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Fairseq-S2T
Commit a2353895 by xuchen
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add the shrink method into the sate architecture

parent da4e7dc3
正在显示 包含 2137 行增加 和 15 行删除
egs/libri_trans/asr/binary.sh 0 → 100644
set -e
eval=1
lcrm=0
tokenizer=0
root_dir=~/st/Fairseq-S2T
data_dir=~/st/data/test
vocab_dir=~/st/data/mustc/st/en-de
asr_vocab_prefix=spm_unigram10000_st_share
src_lang=en
tgt_lang=de
subsets=(2019)
cp -r ${vocab_dir}/${asr_vocab_prefix}.* ${data_dir}/${src_lang}-${tgt_lang}
rm -rf ${data_dir}/${src_lang}-${tgt_lang}/fbank80.zip
splits=$(echo ${subsets[*]} | sed 's/ /,/g')
cmd="python ${root_dir}/examples/speech_to_text/prep_st_data.py
--data-root ${data_dir}
--output-root ${data_dir}
--splits ${splits}
--task asr
--src-lang ${src_lang}
--tgt-lang ${tgt_lang}
--add-src
--share
--asr-prefix ${asr_vocab_prefix}
--cmvn-type utterance"
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/libri_trans/asr/conf/base.yaml 0 → 100644
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_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/libri_trans/asr/conf/basis.yaml 0 → 100644
train-subset: train
valid-subset: dev
max-epoch: 100
max-update: 100000
patience: 20
best_checkpoint_metric: loss
maximize_best_checkpoint_metric: False
no-epoch-checkpoints: True
#keep-last-epochs: 10
keep-best-checkpoints: 10
num-workers: 8
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
\ No newline at end of file
egs/libri_trans/asr/conf/conformer.yaml 0 → 100644
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
egs/libri_trans/asr/conf/ctc.yaml 0 → 100644
ctc-weight: 0.3
post-process: sentencepiece
egs/libri_trans/asr/conf/debug.yaml 0 → 100644
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: 2
text-encoder-layers: 2
decoder-layers: 2
encoder-attention-heads: 4
#load-pretrained-encoder-from:
#load-pretrained-acoustic-encoder-from:
#load-pretrained-text-encoder-from:
#load-pretrained-decoder-from:
#macaron-style: True
#use-cnn-module: True
#cnn-module-kernel: 31
#acoustic-encoder: pds
acoustic-encoder: transformer
adapter: shrink
encoder-embed-dim: 256
pds-stages: 4
#pds-dropout: 0
pds-layers: 3_3_3_3
pds-ratios: 2_2_1_2
pds-fusion: True
pds-fusion-method: all_conv
pds-embed-dims: 256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1
pds-kernel-sizes: 5_5_5_5
pds-ffn-ratios: 8_8_8_8
pds-attn-heads: 4_4_4_4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
\ No newline at end of file
egs/libri_trans/asr/conf/dlcl.yaml 0 → 100644
use-enc-dlcl: True
use-dec-dlcl: True
egs/libri_trans/asr/conf/local_attn.yaml 0 → 100644
encoder-attention-type: local
hard-mask-window: 0
gauss-mask-sigma: 3
init-mask-weight: 0
\ No newline at end of file
egs/libri_trans/asr/conf/pds_base.yaml 0 → 100644
arch: pdss2t_transformer_s_8
pds-fusion: True
ctc-layer: 12
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_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
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
egs/libri_trans/asr/conf/pds_base_16.yaml 0 → 100644
arch: pdss2t_transformer_s_16
encoder-embed-dim: 256
pds-stages: 4
ctc-layer: 12
pds-layers: 2_2_6_2
pds-ratios: 2_2_2_2
pds-fusion: True
pds-fusion-method: all_conv
pds-embed-dims: 256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1
pds-kernel-sizes: 5_5_5_5
pds-ffn-ratios: 8_8_8_8
pds-attn-heads: 4_4_4_4
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_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
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
egs/libri_trans/asr/conf/pds_base_32.yaml 0 → 100644
arch: pdss2t_transformer_s_32
encoder-embed-dim: 256
pds-stages: 5
ctc-layer: 12
pds-layers: 2_2_3_3_2
pds-ratios: 2_2_2_2_2
pds-fusion: True
pds-fusion-method: all_conv
pds-embed-dims: 256_256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1_1
pds-kernel-sizes: 5_5_5_5_5
pds-ffn-ratios: 8_8_8_8_8
pds-attn-heads: 4_4_4_4_4
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_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
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
egs/libri_trans/asr/conf/pds_base_8.yaml 0 → 100644
arch: pdss2t_transformer_s_8
encoder-embed-dim: 256
pds-stages: 4
ctc-layer: 12
pds-layers: 3_3_3_3
pds-ratios: 2_2_1_2
pds-fusion: True
pds-fusion-method: all_conv
pds-embed-dims: 256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1
pds-kernel-sizes: 5_5_5_5
pds-ffn-ratios: 8_8_8_8
