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egs/aishell2/s5/local/nnet3/tuning/finetune_tdnn_1a.sh
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# !/bin/bash
# This script uses weight transfer as a transfer learning method to transfer
# already trained neural net model on aishell2 to a finetune data set.
. ./path.sh
. ./cmd.sh
data_set=finetune
data_dir=data/${data_set}
ali_dir=exp/${data_set}_ali
src_dir=exp/nnet3/tdnn_sp
dir=${src_dir}_${data_set}
num_jobs_initial=1
num_jobs_final=1
num_epochs=5
initial_effective_lrate=0.0005
final_effective_lrate=0.00002
minibatch_size=1024
stage=1
train_stage=-10
nj=4
if [ $stage -le 1 ]; then
# align new data(finetune set) with GMM, we probably replace GMM with NN later
steps/make_mfcc.sh \
--cmd "$train_cmd" --nj $nj --mfcc-config conf/mfcc.conf \
${data_dir} exp/make_mfcc/${data_set} mfcc
steps/compute_cmvn_stats.sh ${data_dir} exp/make_mfcc/${data_set} mfcc || exit 1;
utils/fix_data_dir.sh ${data_dir} || exit 1;
steps/align_si.sh --cmd "$train_cmd" --nj ${nj} ${data_dir} data/lang exp/tri3 ${ali_dir}
# extract mfcc_hires for AM finetuning
utils/copy_data_dir.sh ${data_dir} ${data_dir}_hires
rm -f ${data_dir}_hires/{cmvn.scp,feats.scp}
#utils/data/perturb_data_dir_volume.sh ${data_dir}_hires || exit 1;
steps/make_mfcc.sh \
--cmd "$train_cmd" --nj $nj --mfcc-config conf/mfcc_hires.conf \
${data_dir}_hires exp/make_mfcc/${data_set}_hires mfcc_hires
steps/compute_cmvn_stats.sh ${data_dir}_hires exp/make_mfcc/${data_set}_hires mfcc_hires
fi
if [ $stage -le 2 ]; then
$train_cmd $dir/log/generate_input_model.log \
nnet3-am-copy --raw=true $src_dir/final.mdl $dir/input.raw
if [ $stage -le 3 ]; then
steps/nnet3/train_dnn.py --stage=$train_stage \
--cmd="$decode_cmd" \
--feat.cmvn-opts="--norm-means=false --norm-vars=false" \
--trainer.input-model $dir/input.raw \
--trainer.num-epochs $num_epochs \
--trainer.optimization.num-jobs-initial $num_jobs_initial \
--trainer.optimization.num-jobs-final $num_jobs_final \
--trainer.optimization.initial-effective-lrate $initial_effective_lrate \
--trainer.optimization.final-effective-lrate $final_effective_lrate \
--trainer.optimization.minibatch-size $minibatch_size \
--feat-dir ${data_dir}_hires \
--lang data/lang \
--ali-dir ${ali_dir} \
--dir $dir || exit 1;
fi
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