#!/usr/bin/env bash
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# Set bash to 'debug' mode, it will exit on :
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# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
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set -e
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set -u
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set -o pipefail
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log() {
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local fname=${BASH_SOURCE[1]##*/}
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echo -e "$(date '+%Y-%m-%dT%H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
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}
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min() {
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local a b
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a=$1
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for b in "$@"; do
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if [ "${b}" -le "${a}" ]; then
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a="${b}"
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fi
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done
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echo "${a}"
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}
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SECONDS=0
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# General configuration
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stage=1 # Processes starts from the specified stage.
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stop_stage=10000 # Processes is stopped at the specified stage.
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skip_data_prep=true # Skip data preparation stages.
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skip_train=false # Skip training stages.
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skip_eval=false # Skip decoding and evaluation stages.
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skip_upload=true # Skip packing and uploading stages.
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skip_upload_hf=true # Skip uploading to hugging face stages.
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cuda_cmd=utils/run.pl
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decode_cmd=utils/run.pl
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ngpu=1 # The number of gpus ("0" uses cpu, otherwise use gpu).
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njob=1 # the number of jobs for each gpu
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gpuid_list=
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num_nodes=1 # The number of nodes.
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nj=32 # The number of parallel jobs.
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inference_nj=32 # The number of parallel jobs in decoding.
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gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
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datadir="./"
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dumpdir=dump # Directory to dump features.
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expdir=exp # Directory to save experiments.
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python=python # Specify python to execute funasr commands.
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# Data preparation related
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local_data_opts= # The options given to local/data.sh.
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# Speed perturbation related
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speed_perturb_factors= # perturbation factors, e.g. "0.9 1.0 1.1" (separated by space).
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# Feature extraction related
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feats_type=fbank # Feature type (raw or fbank_pitch).
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feats_dim=
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audio_format=flac # Audio format: wav, flac, wav.ark, flac.ark (only in feats_type=raw).
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fs=16k # Sampling rate.
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min_wav_duration=0.1 # Minimum duration in second.
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max_wav_duration=20 # Maximum duration in second.
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# Tokenization related
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token_type=bpe # Tokenization type (char or bpe).
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nbpe=30 # The number of BPE vocabulary.
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bpemode=unigram # Mode of BPE (unigram or bpe).
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oov="<unk>" # Out of vocabulary symbol.
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blank="<blank>" # CTC blank symbol
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sos_eos="<sos/eos>" # sos and eos symbole
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bpe_input_sentence_size=100000000 # Size of input sentence for BPE.
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bpe_nlsyms= # non-linguistic symbols list, separated by a comma, for BPE
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bpe_char_cover=1.0 # character coverage when modeling BPE
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# Ngram model related
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use_ngram=false
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ngram_exp=
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ngram_num=3
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# Language model related
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use_lm=false # Use language model for ASR decoding.
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lm_tag= # Suffix to the result dir for language model training.
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lm_exp= # Specify the directory path for LM experiment.
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# If this option is specified, lm_tag is ignored.
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lm_stats_dir= # Specify the directory path for LM statistics.
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lm_config= # Config for language model training.
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lm_args= # Arguments for language model training, e.g., "--max_epoch 10".
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# Note that it will overwrite args in lm config.
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use_word_lm=false # Whether to use word language model.
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num_splits_lm=1 # Number of splitting for lm corpus.
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# shellcheck disable=SC2034
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word_vocab_size=10000 # Size of word vocabulary.
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# ASR model related
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asr_tag= # Suffix to the result dir for asr model training.
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asr_exp= # Specify the directory path for ASR experiment.
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# If this option is specified, asr_tag is ignored.
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asr_stats_dir= # Specify the directory path for ASR statistics.
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asr_config= # Config for asr model training.
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asr_args= # Arguments for asr model training, e.g., "--max_epoch 10".
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# Note that it will overwrite args in asr config.
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pretrained_model= # Pretrained model to load
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ignore_init_mismatch=false # Ignore initial mismatch
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feats_normalize=global_mvn # Normalizaton layer type.
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num_splits_asr=1 # Number of splitting for lm corpus.
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# Upload model related
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hf_repo=
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# Decoding related
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use_k2=false # Whether to use k2 based decoder
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k2_ctc_decoding=true
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use_nbest_rescoring=true # use transformer-decoder
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# and transformer language model for nbest rescoring
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num_paths=1000 # The 3rd argument of k2.random_paths.