pds-attn-heads: 4_4_4_4
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_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
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
egs/libri_trans/asr/conf/rpr.yaml 0 → 100644
encoder-attention-type: rel_selfattn
#encoder-attention-type: relative
#max-encoder-relative-length: 100
egs/libri_trans/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
len_penalty=1.0
max_tokens=80000
dec_model=checkpoint_best.pt
cmd="./run.sh
--stage 2
--stop_stage 2
--gpu_num ${gpu_num}
--exp_name ${exp_name}
--n_average ${n_average}
--beam_size ${beam_size}
--len_penalty ${len_penalty}
--max_tokens ${max_tokens}
--dec_model ${dec_model}
"
if [[ -n ${data_dir} ]]; then
cmd="$cmd --data_dir ${data_dir}"
fi
if [[ ${#test_subset[@]} -ne 0 ]]; then
subsets=$(echo ${test_subset[*]} | sed 's/ /,/g')
cmd="$cmd --test_subset ${subsets}"
fi
echo $cmd
eval $cmd
egs/libri_trans/asr/local/monitor.sh 0 → 100755
gpu_num=4
cmd="sh train.sh"
while :
do
record=$(mktemp -t temp.record.XXXXXX)
gpustat > $record
all_devices=$(seq 0 "$(sed '1,2d' ${record} | wc -l)");
count=0
for dev in ${all_devices[@]}
do
line=$((dev + 2))
use=$(head -n $line ${record} | tail -1 | cut -d '|' -f3 | cut -d '/' -f1)
if [[ $use -lt 100 ]]; then
device[$count]=$dev
count=$((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/libri_trans/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/libri_trans/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 "$(sed '1,2d' ${record} | wc -l)");
count=0
for dev in ${all_devices[@]}
do
line=$((dev + 2))
use=$(head -n $line ${record} | tail -1 | cut -d '|' -f3 | cut -d '/' -f1)
if [[ $use -lt 100 ]]; then
device[$count]=$dev
count=$((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 $?
}
egs/libri_trans/asr/run.sh 0 → 100755
#! /bin/bash
# Processing MuST-C Datasets
# Copyright 2021 Natural Language Processing Laboratory
# Xu Chen (xuchenneu@163.com)
# Set bash to 'debug' mode, it will exit on :
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
set -e
#set -u
set -o pipefail
export PYTHONIOENCODING=UTF-8
eval=1
time=$(date "+%m%d_%H%M")
stage=0
stop_stage=0
######## hardware ########
# devices
#device=()
gpu_num=8
update_freq=1
root_dir=~/st
code_dir=${root_dir}/Fairseq-S2T
pwd_dir=$PWD
# dataset
src_lang=en
tgt_lang=fr
lang=${src_lang}-${tgt_lang}
dataset=libri_trans
task=speech_to_text
vocab_type=unigram
vocab_size=1000
speed_perturb=1
lcrm=1
tokenizer=0
use_raw_audio=1
use_specific_dict=0
specific_prefix=st
specific_dir=${root_dir}/data/${dataset}/st
asr_vocab_prefix=spm_unigram1000_st_share
org_data_dir=${root_dir}/data/${dataset}
data_dir=${root_dir}/data/${dataset}/asr
train_split=train
valid_split=dev
test_split=test
test_subset=test
# exp
exp_prefix=$(date "+%m%d")
extra_tag=
extra_parameter=
exp_tag=baseline
exp_name=
# config
train_config=base,ctc
data_config=config.yaml
# training setting
fp16=1
max_tokens=40000
step_valid=0
# decoding setting
dec_model=checkpoint_best.pt
n_average=10
beam_size=5
len_penalty=1.0
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
if [[ ${use_raw_audio} -eq 1 ]]; then
data_dir=${data_dir}_raw
exp_prefix=${exp_prefix}_raw
fi
. ./local/parse_options.sh || exit 1;
if [[ -z ${exp_name} ]]; then
config_string=${train_config//,/_}
exp_name=${exp_prefix}_${config_string}_${exp_tag}
if [[ -n ${extra_tag} ]]; then
exp_name=${exp_name}_${extra_tag}
fi
fi
model_dir=${code_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} ]]; then
mkdir -p ${data_dir}
fi
feature_zip=fbank80.zip
if [[ ${speed_perturb} -eq 1 ]]; then
feature_zip=fbank80_sp.zip
fi
if [[ ! -f ${data_dir}/${feature_zip} && -f ${data_dir}/../feature_zip ]]; then
ln -s ${data_dir}/../feature_zip ${data_dir}
fi
cmd="python ${code_dir}/examples/speech_to_text/prep_audio_data.py
--data-root ${org_data_dir}
--output-root ${data_dir}
--task asr
--src-lang ${src_lang}
--splits ${valid_split},${test_split},${train_split}
--vocab-type ${vocab_type}
--vocab-size ${vocab_size}"
if [[ ${use_raw_audio} -eq 1 ]]; then
cmd="$cmd
--raw"
fi
if [[ ${use_specific_dict} -eq 1 ]]; then
cp -r ${specific_dir}/${asr_vocab_prefix}.* ${data_dir}
cmd="$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
if [[ ${tokenizer} -eq 1 ]]; then
cmd="$cmd
--tokenizer"
fi
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval ${cmd}
if [[ -f ${data_dir}/${feature_zip} && ! -f ${data_dir}/../${feature_zip} ]]; then
mv ${data_dir}/${feature_zip} ${data_dir}/..
