 Johns Hopkins University (Author: Daniel Povey);
# Arnab Ghoshal, Karel Vesely
# 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
#
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
# MERCHANTABLITY OR NON-INFRINGEMENT.
# See the Apache 2 License for the specific language governing permissions and
# limitations under the License.
# Parse command-line options.
# To be sourced by another script (as in ". parse_options.sh").
# Option format is: --option-name arg
# and shell variable "option_name" gets set to value "arg."
# The exception is --help, which takes no arguments, but prints the
# $help_message variable (if defined).
###
### The --config file options have lower priority to command line
### options, so we need to import them first...
###
# Now import all the configs specified by command-line, in left-to-right order
for ((argpos=1; argpos<$#; argpos++)); do
if [ "${!argpos}" == "--config" ]; then
argpos_plus1=$((argpos+1))
config=${!argpos_plus1}
[ ! -r $config ] && echo "$0: missing config '$config'" && exit 1
. $config # source the config file.
fi
done
###
### Now we process the command line options
###
while true; do
[ -z "${1:-}" ] && break; # break if there are no arguments
case "$1" in
# If the enclosing script is called with --help option, print the help
# message and exit. Scripts should put help messages in $help_message
--help|-h) if [ -z "$help_message" ]; then echo "No help found." 1>&2;
else printf "$help_message\n" 1>&2 ; fi;
exit 0 ;;
--*=*) echo "$0: options to scripts must be of the form --name value, got '$1'"
exit 1 ;;
# If the first command-line argument begins with "--" (e.g. --foo-bar),
# then work out the variable name as $name, which will equal "foo_bar".
--*) name=`echo "$1" | sed s/^--// | sed s/-/_/g`;
# Next we test whether the variable in question is undefned-- if so it's
# an invalid option and we die. Note: $0 evaluates to the name of the
# enclosing script.
# The test [ -z ${foo_bar+xxx} ] will return true if the variable foo_bar
# is undefined. We then have to wrap this test inside "eval" because
# foo_bar is itself inside a variable ($name).
eval '[ -z "${'$name'+xxx}" ]' && echo "$0: invalid option $1" 1>&2 && exit 1;
oldval="`eval echo \\$$name`";
# Work out whether we seem to be expecting a Boolean argument.
if [ "$oldval" == "true" ] || [ "$oldval" == "false" ]; then
was_bool=true;
else
was_bool=false;
fi
# Set the variable to the right value-- the escaped quotes make it work if
# the option had spaces, like --cmd "queue.pl -sync y"
eval $name=\"$2\";
# Check that Boolean-valued arguments are really Boolean.
if $was_bool && [[ "$2" != "true" && "$2" != "false" ]]; then
echo "$0: expected \"true\" or \"false\": $1 $2" 1>&2
exit 1;
fi
shift 2;
;;
*) break;
esac
done
# Check for an empty argument to the --cmd option, which can easily occur as a
# result of scripting errors.
[ ! -z "${cmd+xxx}" ] && [ -z "$cmd" ] && echo "$0: empty argument to --cmd option" 1>&2 && exit 1;
true; # so this script returns exit code 0.
egs/covost/st/local/path.sh 0 → 100755
MAIN_ROOT=$PWD/../../..
KALDI_ROOT=$MAIN_ROOT/tools/kaldi
export PATH=$PWD/utils/:$KALDI_ROOT/tools/openfst/bin:$PATH
[ ! -f $KALDI_ROOT/tools/config/common_path.sh ] && echo >&2 "The standard file $KALDI_ROOT/tools/config/common_path.sh is not present -> Exit!" && exit 1
. $KALDI_ROOT/tools/config/common_path.sh
export LC_ALL=C
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$MAIN_ROOT/src/lib
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:$MAIN_ROOT/tools/chainer_ctc/ext/warp-ctc/build
. "${MAIN_ROOT}"/tools/activate_python.sh && . "${MAIN_ROOT}"/tools/extra_path.sh
export PATH=$MAIN_ROOT/utils:$MAIN_ROOT/espnet/bin:$PATH
export OMP_NUM_THREADS=1
# check extra module installation
if ! which tokenizer.perl > /dev/null; then
echo "Error: it seems that moses is not installed." >&2
echo "Error: please install moses as follows." >&2
echo "Error: cd ${MAIN_ROOT}/tools && make moses.done" >&2
return 1
fi
# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
export PYTHONIOENCODING=UTF-8
egs/covost/st/local/utils.sh 0 → 100755
get_devices(){
gpu_num=$1
use_cpu=$2
device=()
while :
do
record=`mktemp -t temp.record.XXXXXX`
gpustat > $record
all_devices=$(seq 0 `cat $record | sed '1,2d' | wc -l`);
count=0
for dev in ${all_devices[@]}
do
line=`expr $dev + 2`
use=`cat $record | head -n $line | tail -1 | cut -d '|' -f3 | cut -d '/' -f1`
if [[ $use -lt 100 ]]; then
device[$count]=$dev
count=`expr $count + 1`
if [[ $count -eq $gpu_num ]]; then
break
fi
fi
done
if [[ ${#device[@]} -lt $gpu_num ]]; then
if [[ $use_cpu -eq 1 ]]; then
device=(-1)
else
sleep 60s
fi
else
break
fi
done
echo ${device[*]} | sed 's/ /,/g'
return $?
