add the general scrips for s2t

egs/templete/asr/conf/train.yaml

+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/templete/asr/conf/train_ctc.yaml

+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/templete/asr/conf/train_ctc_conformer.yaml

+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_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/templete/asr/conf/train_ctc_conformer_rpr.yaml

+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_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
+
+encoder-attention-type: relative
+decoder-attention-type: relative
+max-encoder-relative-length: 100
+max-decoder-relative-length: 20
+
+#decoder-embed-dim: 256
+#decoder-ffn-embed-dim: 2048
+#decoder-attention-heads: 4
+#attention-dropout: 0.1
+#activation-dropout: 0.1

egs/templete/asr/conf/train_ctc_debug.yaml

+#train-subset: train-clean-100,train-clean-360,train-other-500
+train-subset: train-clean-100
+valid-subset: dev-clean
+
+max-epoch: 100
+max-update: 300000
+
+num-workers: 0
+patience: 10
+no-progress-bar: True
+log-interval: 100
+seed: 1
+report-accuracy: True
+
+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: 3
+decoder-layers: 3
+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/templete/asr/conf/train_ctc_rpr.yaml

+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-encoder-relative-length: 100
+max-decoder-relative-length: 20
+
+decoder-embed-dim: 256
+decoder-ffn-embed-dim: 2048
+decoder-attention-heads: 4
+attention-dropout: 0.1
+activation-dropout: 0.1

egs/templete/asr/conf/train_ctc_sate.yaml

+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-acoustic-encoder-from:
+#load-pretrained-text-encoder-from:
+#load-pretrained-decoder-from:
+
+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/templete/asr/conf/train_ctc_sate_conformer.yaml

+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-acoustic-encoder-from:
+#load-pretrained-text-encoder-from:
+#load-pretrained-decoder-from:
+
+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/templete/asr/conf/train_ctc_sate_conformer_rpr.yaml

+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-acoustic-encoder-from:
+#load-pretrained-text-encoder-from:
+#load-pretrained-decoder-from:
+
+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
+
+encoder-attention-type: relative
+decoder-attention-type: relative
+max-encoder-relative-length: 100
+max-decoder-relative-length: 20
+
+decoder-embed-dim: 256
+decoder-ffn-embed-dim: 2048
+decoder-attention-heads: 4
+attention-dropout: 0.1
+activation-dropout: 0.1

egs/templete/asr/conf/train_ctc_sate_rpr.yaml

+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-acoustic-encoder-from:
+#load-pretrained-text-encoder-from:
+#load-pretrained-decoder-from:
+
+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-encoder-relative-length: 100
+max-decoder-relative-length: 20
+
+#decoder-embed-dim: 256
+#decoder-ffn-embed-dim: 2048
+#decoder-attention-heads: 4
+#attention-dropout: 0.1
+#activation-dropout: 0.1

egs/templete/asr/conf/train_ctc_templete.yaml

+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
+
+#decoder-embed-dim: 256
+#decoder-ffn-embed-dim: 2048
+#decoder-attention-heads: 4
+#attention-dropout: 0.1
+#activation-dropout: 0.1
+
+# conformer
+#macaron-style: True
+#use-cnn-module: True
+#cnn-module-kernel: 31
+
+# relative position encoding
+#encoder-attention-type: relative
+#decoder-attention-type: relative
+#max-encoder-relative-length: 100
+#max-decoder-relative-length: 20
+
+

egs/templete/asr/conf/train_sate.yaml

+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-acoustic-encoder-from:
+#load-pretrained-text-encoder-from:
+#load-pretrained-decoder-from:
+
+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/templete/asr/conf/train_sate_rpr.yaml

+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-encoder-from:
+#load-pretrained-acoustic-encoder-from:
+#load-pretrained-text-encoder-from:
+#load-pretrained-decoder-from:
+
+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-encoder-relative-length: 100
+max-decoder-relative-length: 20
+
+#decoder-embed-dim: 256
+#decoder-ffn-embed-dim: 2048
+#decoder-attention-heads: 4
+#attention-dropout: 0.1
+#activation-dropout: 0.1

egs/templete/asr/decode.sh

+#! /bin/bash
+
+gpu_num=1
+
+data_dir=
+test_subset=(test-cleam test-other)
+
+exp_name=
+if [ "$#" -eq 1 ]; then
+    exp_name=$1
+fi
+
+n_average=10
+beam_size=5
+len_penalty=1.0
+max_tokens=10000
+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 [[ -n ${test_subset} ]]; then
+    test_subset=`echo ${test_subset[*]} | sed 's/ /,/g'`
+    cmd="$cmd --test_subset ${test_subset}"
+fi
+
+echo $cmd
+eval $cmd

egs/templete/asr/local/monitor.sh

+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/templete/asr/local/parse_options.sh

