#!/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=false # 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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ngpu=1 # The number of gpus ("0" uses cpu, otherwise use gpu).
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num_nodes=1 # The number of nodes.
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nj=16 # The number of parallel jobs.
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inference_nj=16 # The number of parallel jobs in decoding.
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gpu_inference=false # Whether to perform gpu decoding.
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njob_infer=4
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dumpdir=dump2 # Directory to dump features.
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expdir=exp # Directory to save experiments.
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python=python3 # Specify python to execute espnet commands.
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device=0
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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=raw # Feature type (raw or fbank_pitch).
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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=16000 # 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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# Language model related
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use_lm=true # 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 direcotry 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 direcotry 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 direcotry path for ASR experiment.
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# If this option is specified, asr_tag is ignored.
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sa_asr_exp=
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asr_stats_dir= # Specify the direcotry path for ASR statistics.
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asr_config= # Config for asr model training.
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sa_asr_config=
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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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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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# Decoding related
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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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sa_asr_inference_tag=
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sa_asr_inference_args=
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inference_lm=valid.loss.ave.pb # Language modle path for decoding.
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inference_asr_model=valid.acc.ave.pb # 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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inference_sa_asr_model=valid.acc_spk.ave.pb
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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=zh # 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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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="${python}").
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--device # Which GPUs are use for local training (defalut="${device}").
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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="${lm_tag}").
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--lm_exp # Specify the direcotry path for LM experiment.
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# If this option is specified, lm_tag is ignored (default="${lm_exp}").
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--lm_stats_dir # Specify the direcotry 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="${asr_tag}").
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--asr_exp # Specify the direcotry path for ASR experiment.
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# If this option is specified, asr_tag is ignored (default="${asr_exp}").
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--asr_stats_dir # Specify the direcotry 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="${asr_args}").
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# e.g., --asr_args "--max_epoch 10"
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# Note that it will overwrite args in asr config.
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--feats_normalize # Normalizaton layer type (default="${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 modle 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="${download_model}").
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# [Task dependent] Set the datadir name created by local/data.sh
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--train_set # Name of training set (required).
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--valid_set # Name of validation set used for monitoring/tuning network training (required).
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--test_sets # Names of test sets.
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# Multiple items (e.g., both dev and eval sets) can be specified (required).
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--bpe_train_text # Text file path of bpe training set.
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--lm_train_text # Text file path of language model training set.
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--lm_dev_text # Text file path of language model development set (default="${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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EOF
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)
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log "$0 $*"
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# Save command line args for logging (they will be lost after utils/parse_options.sh)
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run_args=$(python -m funasr.utils.cli_utils $0 "$@")
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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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. ./path.sh
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# Check required arguments
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[ -z "${train_set}" ] && { log "${help_message}"; log "Error: --train_set is required"; exit 2; };
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[ -z "${valid_set}" ] && { log "${help_message}"; log "Error: --valid_set is required"; exit 2; };
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[ -z "${test_sets}" ] && { log "${help_message}"; log "Error: --test_sets is required"; exit 2; };
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# Check feature type
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if [ "${feats_type}" = raw ]; then
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data_feats=${dumpdir}/raw
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elif [ "${feats_type}" = fbank_pitch ]; then
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data_feats=${dumpdir}/fbank_pitch
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elif [ "${feats_type}" = fbank ]; then
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data_feats=${dumpdir}/fbank
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elif [ "${feats_type}" == extracted ]; then
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data_feats=${dumpdir}/extracted
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else
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log "${help_message}"
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log "Error: not supported: --feats_type ${feats_type}"
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exit 2
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fi
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# Use the same text as ASR for bpe training if not specified.
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[ -z "${bpe_train_text}" ] && bpe_train_text="${data_feats}/${train_set}/text"
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# Use the same text as ASR for lm training if not specified.
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[ -z "${lm_train_text}" ] && lm_train_text="${data_feats}/${train_set}/text"
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# Use the same text as ASR for lm training if not specified.