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nll_batch_size=100 # Affect GPU memory usage when computing nll
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# during nbest rescoring
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k2_config=./conf/decode_asr_transformer_with_k2.yaml
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use_streaming=false # Whether to use streaming decoding
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use_maskctc=false # Whether to use maskctc decoding
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batch_size=1
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inference_tag= # Suffix to the result dir for decoding.
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inference_config= # Config for decoding.
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inference_args= # Arguments for decoding, e.g., "--lm_weight 0.1".
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# Note that it will overwrite args in inference config.
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inference_lm=valid.loss.ave.pth # Language model path for decoding.
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inference_ngram=${ngram_num}gram.bin
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inference_asr_model=valid.acc.ave.pth # ASR model path for decoding.
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# e.g.
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# inference_asr_model=train.loss.best.pth
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# inference_asr_model=3epoch.pth
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# inference_asr_model=valid.acc.best.pth
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# inference_asr_model=valid.loss.ave.pth
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download_model= # Download a model from Model Zoo and use it for decoding.
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# [Task dependent] Set the datadir name created by local/data.sh
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train_set= # Name of training set.
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valid_set= # Name of validation set used for monitoring/tuning network training.
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test_sets= # Names of test sets. Multiple items (e.g., both dev and eval sets) can be specified.
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bpe_train_text= # Text file path of bpe training set.
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lm_train_text= # Text file path of language model training set.
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lm_dev_text= # Text file path of language model development set.
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lm_test_text= # Text file path of language model evaluation set.
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nlsyms_txt=none # Non-linguistic symbol list if existing.
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cleaner=none # Text cleaner.
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g2p=none # g2p method (needed if token_type=phn).
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lang=noinfo # The language type of corpus.
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score_opts= # The options given to sclite scoring
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local_score_opts= # The options given to local/score.sh.
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asr_speech_fold_length=800 # fold_length for speech data during ASR training.
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asr_text_fold_length=150 # fold_length for text data during ASR training.
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lm_fold_length=150 # fold_length for LM training.
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oss_path=
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token_list=
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scp=
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text=
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mode=
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help_message=$(cat << EOF
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Usage: $0 --train-set "<train_set_name>" --valid-set "<valid_set_name>" --test_sets "<test_set_names>"
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Options:
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# General configuration
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--stage # Processes starts from the specified stage (default="${stage}").
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--stop_stage # Processes is stopped at the specified stage (default="${stop_stage}").
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--skip_data_prep # Skip data preparation stages (default="${skip_data_prep}").
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--skip_train # Skip training stages (default="${skip_train}").
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--skip_eval # Skip decoding and evaluation stages (default="${skip_eval}").
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--skip_upload # Skip packing and uploading stages (default="${skip_upload}").
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--ngpu # The number of gpus ("0" uses cpu, otherwise use gpu, default="${ngpu}").
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--num_nodes # The number of nodes (default="${num_nodes}").
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--nj # The number of parallel jobs (default="${nj}").
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--inference_nj # The number of parallel jobs in decoding (default="${inference_nj}").
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--gpu_inference # Whether to perform gpu decoding (default="${gpu_inference}").
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--dumpdir # Directory to dump features (default="${dumpdir}").
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--expdir # Directory to save experiments (default="${expdir}").
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--python # Specify python to execute espnet commands (default="${python}").
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# Data preparation related
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--local_data_opts # The options given to local/data.sh (default="${local_data_opts}").
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# Speed perturbation related
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--speed_perturb_factors # speed perturbation factors, e.g. "0.9 1.0 1.1" (separated by space, default="${speed_perturb_factors}").
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# Feature extraction related
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--feats_type # Feature type (raw, fbank_pitch or extracted, default="${feats_type}").
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--audio_format # Audio format: wav, flac, wav.ark, flac.ark (only in feats_type=raw, default="${audio_format}").
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--fs # Sampling rate (default="${fs}").
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--min_wav_duration # Minimum duration in second (default="${min_wav_duration}").
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--max_wav_duration # Maximum duration in second (default="${max_wav_duration}").
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# Tokenization related
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--token_type # Tokenization type (char or bpe, default="${token_type}").
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--nbpe # The number of BPE vocabulary (default="${nbpe}").
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--bpemode # Mode of BPE (unigram or bpe, default="${bpemode}").
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--oov # Out of vocabulary symbol (default="${oov}").
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--blank # CTC blank symbol (default="${blank}").
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--sos_eos # sos and eos symbole (default="${sos_eos}").
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--bpe_input_sentence_size # Size of input sentence for BPE (default="${bpe_input_sentence_size}").
