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FunASR/egs/alimeeting/sa_asr_deprecated/asr_local.sh
yhliang e8528b8f62
Dev lyh (#645) 
* update

* update

* fix bug

* fix bug
2023-06-16 20:16:47 +08:00

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#!/usr/bin/env bash
# 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
log() {
local fname=${BASH_SOURCE[1]##*/}
echo -e "$(date '+%Y-%m-%dT%H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
}
min() {
local a b
a=$1
for b in "$@"; do
if [ "${b}" -le "${a}" ]; then
a="${b}"
fi
done
echo "${a}"
}
SECONDS=0
# General configuration
stage=1 # Processes starts from the specified stage.
stop_stage=10000 # Processes is stopped at the specified stage.
skip_data_prep=false # Skip data preparation stages.
skip_train=false # Skip training stages.
skip_eval=false # Skip decoding and evaluation stages.
skip_upload=true # Skip packing and uploading stages.
ngpu=1 # The number of gpus ("0" uses cpu, otherwise use gpu).
num_nodes=1 # The number of nodes.
nj=16 # The number of parallel jobs.
inference_nj=16 # The number of parallel jobs in decoding.
gpu_inference=false # Whether to perform gpu decoding.
njob_infer=4
dumpdir=dump2 # Directory to dump features.
expdir=exp # Directory to save experiments.
python=python3 # Specify python to execute espnet commands.
device=0
# Data preparation related
local_data_opts= # The options given to local/data.sh.
# Speed perturbation related
speed_perturb_factors= # perturbation factors, e.g. "0.9 1.0 1.1" (separated by space).
# Feature extraction related
feats_type=raw # Feature type (raw or fbank_pitch).
audio_format=flac # Audio format: wav, flac, wav.ark, flac.ark (only in feats_type=raw).
fs=16000 # Sampling rate.
min_wav_duration=0.1 # Minimum duration in second.
max_wav_duration=20 # Maximum duration in second.
# Tokenization related
token_type=bpe # Tokenization type (char or bpe).
nbpe=30 # The number of BPE vocabulary.
bpemode=unigram # Mode of BPE (unigram or bpe).
oov="<unk>" # Out of vocabulary symbol.
blank="<blank>" # CTC blank symbol
sos_eos="<sos/eos>" # sos and eos symbole
bpe_input_sentence_size=100000000 # Size of input sentence for BPE.
bpe_nlsyms= # non-linguistic symbols list, separated by a comma, for BPE
bpe_char_cover=1.0 # character coverage when modeling BPE
# Language model related
use_lm=true # Use language model for ASR decoding.
lm_tag= # Suffix to the result dir for language model training.
lm_exp= # Specify the direcotry path for LM experiment.
# If this option is specified, lm_tag is ignored.
lm_stats_dir= # Specify the direcotry path for LM statistics.
lm_config= # Config for language model training.
lm_args= # Arguments for language model training, e.g., "--max_epoch 10".
# Note that it will overwrite args in lm config.
use_word_lm=false # Whether to use word language model.
num_splits_lm=1 # Number of splitting for lm corpus.
# shellcheck disable=SC2034
word_vocab_size=10000 # Size of word vocabulary.
# ASR model related
asr_tag= # Suffix to the result dir for asr model training.
asr_exp= # Specify the direcotry path for ASR experiment.
# If this option is specified, asr_tag is ignored.
sa_asr_exp=
asr_stats_dir= # Specify the direcotry path for ASR statistics.
asr_config= # Config for asr model training.
sa_asr_config=
asr_args= # Arguments for asr model training, e.g., "--max_epoch 10".
# Note that it will overwrite args in asr config.
feats_normalize=global_mvn # Normalizaton layer type.
num_splits_asr=1 # Number of splitting for lm corpus.
# Decoding related
inference_tag= # Suffix to the result dir for decoding.
inference_config= # Config for decoding.
inference_args= # Arguments for decoding, e.g., "--lm_weight 0.1".
# Note that it will overwrite args in inference config.
sa_asr_inference_tag=
sa_asr_inference_args=
inference_lm=valid.loss.ave.pb # Language modle path for decoding.
inference_asr_model=valid.acc.ave.pb # ASR model path for decoding.
# e.g.
# inference_asr_model=train.loss.best.pth
# inference_asr_model=3epoch.pth
# inference_asr_model=valid.acc.best.pth
# inference_asr_model=valid.loss.ave.pth
inference_sa_asr_model=valid.acc_spk.ave.pb
infer_with_pretrained_model=false # Use pretrained model for decoding
download_sa_asr_model= # Download the SA-ASR model from ModelScope and use it for decoding.
# [Task dependent] Set the datadir name created by local/data.sh
train_set= # Name of training set.
valid_set= # Name of validation set used for monitoring/tuning network training.
test_sets= # Names of test sets. Multiple items (e.g., both dev and eval sets) can be specified.
bpe_train_text= # Text file path of bpe training set.
lm_train_text= # Text file path of language model training set.
lm_dev_text= # Text file path of language model development set.
lm_test_text= # Text file path of language model evaluation set.
nlsyms_txt=none # Non-linguistic symbol list if existing.
cleaner=none # Text cleaner.
g2p=none # g2p method (needed if token_type=phn).
lang=zh # The language type of corpus.
score_opts= # The options given to sclite scoring
local_score_opts= # The options given to local/score.sh.
help_message=$(cat << EOF
Usage: $0 --train-set "<train_set_name>" --valid-set "<valid_set_name>" --test_sets "<test_set_names>"
Options:
# General configuration
--stage # Processes starts from the specified stage (default="${stage}").
--stop_stage # Processes is stopped at the specified stage (default="${stop_stage}").
--skip_data_prep # Skip data preparation stages (default="${skip_data_prep}").
--skip_train # Skip training stages (default="${skip_train}").
--skip_eval # Skip decoding and evaluation stages (default="${skip_eval}").
--skip_upload # Skip packing and uploading stages (default="${skip_upload}").
--ngpu # The number of gpus ("0" uses cpu, otherwise use gpu, default="${ngpu}").
--num_nodes # The number of nodes (default="${num_nodes}").
--nj # The number of parallel jobs (default="${nj}").
--inference_nj # The number of parallel jobs in decoding (default="${inference_nj}").
--gpu_inference # Whether to perform gpu decoding (default="${gpu_inference}").
--dumpdir # Directory to dump features (default="${dumpdir}").
--expdir # Directory to save experiments (default="${expdir}").
--python # Specify python to execute espnet commands (default="${python}").
--device # Which GPUs are use for local training (defalut="${device}").
# Data preparation related
--local_data_opts # The options given to local/data.sh (default="${local_data_opts}").
# Speed perturbation related
--speed_perturb_factors # speed perturbation factors, e.g. "0.9 1.0 1.1" (separated by space, default="${speed_perturb_factors}").
# Feature extraction related
--feats_type # Feature type (raw, fbank_pitch or extracted, default="${feats_type}").
--audio_format # Audio format: wav, flac, wav.ark, flac.ark (only in feats_type=raw, default="${audio_format}").
--fs # Sampling rate (default="${fs}").
--min_wav_duration # Minimum duration in second (default="${min_wav_duration}").
--max_wav_duration # Maximum duration in second (default="${max_wav_duration}").
# Tokenization related
--token_type # Tokenization type (char or bpe, default="${token_type}").