+#!/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/templete/asr/local/path.sh

+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/templete/asr/local/utils.sh

+
+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/templete/asr/run.sh

+#! /bin/bash
+
+# Processing LibriSpeech 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
+lang=${src_lang}
+
+dataset=
+task=speech_to_text
+vocab_type=unigram
+vocab_size=10000
+speed_perturb=0
+lcrm=1
+tokenizer=0
+
+use_specific_dict=0
+specific_prefix=valid
+specific_dir=/home/xuchen/st/data/mustc/st_lcrm/en-de
+asr_vocab_prefix=spm_unigram10000_st_share
+
+org_data_dir=/media/data/${dataset}
+data_dir=~/st/data/${dataset}
+train_split=train
+valid_split=valid
+test_split=test
+test_subset=dev-clean,dev-other,test-clean,test-other
+
+# exp
+exp_prefix=${time}
+extra_tag=
+extra_parameter=
+exp_tag=baseline
+exp_name=
+
+# config
+train_config=train_ctc.yaml
+data_config=config.yaml
+
+# training setting
+fp16=1
+max_tokens=20000
+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 [[ ${use_specific_dict} -eq 1 ]]; then
+    data_dir=${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: Data Preparation"
+
+    if [[ ! -e ${data_dir} ]]; then
+        mkdir -p ${data_dir}
+    fi
+    source ~/tools/audio/bin/activate
+
+    cmd="python ${root_dir}/examples/speech_to_text/prep_asr_data.py
+        --data-root ${org_data_dir}
+        --output-root ${data_dir}
+		--task asr
+		--splits ${train_split},${valid_split},${test_split}
+		--lang ${lang}
+        --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
+    if [[ ${tokenizer} -eq 1 ]]; then
+        cmd="$cmd
+        --tokenizer"
+    fi
+    echo -e "\033[34mRun command: \n${cmd} \033[0m"
+    [[ $eval -eq 1 ]] && eval $cmd
+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}
+    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}
+        --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} | ${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 $cmd
+	else
+		dec_model=${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}
+
+	#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}
+  		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}
+        --lenpen ${len_penalty}
+        --scoring wer"
+    	echo -e "\033[34mRun command: \n${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/templete/asr/train.sh

+#! /bin/bash
+
+# training the model
+
+gpu_num=8
+update_freq=2
+max_tokens=20000
+
+extra_tag=
+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/templete/mt/binary.sh

+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="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/templete/mt/conf/train.yaml

+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/templete/mt/conf/train_dlcl.yaml

+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
+
+use-enc-dlcl: True
+use-dec-dlcl: True
\ No newline at end of file

egs/templete/mt/conf/train_dlcl_rpr.yaml

+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-encoder-relative-length: 20
+max-decoder-relative-length: 20
+
+use-enc-dlcl: True
+use-dec-dlcl: True

egs/templete/mt/conf/train_rpr.yaml

+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-encoder-relative-length: 20
+max-decoder-relative-length: 20

egs/templete/mt/conf/train_s.yaml

+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/templete/mt/decode.sh

+#! /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
+len_penalty=1.0
+max_tokens=10000
+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 [[ -n ${test_subset} ]]; then
+    test_subset=`echo ${test_subset[*]} | sed 's/ /,/g'`
+    cmd="$cmd --test_subset ${test_subset}"
+fi
+
+echo $cmd
+eval $cmd

egs/templete/mt/local/lower_rm.py

+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/templete/mt/local/monitor.sh

+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/templete/mt/local/parse_options.sh

+#!/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/templete/mt/local/path.sh

+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/templete/mt/local/utils.sh