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--bpe_nlsyms # Non-linguistic symbol list for sentencepiece, separated by a comma. (default="${bpe_nlsyms}").
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--bpe_char_cover # Character coverage when modeling BPE (default="${bpe_char_cover}").
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# Language model related
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--lm_tag # Suffix to the result dir for language model training (default="${lm_tag}").
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--lm_exp # Specify the directory path for LM experiment.
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# If this option is specified, lm_tag is ignored (default="${lm_exp}").
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--lm_stats_dir # Specify the directory path for LM statistics (default="${lm_stats_dir}").
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--lm_config # Config for language model training (default="${lm_config}").
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--lm_args # Arguments for language model training (default="${lm_args}").
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# e.g., --lm_args "--max_epoch 10"
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# Note that it will overwrite args in lm config.
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--use_word_lm # Whether to use word language model (default="${use_word_lm}").
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--word_vocab_size # Size of word vocabulary (default="${word_vocab_size}").
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--num_splits_lm # Number of splitting for lm corpus (default="${num_splits_lm}").
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# ASR model related
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--asr_tag # Suffix to the result dir for asr model training (default="${asr_tag}").
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--asr_exp # Specify the directory path for ASR experiment.
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# If this option is specified, asr_tag is ignored (default="${asr_exp}").
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--asr_stats_dir # Specify the directory path for ASR statistics (default="${asr_stats_dir}").
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--asr_config # Config for asr model training (default="${asr_config}").
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--asr_args # Arguments for asr model training (default="${asr_args}").
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# e.g., --asr_args "--max_epoch 10"
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# Note that it will overwrite args in asr config.
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--pretrained_model= # Pretrained model to load (default="${pretrained_model}").
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--ignore_init_mismatch= # Ignore mismatch parameter init with pretrained model (default="${ignore_init_mismatch}").
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--feats_normalize # Normalizaton layer type (default="${feats_normalize}").
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--num_splits_asr # Number of splitting for lm corpus (default="${num_splits_asr}").
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# Decoding related
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--inference_tag # Suffix to the result dir for decoding (default="${inference_tag}").
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--inference_config # Config for decoding (default="${inference_config}").
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--inference_args # Arguments for decoding (default="${inference_args}").
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# e.g., --inference_args "--lm_weight 0.1"
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# Note that it will overwrite args in inference config.
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--inference_lm # Language model path for decoding (default="${inference_lm}").
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--inference_asr_model # ASR model path for decoding (default="${inference_asr_model}").
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--download_model # Download a model from Model Zoo and use it for decoding (default="${download_model}").
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--use_streaming # Whether to use streaming decoding (default="${use_streaming}").
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--use_maskctc # Whether to use maskctc decoding (default="${use_streaming}").
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# [Task dependent] Set the datadir name created by local/data.sh
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--train_set # Name of training set (required).
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--valid_set # Name of validation set used for monitoring/tuning network training (required).
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--test_sets # Names of test sets.
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# Multiple items (e.g., both dev and eval sets) can be specified (required).
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--bpe_train_text # Text file path of bpe training set.
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--lm_train_text # Text file path of language model training set.
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--lm_dev_text # Text file path of language model development set (default="${lm_dev_text}").
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--lm_test_text # Text file path of language model evaluation set (default="${lm_test_text}").
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--nlsyms_txt # Non-linguistic symbol list if existing (default="${nlsyms_txt}").
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--cleaner # Text cleaner (default="${cleaner}").
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--g2p # g2p method (default="${g2p}").
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--lang # The language type of corpus (default=${lang}).
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--score_opts # The options given to sclite scoring (default="{score_opts}").
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--local_score_opts # The options given to local/score.sh (default="{local_score_opts}").
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--asr_speech_fold_length # fold_length for speech data during ASR training (default="${asr_speech_fold_length}").
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--asr_text_fold_length # fold_length for text data during ASR training (default="${asr_text_fold_length}").
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--lm_fold_length # fold_length for LM training (default="${lm_fold_length}").
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EOF
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)
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log "$0 $*"
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# Save command line args for logging (they will be lost after utils/parse_options.sh)
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run_args=$(utils/print_args.py $0 "$@")
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. utils/parse_options.sh
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if [ $# -ne 0 ]; then
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log "${help_message}"
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log "Error: No positional arguments are required."