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[ -z "${lm_dev_text}" ] && lm_dev_text="${data_feats}/${valid_set}/text"
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# Use the text of the 1st evaldir if lm_test is not specified
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[ -z "${lm_test_text}" ] && lm_test_text="${data_feats}/${test_sets%% *}/text"
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# Check tokenization type
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if [ "${lang}" != noinfo ]; then
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token_listdir=data/${lang}_token_list
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else
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token_listdir=data/token_list
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fi
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bpedir="${token_listdir}/bpe_${bpemode}${nbpe}"
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bpeprefix="${bpedir}"/bpe
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bpemodel="${bpeprefix}".model
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bpetoken_list="${bpedir}"/tokens.txt
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chartoken_list="${token_listdir}"/char/tokens.txt
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# NOTE: keep for future development.
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# shellcheck disable=SC2034
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wordtoken_list="${token_listdir}"/word/tokens.txt
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if [ "${token_type}" = bpe ]; then
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token_list="${bpetoken_list}"
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elif [ "${token_type}" = char ]; then
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token_list="${chartoken_list}"
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bpemodel=none
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elif [ "${token_type}" = word ]; then
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token_list="${wordtoken_list}"
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bpemodel=none
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else
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log "Error: not supported --token_type '${token_type}'"
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exit 2
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fi
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if ${use_word_lm}; then
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log "Error: Word LM is not supported yet"
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exit 2
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lm_token_list="${wordtoken_list}"
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lm_token_type=word
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else
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lm_token_list="${token_list}"
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lm_token_type="${token_type}"
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fi
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# Set tag for naming of model directory
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if [ -z "${asr_tag}" ]; then
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if [ -n "${asr_config}" ]; then
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asr_tag="$(basename "${asr_config}" .yaml)_${feats_type}"
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else
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asr_tag="train_${feats_type}"
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fi
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if [ "${lang}" != noinfo ]; then
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asr_tag+="_${lang}_${token_type}"
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else
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asr_tag+="_${token_type}"
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fi
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if [ "${token_type}" = bpe ]; then
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asr_tag+="${nbpe}"
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fi
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# Add overwritten arg's info
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if [ -n "${asr_args}" ]; then
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asr_tag+="$(echo "${asr_args}" | sed -e "s/--/\_/g" -e "s/[ |=/]//g")"
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fi
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if [ -n "${speed_perturb_factors}" ]; then
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asr_tag+="_sp"
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fi
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fi
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if [ -z "${lm_tag}" ]; then
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if [ -n "${lm_config}" ]; then
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lm_tag="$(basename "${lm_config}" .yaml)"
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else
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lm_tag="train"
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fi
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if [ "${lang}" != noinfo ]; then
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lm_tag+="_${lang}_${lm_token_type}"
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else
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lm_tag+="_${lm_token_type}"
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fi
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if [ "${lm_token_type}" = bpe ]; then
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lm_tag+="${nbpe}"
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fi
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# Add overwritten arg's info
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if [ -n "${lm_args}" ]; then
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lm_tag+="$(echo "${lm_args}" | sed -e "s/--/\_/g" -e "s/[ |=/]//g")"
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fi
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fi