ln -s ${data_dir}/../${feature_zip} ${data_dir}
fi
fi
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 ${model_dir}
extra_parameter="${extra_parameter}
--train-config ${pwd_dir}/conf/basis.yaml"
cp ${pwd_dir}/conf/basis.yaml ${model_dir}
config_list="${train_config//,/ }"
idx=1
for config in ${config_list[@]}
do
config_path=${pwd_dir}/conf/${config}.yaml
if [[ ! -f ${config_path} ]]; then
echo "No config file ${config_path}"
exit
fi
cp ${config_path} ${model_dir}
extra_parameter="${extra_parameter}
--train-config${idx} ${config_path}"
idx=$((idx + 1))
done
cmd="python3 -u ${code_dir}/fairseq_cli/train.py
${data_dir}
--config-yaml ${data_config}
--task ${task}
--max-tokens ${max_tokens}
--skip-invalid-size-inputs-valid-test
--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=500
keep_interval_updates=10
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} | ${exp_name} | ${model_dir} " >> $log
tail -n 50 ${log} > 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
if [[ ! -f ${model_dir}/${dec_model} ]]; then
cmd="python ${code_dir}/scripts/average_checkpoints.py
--inputs ${model_dir}
--num-best-checkpoints ${n_average}
--output ${model_dir}/${dec_model}"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
[[ $eval -eq 1 ]] && eval $cmd
fi
else
dec_model=${dec_model}
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}
result_file=${model_dir}/decode_result
[[ -f ${result_file} ]] && rm ${result_file}
test_subset=${test_subset//,/ }
for subset in ${test_subset[@]}; do
subset=${subset}
cmd="python ${code_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}
--lenpen ${len_penalty}
--scoring wer
--wer-tokenizer 13a
--wer-lowercase
--wer-remove-punct
"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
if [[ $eval -eq 1 ]]; then
eval $cmd
tail -n 1 ${model_dir}/generate-${subset}.txt >> ${result_file}
fi
done
cat ${result_file}
fi
egs/libri_trans/asr/train.sh 0 → 100755
#! /bin/bash
# training the model
gpu_num=2
update_freq=1
max_tokens=40000
extra_tag=
extra_parameter=
#extra_tag="${extra_tag}"
#extra_parameter="${extra_parameter} "
exp_tag=
#config_list=(base)
#config_list=(ctc)
#config_list=(base conformer)
#config_list=(pds_base_16)
config_list=(pds_base_8 conformer rpr)
# exp full name
exp_name=
train_config=$(echo ${config_list[*]} | sed 's/ /,/g')
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 ${exp_name} ]]; then
cmd="$cmd --exp_name ${exp_name}"
fi
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/libri_trans/mt/binary.sh 0 → 100755
set -e
eval=1
lcrm=0
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="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
> ${src_file}.spm"
echo -e "\033[34mRun command: \n${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/libri_trans/mt/conf/base.yaml 0 → 100644
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/libri_trans/mt/conf/base_s.yaml 0 → 100644
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/libri_trans/mt/conf/basis.yaml 0 → 100644
train-subset: train
valid-subset: valid
max-epoch: 50
max-update: 100000
patience: 20
best_checkpoint_metric: loss
maximize_best_checkpoint_metric: False
no-epoch-checkpoints: True
#keep-last-epochs: 10
keep-best-checkpoints: 10
num-workers: 8
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
\ No newline at end of file
egs/libri_trans/mt/conf/dlcl.yaml 0 → 100644
use-enc-dlcl: True
use-dec-dlcl: True
egs/libri_trans/mt/conf/rpr.yaml 0 → 100644
#encoder-attention-type: rel_selfattn
encoder-attention-type: relative