--nbpe # The number of BPE vocabulary (default="${nbpe}").
--bpemode # Mode of BPE (unigram or bpe, default="${bpemode}").
--oov # Out of vocabulary symbol (default="${oov}").
--blank # CTC blank symbol (default="${blank}").
--sos_eos # sos and eos symbole (default="${sos_eos}").
--bpe_input_sentence_size # Size of input sentence for BPE (default="${bpe_input_sentence_size}").
--bpe_nlsyms # Non-linguistic symbol list for sentencepiece, separated by a comma. (default="${bpe_nlsyms}").
--bpe_char_cover # Character coverage when modeling BPE (default="${bpe_char_cover}").
# Language model related
--lm_tag # Suffix to the result dir for language model training (default="${lm_tag}").
--lm_exp # Specify the direcotry path for LM experiment.
# If this option is specified, lm_tag is ignored (default="${lm_exp}").
--lm_stats_dir # Specify the direcotry path for LM statistics (default="${lm_stats_dir}").
--lm_config # Config for language model training (default="${lm_config}").
--lm_args # Arguments for language model training (default="${lm_args}").
# e.g., --lm_args "--max_epoch 10"
# Note that it will overwrite args in lm config.
--use_word_lm # Whether to use word language model (default="${use_word_lm}").
--word_vocab_size # Size of word vocabulary (default="${word_vocab_size}").
--num_splits_lm # Number of splitting for lm corpus (default="${num_splits_lm}").
# ASR model related
--asr_tag # Suffix to the result dir for asr model training (default="${asr_tag}").
--asr_exp # Specify the direcotry path for ASR experiment.
# If this option is specified, asr_tag is ignored (default="${asr_exp}").
--asr_stats_dir # Specify the direcotry path for ASR statistics (default="${asr_stats_dir}").
--asr_config # Config for asr model training (default="${asr_config}").
--asr_args # Arguments for asr model training (default="${asr_args}").
# e.g., --asr_args "--max_epoch 10"
# Note that it will overwrite args in asr config.
--feats_normalize # Normalizaton layer type (default="${feats_normalize}").
--num_splits_asr # Number of splitting for lm corpus (default="${num_splits_asr}").
# Decoding related
--inference_tag # Suffix to the result dir for decoding (default="${inference_tag}").
--inference_config # Config for decoding (default="${inference_config}").
--inference_args # Arguments for decoding (default="${inference_args}").
# e.g., --inference_args "--lm_weight 0.1"
# Note that it will overwrite args in inference config.
--inference_lm # Language modle path for decoding (default="${inference_lm}").
--inference_asr_model # ASR model path for decoding (default="${inference_asr_model}").
--infer_with_pretrained_model # Use pretrained model for decoding (default="${infer_with_pretrained_model}").
--download_sa_asr_model= # Download the SA-ASR model from ModelScope and use it for decoding(default="${download_sa_asr_model}").
# [Task dependent] Set the datadir name created by local/data.sh
--train_set # Name of training set (required).
--valid_set # Name of validation set used for monitoring/tuning network training (required).
--test_sets # Names of test sets.
# Multiple items (e.g., both dev and eval sets) can be specified (required).
--bpe_train_text # Text file path of bpe training set.
--lm_train_text # Text file path of language model training set.
--lm_dev_text # Text file path of language model development set (default="${lm_dev_text}").
--lm_test_text # Text file path of language model evaluation set (default="${lm_test_text}").
--nlsyms_txt # Non-linguistic symbol list if existing (default="${nlsyms_txt}").
--cleaner # Text cleaner (default="${cleaner}").
--g2p # g2p method (default="${g2p}").
--lang # The language type of corpus (default=${lang}).
--score_opts # The options given to sclite scoring (default="{score_opts}").
--local_score_opts # The options given to local/score.sh (default="{local_score_opts}").
EOF
)
log "$0 $*"
# Save command line args for logging (they will be lost after utils/parse_options.sh)
run_args=$(python -m funasr.utils.cli_utils $0 "$@")
. utils/parse_options.sh
if [ $# -ne 0 ]; then
log "${help_message}"
log "Error: No positional arguments are required."
exit 2
fi
. ./path.sh
# Check required arguments
[ -z "${train_set}" ] && { log "${help_message}"; log "Error: --train_set is required"; exit 2; };
[ -z "${valid_set}" ] && { log "${help_message}"; log "Error: --valid_set is required"; exit 2; };
[ -z "${test_sets}" ] && { log "${help_message}"; log "Error: --test_sets is required"; exit 2; };
# Check feature type
if [ "${feats_type}" = raw ]; then
data_feats=${dumpdir}/raw
elif [ "${feats_type}" = fbank_pitch ]; then
data_feats=${dumpdir}/fbank_pitch
elif [ "${feats_type}" = fbank ]; then
data_feats=${dumpdir}/fbank
elif [ "${feats_type}" == extracted ]; then
data_feats=${dumpdir}/extracted
else
log "${help_message}"
log "Error: not supported: --feats_type ${feats_type}"
exit 2
fi
# Use the same text as ASR for bpe training if not specified.
[ -z "${bpe_train_text}" ] && bpe_train_text="${data_feats}/${train_set}/text"
# Use the same text as ASR for lm training if not specified.
[ -z "${lm_train_text}" ] && lm_train_text="${data_feats}/${train_set}/text"
# Use the same text as ASR for lm training if not specified.
[ -z "${lm_dev_text}" ] && lm_dev_text="${data_feats}/${valid_set}/text"
# Use the text of the 1st evaldir if lm_test is not specified
[ -z "${lm_test_text}" ] && lm_test_text="${data_feats}/${test_sets%% *}/text"
# Check tokenization type
if [ "${lang}" != noinfo ]; then
token_listdir=data/${lang}_token_list
else
token_listdir=data/token_list
fi
bpedir="${token_listdir}/bpe_${bpemode}${nbpe}"