+
+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/templete/mt/run.sh

+#! /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=
+task=translation
+vocab_type=unigram
+vocab_size=10000
+share_dict=1
+lcrm=1
+tokenizer=1
+
+use_specific_dict=0
+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/${lang}
+train_subset=train
+valid_subset=dev
+trans_subset=tst-COMMON
+test_subset=test
+
+# exp
+exp_prefix=${time}
+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
+dec_model=checkpoint_best.pt
+n_average=10
+beam_size=5
+len_penalty=1.0
+
+if [[ ${use_specific_dict} -eq 1 ]]; then
+    exp_prefix=${specific_prefix}_${exp_prefix}
+    data_dir=${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
+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=${valid_subset}.tok
+    trans_subset=${trans_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 ${exp_name} ]]; then
+    exp_name=${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/$dataset/mt/${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},${trans_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} ${trans_subset}; do
+    {
+        cmd="cat ${org_data_dir}/${lang}/data/${split}.${src_lang}"
+        if [[ ${lcrm} -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}
+        --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=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 $cmd
+	else
+		dec_model=${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}_st
+  		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}
+        --lenpen ${len_penalty}
+        --post-process sentencepiece
+        --scoring sacrebleu"
+
+        if [[ ${tokenizer} -eq 1 ]]; then
+            cmd="${cmd}
+        --tokenizer moses
+        --moses-source-lang ${src_lang}
+        --moses-target-lang ${tgt_lang}"
+        fi
+
+    	echo -e "\033[34mRun command: \n${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/templete/mt/train.sh

+#! /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/templete/st/binary.sh

+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)
+
+source ~/tools/audio/bin/activate
+
+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}
+deactivate

egs/templete/st/conf/train.yaml

+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/templete/st/conf/train_ctc.yaml

+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/templete/st/conf/train_ctc_conformer.yaml

+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_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/templete/st/conf/train_ctc_conformer_rpr.yaml

+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_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
+
+encoder-attention-type: relative
+decoder-attention-type: relative
+max-encoder-relative-length: 100
+max-decoder-relative-length: 20
+
+#decoder-embed-dim: 256
+#decoder-ffn-embed-dim: 2048
+#decoder-attention-heads: 4
+#attention-dropout: 0.1
+#activation-dropout: 0.1

egs/templete/st/conf/train_ctc_rpr.yaml

+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-encoder-relative-length: 100
+max-decoder-relative-length: 20
+
+decoder-embed-dim: 256
+decoder-ffn-embed-dim: 2048
+decoder-attention-heads: 4
+attention-dropout: 0.1
+activation-dropout: 0.1

egs/templete/st/conf/train_ctc_sate.yaml

+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-acoustic-encoder-from:
+#load-pretrained-text-encoder-from:
+#load-pretrained-decoder-from:
+
+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/templete/st/conf/train_ctc_sate_conformer.yaml

+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-acoustic-encoder-from:
+#load-pretrained-text-encoder-from:
+#load-pretrained-decoder-from:
+
+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/templete/st/conf/train_ctc_sate_conformer_rpr.yaml

+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-acoustic-encoder-from:
+#load-pretrained-text-encoder-from:
+#load-pretrained-decoder-from:
+
+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
+
+encoder-attention-type: relative
+decoder-attention-type: relative
+max-encoder-relative-length: 100
+max-decoder-relative-length: 20
+
+decoder-embed-dim: 256
+decoder-ffn-embed-dim: 2048
+decoder-attention-heads: 4
+attention-dropout: 0.1
+activation-dropout: 0.1

egs/templete/st/conf/train_ctc_sate_rpr.yaml

+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-acoustic-encoder-from:
+#load-pretrained-text-encoder-from:
+#load-pretrained-decoder-from:
+
+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-encoder-relative-length: 100
+max-decoder-relative-length: 20
+
+#decoder-embed-dim: 256
+#decoder-ffn-embed-dim: 2048
+#decoder-attention-heads: 4
+#attention-dropout: 0.1
+#activation-dropout: 0.1

egs/templete/st/conf/train_ctc_templete.yaml

+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
+
+#decoder-embed-dim: 256
+#decoder-ffn-embed-dim: 2048
+#decoder-attention-heads: 4
+#attention-dropout: 0.1
+#activation-dropout: 0.1
+
+# conformer
+#macaron-style: True
+#use-cnn-module: True
+#cnn-module-kernel: 31
+
+# relative position encoding
+#encoder-attention-type: relative
+#decoder-attention-type: relative
+#max-encoder-relative-length: 100
+#max-decoder-relative-length: 20
+
+

egs/templete/st/conf/train_sate.yaml

+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-acoustic-encoder-from:
+#load-pretrained-text-encoder-from:
+#load-pretrained-decoder-from:
+
+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/templete/st/conf/train_sate_rpr.yaml