}
egs/covost/st/run.sh 0 → 100755
#! /bin/bash
# Processing MuST-C Datasets
# Copyright 2021 Natural Language Processing Laboratory
# Xu Chen (xuchenneu@163.com)
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
#set -u
set -o pipefail
export PYTHONIOENCODING=UTF-8
eval=1
time=$(date "+%m%d_%H%M")
stage=0
stop_stage=0
######## hardware ########
# devices
#device=()
gpu_num=8
update_freq=1
root_dir=~/st/Fairseq-S2T
pwd_dir=$PWD
# dataset
src_lang=en
tgt_lang=de
lang=${src_lang}-${tgt_lang}
dataset=mustc
task=speech_to_text
vocab_type=unigram
asr_vocab_size=5000
vocab_size=10000
share_dict=1
speed_perturb=0
lcrm=1
tokenizer=1
use_specific_dict=0
specific_prefix=valid
specific_dir=/home/xuchen/st/data/mustc/st_lcrm/en-de
asr_vocab_prefix=spm_unigram10000_st_share
st_vocab_prefix=spm_unigram10000_st_share
org_data_dir=/media/data/${dataset}
data_dir=~/st/data/${dataset}/st
test_subset=tst-COMMON
# exp
exp_prefix=${time}
extra_tag=
extra_parameter=
exp_tag=baseline
exp_name=
# config
train_config=train_ctc.yaml
# training setting
fp16=1
max_tokens=40000
step_valid=0
bleu_valid=0
# decoding setting
n_average=10
beam_size=5
if [[ ${share_dict} -eq 1 ]]; then
data_config=config_st_share.yaml
else
data_config=config_st.yaml
fi
if [[ ${speed_perturb} -eq 1 ]]; then
data_dir=${data_dir}_sp
exp_prefix=${exp_prefix}_sp
fi
if [[ ${lcrm} -eq 1 ]]; then
data_dir=${data_dir}_lcrm
exp_prefix=${exp_prefix}_lcrm
fi
if [[ ${use_specific_dict} -eq 1 ]]; then
data_dir=${data_dir}_${specific_prefix}
exp_prefix=${exp_prefix}_${specific_prefix}
fi
if [[ ${tokenizer} -eq 1 ]]; then
data_dir=${data_dir}_tok
exp_prefix=${exp_prefix}_tok
fi
. ./local/parse_options.sh || exit 1;
# full path
train_config=$pwd_dir/conf/${train_config}
if [[ -z ${exp_name} ]]; then
exp_name=${exp_prefix}_$(basename ${train_config%.*})_${exp_tag}
if [[ -n ${extra_tag} ]]; then
exp_name=${exp_name}_${extra_tag}
fi
fi
model_dir=$root_dir/../checkpoints/$dataset/st/${exp_name}
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
echo "stage -1: Data Download"
