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#!/usr/bin/env bash
export PYTHONPATH=""
source /esat/spchtemp/scratch/jponcele/anaconda3/bin/activate espnet2
python --version
# 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
####################################################
stage=12
stop_stage=12
####################################################
# notes: geen speed perturbation, geen LM, geen word LM, geen NGRAM LM
# EXP
outdir=/esat/spchtemp/scratch/jponcele/espnet2
expdir=${outdir}/exp/exp-fbp-new
asr_tag=train_asr_conformer_fbp_2_specaug_new_bpe5000_digits_lr_v6_subs_C8_new
nj=8
ngpu=1
# DATA
train_set=train_s
valid_set=valid_s_filtered
test_sets="aphasia_lowthres" #"sabed" #dev_s subs_annot"
traincomps="a;b;c;d;f;g;h;i;j;k;l;m;n;o"
decodecomps="b;f;g;h;i;j;k;l;m;n;o"
local_data_opts="--repstr false --lowercase true --outdir data --traincomps ${traincomps} --decodecomps ${decodecomps}"
feats_type=fbank_pitch
# LM
use_word_lm=false # not yet supported!
use_lm=false
lm_config=conf/train_lm_transformer.yaml
use_ngram=false
# ASR
feats_normalize=utterance_mvn # recommended for pretrained models instead of globalmvn
asr_config=conf/tuning/train_asr_conformer_v6.yaml
inference_config=conf/decode.yaml
inference_nj=64
inference_asr_model=init_model.pth #valid.acc.ave.pth
asr_args="" # "--input_size 0" # to use raw audio for w2v2 encoder
#decode_ngram=/esat/spchtemp/scratch/jponcele/espnet2/lm/nbest_words_100k_3gram_tabs.arpa
pretrained_model="/esat/spchtemp/scratch/jponcele/espnet2/exp/exp-subs-chained/st_train_subtitling_chained_PL_C8_new/averaged_model_81epochs.pth"
overwrite_bpemodel="/users/spraak/jponcele/espnet/egs2/cgn/st1/data/vl_token_list_/joint_bpe_unigram5000/bpe.model"
overwrite_token_list="/users/spraak/jponcele/espnet/egs2/cgn/st1/data/vl_token_list_/joint_bpe_unigram5000/tokens.txt"
./asr.sh \
--stage ${stage} \
--stop_stage ${stop_stage} \
--ngpu ${ngpu} \
--nj ${nj} \
--gpu_inference false \
--dumpdir ${outdir}/dump \
--expdir ${expdir} \
--feats_type ${feats_type} \
--local_data_opts "${local_data_opts}" \
--audio_format wav \
--min_wav_duration 0.1 \
--max_wav_duration 30 \
--token_type bpe \
--nbpe 5000 \
--bpe_split_digits true \
--bpemode unigram \
--oov "<unk>" \
--lang "vl" \
--use_lm ${use_lm} \
--use_word_lm ${use_word_lm} \
--lm_config ${lm_config} \
--use_ngram ${use_ngram} \
--asr_config ${asr_config} \
--asr_args "${asr_args}" \
--asr_tag ${asr_tag} \
--inference_config ${inference_config} \
--inference_nj ${inference_nj} \
--feats_normalize ${feats_normalize} \
--train_set "${train_set}" \
--valid_set "${valid_set}" \
--test_sets "${test_sets}" \
--inference_asr_model "${inference_asr_model}" \
--pretrained_model ${pretrained_model} \
--overwrite_bpemodel ${overwrite_bpemodel} \
--overwrite_token_list ${overwrite_token_list} \
# --decode_ngram ${decode_ngram} \
#train_set=train_si284
#valid_set=test_dev93
#test_sets="test_dev93 test_eval92"
#
#./asr.sh \
# --lang "en" \
# --use_lm true \
# --token_type char \
# --nbpe 80 \
# --nlsyms_txt data/nlsyms.txt \
# --lm_config conf/train_lm_transformer.yaml \
# --asr_config conf/train_asr_transformer.yaml \
# --inference_config conf/decode.yaml \
# --train_set "${train_set}" \
# --valid_set "${valid_set}" \
# --test_sets "${test_sets}" \
# --bpe_train_text "data/train_si284/text" \
# --lm_train_text "data/train_si284/text data/local/other_text/text" "$@"
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