+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-encoder-from:
+#load-pretrained-acoustic-encoder-from:
+#load-pretrained-text-encoder-from:
+#load-pretrained-decoder-from:
+
+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-encoder-relative-length: 100
+max-decoder-relative-length: 20
+
+#decoder-embed-dim: 256
+#decoder-ffn-embed-dim: 2048
+#decoder-attention-heads: 4
+#attention-dropout: 0.1
+#activation-dropout: 0.1

egs/templete/st/decode.sh

+#! /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=10000
+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 [[ -n ${test_subset} ]]; then
+    test_subset=`echo ${test_subset[*]} | sed 's/ /,/g'`
+    cmd="$cmd --test_subset ${test_subset}"
+fi
+
+echo $cmd
+eval $cmd

egs/templete/st/ensemble.sh

+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/templete/st/local/monitor.sh

+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/templete/st/local/parse_options.sh

+#!/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/templete/st/local/path.sh

+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/templete/st/local/utils.sh

+
+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/templete/st/run.sh

+#! /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-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
+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
+dec_model=checkpoint_best.pt
+n_average=10
+beam_size=5
+len_penalty=1.0
+
+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}.* ${data_dir}/${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 ${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}
+        --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 [[ $bleu_valid -eq 1 ]]; then
+        cmd="$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 $cmd
+	else
+		dec_model=${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}_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}
+        --lenpen ${len_penalty}
+        --scoring sacrebleu"
+    	echo -e "\033[34mRun command: \n${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/templete/st/train.sh

+#! /bin/bash
+
+# training the model
+
+gpu_num=8
+update_freq=2
+max_tokens=20000
+
+exp_name=
+extra_tag=
+extra_parameter=
+
+#extra_tag="${extra_tag}"
+#extra_parameter="${extra_parameter} "
+
+#extra_tag="${extra_tag}_encdlcl"
+#extra_parameter="${extra_parameter} --use-enc-dlcl"
+
+#extra_tag="${extra_tag}_decdlcl"
+#extra_parameter="${extra_parameter} --use-dec-dlcl"
+
+exp_tag=baseline
+train_config=train_ctc.yaml
+#train_config=train_ctc_conformer.yaml
+#train_config=train_ctc_conformer_rpr.yaml
+#train_config=train_ctc_sate.yaml
+#train_config=train_ctc_sate_rpr.yaml
+#train_config=train_ctc_sate_conformer.yaml
+#train_config=train_ctc_sate_conformer_rpr.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 ${exp_name} ]]; then
+    cmd="$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