# pass
fi
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
### Task dependent. You have to make data the following preparation part by yourself.
### But you can utilize Kaldi recipes in most cases
echo "stage 0: ASR Data Preparation"
if [[ ! -e ${data_dir}/${lang} ]]; then
mkdir -p ${data_dir}/${lang}
fi
source ~/tools/audio/bin/activate
cmd="python ${root_dir}/examples/speech_to_text/prep_mustc_data.py
--data-root ${org_data_dir}
--output-root ${data_dir}
--task asr
--vocab-type ${vocab_type}
--vocab-size ${asr_vocab_size}"
if [[ ${speed_perturb} -eq 1 ]]; then
cmd="$cmd
--speed-perturb"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 && ${share_dict} -ne 1 && ${use_specific_dict} -ne 1 ]] && eval $cmd
asr_prefix=spm_${vocab_type}${asr_vocab_size}_asr
echo "stage 0: ST Data Preparation"
cmd="python ${root_dir}/examples/speech_to_text/prep_mustc_data.py
--data-root ${org_data_dir}
--output-root ${data_dir}
--task st
--add-src
--cmvn-type utterance
--vocab-type ${vocab_type}
--vocab-size ${vocab_size}"
if [[ ${use_specific_dict} -eq 1 ]]; then
cp -r ${specific_dir}/${asr_vocab_prefix}.* ./
cp -r ${specific_dir}/${st_vocab_prefix}.* ./
if [[ $share_dict -eq 1 ]]; then
cmd="$cmd
--share
--st-spm-prefix ${st_vocab_prefix}"
else
cmd="$cmd
--st-spm-prefix ${st_vocab_prefix}
--asr-prefix ${asr_vocab_prefix}"
fi
else
if [[ $share_dict -eq 1 ]]; then
cmd="$cmd
--share"
else
cmd="$cmd
--asr-prefix ${asr_prefix}"
fi
fi
if [[ ${speed_perturb} -eq 1 ]]; then
cmd="$cmd
--speed-perturb"
fi
if [[ ${lcrm} -eq 1 ]]; then
cmd="$cmd
--lowercase-src
--rm-punc-src"
fi
if [[ ${tokenizer} -eq 1 ]]; then
cmd="$cmd
--tokenizer"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
deactivate
fi
data_dir=${data_dir}/${lang}
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "stage 1: ST Network Training"
[[ ! -d ${data_dir} ]] && echo "The data dir ${data_dir} is not existing!" && exit 1;
if [[ -z ${device} || ${#device[@]} -eq 0 ]]; then
if [[ ${gpu_num} -eq 0 ]]; then
device=()
else
source ./local/utils.sh
device=$(get_devices $gpu_num 0)
fi
fi
echo -e "dev=${device} data=${data_dir} model=${model_dir}"
if [[ ! -d ${model_dir} ]]; then
mkdir -p ${model_dir}
else
echo "${model_dir} exists."
fi
cp ${BASH_SOURCE[0]} ${model_dir}
cp ${PWD}/train.sh ${model_dir}
cp ${train_config} ${model_dir}
cmd="python3 -u ${root_dir}/fairseq_cli/train.py
${data_dir}
--config-yaml ${data_config}
--train-config ${train_config}
--task ${task}
--max-tokens ${max_tokens}
--update-freq ${update_freq}
--log-interval 100
--save-dir ${model_dir}
--tensorboard-logdir ${model_dir}"
if [[ -n ${extra_parameter} ]]; then
cmd="${cmd}
${extra_parameter}"
fi
if [[ ${gpu_num} -gt 0 ]]; then
cmd="${cmd}
--distributed-world-size $gpu_num
--ddp-backend no_c10d"
fi
if [[ $fp16 -eq 1 ]]; then
cmd="${cmd}
--fp16"
fi
if [[ $step_valid -eq 1 ]]; then
validate_interval=10000
save_interval=10000
no_epoch_checkpoints=1
save_interval_updates=5000
keep_interval_updates=3
else
validate_interval=1
keep_last_epochs=10
fi
if [[ $bleu_valid -eq 1 ]]; then
cmd="$cmd
--eval-bleu
--eval-bleu-args '{\"beam\": 1}'
--eval-tokenized-bleu
--eval-bleu-remove-bpe
--best-checkpoint-metric bleu
--maximize-best-checkpoint-metric"
fi
if [[ -n $no_epoch_checkpoints && $no_epoch_checkpoints -eq 1 ]]; then
cmd="$cmd
--no-epoch-checkpoints"
fi
if [[ -n $validate_interval ]]; then
cmd="${cmd}
--validate-interval $validate_interval "
fi
if [[ -n $save_interval ]]; then
cmd="${cmd}
--save-interval $save_interval "
fi
if [[ -n $keep_last_epochs ]]; then
cmd="${cmd}
--keep-last-epochs $keep_last_epochs "
fi
if [[ -n $save_interval_updates ]]; then
cmd="${cmd}
--save-interval-updates $save_interval_updates"
if [[ -n $keep_interval_updates ]]; then
cmd="${cmd}
--keep-interval-updates $keep_interval_updates"
fi
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
# save info
log=./history.log
echo "${time} | ${device} | ${data_dir} | ${model_dir} " >> $log
cat $log | tail -n 50 > tmp.log
mv tmp.log $log
export CUDA_VISIBLE_DEVICES=${device}
cmd="nohup ${cmd} >> ${model_dir}/train.log 2>&1 &"
if [[ $eval -eq 1 ]]; then
eval $cmd
sleep 2s
tail -n `wc -l ${model_dir}/train.log | awk '{print $1+1}'` -f ${model_dir}/train.log
fi
fi
wait
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "stage 2: ST Decoding"
if [[ ${n_average} -ne 1 ]]; then
# Average models
dec_model=avg_${n_average}_checkpoint.pt
cmd="python ${root_dir}/scripts/average_checkpoints.py
--inputs ${model_dir}
--num-epoch-checkpoints ${n_average}
--output ${model_dir}/${dec_model}"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval $cmd
else
dec_model=checkpoint_best.pt
fi
if [[ -z ${device} || ${#device[@]} -eq 0 ]]; then
if [[ ${gpu_num} -eq 0 ]]; then
device=()
else
source ./local/utils.sh