decoder-attention-type: relative
max-encoder-relative-length: 20
max-decoder-relative-length: 20
egs/libri_trans/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
sacrebleu=1
n_average=10
beam_size=5
len_penalty=1.0
max_tokens=80000
dec_model=checkpoint_best.pt
cmd="./run.sh
--stage 2
--stop_stage 2
--gpu_num ${gpu_num}
--exp_name ${exp_name}
--sacrebleu ${sacrebleu}
--n_average ${n_average}
--beam_size ${beam_size}
--len_penalty ${len_penalty}
--max_tokens ${max_tokens}
--dec_model ${dec_model}
"
if [[ -n ${data_dir} ]]; then
cmd="$cmd --data_dir ${data_dir}"
fi
if [[ -n ${test_subset} ]]; then
test_subset=`echo ${test_subset[*]} | sed 's/ /,/g'`
cmd="$cmd --test_subset ${test_subset}"
fi
echo $cmd
eval $cmd
egs/libri_trans/mt/local/lower_rm.py 0 → 100644
import sys
import string
in_file = sys.argv[1]
with open(in_file, "r", encoding="utf-8") as f:
for line in f.readlines():
line = line.strip().lower()
for w in string.punctuation:
line = line.replace(w, "")
line = line.replace(" ", "")
print(line)
egs/libri_trans/mt/local/monitor.sh 0 → 100755
gpu_num=4
cmd="sh train.sh"
while :
do
record=$(mktemp -t temp.record.XXXXXX)
gpustat > $record
all_devices=$(seq 0 "$(sed '1,2d' ${record} | wc -l)");
count=0
for dev in ${all_devices[@]}
do
line=$((dev + 2))
use=$(head -n $line ${record} | tail -1 | cut -d '|' -f3 | cut -d '/' -f1)
if [[ $use -lt 100 ]]; then
device[$count]=$dev
count=$((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/libri_trans/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/libri_trans/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 "$(sed '1,2d' ${record} | wc -l)");
count=0
for dev in ${all_devices[@]}
do
line=$((dev + 2))
use=$(head -n $line ${record} | tail -1 | cut -d '|' -f3 | cut -d '/' -f1)
if [[ $use -lt 100 ]]; then
device[$count]=$dev
count=$((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 $?
}
egs/libri_trans/mt/train.sh 0 → 100755
#! /bin/bash
# training the model
gpu_num=1
update_freq=1
max_tokens=8192
exp_tag=baseline
config_list=(base)
# exp full name
exp_name=
extra_tag=
extra_parameter=
#extra_tag="${extra_tag}"
#extra_parameter="${extra_parameter} "
train_config=$(echo ${config_list[*]} | sed 's/ /,/g')
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 ${exp_name} ]]; then
cmd="$cmd --exp_name ${exp_name}"
fi
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 ${cmd}
egs/libri_trans/st/binary.sh 0 → 100644
set -e
eval=1
lcrm=1
tokenizer=0
root_dir=~/st/Fairseq-S2T
data_dir=/home/xuchen/st/data/test
vocab_dir=/home/xuchen/st/data/mustc/st_lcrm/en-de
asr_vocab_prefix=spm_unigram10000_st_share
st_vocab_prefix=spm_unigram10000_st_share
src_lang=en
tgt_lang=de
splits=(2019)
splits=$(echo ${splits[*]} | sed 's/ /_/g')
cp -r ${vocab_dir}/${asr_vocab_prefix}.* ${data_dir}/${src_lang}-${tgt_lang}
cp -r ${vocab_dir}/${st_vocab_prefix}.* ${data_dir}/${src_lang}-${tgt_lang}
rm -rf ${data_dir}/${src_lang}-${tgt_lang}/fbank80.zip
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
--share
--asr-prefix ${asr_vocab_prefix}
--st-spm-prefix ${st_vocab_prefix}
--cmvn-type utterance"
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/libri_trans/st/conf/base.yaml 0 → 100644
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_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
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
\ No newline at end of file
egs/libri_trans/st/conf/basis.yaml 0 → 100644
train-subset: train
valid-subset: dev
max-epoch: 100
max-update: 100000
patience: 20