examples/speech_to_text/prep_asr_data.py

+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+import logging
+import os
+from pathlib import Path
+import shutil
+from itertools import groupby
+from tempfile import NamedTemporaryFile
+import string
+import csv
+
+import numpy as np
+import pandas as pd
+import torchaudio
+from examples.speech_to_text.data_utils import (
+    create_zip,
+    extract_fbank_features,
+    filter_manifest_df,
+    gen_config_yaml,
+    gen_vocab,
+    get_zip_manifest,
+    load_df_from_tsv,
+    save_df_to_tsv,
+    cal_gcmvn_stats,
+)
+from torch.utils.data import Dataset
+from tqdm import tqdm
+
+
+log = logging.getLogger(__name__)
+
+
+MANIFEST_COLUMNS = ["id", "audio", "n_frames", "tgt_text", "speaker"]
+
+
+class ASR_Dataset(Dataset):
+    """
+    Create a Dataset for MuST-C. Each item is a tuple of the form:
+    waveform, sample_rate, source utterance, target utterance, speaker_id,
+    utterance_id
+    """
+
+    def __init__(self, root: str, lang, split: str, speed_perturb: bool = False, tokenizer: bool = False) -> None:
+        _root = Path(root) / f"{lang}" / split
+        wav_root, txt_root = _root / "wav", _root / "txt"
+        if tokenizer:
+            txt_root = _root / "txt.tok"
+        assert _root.is_dir() and wav_root.is_dir() and txt_root.is_dir(), (_root, wav_root, txt_root)
+        # Load audio segments
+        try:
+            import yaml
+        except ImportError:
+            print("Please install PyYAML to load the MuST-C YAML files")
+        with open(txt_root / f"{split}.yaml") as f:
+            segments = yaml.load(f, Loader=yaml.BaseLoader)
+
+        self.speed_perturb = [0.9, 1.0, 1.1] if speed_perturb and split.startswith("train") else None
+        # Load source and target utterances
+        with open(txt_root / f"{split}.{lang}") as f:
+            utterances = [r.strip() for r in f]
+        assert len(segments) == len(utterances)
+        for i, u in enumerate(utterances):
+            segments[i][lang] = u
+        # Gather info
+        self.data = []
+        for wav_filename, _seg_group in groupby(segments, lambda x: x["wav"]):
+            wav_path = wav_root / wav_filename
+            try:
+                sample_rate = torchaudio.info(wav_path.as_posix())[0].rate
+            except TypeError:
+                sample_rate = torchaudio.info(wav_path.as_posix()).sample_rate
+            seg_group = sorted(_seg_group, key=lambda x: float(x["offset"]))
+            for i, segment in enumerate(seg_group):
+                offset = int(float(segment["offset"]) * sample_rate)
+                n_frames = int(float(segment["duration"]) * sample_rate)
+                _id = f"{split}_{wav_path.stem}_{i}"
+                self.data.append(
+                    (
+                        wav_path.as_posix(),
+                        offset,
+                        n_frames,
+                        sample_rate,
+                        segment[lang],
+                        segment["speaker_id"] if "speaker_id" in segment else "spk1",
+                        _id,
+                    )
+                )
+
+    def __getitem__(self, n: int):
+        wav_path, offset, n_frames, sr, utt, spk_id, utt_id = self.data[n]
+
+        items = []
+        if self.speed_perturb is None:
+            waveform, _ = torchaudio.load(wav_path, frame_offset=offset, num_frames=n_frames)
+            items.append([waveform, sr, n_frames, utt, spk_id, utt_id])
+        else:
+            for speed in self.speed_perturb:
+                sp_utt_id = f"sp{speed}_" + utt_id
+                sp_n_frames = n_frames / speed
+                if speed == 1.0:
+                    waveform, _ = torchaudio.load(wav_path, frame_offset=offset, num_frames=n_frames)
+                else:
+                    waveform, _ = torchaudio.load(wav_path, frame_offset=offset, num_frames=n_frames)
+                    effects = [
+                        ["speed", f"{speed}"],
+                        ["rate", f"{sr}"]
+                    ]
+                    waveform, _ = torchaudio.sox_effects.apply_effects_tensor(waveform, sr, effects)
+
+                items.append([waveform, sr, sp_n_frames, utt, spk_id, sp_utt_id])
+        return items
+
+    def get_wav(self, n: int, speed_perturb=1.0):
+        wav_path, offset, n_frames, sr, utt, spk_id, utt_id = self.data[n]
+
+        if self.speed_perturb is None or speed_perturb == 1.0:
+            waveform, _ = torchaudio.load(wav_path, frame_offset=offset, num_frames=n_frames)
+        else:
+            waveform, _ = torchaudio.load(wav_path, frame_offset=offset, num_frames=n_frames)
+            effects = [
+                ["speed", f"{speed_perturb}"],
+                ["rate", f"{sr}"]
+            ]
+            waveform, _ = torchaudio.sox_effects.apply_effects_tensor(waveform, sr, effects)