bpeprefix="${bpedir}"/bpe
bpemodel="${bpeprefix}".model
bpetoken_list="${bpedir}"/tokens.txt
chartoken_list="${token_listdir}"/char/tokens.txt
# NOTE: keep for future development.
# shellcheck disable=SC2034
wordtoken_list="${token_listdir}"/word/tokens.txt
if [ "${token_type}" = bpe ]; then
token_list="${bpetoken_list}"
elif [ "${token_type}" = char ]; then
token_list="${chartoken_list}"
bpemodel=none
elif [ "${token_type}" = word ]; then
token_list="${wordtoken_list}"
bpemodel=none
else
log "Error: not supported --token_type '${token_type}'"
exit 2
fi
if ${use_word_lm}; then
log "Error: Word LM is not supported yet"
exit 2
lm_token_list="${wordtoken_list}"
lm_token_type=word
else
lm_token_list="${token_list}"
lm_token_type="${token_type}"
fi
if ${infer_with_pretrained_model}; then
skip_train=true
fi
# Set tag for naming of model directory
if [ -z "${asr_tag}" ]; then
if [ -n "${asr_config}" ]; then
asr_tag="$(basename "${asr_config}" .yaml)_${feats_type}"
else
asr_tag="train_${feats_type}"
fi
if [ "${lang}" != noinfo ]; then
asr_tag+="_${lang}_${token_type}"
else
asr_tag+="_${token_type}"
fi
if [ "${token_type}" = bpe ]; then
asr_tag+="${nbpe}"
fi
# Add overwritten arg's info
if [ -n "${asr_args}" ]; then
asr_tag+="$(echo "${asr_args}" | sed -e "s/--/\_/g" -e "s/[ |=/]//g")"
fi
if [ -n "${speed_perturb_factors}" ]; then
asr_tag+="_sp"
fi
fi
if [ -z "${lm_tag}" ]; then
if [ -n "${lm_config}" ]; then
lm_tag="$(basename "${lm_config}" .yaml)"
else
lm_tag="train"
fi
if [ "${lang}" != noinfo ]; then
lm_tag+="_${lang}_${lm_token_type}"
else
lm_tag+="_${lm_token_type}"
fi
if [ "${lm_token_type}" = bpe ]; then
lm_tag+="${nbpe}"
fi
# Add overwritten arg's info
if [ -n "${lm_args}" ]; then
lm_tag+="$(echo "${lm_args}" | sed -e "s/--/\_/g" -e "s/[ |=/]//g")"
fi
fi
# The directory used for collect-stats mode
if [ -z "${asr_stats_dir}" ]; then
if [ "${lang}" != noinfo ]; then
asr_stats_dir="${expdir}/asr_stats_${feats_type}_${lang}_${token_type}"
else
asr_stats_dir="${expdir}/asr_stats_${feats_type}_${token_type}"
fi
if [ "${token_type}" = bpe ]; then
asr_stats_dir+="${nbpe}"
fi
if [ -n "${speed_perturb_factors}" ]; then
asr_stats_dir+="_sp"
fi
fi
if [ -z "${lm_stats_dir}" ]; then
if [ "${lang}" != noinfo ]; then
lm_stats_dir="${expdir}/lm_stats_${lang}_${lm_token_type}"
else
lm_stats_dir="${expdir}/lm_stats_${lm_token_type}"
fi
if [ "${lm_token_type}" = bpe ]; then
lm_stats_dir+="${nbpe}"
fi
fi
# The directory used for training commands
if [ -z "${asr_exp}" ]; then
asr_exp="${expdir}/asr_${asr_tag}"
fi
if [ -z "${lm_exp}" ]; then
lm_exp="${expdir}/lm_${lm_tag}"
fi
if [ -z "${inference_tag}" ]; then
if [ -n "${inference_config}" ]; then
inference_tag="$(basename "${inference_config}" .yaml)"
else
inference_tag=inference
fi
# Add overwritten arg's info
if [ -n "${inference_args}" ]; then
inference_tag+="$(echo "${inference_args}" | sed -e "s/--/\_/g" -e "s/[ |=]//g")"
fi
if "${use_lm}"; then
inference_tag+="_lm_$(basename "${lm_exp}")_$(echo "${inference_lm}" | sed -e "s/\//_/g" -e "s/\.[^.]*$//g")"
fi
inference_tag+="_asr_model_$(echo "${inference_asr_model}" | sed -e "s/\//_/g" -e "s/\.[^.]*$//g")"
fi
if [ -z "${sa_asr_inference_tag}" ]; then
if [ -n "${inference_config}" ]; then
sa_asr_inference_tag="$(basename "${inference_config}" .yaml)"
else
sa_asr_inference_tag=sa_asr_inference
fi
# Add overwritten arg's info
if [ -n "${sa_asr_inference_args}" ]; then
sa_asr_inference_tag+="$(echo "${sa_asr_inference_args}" | sed -e "s/--/\_/g" -e "s/[ |=]//g")"
fi
if "${use_lm}"; then
sa_asr_inference_tag+="_lm_$(basename "${lm_exp}")_$(echo "${inference_lm}" | sed -e "s/\//_/g" -e "s/\.[^.]*$//g")"
fi
sa_asr_inference_tag+="_asr_model_$(echo "${inference_sa_asr_model}" | sed -e "s/\//_/g" -e "s/\.[^.]*$//g")"
fi
train_cmd="run.pl"
cuda_cmd="run.pl"
decode_cmd="run.pl"
# ========================== Main stages start from here. ==========================
if ! "${skip_data_prep}"; then
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
log "Stage 1: Data preparation for data/${train_set}, data/${valid_set}, etc."
./local/alimeeting_data_prep.sh --tgt Test
./local/alimeeting_data_prep.sh --tgt Eval
./local/alimeeting_data_prep.sh --tgt Train
fi
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
if [ -n "${speed_perturb_factors}" ]; then
log "Stage 2: Speed perturbation: data/${train_set} -> data/${train_set}_sp"
for factor in ${speed_perturb_factors}; do
if [[ $(bc <<<"${factor} != 1.0") == 1 ]]; then
local/perturb_data_dir_speed.sh "${factor}" "data/${train_set}" "data/${train_set}_sp${factor}"
_dirs+="data/${train_set}_sp${factor} "
else
# If speed factor is 1, same as the original
_dirs+="data/${train_set} "
fi
done
local/combine_data.sh "data/${train_set}_sp" ${_dirs}
else
log "Skip stage 2: Speed perturbation"
fi
fi
if [ -n "${speed_perturb_factors}" ]; then
train_set="${train_set}_sp"
fi
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
if [ "${feats_type}" = raw ]; then
log "Stage 3: Format wav.scp: data/ -> ${data_feats}"
# ====== Recreating "wav.scp" ======
# Kaldi-wav.scp, which can describe the file path with unix-pipe, like "cat /some/path |",
# shouldn't be used in training process.
# "format_wav_scp.sh" dumps such pipe-style-wav to real audio file
# and it can also change the audio-format and sampling rate.
# If nothing is need, then format_wav_scp.sh does nothing:
# i.e. the input file format and rate is same as the output.
for dset in "${train_set}" "${valid_set}" "${test_sets}" ; do
if [ "${dset}" = "${train_set}" ] || [ "${dset}" = "${valid_set}" ]; then
_suf="/org"
else
if [ "${dset}" = "${test_sets}" ] && [ "${test_sets}" = "Test_Ali_far" ]; then
_suf="/org"
else
_suf=""
fi
fi
local/copy_data_dir.sh --validate_opts --non-print data/"${dset}" "${data_feats}${_suf}/${dset}"
if [ "${dset}" = "Train_Ali_far" ] || [ "${dset}" = "Eval_Ali_far" ] || [ "${dset}" = "Test_Ali_far" ]; then
cp data/"${dset}"/utt2spk_all_fifo "${data_feats}${_suf}/${dset}/"
fi
rm -f ${data_feats}${_suf}/${dset}/{segments,wav.scp,reco2file_and_channel,reco2dur}