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# The directory used for collect-stats mode
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if [ -z "${asr_stats_dir}" ]; then
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if [ "${lang}" != noinfo ]; then
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asr_stats_dir="${expdir}/asr_stats_${feats_type}_${lang}_${token_type}"
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else
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asr_stats_dir="${expdir}/asr_stats_${feats_type}_${token_type}"
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fi
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if [ "${token_type}" = bpe ]; then
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asr_stats_dir+="${nbpe}"
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fi
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if [ -n "${speed_perturb_factors}" ]; then
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asr_stats_dir+="_sp"
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fi
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fi
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if [ -z "${lm_stats_dir}" ]; then
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if [ "${lang}" != noinfo ]; then
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lm_stats_dir="${expdir}/lm_stats_${lang}_${lm_token_type}"
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else
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lm_stats_dir="${expdir}/lm_stats_${lm_token_type}"
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fi
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if [ "${lm_token_type}" = bpe ]; then
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lm_stats_dir+="${nbpe}"
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fi
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fi
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# The directory used for training commands
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if [ -z "${asr_exp}" ]; then
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asr_exp="${expdir}/asr_${asr_tag}"
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fi
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if [ -z "${lm_exp}" ]; then
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lm_exp="${expdir}/lm_${lm_tag}"
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fi
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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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# Add overwritten arg's info
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if [ -n "${inference_args}" ]; then
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inference_tag+="$(echo "${inference_args}" | sed -e "s/--/\_/g" -e "s/[ |=]//g")"
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fi
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if "${use_lm}"; then
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inference_tag+="_lm_$(basename "${lm_exp}")_$(echo "${inference_lm}" | sed -e "s/\//_/g" -e "s/\.[^.]*$//g")"
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fi
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inference_tag+="_asr_model_$(echo "${inference_asr_model}" | sed -e "s/\//_/g" -e "s/\.[^.]*$//g")"
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fi
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if [ -z "${sa_asr_inference_tag}" ]; then
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if [ -n "${inference_config}" ]; then
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sa_asr_inference_tag="$(basename "${inference_config}" .yaml)"
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else
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sa_asr_inference_tag=sa_asr_inference
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fi
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# Add overwritten arg's info
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if [ -n "${sa_asr_inference_args}" ]; then
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sa_asr_inference_tag+="$(echo "${sa_asr_inference_args}" | sed -e "s/--/\_/g" -e "s/[ |=]//g")"
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fi
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if "${use_lm}"; then
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sa_asr_inference_tag+="_lm_$(basename "${lm_exp}")_$(echo "${inference_lm}" | sed -e "s/\//_/g" -e "s/\.[^.]*$//g")"
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fi
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sa_asr_inference_tag+="_asr_model_$(echo "${inference_sa_asr_model}" | sed -e "s/\//_/g" -e "s/\.[^.]*$//g")"
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fi
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train_cmd="run.pl"
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cuda_cmd="run.pl"
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decode_cmd="run.pl"
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# ========================== Main stages start from here. ==========================
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if ! "${skip_data_prep}"; then
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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if [ "${feats_type}" = raw ]; then
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log "Stage 1: Format wav.scp: data/ -> ${data_feats}"
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# ====== Recreating "wav.scp" ======
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# Kaldi-wav.scp, which can describe the file path with unix-pipe, like "cat /some/path |",
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# shouldn't be used in training process.
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# "format_wav_scp.sh" dumps such pipe-style-wav to real audio file
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# and it can also change the audio-format and sampling rate.
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# If nothing is need, then format_wav_scp.sh does nothing:
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# i.e. the input file format and rate is same as the output.