+        return waveform
+
+    def get_fast(self, n: int):
+        wav_path, offset, n_frames, sr, utt, spk_id, utt_id = self.data[n]
+
+        items = []
+        if self.speed_perturb is None:
+            items.append([wav_path, sr, n_frames, utt, spk_id, utt_id])
+        else:
+            for speed in self.speed_perturb:
+                sp_utt_id = f"sp{speed}_" + utt_id
+                sp_n_frames = n_frames / speed
+                items.append([wav_path, sr, sp_n_frames, utt, spk_id, sp_utt_id])
+        return items
+
+    def get_text(self):
+        src_text = []
+        for item in self.data:
+            src_text.append(item[4])
+        return src_text
+
+    def __len__(self) -> int:
+        return len(self.data)
+
+
+def process(args):
+    root = Path(args.data_root).absolute()
+    splits = args.splits.split(",")
+    lang = args.lang
+    cur_root = root / f"{lang}"
+    if not cur_root.is_dir():
+        print(f"{cur_root.as_posix()} does not exist. Skipped.")
+        return
+    if args.output_root is None:
+        output_root = cur_root
+    else:
+        output_root = Path(args.output_root).absolute() / f"{lang}"
+
+    # Extract features
+    if args.speed_perturb:
+        zip_path = output_root / "fbank80_sp.zip"
+    else:
+        zip_path = output_root / "fbank80.zip"
+    index = 0
+
+    gen_feature_flag = False
+    if not Path.exists(zip_path):
+        gen_feature_flag = True
+
+    if args.overwrite or gen_feature_flag:
+        if args.speed_perturb:
+            feature_root = output_root / "fbank80_sp"
+        else:
+            feature_root = output_root / "fbank80"
+        feature_root.mkdir(exist_ok=True)
+
+        for split in splits:
+            print(f"Fetching split {split}...")
+            dataset = ASR_Dataset(root.as_posix(), lang, split, args.speed_perturb, args.tokenizer)
+            is_train_split = split.startswith("train")
+            print("Extracting log mel filter bank features...")
+            if is_train_split and args.cmvn_type == "global":
+                print("And estimating cepstral mean and variance stats...")
+                gcmvn_feature_list = []
+
+            for idx in tqdm(range(len(dataset))):
+                items = dataset.get_fast(idx)
+                for item in items:
+                    index += 1
+                    wav_path, sr, _, _, _, utt_id = item
+
+                    features_path = (feature_root / f"{utt_id}.npy").as_posix()
+                    if not os.path.exists(features_path):
+                        sp = 1.0
+                        if dataset.speed_perturb is not None:
+                            sp = float(utt_id.split("_")[0].replace("sp", ""))
+                        waveform = dataset.get_wav(idx, sp)
+                        if waveform.shape[1] == 0:
+                            continue
+                        features = extract_fbank_features(waveform, sr, Path(features_path))
+
+                        if split == 'train' and args.cmvn_type == "global" and not utt_id.startswith("sp"):
+                            if len(gcmvn_feature_list) < args.gcmvn_max_num:
+                                gcmvn_feature_list.append(features)
+
+                if is_train_split and args.size != -1 and index > args.size:
+                    break
+
+            if is_train_split and args.cmvn_type == "global":
+                # Estimate and save cmv
+                stats = cal_gcmvn_stats(gcmvn_feature_list)
+                with open(output_root / "gcmvn.npz", "wb") as f:
+                    np.savez(f, mean=stats["mean"], std=stats["std"])
+
+        # Pack features into ZIP
+        print("ZIPing features...")
+        create_zip(feature_root, zip_path)
+
+        # Clean up
+        shutil.rmtree(feature_root)
+
+    gen_manifest_flag = False
+    for split in splits:
+        if not Path.exists(output_root / f"{split}_{args.task}.tsv"):
+            gen_manifest_flag = True
+            break
+
+    train_text = []
+    if args.overwrite or gen_manifest_flag:
+        print("Fetching ZIP manifest...")
+        zip_manifest = get_zip_manifest(zip_path)
+        # Generate TSV manifest
+        print("Generating manifest...")
+        for split in splits:
+            is_train_split = split.startswith("train")
+            manifest = {c: [] for c in MANIFEST_COLUMNS}
+            if args.task == "st" and args.add_src:
+                manifest["src_text"] = []
+
+            dataset = ASR_Dataset(args.data_root, lang, split, args.speed_perturb, args.tokenizer)
+            for idx in range(len(dataset)):
+                items = dataset.get_fast(idx)
+                for item in items:
+                    _, sr, n_frames, utt, speaker_id, utt_id = item
+                    manifest["id"].append(utt_id)
+                    manifest["audio"].append(zip_manifest[utt_id])