_opts=
if [ -e data/"${dset}"/segments ]; then
# "segments" is used for splitting wav files which are written in "wav".scp
# into utterances. The file format of segments:
# <segment_id> <record_id> <start_time> <end_time>
# "e.g. call-861225-A-0050-0065 call-861225-A 5.0 6.5"
# Where the time is written in seconds.
_opts+="--segments data/${dset}/segments "
fi
# shellcheck disable=SC2086
local/format_wav_scp.sh --nj "${nj}" --cmd "${train_cmd}" \
--audio-format "${audio_format}" --fs "${fs}" ${_opts} \
"data/${dset}/wav.scp" "${data_feats}${_suf}/${dset}"
echo "${feats_type}" > "${data_feats}${_suf}/${dset}/feats_type"
done
else
log "Error: not supported: --feats_type ${feats_type}"
exit 2
fi
fi
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
log "Stage 4: Remove long/short data: ${data_feats}/org -> ${data_feats}"
# NOTE(kamo): Not applying to test_sets to keep original data
if [ "${test_sets}" = "Test_Ali_far" ]; then
rm_dset="${train_set} ${valid_set} ${test_sets}"
else
rm_dset="${train_set} ${valid_set}"
fi
for dset in $rm_dset; do
# Copy data dir
local/copy_data_dir.sh --validate_opts --non-print "${data_feats}/org/${dset}" "${data_feats}/${dset}"
cp "${data_feats}/org/${dset}/feats_type" "${data_feats}/${dset}/feats_type"
# Remove short utterances
_feats_type="$(<${data_feats}/${dset}/feats_type)"
if [ "${_feats_type}" = raw ]; then
_fs=$(python3 -c "import humanfriendly as h;print(h.parse_size('${fs}'))")
_min_length=$(python3 -c "print(int(${min_wav_duration} * ${_fs}))")
_max_length=$(python3 -c "print(int(${max_wav_duration} * ${_fs}))")
# utt2num_samples is created by format_wav_scp.sh
<"${data_feats}/org/${dset}/utt2num_samples" \
awk -v min_length="${_min_length}" -v max_length="${_max_length}" \
'{ if ($2 > min_length && $2 < max_length ) print $0; }' \
>"${data_feats}/${dset}/utt2num_samples"
<"${data_feats}/org/${dset}/wav.scp" \
utils/filter_scp.pl "${data_feats}/${dset}/utt2num_samples" \
>"${data_feats}/${dset}/wav.scp"
else
# Get frame shift in ms from conf/fbank.conf
_frame_shift=
if [ -f conf/fbank.conf ] && [ "$(<conf/fbank.conf grep -c frame-shift)" -gt 0 ]; then
# Assume using conf/fbank.conf for feature extraction
_frame_shift="$(<conf/fbank.conf grep frame-shift | sed -e 's/[-a-z =]*\([0-9]*\)/\1/g')"
fi
if [ -z "${_frame_shift}" ]; then
# If not existing, use the default number in Kaldi (=10ms).
# If you are using different number, you have to change the following value manually.
_frame_shift=10
fi
_min_length=$(python3 -c "print(int(${min_wav_duration} / ${_frame_shift} * 1000))")
_max_length=$(python3 -c "print(int(${max_wav_duration} / ${_frame_shift} * 1000))")
cp "${data_feats}/org/${dset}/feats_dim" "${data_feats}/${dset}/feats_dim"
<"${data_feats}/org/${dset}/feats_shape" awk -F, ' { print $1 } ' \
| awk -v min_length="${_min_length}" -v max_length="${_max_length}" \
'{ if ($2 > min_length && $2 < max_length) print $0; }' \
>"${data_feats}/${dset}/feats_shape"
<"${data_feats}/org/${dset}/feats.scp" \
utils/filter_scp.pl "${data_feats}/${dset}/feats_shape" \
>"${data_feats}/${dset}/feats.scp"
fi
# Remove empty text
<"${data_feats}/org/${dset}/text" \
awk ' { if( NF != 1 ) print $0; } ' >"${data_feats}/${dset}/text"
# fix_data_dir.sh leaves only utts which exist in all files
local/fix_data_dir.sh "${data_feats}/${dset}"
# generate uttid
cut -d ' ' -f 1 "${data_feats}/${dset}/wav.scp" > "${data_feats}/${dset}/uttid"
if [ "${dset}" = "Train_Ali_far" ] || [ "${dset}" = "Eval_Ali_far" ] || [ "${dset}" = "Test_Ali_far" ]; then
# filter utt2spk_all_fifo
python local/filter_utt2spk_all_fifo.py ${data_feats}/${dset}/uttid ${data_feats}/org/${dset} ${data_feats}/${dset}
fi
done
# shellcheck disable=SC2002
cat ${lm_train_text} | awk ' { if( NF != 1 ) print $0; } ' > "${data_feats}/lm_train.txt"
fi
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
log "Stage 5: Dictionary Preparation"
mkdir -p data/${lang}_token_list/char/
echo "make a dictionary"
echo "<blank>" > ${token_list}
echo "<s>" >> ${token_list}
echo "</s>" >> ${token_list}
utils/text2token.py -s 1 -n 1 --space "" ${data_feats}/lm_train.txt | cut -f 2- -d" " | tr " " "\n" \
| sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
num_token=$(cat ${token_list} | wc -l)
echo "<unk>" >> ${token_list}
vocab_size=$(cat ${token_list} | wc -l)
fi
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
log "Stage 6: Generate speaker settings"
mkdir -p "profile_log"
for dset in "${train_set}" "${valid_set}" "${test_sets}"; do
# generate text_id spk2id
python local/process_sot_fifo_textchar2spk.py --path ${data_feats}/${dset}
log "Successfully generate ${data_feats}/${dset}/text_id ${data_feats}/${dset}/spk2id"
# generate text_id_train for sot
python local/process_text_id.py ${data_feats}/${dset}
log "Successfully generate ${data_feats}/${dset}/text_id_train"
# generate oracle_embedding from single-speaker audio segment
log "oracle_embedding is being generated in the background, and the log is profile_log/gen_oracle_embedding_${dset}.log"
python local/gen_oracle_embedding.py "${data_feats}/${dset}" "data/local/${dset}_correct_single_speaker" &> "profile_log/gen_oracle_embedding_${dset}.log"
log "Successfully generate oracle embedding for ${dset} (${data_feats}/${dset}/oracle_embedding.scp)"
# generate oracle_profile and cluster_profile from oracle_embedding and cluster_embedding (padding the speaker during training)
if [ "${dset}" = "${train_set}" ]; then
python local/gen_oracle_profile_padding.py ${data_feats}/${dset}
log "Successfully generate oracle profile for ${dset} (${data_feats}/${dset}/oracle_profile_padding.scp)"
else
python local/gen_oracle_profile_nopadding.py ${data_feats}/${dset}
log "Successfully generate oracle profile for ${dset} (${data_feats}/${dset}/oracle_profile_nopadding.scp)"
fi
# generate cluster_profile with spectral-cluster directly (for infering and without oracle information)
if [ "${dset}" = "${valid_set}" ] || [ "${dset}" = "${test_sets}" ]; then
log "cluster_profile is being generated in the background, and the log is profile_log/gen_cluster_profile_infer_${dset}.log"
python local/gen_cluster_profile_infer.py "${data_feats}/${dset}" "data/local/${dset}" 0.996 0.815 &> "profile_log/gen_cluster_profile_infer_${dset}.log"
log "Successfully generate cluster profile for ${dset} (${data_feats}/${dset}/cluster_profile_infer.scp)"
fi
done
fi
else
log "Skip the stages for data preparation"
fi
# ========================== Data preparation is done here. ==========================
if ! "${skip_train}"; then
if "${use_lm}"; then
if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
log "Stage 7: LM collect stats: train_set=${data_feats}/lm_train.txt, dev_set=${lm_dev_text}"