device=$(get_devices $gpu_num 0)
fi
fi
export CUDA_VISIBLE_DEVICES=${device}
#tmp_file=$(mktemp ${model_dir}/tmp-XXXXX)
#trap 'rm -rf ${tmp_file}' EXIT
result_file=${model_dir}/decode_result
[[ -f ${result_file} ]] && rm ${result_file}
test_subset=(${test_subset//,/ })
for subset in ${test_subset[@]}; do
subset=${subset}_st
cmd="python ${root_dir}/fairseq_cli/generate.py
${data_dir}
--config-yaml ${data_config}
--gen-subset ${subset}
--task speech_to_text
--path ${model_dir}/${dec_model}
--results-path ${model_dir}
--max-tokens ${max_tokens}
--beam ${beam_size}
--scoring sacrebleu"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
if [[ $eval -eq 1 ]]; then
eval $cmd
tail -n 1 ${model_dir}/generate-${subset}.txt >> ${result_file}
fi
done
cat ${result_file}
fi
egs/covost/st/train.sh 0 → 100755
#! /bin/bash
# training the model
gpu_num=8
update_freq=2
max_tokens=20000
extra_tag=lcrm
extra_parameter=
#extra_tag="${extra_tag}"
#extra_parameter="${extra_parameter} "
exp_tag=baseline
train_config=train_ctc_sate.yaml
#train_config=train_ctc.yaml
cmd="./run.sh
--stage 1
--stop_stage 1
--gpu_num ${gpu_num}
--update_freq ${update_freq}
--train_config ${train_config}
--max_tokens ${max_tokens}
"
if [[ -n ${exp_tag} ]]; then
cmd="$cmd --exp_tag ${exp_tag}"
fi
if [[ -n ${extra_tag} ]]; then
cmd="$cmd --extra_tag ${extra_tag}"
fi
if [[ -n ${extra_parameter} ]]; then
cmd="$cmd --extra_parameter \"${extra_parameter}\""
fi
echo $cmd
eval $cmd
egs/mustc/asr/conf/train_ctc_conformer.yaml 0 → 100644
train-subset: train_asr
valid-subset: dev_asr
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: s2t_conformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/mustc/asr/conf/train_ctc_conformer_m.yaml 0 → 100644
train-subset: train_asr
valid-subset: dev_asr
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: s2t_conformer_m
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 1e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
#dropout: 0.1
#activation-fn: relu
#encoder-embed-dim: 256
#encoder-ffn-embed-dim: 2048
#encoder-layers: 12
#decoder-layers: 6
#encoder-attention-heads: 4
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/mustc/asr/conf/train_ctc_rpr.yaml 0 → 100644
train-subset: train_asr
valid-subset: dev_asr
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
arch: s2t_transformer_s
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
ctc-weight: 0.3
criterion: label_smoothed_cross_entropy_with_ctc
label_smoothing: 0.1
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
decoder-layers: 6
encoder-attention-heads: 4
encoder-attention-type: relative
#decoder-attention-type: relative
max-relative-length: 100
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/mustc/asr/run.sh
......@@ -20,7 +20,7 @@ stop_stage=0
######## hardware ########
# devices
device=()
#device=()
gpu_num=8
update_freq=1
......@@ -37,12 +37,19 @@ task=speech_to_text
vocab_type=unigram
vocab_size=5000
speed_perturb=0
lcrm=1
use_specific_dict=0
specific_prefix=valid
specific_dir=/home/xuchen/st/data/mustc/st_lcrm/en-de
asr_vocab_prefix=spm_unigram10000_st_share
org_data_dir=/media/data/${dataset}
data_dir=~/st/data/${dataset}/asr_lcrm
data_dir=~/st/data/${dataset}/asr
test_subset=tst-COMMON
# exp
exp_prefix=${time}
extra_tag=
extra_parameter=
exp_tag=baseline
......@@ -64,6 +71,15 @@ beam_size=5
if [[ ${speed_perturb} -eq 1 ]]; then
data_dir=${data_dir}_sp
exp_prefix=${exp_prefix}_sp
fi
if [[ ${lcrm} -eq 1 ]]; then
data_dir=${data_dir}_lcrm
exp_prefix=${exp_prefix}_lcrm
fi
if [[ ${use_specific_dict} -eq 1 ]]; then
data_dir=${data_dir}_${specific_prefix}
exp_prefix=${exp_prefix}_${specific_prefix}
fi
. ./local/parse_options.sh || exit 1;
......@@ -71,13 +87,10 @@ fi
# full path
train_config=$pwd_dir/conf/${train_config}
if [[ -z ${exp_name} ]]; then
exp_name=$(basename ${train_config%.*})_${exp_tag}
exp_name=${exp_prefix}_$(basename ${train_config%.*})_${exp_tag}
if [[ -n ${extra_tag} ]]; then
exp_name=${exp_name}_${extra_tag}
fi
if [[ ${speed_perturb} -eq 1 ]]; then
exp_name=sp_${exp_name}
fi
fi
model_dir=$root_dir/../checkpoints/$dataset/asr/${exp_name}
......@@ -99,16 +112,26 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
--data-root ${org_data_dir}
--output-root ${data_dir}
--task asr
--lowercase-src
--rm-punc-src
--vocab-type ${vocab_type}
--vocab-size ${vocab_size}"
if [[ ${use_specific_dict} -eq 1 ]]; then
cp -r ${specific_dir}/${asr_vocab_prefix}.* ${data_dir}/${lang}
cmd="$cmd
--asr-prefix ${asr_vocab_prefix}"
fi
if [[ ${speed_perturb} -eq 1 ]]; then
cmd="$cmd
--speed-perturb"
fi
if [[ ${lcrm} -eq 1 ]]; then
cmd="$cmd
--lowercase-src
--rm-punc-src"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval $cmd
[[ $eval -eq 1 ]] && eval ${cmd}
deactivate
fi
data_dir=${data_dir}/${lang}
......