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exit 2
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fi
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# set absolute dump dir path
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dumpdir=${datadir}/${dumpdir}
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if [ -z "${inference_tag}" ]; then
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if [ -n "${inference_config}" ]; then
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inference_tag="$(basename "${inference_config}" .yaml)"
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else
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inference_tag=inference
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fi
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if "${use_k2}"; then
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inference_tag+="_use_k2"
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inference_tag+="_k2_ctc_decoding_${k2_ctc_decoding}"
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inference_tag+="_use_nbest_rescoring_${use_nbest_rescoring}"
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fi
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fi
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# ========================== Main stages start from here. ==========================
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if [ ${stage} -le 12 ] && [ ${stop_stage} -ge 12 ]; then
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log "Stage 12: Decoding: training_dir=${asr_exp}"
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if ${gpu_inference}; then
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_cmd="${cuda_cmd}"
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_ngpu=1
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else
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_cmd="${decode_cmd}"
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_ngpu=0
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fi
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_opts=
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if [ -n "${inference_config}" ]; then
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_opts+="--config ${inference_config} "
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fi
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if "${use_lm}"; then
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if "${use_word_lm}"; then
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_opts+="--word_lm_train_config ${lm_exp}/config.yaml "
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_opts+="--word_lm_file ${lm_exp}/${inference_lm} "
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else
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_opts+="--lm_train_config ${lm_exp}/config.yaml "
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_opts+="--lm_file ${lm_exp}/${inference_lm} "
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fi
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fi
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if "${use_ngram}"; then
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_opts+="--ngram_file ${ngram_exp}/${inference_ngram}"
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inference_tag=${inference_tag}.${inference_ngram}
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fi
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# 2. Generate run.sh
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log "Generate '${asr_exp}/${inference_tag}/run.sh'. You can resume the process from stage 12 using this script"
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mkdir -p "${asr_exp}/${inference_tag}"; echo "${run_args} --stage 12 \"\$@\"; exit \$?" > "${asr_exp}/${inference_tag}/run.sh"; chmod +x "${asr_exp}/${inference_tag}/run.sh"
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if "${use_streaming}"; then
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asr_inference_tool="funasr.bin.asr_inference_streaming"
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elif "${use_maskctc}"; then
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asr_inference_tool="funasr.bin.asr_inference_maskctc"
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else
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asr_inference_tool="funasr.bin.asr_inference_launch"
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fi
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for dset in ${test_sets}; do
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if [ $feats_type == "ark_wav" ]; then
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_data="${dumpdir}/wav/${dset}"
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else
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_data="${dumpdir}/$feats_type/${dset}"
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fi
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_dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
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_logdir="${_dir}/logdir"
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if [ -d ${_dir} ]; then
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#echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
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rm -r ${_dir}
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fi
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mkdir -p "${_logdir}"
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_scp=$scp
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_type=kaldi_ark
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# 1. Split the key file
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key_file=${_data}/${_scp}
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split_scps=""
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if "${use_k2}"; then
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# Now only _nj=1 is verified if using k2
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_nj=1
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else
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_nj=$(min "${inference_nj}" "$(<${key_file} wc -l)")
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fi
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for n in $(seq "${_nj}"); do
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split_scps+=" ${_logdir}/keys.${n}.scp"
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done
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# shellcheck disable=SC2086
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utils/split_scp.pl "${key_file}" ${split_scps}
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# 2. Submit decoding jobs
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log "Decoding started... log: '${_logdir}/asr_inference.*.log'"
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# shellcheck disable=SC2086
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${_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
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${python} -m ${asr_inference_tool} \
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--batch_size ${batch_size} \
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--ngpu "${_ngpu}" \
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--njob ${njob} \
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--gpuid_list ${gpuid_list} \
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--data_path_and_name_and_type "${_data}/${_scp},speech,${_type}" \
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--key_file "${_logdir}"/keys.JOB.scp \
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--asr_train_config "${asr_exp}"/config.yaml \
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--asr_model_file "${asr_exp}"/"${inference_asr_model}" \
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--output_dir "${_logdir}"/output.JOB \
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--mode $mode \
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${_opts} ${inference_args}
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# 3. Concatenates the output files from each jobs
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for f in token token_int score text; do
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if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
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for i in $(seq "${_nj}"); do
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cat "${_logdir}/output.${i}/1best_recog/${f}"
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done | sort -k1 >"${_dir}/${f}"
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fi
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done
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python utils/proce_text.py ${_dir}/text ${_dir}/${text}.proc
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python utils/proce_text.py ${_data}/text ${_data}/${text}.proc
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python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
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tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
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cat ${_dir}/text.cer.txt
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done
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fi
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log "Successfully finished. [elapsed=${SECONDS}s]"
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