_opts=
if [ -n "${lm_config}" ]; then
# To generate the config file: e.g.
# % python3 -m espnet2.bin.lm_train --print_config --optim adam
_opts+="--config ${lm_config} "
fi
# 1. Split the key file
_logdir="${lm_stats_dir}/logdir"
mkdir -p "${_logdir}"
# Get the minimum number among ${nj} and the number lines of input files
_nj=$(min "${nj}" "$(<${data_feats}/lm_train.txt wc -l)" "$(<${lm_dev_text} wc -l)")
key_file="${data_feats}/lm_train.txt"
split_scps=""
for n in $(seq ${_nj}); do
split_scps+=" ${_logdir}/train.${n}.scp"
done
# shellcheck disable=SC2086
utils/split_scp.pl "${key_file}" ${split_scps}
key_file="${lm_dev_text}"
split_scps=""
for n in $(seq ${_nj}); do
split_scps+=" ${_logdir}/dev.${n}.scp"
done
# shellcheck disable=SC2086
utils/split_scp.pl "${key_file}" ${split_scps}
# 2. Generate run.sh
log "Generate '${lm_stats_dir}/run.sh'. You can resume the process from stage 6 using this script"
mkdir -p "${lm_stats_dir}"; echo "${run_args} --stage 6 \"\$@\"; exit \$?" > "${lm_stats_dir}/run.sh"; chmod +x "${lm_stats_dir}/run.sh"
# 3. Submit jobs
log "LM collect-stats started... log: '${_logdir}/stats.*.log'"
# NOTE: --*_shape_file doesn't require length information if --batch_type=unsorted,
# but it's used only for deciding the sample ids.
# shellcheck disable=SC2086
${train_cmd} JOB=1:"${_nj}" "${_logdir}"/stats.JOB.log \
${python} -m funasr.bin.lm_train \
--collect_stats true \
--use_preprocessor true \
--bpemodel "${bpemodel}" \
--token_type "${lm_token_type}"\
--token_list "${lm_token_list}" \
--non_linguistic_symbols "${nlsyms_txt}" \
--cleaner "${cleaner}" \
--g2p "${g2p}" \
--train_data_path_and_name_and_type "${data_feats}/lm_train.txt,text,text" \
--valid_data_path_and_name_and_type "${lm_dev_text},text,text" \
--train_shape_file "${_logdir}/train.JOB.scp" \
--valid_shape_file "${_logdir}/dev.JOB.scp" \
--output_dir "${_logdir}/stats.JOB" \
${_opts} ${lm_args} || { cat "${_logdir}"/stats.1.log; exit 1; }
# 4. Aggregate shape files
_opts=
for i in $(seq "${_nj}"); do
_opts+="--input_dir ${_logdir}/stats.${i} "
done
# shellcheck disable=SC2086
${python} -m funasr.bin.aggregate_stats_dirs ${_opts} --output_dir "${lm_stats_dir}"
# Append the num-tokens at the last dimensions. This is used for batch-bins count
<"${lm_stats_dir}/train/text_shape" \
awk -v N="$(<${lm_token_list} wc -l)" '{ print $0 "," N }' \
>"${lm_stats_dir}/train/text_shape.${lm_token_type}"
<"${lm_stats_dir}/valid/text_shape" \
awk -v N="$(<${lm_token_list} wc -l)" '{ print $0 "," N }' \
>"${lm_stats_dir}/valid/text_shape.${lm_token_type}"
fi
if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then
log "Stage 8: LM Training: train_set=${data_feats}/lm_train.txt, dev_set=${lm_dev_text}"
_opts=
if [ -n "${lm_config}" ]; then
# To generate the config file: e.g.
# % python3 -m espnet2.bin.lm_train --print_config --optim adam
_opts+="--config ${lm_config} "
fi
if [ "${num_splits_lm}" -gt 1 ]; then
# If you met a memory error when parsing text files, this option may help you.
# The corpus is split into subsets and each subset is used for training one by one in order,
# so the memory footprint can be limited to the memory required for each dataset.
_split_dir="${lm_stats_dir}/splits${num_splits_lm}"
if [ ! -f "${_split_dir}/.done" ]; then
rm -f "${_split_dir}/.done"
${python} -m espnet2.bin.split_scps \
--scps "${data_feats}/lm_train.txt" "${lm_stats_dir}/train/text_shape.${lm_token_type}" \
--num_splits "${num_splits_lm}" \
--output_dir "${_split_dir}"
touch "${_split_dir}/.done"
else
log "${_split_dir}/.done exists. Spliting is skipped"
fi
_opts+="--train_data_path_and_name_and_type ${_split_dir}/lm_train.txt,text,text "
_opts+="--train_shape_file ${_split_dir}/text_shape.${lm_token_type} "
_opts+="--multiple_iterator true "
else
_opts+="--train_data_path_and_name_and_type ${data_feats}/lm_train.txt,text,text "
_opts+="--train_shape_file ${lm_stats_dir}/train/text_shape.${lm_token_type} "
fi
# NOTE(kamo): --fold_length is used only if --batch_type=folded and it's ignored in the other case
log "Generate '${lm_exp}/run.sh'. You can resume the process from stage 8 using this script"
mkdir -p "${lm_exp}"; echo "${run_args} --stage 8 \"\$@\"; exit \$?" > "${lm_exp}/run.sh"; chmod +x "${lm_exp}/run.sh"
log "LM training started... log: '${lm_exp}/train.log'"
if echo "${cuda_cmd}" | grep -e queue.pl -e queue-freegpu.pl &> /dev/null; then
# SGE can't include "/" in a job name
jobname="$(basename ${lm_exp})"
else
jobname="${lm_exp}/train.log"
fi
mkdir -p ${lm_exp}
mkdir -p ${lm_exp}/log
INIT_FILE=${lm_exp}/ddp_init
if [ -f $INIT_FILE ];then
rm -f $INIT_FILE
fi
init_method=file://$(readlink -f $INIT_FILE)
echo "$0: init method is $init_method"
for ((i = 0; i < $ngpu; ++i)); do
{
# i=0
rank=$i
local_rank=$i
gpu_id=$(echo $device | cut -d',' -f$[$i+1])
lm_train.py \
--gpu_id $gpu_id \
--use_preprocessor true \
--bpemodel ${bpemodel} \
--token_type ${token_type} \
--token_list ${token_list} \
--non_linguistic_symbols ${nlsyms_txt} \
--cleaner ${cleaner} \
--g2p ${g2p} \
--valid_data_path_and_name_and_type "${lm_dev_text},text,text" \
--valid_shape_file "${lm_stats_dir}/valid/text_shape.${lm_token_type}" \
--resume true \
--output_dir ${lm_exp} \
--config $lm_config \
--ngpu $ngpu \
--num_worker_count 1 \
--multiprocessing_distributed true \
--dist_init_method $init_method \
--dist_world_size $ngpu \
--dist_rank $rank \
--local_rank $local_rank \
${_opts} 1> ${lm_exp}/log/train.log.$i 2>&1
} &
done
wait
fi
if [ ${stage} -le 9 ] && [ ${stop_stage} -ge 9 ]; then
log "Stage 9: Calc perplexity: ${lm_test_text}"
_opts=
# TODO(kamo): Parallelize?
log "Perplexity calculation started... log: '${lm_exp}/perplexity_test/lm_calc_perplexity.log'"
# shellcheck disable=SC2086
CUDA_VISIBLE_DEVICES=${device}\
${cuda_cmd} --gpu "${ngpu}" "${lm_exp}"/perplexity_test/lm_calc_perplexity.log \
${python} -m funasr.bin.lm_calc_perplexity \
--ngpu "${ngpu}" \
--data_path_and_name_and_type "${lm_test_text},text,text" \
--train_config "${lm_exp}"/config.yaml \
--model_file "${lm_exp}/${inference_lm}" \
--output_dir "${lm_exp}/perplexity_test" \
${_opts}
log "PPL: ${lm_test_text}: $(cat ${lm_exp}/perplexity_test/ppl)"
fi
else
log "Stage 7-9: Skip lm-related stages: use_lm=${use_lm}"
fi
if [ ${stage} -le 10 ] && [ ${stop_stage} -ge 10 ]; then
_asr_train_dir="${data_feats}/${train_set}"
_asr_valid_dir="${data_feats}/${valid_set}"
log "Stage 10: ASR collect stats: train_set=${_asr_train_dir}, valid_set=${_asr_valid_dir}"
