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egs/csj/s5/local/csj_run_rnnlm.sh
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#!/bin/bash
# Copyright 2016 Tokyo Institute of Technology (Authors: Tomohiro Tanaka, Takafumi Moriya and Takahiro Shinozaki)
# 2016 Mitsubishi Electric Research Laboratories (Author: Shinji Watanabe)
# Apache 2.0
# Acknowledgement This work was supported by JSPS KAKENHI Grant Number 26280055.
[ -f ./path.sh ] && . ./path.sh
. utils/parse_options.sh
. ./cmd.sh
if [ -e data/train_dev ] ;then
dev_set=train_dev
fi
rnnlm_ver=rnnlm-0.3e
#:<<"#SKIP"
echo h30 Begin
local/csj_train_rnnlms.sh --dict-suffix "_nosp" data/local/rnnlm.h30
sleep 20; # wait till tools compiled.
echo h100 Begin
local/csj_train_rnnlms.sh --dict-suffix "_nosp" \
--hidden 100 --nwords 10000 --class 200 \
--direct 0 data/local/rnnlm.h100
echo h200 Begin
local/csj_train_rnnlms.sh --dict-suffix "_nosp" \
--hidden 200 --nwords 10000 --class 200 \
--direct 0 data/local/rnnlm.h200
echo h300 Begin
local/csj_train_rnnlms.sh --dict-suffix "_nosp" \
--hidden 300 --nwords 10000 --class 200 \
--direct 0 data/local/rnnlm.h300
echo h400 Begin
local/csj_train_rnnlms.sh --dict-suffix "_nosp" \
--hidden 400 --nwords 10000 --class 200 \
--direct 0 data/local/rnnlm.h400
echo h500 Begin
local/csj_train_rnnlms.sh --dict-suffix "_nosp" \
--hidden 500 --nwords 10000 --class 200 \
--direct 0 data/local/rnnlm.h500
#SKIP
echo Begin rescoring
sourceresult=dnn5b_pretrain-dbn_dnn_smbr_i1lats
acwt=17
for dict in rnnlm.h30 rnnlm.h100 rnnlm.h200 rnnlm.h300 rnnlm.h400 rnnlm.h500 ;do
for eval_num in eval1 eval2 eval3 $dev_set ;do
dir=data/local/$dict
sourcedir=exp/${sourceresult}/decode_${eval_num}_csj
resultsdir=${sourcedir}_${dict}
echo "rnnlm0.5"
steps/rnnlmrescore.sh --rnnlm_ver $rnnlm_ver \
--N 100 --cmd "$decode_cmd --mem 1G" --inv-acwt $acwt 0.5 \
data/lang_csj_tg $dir data/$eval_num $sourcedir ${resultsdir}_L0.5
rm -rf ${resultsdir}_L0.25
rm -rf ${resultsdir}_L0.75
cp -rp ${resultsdir}_L0.5 ${resultsdir}_L0.25
cp -rp ${resultsdir}_L0.5 ${resultsdir}_L0.75
echo "rnnlm0.25"
steps/rnnlmrescore.sh --rnnlm_ver $rnnlm_ver \
--stage 7 --N 100 --cmd "$decode_cmd --mem 1G" --inv-acwt $acwt 0.25 \
data/lang_csj_tg $dir data/$eval_num $sourcedir ${resultsdir}_L0.25
echo "rnnlm0.75"
steps/rnnlmrescore.sh --rnnlm_ver $rnnlm_ver \
--stage 7 --N 100 --cmd "$decode_cmd --mem 1G" --inv-acwt $acwt 0.75 \
data/lang_csj_tg $dir data/$eval_num $sourcedir ${resultsdir}_L0.75
done
done
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