egs/mustc/mt/run.sh
......@@ -32,18 +32,19 @@ src_lang=en
tgt_lang=de
lang=${src_lang}-${tgt_lang}
dataset=mustc
dataset=mustc-v2
task=translation
vocab_type=unigram
vocab_size=10000
share_dict=1
lc_rm=1
lcrm=1
tokenizer=1
use_specific_dict=1
specific_prefix=st_share10k_lcrm
specific_dir=/home/xuchen/st/data/mustc/st_lcrm/en-de
src_vocab_prefix=spm_unigram10000_st_share
tgt_vocab_prefix=spm_unigram10000_st_share
specific_prefix=wmt_share32k
specific_dir=/home/xuchen/st/data/wmt/mt_lcrm/en-de/unigram32000_share
src_vocab_prefix=spm_unigram32000_share
tgt_vocab_prefix=spm_unigram32000_share
org_data_dir=/media/data/${dataset}
data_dir=~/st/data/${dataset}/mt/${lang}
......@@ -53,6 +54,7 @@ test_subset=tst-COMMON
trans_set=test
# exp
exp_prefix=${time}
extra_tag=
extra_parameter=
exp_tag=baseline
......@@ -72,7 +74,7 @@ n_average=10
beam_size=5
if [[ ${use_specific_dict} -eq 1 ]]; then
exp_tag=${specific_prefix}_${exp_tag}
exp_prefix=${specific_prefix}_${exp_prefix}
data_dir=${data_dir}/${specific_prefix}
mkdir -p ${data_dir}
else
......@@ -85,13 +87,24 @@ else
tgt_vocab_prefix=spm_${vocab_type}${vocab_size}_share
fi
fi
if [[ ${lcrm} -eq 1 ]]; then
data_dir=${data_dir}_lcrm
exp_prefix=${exp_prefix}_lcrm
fi
if [[ ${tokenizer} -eq 1 ]]; then
train_subset=${train_subset}.tok
valid_subset=${valid_subset}.tok
test_subset=${test_subset}.tok
data_dir=${data_dir}_tok
exp_prefix=${exp_prefix}_tok
fi
. ./local/parse_options.sh || exit 1;
# full path
train_config=$pwd_dir/conf/${train_config}
if [[ -z ${exp_name} ]]; then
exp_name=$(basename ${train_config%.*})_${exp_tag}
exp_name=${exp_prefix}_$(basename ${train_config%.*})_${exp_tag}
if [[ -n ${extra_tag} ]]; then
exp_name=${exp_name}_${extra_tag}
fi
......@@ -136,7 +149,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
for split in ${train_subset} ${valid_subset} ${test_subset}; do
{
cmd="cat ${org_data_dir}/${lang}/data/${split}.${src_lang}"
if [[ ${lc_rm} -eq 1 ]]; then
if [[ ${lcrm} -eq 1 ]]; then
cmd="python local/lower_rm.py ${org_data_dir}/${lang}/data/${split}.${src_lang}"
fi
cmd="${cmd}
......