_opts=
if [ -n "${asr_config}" ]; then
# To generate the config file: e.g.
# % python3 -m espnet2.bin.asr_train --print_config --optim adam
_opts+="--config ${asr_config} "
fi
_feats_type="$(<${_asr_train_dir}/feats_type)"
if [ "${_feats_type}" = raw ]; then
_scp=wav.scp
if [[ "${audio_format}" == *ark* ]]; then
_type=kaldi_ark
else
# "sound" supports "wav", "flac", etc.
_type=sound
fi
_opts+="--frontend_conf fs=${fs} "
else
_scp=feats.scp
_type=kaldi_ark
_input_size="$(<${_asr_train_dir}/feats_dim)"
_opts+="--input_size=${_input_size} "
fi
# 1. Split the key file
_logdir="${asr_stats_dir}/logdir"
mkdir -p "${_logdir}"
# Get the minimum number among ${nj} and the number lines of input files
_nj=$(min "${nj}" "$(<${_asr_train_dir}/${_scp} wc -l)" "$(<${_asr_valid_dir}/${_scp} wc -l)")
key_file="${_asr_train_dir}/${_scp}"
split_scps=""
for n in $(seq "${_nj}"); do
split_scps+=" ${_logdir}/train.${n}.scp"
done
# shellcheck disable=SC2086
utils/split_scp.pl "${key_file}" ${split_scps}
key_file="${_asr_valid_dir}/${_scp}"
split_scps=""
for n in $(seq "${_nj}"); do
split_scps+=" ${_logdir}/valid.${n}.scp"
done
# shellcheck disable=SC2086
utils/split_scp.pl "${key_file}" ${split_scps}
# 2. Generate run.sh
log "Generate '${asr_stats_dir}/run.sh'. You can resume the process from stage 9 using this script"
mkdir -p "${asr_stats_dir}"; echo "${run_args} --stage 9 \"\$@\"; exit \$?" > "${asr_stats_dir}/run.sh"; chmod +x "${asr_stats_dir}/run.sh"
# 3. Submit jobs
log "ASR collect-stats started... log: '${_logdir}/stats.*.log'"
# NOTE: --*_shape_file doesn't require length information if --batch_type=unsorted,
# but it's used only for deciding the sample ids.
# shellcheck disable=SC2086
${train_cmd} JOB=1:"${_nj}" "${_logdir}"/stats.JOB.log \
${python} -m funasr.bin.asr_train \
--collect_stats true \
--mc true \
--use_preprocessor true \
--bpemodel "${bpemodel}" \
--token_type "${token_type}" \
--token_list "${token_list}" \
--split_with_space false \
--non_linguistic_symbols "${nlsyms_txt}" \
--cleaner "${cleaner}" \
--g2p "${g2p}" \
--train_data_path_and_name_and_type "${_asr_train_dir}/${_scp},speech,${_type}" \
--train_data_path_and_name_and_type "${_asr_train_dir}/text,text,text" \
--valid_data_path_and_name_and_type "${_asr_valid_dir}/${_scp},speech,${_type}" \
--valid_data_path_and_name_and_type "${_asr_valid_dir}/text,text,text" \
--train_shape_file "${_logdir}/train.JOB.scp" \
--valid_shape_file "${_logdir}/valid.JOB.scp" \
--output_dir "${_logdir}/stats.JOB" \
${_opts} ${asr_args} || { cat "${_logdir}"/stats.1.log; exit 1; }
# 4. Aggregate shape files
_opts=
for i in $(seq "${_nj}"); do
_opts+="--input_dir ${_logdir}/stats.${i} "
done
# shellcheck disable=SC2086
${python} -m funasr.bin.aggregate_stats_dirs ${_opts} --output_dir "${asr_stats_dir}"
# Append the num-tokens at the last dimensions. This is used for batch-bins count
<"${asr_stats_dir}/train/text_shape" \
awk -v N="$(<${token_list} wc -l)" '{ print $0 "," N }' \
>"${asr_stats_dir}/train/text_shape.${token_type}"
<"${asr_stats_dir}/valid/text_shape" \
awk -v N="$(<${token_list} wc -l)" '{ print $0 "," N }' \
>"${asr_stats_dir}/valid/text_shape.${token_type}"
fi
if [ ${stage} -le 11 ] && [ ${stop_stage} -ge 11 ]; then
_asr_train_dir="${data_feats}/${train_set}"
_asr_valid_dir="${data_feats}/${valid_set}"
log "Stage 11: ASR Training: train_set=${_asr_train_dir}, valid_set=${_asr_valid_dir}"
_opts=
if [ -n "${asr_config}" ]; then
# To generate the config file: e.g.
# % python3 -m espnet2.bin.asr_train --print_config --optim adam
_opts+="--config ${asr_config} "
fi
_feats_type="$(<${_asr_train_dir}/feats_type)"
if [ "${_feats_type}" = raw ]; then
_scp=wav.scp
# "sound" supports "wav", "flac", etc.
if [[ "${audio_format}" == *ark* ]]; then
_type=kaldi_ark
else
_type=sound
fi
_opts+="--frontend_conf fs=${fs} "
else
_scp=feats.scp
_type=kaldi_ark
_input_size="$(<${_asr_train_dir}/feats_dim)"
_opts+="--input_size=${_input_size} "
fi
if [ "${feats_normalize}" = global_mvn ]; then
# Default normalization is utterance_mvn and changes to global_mvn
_opts+="--normalize=global_mvn --normalize_conf stats_file=${asr_stats_dir}/train/feats_stats.npz "