best_checkpoint_metric: loss
maximize_best_checkpoint_metric: False
no-epoch-checkpoints: True
#keep-last-epochs: 10
keep-best-checkpoints: 10
num-workers: 8
no-progress-bar: True
log-interval: 100
seed: 1
report-accuracy: True
skip-invalid-size-inputs-valid-test: True
\ No newline at end of file
egs/libri_trans/st/conf/conformer.yaml 0 → 100644
macaron-style: True
use-cnn-module: True
cnn-module-kernel: 31
egs/libri_trans/st/conf/ctc.yaml 0 → 100644
ctc-weight: 0.3
post-process: sentencepiece
\ No newline at end of file
egs/libri_trans/st/conf/dlcl.yaml 0 → 100644
use-enc-dlcl: True
use-dec-dlcl: True
egs/libri_trans/st/conf/local_attn.yaml 0 → 100644
encoder-attention-type: local
hard-mask-window: 0
gauss-mask-sigma: 3
init-mask-weight: 0
\ No newline at end of file
egs/libri_trans/st/conf/pds_base.yaml 0 → 100644
arch: pdss2t_transformer_s_8
pds-fusion: True
ctc-layer: 12
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_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
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
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
\ No newline at end of file
egs/libri_trans/st/conf/pds_base_16.yaml 0 → 100644
arch: pdss2t_transformer_s_16
encoder-embed-dim: 256
pds-stages: 4
ctc-layer: 12
pds-layers: 2_2_6_2
pds-ratios: 2_2_2_2
pds-fusion: True
pds-fusion-method: all_conv
pds-embed-dims: 256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1
pds-kernel-sizes: 5_5_5_5
pds-ffn-ratios: 8_8_8_8
pds-attn-heads: 4_4_4_4
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_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
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
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
\ No newline at end of file
egs/libri_trans/st/conf/pds_base_32.yaml 0 → 100644
arch: pdss2t_transformer_s_32
encoder-embed-dim: 256
pds-stages: 5
ctc-layer: 12
pds-layers: 2_2_3_3_2
pds-ratios: 2_2_2_2_2
pds-fusion: True
pds-fusion-method: all_conv
pds-embed-dims: 256_256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1_1
pds-kernel-sizes: 5_5_5_5_5
pds-ffn-ratios: 8_8_8_8_8
pds-attn-heads: 4_4_4_4_4
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_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
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
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
\ No newline at end of file
egs/libri_trans/st/conf/pds_base_8.yaml 0 → 100644
arch: pdss2t_transformer_s_8
encoder-embed-dim: 256
pds-stages: 4
ctc-layer: 12
pds-layers: 3_3_3_3
pds-ratios: 2_2_1_2
pds-fusion: True
pds-fusion-method: all_conv
pds-embed-dims: 256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1
pds-kernel-sizes: 5_5_5_5
pds-ffn-ratios: 8_8_8_8
pds-attn-heads: 4_4_4_4
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_with_ctc
label_smoothing: 0.1
dropout: 0.1
activation-fn: relu
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
#load-pretrained-encoder-from:
#load-pretrained-decoder-from:
\ No newline at end of file
egs/libri_trans/st/conf/rpr.yaml 0 → 100644
encoder-attention-type: rel_selfattn
#encoder-attention-type: relative
#decoder-attention-type: relative
#max-encoder-relative-length: 100
#max-decoder-relative-length: 20
egs/libri_trans/st/conf/sate_ctc.yaml 0 → 100644
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
#load-pretrained-encoder-from:
#load-pretrained-acoustic-encoder-from:
#load-pretrained-text-encoder-from:
#load-pretrained-decoder-from:
#macaron-style: True
#use-cnn-module: True