egs/mustc/st/binary.sh 0 → 100644
set -e
eval=1
root_dir=~/st/Fairseq-S2T
data_dir=/home/xuchen/st/data/test
vocab_dir=/home/xuchen/st/data/mustc/st_lcrm/en-de
asr_vocab_prefix=spm_unigram10000_st_share
st_vocab_prefix=spm_unigram10000_st_share
src_lang=en
tgt_lang=de
splits=(2019)
source ~/tools/audio/bin/activate
splits=`echo ${splits[*]} | sed 's/ /,/g'`
cp -r ${vocab_dir}/${asr_vocab_prefix}.* ${data_dir}
cp -r ${vocab_dir}/${st_vocab_prefix}.* ${data_dir}
cmd="python ${root_dir}/examples/speech_to_text/prep_st_data.py
--data-root ${data_dir}
--output-root ${data_dir}
--splits ${splits}
--task st
--src-lang ${src_lang}
--tgt-lang ${tgt_lang}
--add-src
--lowercase-src
--rm-punc-src
--asr-prefix ${asr_vocab_prefix}
--tgt-prefix ${st_vocab_prefix}
--cmvn-type utterance"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
deactivate
egs/mustc/st/conf/train_ctc_rpr.yaml
train-subset: train_st
valid-subset: dev_st
max-epoch: 100
max-epoch: 50
max-update: 100000
num-workers: 8
......@@ -39,8 +39,8 @@ decoder-layers: 6
encoder-attention-heads: 4
encoder-attention-type: relative
decoder-attention-type: relative
max-relative-length: 20
#decoder-attention-type: relative
max-relative-length: 100
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
......
egs/mustc/st/conf/train_ctc_sate.yaml
......@@ -15,6 +15,10 @@ report-accuracy: True
#load-pretrained-text-encoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
#load-pretrained-decoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
#load-pretrained-acoustic-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_baseline_lcrm/avg_10_checkpoint.pt
#load-pretrained-text-encoder-from: /home/xuchen/st/Fairseq-S2T/../checkpoints/mustc/mt/train_st_share10k_lcrm_baseline/avg_10_checkpoint.pt
#load-pretrained-decoder-from: /home/xuchen/st/Fairseq-S2T/../checkpoints/mustc/mt/train_st_share10k_lcrm_baseline/avg_10_checkpoint.pt
arch: s2t_sate
share-decoder-input-output-embed: True
optimizer: adam
......
egs/mustc/st/conf/train_ctc_sate_rpr.yaml
......@@ -50,8 +50,8 @@ acoustic-encoder: transformer
adapter: league
encoder-attention-type: relative
decoder-attention-type: relative
max-relative-length: 20
#decoder-attention-type: relative
max-relative-length: 100
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
......
egs/mustc/st/conf/train_sate.yaml
......@@ -11,9 +11,13 @@ log-interval: 100
seed: 1
report-accuracy: True
load-pretrained-acoustic-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_st_vocab/avg_10_checkpoint.pt
load-pretrained-text-encoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
load-pretrained-decoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
#load-pretrained-acoustic-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_st_vocab/avg_10_checkpoint.pt
#load-pretrained-text-encoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
#load-pretrained-decoder-from: /home/xuchen/st/checkpoints/mustc/mt/st_share10k_train_baseline/avg_10_checkpoint.pt
load-pretrained-acoustic-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_baseline_lcrm/avg_10_checkpoint.pt
load-pretrained-text-encoder-from: /home/xuchen/st/Fairseq-S2T/../checkpoints/mustc/mt/train_st_share10k_lcrm_baseline/avg_10_checkpoint.pt
load-pretrained-decoder-from: /home/xuchen/st/Fairseq-S2T/../checkpoints/mustc/mt/train_st_share10k_lcrm_baseline/avg_10_checkpoint.pt
arch: s2t_sate
share-decoder-input-output-embed: True
......
egs/mustc/st/conf/train_sate_rpr.yaml 0 → 100644
train-subset: train_st,train_covost
valid-subset: dev_st
max-epoch: 50
max-update: 100000
num-workers: 8
patience: 10
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
load-pretrained-acoustic-encoder-from: /home/xuchen/st/checkpoints/mustc/asr/train_ctc_baseline_lcrm/avg_10_checkpoint.pt
load-pretrained-text-encoder-from: /home/xuchen/st/Fairseq-S2T/../checkpoints/mustc/mt/train_st_share10k_lcrm_baseline/avg_10_checkpoint.pt
load-pretrained-decoder-from: /home/xuchen/st/Fairseq-S2T/../checkpoints/mustc/mt/train_st_share10k_lcrm_baseline/avg_10_checkpoint.pt