fi
if [ "${num_splits_asr}" -gt 1 ]; then
# If you met a memory error when parsing text files, this option may help you.
# The corpus is split into subsets and each subset is used for training one by one in order,
# so the memory footprint can be limited to the memory required for each dataset.
_split_dir="${asr_stats_dir}/splits${num_splits_asr}"
if [ ! -f "${_split_dir}/.done" ]; then
rm -f "${_split_dir}/.done"
${python} -m espnet2.bin.split_scps \
--scps \
"${_asr_train_dir}/${_scp}" \
"${_asr_train_dir}/text" \
"${asr_stats_dir}/train/speech_shape" \
"${asr_stats_dir}/train/text_shape.${token_type}" \
--num_splits "${num_splits_asr}" \
--output_dir "${_split_dir}"
touch "${_split_dir}/.done"
else
log "${_split_dir}/.done exists. Spliting is skipped"
fi
_opts+="--train_data_path_and_name_and_type ${_split_dir}/${_scp},speech,${_type} "
_opts+="--train_data_path_and_name_and_type ${_split_dir}/text,text,text "
_opts+="--train_shape_file ${_split_dir}/speech_shape "
_opts+="--train_shape_file ${_split_dir}/text_shape.${token_type} "
_opts+="--multiple_iterator true "
else
_opts+="--train_data_path_and_name_and_type ${_asr_train_dir}/${_scp},speech,${_type} "
_opts+="--train_data_path_and_name_and_type ${_asr_train_dir}/text,text,text "
_opts+="--train_shape_file ${asr_stats_dir}/train/speech_shape "
_opts+="--train_shape_file ${asr_stats_dir}/train/text_shape.${token_type} "
fi
# log "Generate '${asr_exp}/run.sh'. You can resume the process from stage 10 using this script"
# mkdir -p "${asr_exp}"; echo "${run_args} --stage 10 \"\$@\"; exit \$?" > "${asr_exp}/run.sh"; chmod +x "${asr_exp}/run.sh"
# NOTE(kamo): --fold_length is used only if --batch_type=folded and it's ignored in the other case
log "ASR training started... log: '${asr_exp}/log/train.log'"
# if echo "${cuda_cmd}" | grep -e queue.pl -e queue-freegpu.pl &> /dev/null; then
# # SGE can't include "/" in a job name
# jobname="$(basename ${asr_exp})"
# else
# jobname="${asr_exp}/train.log"
# fi
mkdir -p ${asr_exp}
mkdir -p ${asr_exp}/log
INIT_FILE=${asr_exp}/ddp_init
if [ -f $INIT_FILE ];then
rm -f $INIT_FILE
fi
init_method=file://$(readlink -f $INIT_FILE)
echo "$0: init method is $init_method"
for ((i = 0; i < $ngpu; ++i)); do
{
# i=0
rank=$i
local_rank=$i
gpu_id=$(echo $device | cut -d',' -f$[$i+1])
asr_train.py \
--mc true \
--gpu_id $gpu_id \
--use_preprocessor true \
--bpemodel ${bpemodel} \
--token_type ${token_type} \
--token_list ${token_list} \
--split_with_space false \
--non_linguistic_symbols ${nlsyms_txt} \
--cleaner ${cleaner} \
--g2p ${g2p} \
--valid_data_path_and_name_and_type ${_asr_valid_dir}/${_scp},speech,${_type} \
--valid_data_path_and_name_and_type ${_asr_valid_dir}/text,text,text \
--valid_shape_file ${asr_stats_dir}/valid/speech_shape \
--valid_shape_file ${asr_stats_dir}/valid/text_shape.${token_type} \
--resume true \
--output_dir ${asr_exp} \
--config $asr_config \
--ngpu $ngpu \
--num_worker_count 1 \
--multiprocessing_distributed true \
--dist_init_method $init_method \
--dist_world_size $ngpu \
--dist_rank $rank \
--local_rank $local_rank \
${_opts} 1> ${asr_exp}/log/train.log.$i 2>&1
} &
done
wait
fi
if [ ${stage} -le 12 ] && [ ${stop_stage} -ge 12 ]; then
_asr_train_dir="${data_feats}/${train_set}"
_asr_valid_dir="${data_feats}/${valid_set}"
log "Stage 12: SA-ASR Training: train_set=${_asr_train_dir}, valid_set=${_asr_valid_dir}"
_opts=
if [ -n "${sa_asr_config}" ]; then
# To generate the config file: e.g.
# % python3 -m espnet2.bin.asr_train --print_config --optim adam
_opts+="--config ${sa_asr_config} "
fi
_feats_type="$(<${_asr_train_dir}/feats_type)"
if [ "${_feats_type}" = raw ]; then
_scp=wav.scp
# "sound" supports "wav", "flac", etc.
if [[ "${audio_format}" == *ark* ]]; then
_type=kaldi_ark
else
_type=sound
fi
_opts+="--frontend_conf fs=${fs} "
else
_scp=feats.scp
_type=kaldi_ark
_input_size="$(<${_asr_train_dir}/feats_dim)"
_opts+="--input_size=${_input_size} "
fi
if [ "${feats_normalize}" = global_mvn ]; then
# Default normalization is utterance_mvn and changes to global_mvn
_opts+="--normalize=global_mvn --normalize_conf stats_file=${asr_stats_dir}/train/feats_stats.npz "
fi
if [ "${num_splits_asr}" -gt 1 ]; then
# If you met a memory error when parsing text files, this option may help you.
# The corpus is split into subsets and each subset is used for training one by one in order,
# so the memory footprint can be limited to the memory required for each dataset.
_split_dir="${asr_stats_dir}/splits${num_splits_asr}"
if [ ! -f "${_split_dir}/.done" ]; then
rm -f "${_split_dir}/.done"
${python} -m espnet2.bin.split_scps \
--scps \
"${_asr_train_dir}/${_scp}" \
"${_asr_train_dir}/text" \
"${asr_stats_dir}/train/speech_shape" \
"${asr_stats_dir}/train/text_shape.${token_type}" \
--num_splits "${num_splits_asr}" \
--output_dir "${_split_dir}"
touch "${_split_dir}/.done"
else
log "${_split_dir}/.done exists. Spliting is skipped"
fi
_opts+="--train_data_path_and_name_and_type ${_split_dir}/${_scp},speech,${_type} "
_opts+="--train_data_path_and_name_and_type ${_split_dir}/text,text,text "
_opts+="--train_data_path_and_name_and_type ${_split_dir}/text_id_train,text_id,text_int "
_opts+="--train_data_path_and_name_and_type ${_split_dir}/oracle_profile_padding.scp,profile,npy "
_opts+="--train_shape_file ${_split_dir}/speech_shape "
_opts+="--train_shape_file ${_split_dir}/text_shape.${token_type} "
_opts+="--multiple_iterator true "
else
_opts+="--train_data_path_and_name_and_type ${_asr_train_dir}/${_scp},speech,${_type} "
_opts+="--train_data_path_and_name_and_type ${_asr_train_dir}/text,text,text "
_opts+="--train_data_path_and_name_and_type ${_asr_train_dir}/oracle_profile_padding.scp,profile,npy "
_opts+="--train_data_path_and_name_and_type ${_asr_train_dir}/text_id_train,text_id,text_int "
_opts+="--train_shape_file ${asr_stats_dir}/train/speech_shape "
_opts+="--train_shape_file ${asr_stats_dir}/train/text_shape.${token_type} "
fi
# log "Generate '${asr_exp}/run.sh'. You can resume the process from stage 10 using this script"
# mkdir -p "${asr_exp}"; echo "${run_args} --stage 10 \"\$@\"; exit \$?" > "${asr_exp}/run.sh"; chmod +x "${asr_exp}/run.sh"
# NOTE(kamo): --fold_length is used only if --batch_type=folded and it's ignored in the other case
log "SA-ASR training started... log: '${sa_asr_exp}/log/train.log'"
# if echo "${cuda_cmd}" | grep -e queue.pl -e queue-freegpu.pl &> /dev/null; then
# # SGE can't include "/" in a job name
# jobname="$(basename ${asr_exp})"
# else
# jobname="${asr_exp}/train.log"
# fi
mkdir -p ${sa_asr_exp}