#cnn-module-kernel: 31
#acoustic-encoder: pds
acoustic-encoder: transformer
adapter: league
encoder-embed-dim: 256
pds-stages: 4
#pds-dropout: 0
pds-layers: 3_3_3_3
pds-ratios: 2_2_1_2
pds-fusion: True
pds-fusion-method: all_conv
pds-embed-dims: 256_256_256_256
pds-ds-method: conv
pds-embed-norm: True
pds-position-embed: 1_1_1_1
pds-kernel-sizes: 5_5_5_5
pds-ffn-ratios: 8_8_8_8
pds-attn-heads: 4_4_4_4
decoder-embed-dim: 256
decoder-ffn-embed-dim: 2048
decoder-attention-heads: 4
\ No newline at end of file
egs/libri_trans/st/decode.sh 0 → 100755
#! /bin/bash
gpu_num=1
data_dir=
test_subset=(dev tst-COMMON)
exp_name=
if [ "$#" -eq 1 ]; then
exp_name=$1
fi
sacrebleu=1
n_average=10
beam_size=5
len_penalty=1.0
max_tokens=80000
dec_model=checkpoint_best.pt
cmd="./run.sh
--stage 2
--stop_stage 2
--gpu_num ${gpu_num}
--exp_name ${exp_name}
--sacrebleu ${sacrebleu}
--n_average ${n_average}
--beam_size ${beam_size}
--len_penalty ${len_penalty}
--max_tokens ${max_tokens}
--dec_model ${dec_model}
"
if [[ -n ${data_dir} ]]; then
cmd="$cmd --data_dir ${data_dir}"
fi
if [[ ${#test_subset[@]} -ne 0 ]]; then
subsets=$(echo ${test_subset[*]} | sed 's/ /,/g')
cmd="$cmd --test_subset ${subsets}"
fi
echo $cmd
eval $cmd
egs/libri_trans/st/ensemble.sh 0 → 100644
set -e
gpu_num=1
root_dir=/home/xuchen/st/Fairseq-S2T
ckpt=/home/xuchen/st/checkpoints/mustc-v2/st
model_txt=$1
set=$2
test_subset=$3
#data_dir=/home/xuchen/st/data/mustc-v2/st_lcrm/en-de
#test_subset=(tst-COMMON)
data_dir=/media/data/tst/$set/en-de
#test_subset=(office)
#test_subset=(webrtc1)
#test_subset=(adap2)
data_config=config_st_share.yaml
result_file=./result
beam_size=5
lenpen=0.6
max_tokens=10000
models=()
i=0
for line in `cat $model_txt`; do
i=`expr $i + 1`
model_dir=$ckpt/$line
[[ ! -d $model_dir ]] && echo $model_dir && exit 1;
if [[ -f $model_dir/avg_10_checkpoint.pt ]]; then
model=$model_dir/avg_10_checkpoint.pt
else
model=$model_dir/checkpoint_best.pt
fi
[[ ! -f $model ]] && echo $model && exit 1;
models[$i]=$model
done
models=`echo ${models[*]} | sed 's/ /:/g'`
res_dir=$ckpt/ensemble/$set
i=0
while :
do
if [[ -d $res_dir/$i ]]; then
i=`expr $i + 1`
else
res_dir=$res_dir/$i
break
fi
done
mkdir -p $res_dir
cp $model_txt $res_dir
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}
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 ${models}
--results-path ${res_dir}
--skip-invalid-size-inputs-valid-test
--max-tokens ${max_tokens}
--beam ${beam_size}
--lenpen ${lenpen}
--scoring sacrebleu"
echo -e "\033[34mRun command: \n${cmd} \033[0m"
eval $cmd
tail -n 1 ${res_dir}/generate-${subset}.txt
cd $res_dir
evaluate.sh translation-${subset}.txt $set
cd -
done
egs/libri_trans/st/local/monitor.sh 0 → 100755
gpu_num=4
cmd="sh train.sh"
while :
do
record=$(mktemp -t temp.record.XXXXXX)
gpustat > $record
all_devices=$(seq 0 "$(sed '1,2d' ${record} | wc -l)");
count=0
for dev in ${all_devices[@]}
do
line=$((dev + 2))
use=$(head -n $line ${record} | tail -1 | cut -d '|' -f3 | cut -d '/' -f1)
if [[ $use -lt 100 ]]; then
device[$count]=$dev
count=$((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/libri_trans/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/libri_trans/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 "$(sed '1,2d' ${record} | wc -l)");
count=0
for dev in ${all_devices[@]}
do
line=$((dev + 2))
use=$(head -n $line ${record} | tail -1 | cut -d '|' -f3 | cut -d '/' -f1)
if [[ $use -lt 100 ]]; then
device[$count]=$dev
count=$((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 $?