+                    duration_ms = int(n_frames / sr * 1000)
+                    manifest["n_frames"].append(int(1 + (duration_ms - 25) / 10))
+                    if args.lowercase_src:
+                        utt = utt.lower()
+                    if args.rm_punc_src:
+                        for w in string.punctuation:
+                            utt = utt.replace(w, "")
+                    manifest["tgt_text"].append(utt)
+                    manifest["speaker"].append(speaker_id)
+
+                if is_train_split and args.size != -1 and len(manifest["id"]) > args.size:
+                    break
+            if is_train_split:
+                train_text.extend(manifest["tgt_text"])
+
+            df = pd.DataFrame.from_dict(manifest)
+            df = filter_manifest_df(df, is_train_split=is_train_split)
+            save_df_to_tsv(df, output_root / f"{split}_{args.task}.tsv")
+
+    # Generate vocab
+    v_size_str = "" if args.vocab_type == "char" else str(args.vocab_size)
+    spm_filename_prefix = f"spm_{args.vocab_type}{v_size_str}_{args.task}"
+    gen_vocab_flag = True
+
+    if args.asr_prefix is not None:
+        gen_vocab_flag = False
+        spm_filename_prefix = args.asr_prefix
+
+    if gen_vocab_flag:
+        if len(train_text) == 0:
+            print("Loading the training text to build dictionary...")
+
+            for split in args.SPLITS:
+                if split.startswith("train"):
+                    csv_path = output_root / f"{split}_{args.task}.tsv"
+                    with open(csv_path) as f:
+                        reader = csv.DictReader(
+                            f,
+                            delimiter="\t",
+                            quotechar=None,
+                            doublequote=False,
+                            lineterminator="\n",
+                            quoting=csv.QUOTE_NONE,
+                        )
+
+                    tgt_text = [dict(e)["tgt_text"] for e in reader]
+                    train_text.extend(tgt_text)
+
+        with NamedTemporaryFile(mode="w") as f:
+            for t in train_text:
+                f.write(t + "\n")
+            gen_vocab(
+                Path(f.name),
+                output_root / spm_filename_prefix,
+                args.vocab_type,
+                args.vocab_size,
+            )
+
+    # Generate config YAML
+    yaml_filename = f"config_{args.task}.yaml"
+
+    gen_config_yaml(
+        output_root,
+        spm_filename_prefix + ".model",
+        yaml_filename=yaml_filename,
+        specaugment_policy="lb",
+        cmvn_type=args.cmvn_type,
+        gcmvn_path=(
+            output_root / "gcmvn.npz" if args.cmvn_type == "global"
+            else None
+        ),
+        share_src_and_tgt=True if args.task == "asr" else False
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data-root", "-d", required=True, type=str)
+    parser.add_argument("--output-root", "-o", default=None, type=str)
+    parser.add_argument(
+        "--vocab-type",
+        default="unigram",
+        required=True,
+        type=str,
+        choices=["bpe", "unigram", "char"],
+    ),
+    parser.add_argument("--vocab-size", default=8000, type=int)
+    parser.add_argument("--task", type=str, default="asr", choices=["asr", "st"])
+    parser.add_argument("--lang", type=str, required=True, help="language")
+    parser.add_argument("--splits", type=str, default="train,dev,test", help="dataset splits")
+    parser.add_argument("--speed-perturb", action="store_true", default=False,
+                        help="apply speed perturbation on wave file")
+    parser.add_argument("--share", action="store_true",
+                        help="share the tokenizer and dictionary of the transcription and translation")
+    parser.add_argument("--asr-prefix", type=str, default=None, help="prefix of the asr dict")
+    parser.add_argument("--lowercase-src", action="store_true", help="lowercase the source text")
+    parser.add_argument("--rm-punc-src", action="store_true", help="remove the punctuation of the source text")
+    parser.add_argument("--tokenizer", action="store_true", help="use tokenizer txt")
+    parser.add_argument("--cmvn-type", default="utterance",
+                        choices=["global", "utterance"],
+                        help="The type of cepstral mean and variance normalization")
+    parser.add_argument("--overwrite", action="store_true", help="overwrite the existing files")
+    parser.add_argument("--gcmvn-max-num", default=150000, type=int,
+                        help=(
+                            "Maximum number of sentences to use to estimate"
+                            "global mean and variance"
+                            ))
+    args = parser.parse_args()
+
+    process(args)
+
+
+if __name__ == "__main__":
+    main()