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for dset in "${test_sets}" ; do
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_suf=""
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local/copy_data_dir.sh --validate_opts --non-print data/"${dset}" "${data_feats}${_suf}/${dset}"
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rm -f ${data_feats}${_suf}/${dset}/{segments,wav.scp,reco2file_and_channel,reco2dur}
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_opts=
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if [ -e data/"${dset}"/segments ]; then
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# "segments" is used for splitting wav files which are written in "wav".scp
|
# into utterances. The file format of segments:
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# <segment_id> <record_id> <start_time> <end_time>
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# "e.g. call-861225-A-0050-0065 call-861225-A 5.0 6.5"
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# Where the time is written in seconds.
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_opts+="--segments data/${dset}/segments "
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fi
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# shellcheck disable=SC2086
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local/format_wav_scp.sh --nj "${nj}" --cmd "${train_cmd}" \
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--audio-format "${audio_format}" --fs "${fs}" ${_opts} \
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"data/${dset}/wav.scp" "${data_feats}${_suf}/${dset}"
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echo "${feats_type}" > "${data_feats}${_suf}/${dset}/feats_type"
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done
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else
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log "Error: not supported: --feats_type ${feats_type}"
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exit 2
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fi
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fi
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|
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
log "Stage 2: Generate speaker profile by spectral-cluster"
|
mkdir -p "profile_log"
|
for dset in "${test_sets}"; do
|
# generate cluster_profile with spectral-cluster directly (for infering and without oracle information)
|
python local/gen_cluster_profile_infer.py "${data_feats}/${dset}" "data/${dset}" 0.996 0.815 &> "profile_log/gen_cluster_profile_infer_${dset}.log"
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log "Successfully generate cluster profile for ${dset} (${data_feats}/${dset}/cluster_profile_infer.scp)"
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done
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fi
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else
|
log "Skip the stages for data preparation"
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fi
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|
# ========================== Data preparation is done here. ==========================
|
|
if ! "${skip_eval}"; then
|
|
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
log "Stage 3: Decoding SA-ASR (cluster profile): training_dir=${sa_asr_exp}"
|
|
if ${gpu_inference}; then
|
_cmd="${cuda_cmd}"
|
inference_nj=$[${ngpu}*${njob_infer}]
|
_ngpu=1
|
|
else
|
_cmd="${decode_cmd}"
|
inference_nj=$njob_infer
|
_ngpu=0
|
fi
|
|
_opts=
|
if [ -n "${inference_config}" ]; then
|
_opts+="--config ${inference_config} "
|
fi
|
if "${use_lm}"; then
|
if "${use_word_lm}"; then
|
_opts+="--word_lm_train_config ${lm_exp}/config.yaml "
|
_opts+="--word_lm_file ${lm_exp}/${inference_lm} "
|
else
|
_opts+="--lm_train_config ${lm_exp}/config.yaml "
|
_opts+="--lm_file ${lm_exp}/${inference_lm} "
|
fi
|
fi
|
|
# 2. Generate run.sh
|
log "Generate '${sa_asr_exp}/${sa_asr_inference_tag}.cluster/run.sh'. You can resume the process from stage 17 using this script"
|
mkdir -p "${sa_asr_exp}/${sa_asr_inference_tag}.cluster"; echo "${run_args} --stage 17 \"\$@\"; exit \$?" > "${sa_asr_exp}/${sa_asr_inference_tag}.cluster/run.sh"; chmod +x "${sa_asr_exp}/${sa_asr_inference_tag}.cluster/run.sh"
|
|
for dset in ${test_sets}; do
|
_data="${data_feats}/${dset}"
|
_dir="${sa_asr_exp}/${sa_asr_inference_tag}.cluster/${dset}"
|
_logdir="${_dir}/logdir"
|
mkdir -p "${_logdir}"
|
|
_feats_type="$(<${_data}/feats_type)"
|
if [ "${_feats_type}" = raw ]; then
|
_scp=wav.scp
|
if [[ "${audio_format}" == *ark* ]]; then
|
_type=kaldi_ark
|
else
|
_type=sound
|
fi
|
else
|
_scp=feats.scp
|
_type=kaldi_ark
|
fi
|
|
# 1. Split the key file
|
key_file=${_data}/${_scp}
|
split_scps=""
|
_nj=$(min "${inference_nj}" "$(<${key_file} wc -l)")
|
for n in $(seq "${_nj}"); do
|
split_scps+=" ${_logdir}/keys.${n}.scp"
|
done
|
# shellcheck disable=SC2086
|
utils/split_scp.pl "${key_file}" ${split_scps}
|
|
# 2. Submit decoding jobs
|
log "Decoding started... log: '${_logdir}/sa_asr_inference.*.log'"
|
# shellcheck disable=SC2086
|
${_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
|
python -m funasr.bin.asr_inference_launch \
|
--batch_size 1 \
|
--mc True \
|
--nbest 1 \
|
--ngpu "${_ngpu}" \
|
--njob ${njob_infer} \
|
--gpuid_list ${device} \
|
--data_path_and_name_and_type "${_data}/${_scp},speech,${_type}" \
|
--data_path_and_name_and_type "${_data}/cluster_profile_infer.scp,profile,npy" \
|
--key_file "${_logdir}"/keys.JOB.scp \
|
--allow_variable_data_keys true \
|
--asr_train_config "${sa_asr_exp}"/config.yaml \
|
--asr_model_file "${sa_asr_exp}"/"${inference_sa_asr_model}" \
|
--output_dir "${_logdir}"/output.JOB \
|
--mode sa_asr \
|
${_opts}
|
|
# 3. Concatenates the output files from each jobs
|
for f in token token_int score text text_id; do
|
for i in $(seq "${_nj}"); do
|
cat "${_logdir}/output.${i}/1best_recog/${f}"
|
done | LC_ALL=C sort -k1 >"${_dir}/${f}"
|
done
|
done
|
fi
|
|
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
log "Stage 4: Generate SA-ASR results (cluster profile)"
|
|
for dset in ${test_sets}; do
|
_dir="${sa_asr_exp}/${sa_asr_inference_tag}.cluster/${dset}"
|
|
python local/process_text_spk_merge.py ${_dir}
|
done
|
|
fi
|
|
else
|
log "Skip the evaluation stages"
|
fi
|
|
|
log "Successfully finished. [elapsed=${SECONDS}s]"
|