}
egs/libri_trans/st/train.sh 0 → 100755
#! /bin/bash
# training the model
gpu_num=8
update_freq=1
max_tokens=40000
extra_tag=
extra_parameter=
#extra_tag="${extra_tag}"
#extra_parameter="${extra_parameter} "
exp_tag=
#config_list=(base)
config_list=(ctc)
#config_list=(sate_ctc)
#config_list=(ctc conformer rpr)
#config_list=(base sate)
#config_list=(pds_base)
#config_list=(pds_base conformer)
# exp full name
exp_name=
train_config=$(echo ${config_list[*]} | sed 's/ /,/g')
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 ${exp_name} ]]; then
cmd="$cmd --exp_name ${exp_name}"
fi
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/run.sh
......@@ -62,7 +62,7 @@ exp_tag=baseline
exp_name=
# config
train_config=ctc
train_config=base
data_config=config.yaml
# training setting
......
fairseq/criterions/label_smoothed_cross_entropy_with_ctc.py
......@@ -107,7 +107,10 @@ class LabelSmoothedCrossEntropyCriterionWithCTC(
).contiguous() # (T, B, C) from the encoder
lprobs.batch_first = False
non_padding_mask = ~encoder_out["encoder_padding_mask"][0]
if "ctc_padding_mask" in encoder_out:
non_padding_mask = ~encoder_out["ctc_padding_mask"][0]
else:
non_padding_mask = ~encoder_out["encoder_padding_mask"][0]
input_lengths = non_padding_mask.long().sum(-1)
pad_mask = (transcript["tokens"] != self.pad_idx) & (
......
fairseq/models/speech_to_text/ctc.py
......@@ -4,6 +4,7 @@ import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.modules import (
FairseqDropout,
LayerNorm,
......@@ -50,3 +51,46 @@ class CTC(nn.Module):
def argmax(ctc_logit):
return torch.argmax(ctc_logit, dim=-1)
class CTCCompressStrategy:
@staticmethod
def avg(prob_ctc, predicted, new_lengths, dtype, device):
new_maxlen = max(new_lengths)
weights_matrix = torch.zeros((prob_ctc.shape[0], prob_ctc.shape[1], new_maxlen), dtype=dtype)
for b_idx, pred in enumerate(predicted):
processed_inputs_cnt = 0
for t_idx, same in enumerate(pred):
new_processed_inputs_cnt = processed_inputs_cnt + same[1]
weights_matrix[b_idx, processed_inputs_cnt:new_processed_inputs_cnt, t_idx] = 1.0 / same[1]
processed_inputs_cnt = new_processed_inputs_cnt
return weights_matrix.to(device)
@staticmethod
def weighted(prob_ctc, predicted, new_lengths, dtype, device):
new_maxlen = max(new_lengths)
weights_matrix = torch.zeros((prob_ctc.shape[0], prob_ctc.shape[1], new_maxlen), dtype=dtype, device=device)
for b_idx, pred in enumerate(predicted):
processed_inputs_cnt = 0
for t_idx, same in enumerate(pred):
new_processed_inputs_cnt = processed_inputs_cnt + same[1]
# Get the probabilities of the prediction for the different time steps as weight
weights = prob_ctc[b_idx, processed_inputs_cnt:new_processed_inputs_cnt, same[0]]
weights_matrix[b_idx, processed_inputs_cnt:new_processed_inputs_cnt, t_idx] = \
weights / weights.sum()
processed_inputs_cnt = new_processed_inputs_cnt
return weights_matrix
@staticmethod
def softmax(prob_ctc, predicted, new_lengths, dtype, device):
new_maxlen = max(new_lengths)
weights_matrix = torch.zeros((prob_ctc.shape[0], prob_ctc.shape[1], new_maxlen), dtype=dtype, device=device)
for b_idx, pred in enumerate(predicted):
processed_inputs_cnt = 0
for t_idx, same in enumerate(pred):
new_processed_inputs_cnt = processed_inputs_cnt + same[1]
# Get the probabilities of the prediction for the different time steps as weight
weights = F.softmax(prob_ctc[b_idx, processed_inputs_cnt:new_processed_inputs_cnt, same[0]])
weights_matrix[b_idx, processed_inputs_cnt:new_processed_inputs_cnt, t_idx] = \
weights / weights.sum()
processed_inputs_cnt = new_processed_inputs_cnt
return weights_matrix
fairseq/models/speech_to_text/s2t_sate.py
......@@ -19,6 +19,7 @@ from fairseq.models.speech_to_text import (
S2TConformerEncoder,
PDSS2TTransformerModel,
PDSS2TTransformerEncoder,
CTCCompressStrategy
)
from fairseq.models.speech_to_text.s2t_transformer import Conv1dSubsampler
from fairseq.modules import (
......@@ -68,6 +69,12 @@ class S2TSATEModel(S2TTransformerModel):
help="adapter type",
)
parser.add_argument(
"--ctc-compress-strategy",