examples/speech_to_text/prep_mustc_data.py

@@ -11,7 +11,6 @@ from pathlib import Path
 import shutil
 from itertools import groupby
 from tempfile import NamedTemporaryFile
-from typing import Tuple
 import string
 import csv
 
@@ -47,7 +46,6 @@ class MUSTC(Dataset):
     """
 
     SPLITS = ["dev", "tst-COMMON", "train"]
-    # SPLITS = ["train_debug", "dev"]
     LANGUAGES = ["de", "es", "fr", "it", "nl", "pt", "ro", "ru"]
 
     def __init__(self, root: str, lang: str, split: str, speed_perturb: bool = False, tokenizer: bool = False) -> None:
@@ -81,11 +79,11 @@ class MUSTC(Dataset):
                 sample_rate = torchaudio.info(wav_path.as_posix())[0].rate
             except TypeError:
                 sample_rate = torchaudio.info(wav_path.as_posix()).sample_rate
-            seg_group = sorted(_seg_group, key=lambda x: x["offset"])
+            seg_group = sorted(_seg_group, key=lambda x: float(x["offset"]))
             for i, segment in enumerate(seg_group):
                 offset = int(float(segment["offset"]) * sample_rate)
                 n_frames = int(float(segment["duration"]) * sample_rate)
-                _id = f"{wav_path.stem}_{i}"
+                _id = f"{split}_{wav_path.stem}_{i}"
                 self.data.append(
                     (
                         wav_path.as_posix(),
@@ -435,7 +433,7 @@ def main():
     parser.add_argument("--share", action="store_true",
                         help="share the tokenizer and dictionary of the transcription and translation")
     parser.add_argument("--add-src", action="store_true", help="add the src text for st task")
-    parser.add_argument("--asr-prefix", type=str, help="prefix of the asr dict")
+    parser.add_argument("--asr-prefix", type=str, default=None, help="prefix of the asr dict")
     parser.add_argument("--st-spm-prefix", type=str, default=None, help="prefix of the existing st dict")
     parser.add_argument("--lowercase-src", action="store_true", help="lowercase the source text")
     parser.add_argument("--rm-punc-src", action="store_true", help="remove the punctuation of the source text")

examples/speech_to_text/prep_st_data.py

@@ -45,9 +45,11 @@ class ST_Dataset(Dataset):
     utterance_id
     """
 
-    def __init__(self, root: str, src_lang, tgt_lang: str, split: str, speed_perturb: bool = False) -> None:
+    def __init__(self, root: str, src_lang, tgt_lang: str, split: str, speed_perturb: bool = False, tokenizer: bool = False) -> None:
         _root = Path(root) / f"{src_lang}-{tgt_lang}" / split
         wav_root, txt_root = _root / "wav", _root / "txt"
+        if tokenizer:
+            txt_root = _root / "txt.tok"
         assert _root.is_dir() and wav_root.is_dir() and txt_root.is_dir(), (_root, wav_root, txt_root)
         # Load audio segments
         try:
@@ -192,7 +194,7 @@ def process(args):
 
         for split in splits:
             print(f"Fetching split {split}...")
-            dataset = ST_Dataset(root.as_posix(), src_lang, tgt_lang, split, args.speed_perturb)
+            dataset = ST_Dataset(root.as_posix(), src_lang, tgt_lang, split, args.speed_perturb, args.tokenizer)
             is_train_split = split.startswith("train")
             print("Extracting log mel filter bank features...")
             if is_train_split and args.cmvn_type == "global":
@@ -253,7 +255,7 @@ def process(args):
             if args.task == "st" and args.add_src:
                 manifest["src_text"] = []
 
-            dataset = ST_Dataset(args.data_root, src_lang, tgt_lang, split, args.speed_perturb)
+            dataset = ST_Dataset(args.data_root, src_lang, tgt_lang, split, args.speed_perturb, args.tokenizer)
             for idx in range(len(dataset)):
                 items = dataset.get_fast(idx)
                 for item in items:
@@ -375,15 +377,15 @@ def main():
     parser.add_argument(
         "--vocab-type",
         default="unigram",
+        required=True,
         type=str,
         choices=["bpe", "unigram", "char"],
     ),
     parser.add_argument("--vocab-size", default=8000, type=int)
-    parser.add_argument("--task", type=str, choices=["asr", "st"])
+    parser.add_argument("--task", type=str, default="st", choices=["asr", "st"])
     parser.add_argument("--src-lang", type=str, required=True, help="source language")
     parser.add_argument("--tgt-lang", type=str, required=True, help="target language")
     parser.add_argument("--splits", type=str, default="train,dev,test", help="dataset splits")
-    parser.add_argument("--size", default=-1, type=int)
     parser.add_argument("--speed-perturb", action="store_true", default=False,
                         help="apply speed perturbation on wave file")
     parser.add_argument("--share", action="store_true",
@@ -393,6 +395,7 @@ def main():
     parser.add_argument("--st-spm-prefix", type=str, default=None, help="prefix of the existing st dict")
     parser.add_argument("--lowercase-src", action="store_true", help="lowercase the source text")
     parser.add_argument("--rm-punc-src", action="store_true", help="remove the punctuation of the source text")
+    parser.add_argument("--tokenizer", action="store_true", help="use tokenizer txt")
     parser.add_argument("--cmvn-type", default="utterance",
                         choices=["global", "utterance"],
                         help="The type of cepstral mean and variance normalization")