mkdir -p ${sa_asr_exp}/log
INIT_FILE=${sa_asr_exp}/ddp_init
if [ ! -f "exp/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.pth" ]; then
# download xvector extractor model file
python local/download_xvector_model.py exp
log "Successfully download the pretrained xvector extractor to exp/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.pth"
fi
if [ -f $INIT_FILE ];then
rm -f $INIT_FILE
fi
init_method=file://$(readlink -f $INIT_FILE)
echo "$0: init method is $init_method"
for ((i = 0; i < $ngpu; ++i)); do
{
# i=0
rank=$i
local_rank=$i
gpu_id=$(echo $device | cut -d',' -f$[$i+1])
sa_asr_train.py \
--gpu_id $gpu_id \
--use_preprocessor true \
--unused_parameters true \
--bpemodel ${bpemodel} \
--token_type ${token_type} \
--token_list ${token_list} \
--max_spk_num 4 \
--split_with_space false \
--non_linguistic_symbols ${nlsyms_txt} \
--cleaner ${cleaner} \
--g2p ${g2p} \
--allow_variable_data_keys true \
--init_param "${asr_exp}/valid.acc.ave.pb:encoder:asr_encoder" \
--init_param "${asr_exp}/valid.acc.ave.pb:ctc:ctc" \
--init_param "${asr_exp}/valid.acc.ave.pb:decoder.embed:decoder.embed" \
--init_param "${asr_exp}/valid.acc.ave.pb:decoder.output_layer:decoder.asr_output_layer" \
--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.0.self_attn:decoder.decoder1.self_attn" \
--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.0.src_attn:decoder.decoder3.src_attn" \
--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.0.feed_forward:decoder.decoder3.feed_forward" \
--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.1:decoder.decoder4.0" \
--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.2:decoder.decoder4.1" \
--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.3:decoder.decoder4.2" \
--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.4:decoder.decoder4.3" \
--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.5:decoder.decoder4.4" \
--init_param "exp/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.pth:encoder:spk_encoder" \
--init_param "exp/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.pth:decoder:spk_encoder:decoder.output_dense" \
--valid_data_path_and_name_and_type "${_asr_valid_dir}/${_scp},speech,${_type}" \
--valid_data_path_and_name_and_type "${_asr_valid_dir}/text,text,text" \
--valid_data_path_and_name_and_type "${_asr_valid_dir}/oracle_profile_nopadding.scp,profile,npy" \
--valid_data_path_and_name_and_type "${_asr_valid_dir}/text_id_train,text_id,text_int" \
--valid_shape_file "${asr_stats_dir}/valid/speech_shape" \
--valid_shape_file "${asr_stats_dir}/valid/text_shape.${token_type}" \
--resume true \
--output_dir ${sa_asr_exp} \
--config $sa_asr_config \
--ngpu $ngpu \
--num_worker_count 1 \
--multiprocessing_distributed true \
--dist_init_method $init_method \
--dist_world_size $ngpu \
--dist_rank $rank \
--local_rank $local_rank \
${_opts} 1> ${sa_asr_exp}/log/train.log.$i 2>&1
} &
done
wait
fi
else
log "Skip the training stages"
fi
if ${infer_with_pretrained_model}; then
log "Use ${download_sa_asr_model} for decoding and evaluation"
sa_asr_exp="${expdir}/${download_sa_asr_model}"
mkdir -p "${sa_asr_exp}"
python local/download_pretrained_model_from_modelscope.py $download_sa_asr_model ${expdir}
inference_sa_asr_model="model.pb"
inference_config=${sa_asr_exp}/decoding.yaml
fi
if ! "${skip_eval}"; then
if [ ${stage} -le 13 ] && [ ${stop_stage} -ge 13 ]; then
log "Stage 13: Decoding SA-ASR (oracle profile): training_dir=${sa_asr_exp}"
if ${gpu_inference}; then
_cmd="${cuda_cmd}"
inference_nj=$[${ngpu}*${njob_infer}]
_ngpu=1
else
_cmd="${decode_cmd}"
inference_nj=$inference_nj
_ngpu=0
fi
_opts=
if [ -n "${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}.oracle/run.sh'. You can resume the process from stage 15 using this script"
mkdir -p "${sa_asr_exp}/${sa_asr_inference_tag}.oracle"; echo "${run_args} --stage 15 \"\$@\"; exit \$?" > "${sa_asr_exp}/${sa_asr_inference_tag}.oracle/run.sh"; chmod +x "${sa_asr_exp}/${sa_asr_inference_tag}.oracle/run.sh"
for dset in ${test_sets}; do
_data="${data_feats}/${dset}"
_dir="${sa_asr_exp}/${sa_asr_inference_tag}.oracle/${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 "${_data}/oracle_profile_nopadding.scp,profile,npy" \
--key_file "${_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 14 ] && [ ${stop_stage} -ge 14 ]; then
log "Stage 14: Scoring SA-ASR (oracle profile)"
for dset in ${test_sets}; do
_data="${data_feats}/${dset}"
_dir="${sa_asr_exp}/${sa_asr_inference_tag}.oracle/${dset}"
sed 's/\$//g' ${_data}/text > ${_data}/text_nosrc
sed 's/\$//g' ${_dir}/text > ${_dir}/text_nosrc
python utils/proce_text.py ${_data}/text_nosrc ${_data}/text.proc
python utils/proce_text.py ${_dir}/text_nosrc ${_dir}/text.proc
python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
cat ${_dir}/text.cer.txt
python local/process_text_spk_merge.py ${_dir}
python local/process_text_spk_merge.py ${_data}
python local/compute_cpcer.py ${_data}/text_spk_merge ${_dir}/text_spk_merge ${_dir}/text.cpcer
tail -n 1 ${_dir}/text.cpcer > ${_dir}/text.cpcer.txt
cat ${_dir}/text.cpcer.txt
done
fi
if [ ${stage} -le 15 ] && [ ${stop_stage} -ge 15 ]; then
log "Stage 15: Decoding SA-ASR (cluster profile): training_dir=${sa_asr_exp}"
if ${gpu_inference}; then
_cmd="${cuda_cmd}"
inference_nj=$[${ngpu}*${njob_infer}]
_ngpu=1
else
_cmd="${decode_cmd}"
inference_nj=$inference_nj
_ngpu=0
fi
_opts=
if [ -n "${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 16 ] && [ ${stop_stage} -ge 16 ]; then
log "Stage 16: Scoring SA-ASR (cluster profile)"
for dset in ${test_sets}; do
_data="${data_feats}/${dset}"
_dir="${sa_asr_exp}/${sa_asr_inference_tag}.cluster/${dset}"
sed 's/\$//g' ${_data}/text > ${_data}/text_nosrc
sed 's/\$//g' ${_dir}/text > ${_dir}/text_nosrc
python utils/proce_text.py ${_data}/text_nosrc ${_data}/text.proc
python utils/proce_text.py ${_dir}/text_nosrc ${_dir}/text.proc
python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
cat ${_dir}/text.cer.txt
python local/process_text_spk_merge.py ${_dir}
python local/process_text_spk_merge.py ${_data}
python local/compute_cpcer.py ${_data}/text_spk_merge ${_dir}/text_spk_merge ${_dir}/text.cpcer
tail -n 1 ${_dir}/text.cpcer > ${_dir}/text.cpcer.txt
cat ${_dir}/text.cpcer.txt
done
fi
else
log "Skip the evaluation stages"
fi
log "Successfully finished. [elapsed=${SECONDS}s]"
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