default="avg",
type=str,
help="compress strategy, such as avg, weighted, and softmax",
)
parser.add_argument(
"--share-ctc-and-adapter",
default=False,
action="store_true",
......@@ -143,7 +150,6 @@ class Adapter(nn.Module):
embed_dim = args.encoder_embed_dim
self.adapter_type = args.adapter
if self.adapter_type in ["linear", "league", "gated_league", "gated_league2"]:
self.linear_adapter = nn.Sequential(
nn.Linear(embed_dim, embed_dim),
......@@ -174,11 +180,15 @@ class Adapter(nn.Module):
elif self.adapter_type == "gated_league2":
self.gate_linear1 = nn.Linear(embed_dim, embed_dim)
self.gate_linear2 = nn.Linear(embed_dim, embed_dim)
if self.adapter_type == "shrink":
self.ctc_compress_method = getattr(CTCCompressStrategy, args.ctc_compress_strategy)
def forward(self, x, padding):
representation, distribution = x
batch, seq_len, embed_dim = representation.size()
org_distribution = distribution
if distribution is not None:
distribution = distribution.view(-1, distribution.size(-1))
lengths = (~padding).long().sum(-1)
......@@ -208,6 +218,27 @@ class Adapter(nn.Module):
elif self.adapter_type == "none":
out = representation
elif self.adapter_type == "shrink":
from itertools import groupby
with torch.no_grad():
batch_predicted = []
prob_ctc = org_distribution.transpose(0, 1) # T x B x D -> B x T x D
for b in range(prob_ctc.shape[0]):
predicted = prob_ctc[b][: lengths[b]].argmax(-1).tolist()
batch_predicted.append([(p[0], len(list(p[1]))) for p in groupby(predicted)])
new_lengths = [len(p) for p in batch_predicted]
weights_matrix = self.ctc_compress_method(prob_ctc, batch_predicted, new_lengths,
representation.dtype, representation.device)
# x is T x B x C -> B x C x T; weights_matrix is B x T x T'
compressed_output = representation.permute(1, 2, 0).bmm(weights_matrix) # B x C x T'
out = compressed_output.permute(2, 0, 1)
out_lengths = lengths.new(new_lengths)
padding = lengths_to_padding_mask(out_lengths)
else:
out = None
logging.error("Unsupported adapter type: {}.".format(self.adapter_type))
......@@ -290,13 +321,6 @@ class S2TSATEEncoder(FairseqEncoder):
if args.share_ctc_and_adapter and hasattr(self.adapter, "embed_adapter"):
self.acoustic_encoder.ctc.ctc_projection.weight = self.adapter.embed_adapter.weight
# self.length_adapter = Conv1dSubsampler(
# args.encoder_embed_dim,
# args.conv_channels,
# args.encoder_embed_dim,
# [int(k) for k in args.conv_kernel_sizes.split(",")],
# )
acoustic_encoder_attention_type = args.encoder_attention_type
args.encoder_attention_type = args.text_attention_type
......@@ -321,6 +345,7 @@ class S2TSATEEncoder(FairseqEncoder):
encoder_out = acoustic_encoder_out["encoder_out"][0]
encoder_padding_mask = acoustic_encoder_out["encoder_padding_mask"][0]
ctc_padding_mask = encoder_padding_mask
if "ctc_logit" in acoustic_encoder_out and len(acoustic_encoder_out["ctc_logit"]) > 0:
ctc_logit = acoustic_encoder_out["ctc_logit"][0]
......@@ -343,16 +368,12 @@ class S2TSATEEncoder(FairseqEncoder):
self.history.add(x)
# src_lengths = (~encoder_padding_mask).sum(1)
# x = x.transpose(0, 1)
# x, input_lengths = self.length_adapter(x, src_lengths)
# encoder_padding_mask = lengths_to_padding_mask(input_lengths)
x = self.text_encoder(x, encoder_padding_mask, self.history)
return {
"encoder_out": [x], # T x B x C
"ctc_logit": [ctc_logit], # T x B x C
"ctc_padding_mask": [ctc_padding_mask], # B x T
"encoder_padding_mask": [encoder_padding_mask], # B x T
"encoder_embedding": [], # B x T x C
"encoder_states": [], # List[T x B x C]
......@@ -452,6 +473,7 @@ def base_architecture(args):
# SATE
args.acoustic_encoder = getattr(args, "acoustic_encoder", "transformer")
args.adapter = getattr(args, "adapter", "league")
args.ctc_compress_strategy = getattr(args, "ctc_compress_strategy", "avg")
args.temperature = getattr(args, "temperature", 1.0)
args.text_encoder_layers = getattr(args, "text_encoder_layers", 6)
args.text_attention_type = getattr(args, "text_attention_type", "selfattn")
......
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