arch: s2t_sate
share-decoder-input-output-embed: True
optimizer: adam
clip-norm: 10.0
lr-scheduler: inverse_sqrt
warmup-init-lr: 1e-7
warmup-updates: 10000
lr: 2e-3
#adam_betas: (0.9,0.98)
criterion: label_smoothed_cross_entropy
label_smoothing: 0.1
encoder-normalize-before: True
decoder-normalize-before: True
conv-kernel-sizes: 5,5
conv-channels: 1024
dropout: 0.1
activation-fn: relu
encoder-embed-dim: 256
encoder-ffn-embed-dim: 2048
encoder-layers: 12
text-encoder-layers: 6
decoder-layers: 6
encoder-attention-heads: 4
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
acoustic-encoder: transformer
adapter: league
encoder-attention-type: relative
#decoder-attention-type: relative
max-relative-length: 100
#decoder-embed-dim: 256
#decoder-ffn-embed-dim: 2048
#decoder-attention-heads: 4
#attention-dropout: 0.1
#activation-dropout: 0.1
egs/mustc/st/run.sh
......@@ -20,7 +20,7 @@ stop_stage=0
######## hardware ########
# devices
device=()
#device=()
gpu_num=8
update_freq=1
......@@ -32,19 +32,28 @@ src_lang=en
tgt_lang=de
lang=${src_lang}-${tgt_lang}
dataset=mustc
dataset=mustc-v2
task=speech_to_text
vocab_type=unigram
asr_vocab_size=5000
vocab_size=10000
share_dict=1
speed_perturb=0
lcrm=1
tokenizer=0
use_specific_dict=0
specific_prefix=valid
specific_dir=/home/xuchen/st/data/mustc/st_lcrm/en-de
asr_vocab_prefix=spm_unigram10000_st_share
st_vocab_prefix=spm_unigram10000_st_share
org_data_dir=/media/data/${dataset}
data_dir=~/st/data/${dataset}/st_lcrm
data_dir=~/st/data/${dataset}/st
test_subset=tst-COMMON
# exp
exp_prefix=${time}
extra_tag=
extra_parameter=
exp_tag=baseline
......@@ -68,9 +77,21 @@ if [[ ${share_dict} -eq 1 ]]; then
else
data_config=config_st.yaml
fi
if [[ ${speed_perturb} -eq 1 ]]; then
data_dir=${data_dir}_sp
exp_prefix=${exp_prefix}_sp
fi
if [[ ${lcrm} -eq 1 ]]; then
data_dir=${data_dir}_lcrm
exp_prefix=${exp_prefix}_lcrm
fi
if [[ ${use_specific_dict} -eq 1 ]]; then
data_dir=${data_dir}_${specific_prefix}
exp_prefix=${exp_prefix}_${specific_prefix}
fi
if [[ ${tokenizer} -eq 1 ]]; then
data_dir=${data_dir}_tok
exp_prefix=${exp_prefix}_tok
fi
. ./local/parse_options.sh || exit 1;
......@@ -78,13 +99,10 @@ fi
# full path
train_config=$pwd_dir/conf/${train_config}
if [[ -z ${exp_name} ]]; then
exp_name=$(basename ${train_config%.*})_${exp_tag}
exp_name=${exp_prefix}_$(basename ${train_config%.*})_${exp_tag}
if [[ -n ${extra_tag} ]]; then
exp_name=${exp_name}_${extra_tag}
fi
if [[ ${speed_perturb} -eq 1 ]]; then
exp_name=sp_${exp_name}
fi
fi
model_dir=$root_dir/../checkpoints/$dataset/st/${exp_name}
......@@ -113,7 +131,8 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
--speed-perturb"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 && ${share_dict} -ne 1 ]] && eval $cmd
[[ $eval -eq 1 && ${share_dict} -ne 1 && ${use_specific_dict} -ne 1 ]] && eval $cmd
asr_prefix=spm_${vocab_type}${asr_vocab_size}_asr
echo "stage 0: ST Data Preparation"
cmd="python ${root_dir}/examples/speech_to_text/prep_mustc_data.py
......@@ -121,22 +140,44 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
--output-root ${data_dir}
--task st
--add-src
--lowercase-src
--rm-punc-src
--cmvn-type utterance
--vocab-type ${vocab_type}
--vocab-size ${vocab_size}"
if [[ ${use_specific_dict} -eq 1 ]]; then
cp -r ${specific_dir}/${asr_vocab_prefix}.* ${data_dir}/${lang}
cp -r ${specific_dir}/${st_vocab_prefix}.* ${data_dir}/${lang}
if [[ $share_dict -eq 1 ]]; then
cmd="$cmd
--share
--st-spm-prefix ${st_vocab_prefix}"
else
cmd="$cmd
--st-spm-prefix ${st_vocab_prefix}
--asr-prefix ${asr_vocab_prefix}"
fi
else
if [[ $share_dict -eq 1 ]]; then
cmd="$cmd
--share"
else
cmd="$cmd
--asr-prefix spm_${vocab_type}${asr_vocab_size}_asr"
--asr-prefix ${asr_prefix}"
fi
fi
if [[ ${speed_perturb} -eq 1 ]]; then
cmd="$cmd
--speed-perturb"
fi
if [[ ${lcrm} -eq 1 ]]; then
cmd="$cmd
--lowercase-src
--rm-punc-src"
fi
if [[ ${tokenizer} -eq 1 ]]; then
cmd="$cmd
--tokenizer"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
......
egs/wmt/mt/binary.sh
......@@ -4,14 +4,15 @@ eval=1
root_dir=~/st/Fairseq-S2T
data_dir=/home/xuchen/st/data/wmt/test
vocab_dir=/home/xuchen/st/data/wmt/mt/en-de/unigram32000_share
vocab_dir=/home/xuchen/st/data/wmt/mt_lcrm/en-de/unigram32000_share
src_vocab_prefix=spm_unigram32000_share
tgt_vocab_prefix=spm_unigram32000_share
src_lang=en
tgt_lang=de
tokenize=1
splits=(newstest2014 newstest2016)
lcrm=1
splits=(tst-COMMON newstest2014 newstest2016)
for split in ${splits[@]}; do
src_file=${data_dir}/${split}.${src_lang}
......@@ -29,11 +30,15 @@ for split in ${splits[@]}; do
tgt_file=${tgt_file}.tok
fi
cmd="spm_encode
--model ${vocab_dir}/${src_vocab_prefix}.model
cmd="cat ${src_file}"
if [[ ${lcrm} -eq 1 ]]; then
cmd="python local/lower_rm.py ${src_file}"
fi
cmd="${cmd}
| spm_encode --model ${vocab_dir}/${src_vocab_prefix}.model
--output_format=piece
< ${src_file}
> ${src_file}.spm"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
......
egs/wmt/mt/conf/train_dlcl.yaml
......@@ -2,7 +2,7 @@ train-subset: train
valid-subset: valid
max-epoch: 50
max-update: 100000
max-update: 1000000
num-workers: 8
patience: 10
......
egs/wmt/mt/conf/train_dlcl_rpr.yaml
......@@ -2,7 +2,7 @@ train-subset: train
valid-subset: valid
max-epoch: 50
max-update: 100000
max-update: 1000000
num-workers: 8
patience: 10
......
egs/wmt/mt/conf/train_rpr.yaml
......@@ -2,7 +2,7 @@ train-subset: train
valid-subset: valid
max-epoch: 50
max-update: 100000
max-update: 1000000
num-workers: 8
patience: 10
......
egs/wmt/mt/conf/train_s.yaml
......@@ -2,7 +2,7 @@ train-subset: train
valid-subset: valid
max-epoch: 50
max-update: 100000
max-update: 1000000
num-workers: 8
patience: 10
......
egs/wmt/mt/run.sh
......@@ -37,11 +37,11 @@ task=translation
vocab_type=unigram
vocab_size=32000
share_dict=1
lc_rm=1
lcrm=1
use_specific_dict=0
specific_prefix=st_share10k
specific_dir=/home/xuchen/st/data/mustc/st/en-de
use_specific_dict=1
specific_prefix=st_tok_share10k
specific_dir=/home/xuchen/st/data/mustc/st_lcrm_tok/en-de
src_vocab_prefix=spm_unigram10000_st_share
tgt_vocab_prefix=spm_unigram10000_st_share
......@@ -52,6 +52,7 @@ valid_subset=dev
test_subset=test
# exp
exp_prefix=${time}
extra_tag=
extra_parameter=
exp_tag=baseline
......@@ -71,7 +72,7 @@ n_average=10
beam_size=5
if [[ ${use_specific_dict} -eq 1 ]]; then
exp_tag=${specific_prefix}_${exp_tag}
exp_prefix=${exp_prefix}_${specific_prefix}
data_dir=${data_dir}/${specific_prefix}
mkdir -p ${data_dir}
else
......@@ -85,12 +86,17 @@ else
fi
fi
if [[ ${lcrm} -eq 1 ]]; then
data_dir=${data_dir}_lcrm
exp_prefix=${exp_prefix}_lcrm
fi
. ./local/parse_options.sh || exit 1;
# full path
train_config=$pwd_dir/conf/${train_config}
if [[ -z ${exp_name} ]]; then
exp_name=$(basename ${train_config%.*})_${exp_tag}
exp_name=${exp_prefix}_$(basename ${train_config%.*})_${exp_tag}
if [[ -n ${extra_tag} ]]; then
exp_name=${exp_name}_${extra_tag}
fi
......@@ -137,7 +143,7 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
for split in ${train_subset} ${valid_subset} ${test_subset}; do
{
cmd="cat ${org_data_dir}/${lang}/data/${split}.${src_lang}"
if [[ ${lc_rm} -eq 1 ]]; then
if [[ ${lcrm} -eq 1 ]]; then
cmd="python local/lower_rm.py ${org_data_dir}/${lang}/data/${split}.${src_lang}"
fi
cmd="${cmd}
......
egs/wmt/mt/train.sh
......@@ -24,6 +24,9 @@ cmd="./run.sh
--max_tokens ${max_tokens}
"
if [[ -n ${exp_name} ]]; then
cmd="$cmd --exp_name ${exp_name}"
fi
if [[ -n ${exp_tag} ]]; then
cmd="$cmd --exp_tag ${exp_tag}"
fi
......
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