mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
update repo
This commit is contained in:
嘉渊
parent
e9cafb55ce
commit
d27ec0db77
5
egs/librispeech_100h/conformer/path.sh
Executable file
5
egs/librispeech_100h/conformer/path.sh
Executable file
@ -0,0 +1,5 @@
|
||||
export FUNASR_DIR=$PWD/../../..
|
||||
|
||||
# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
|
||||
export PYTHONIOENCODING=UTF-8
|
||||
export PATH=$FUNASR_DIR/funasr/bin:$PATH
|
||||
262
egs/librispeech_100h/conformer/run.sh
Executable file
262
egs/librispeech_100h/conformer/run.sh
Executable file
@ -0,0 +1,262 @@
|
||||
#!/usr/bin/env bash
|
||||
|
||||
. ./path.sh || exit 1;
|
||||
|
||||
# machines configuration
|
||||
CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
|
||||
gpu_num=8
|
||||
count=1
|
||||
gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
|
||||
# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
|
||||
njob=5
|
||||
train_cmd=utils/run.pl
|
||||
infer_cmd=utils/run.pl
|
||||
|
||||
# general configuration
|
||||
feats_dir="../DATA" #feature output dictionary
|
||||
exp_dir="."
|
||||
lang=en
|
||||
dumpdir=dump/fbank
|
||||
feats_type=fbank
|
||||
token_type=bpe
|
||||
dataset_type=large
|
||||
scp=feats.scp
|
||||
type=kaldi_ark
|
||||
stage=3
|
||||
stop_stage=4
|
||||
|
||||
# feature configuration
|
||||
feats_dim=80
|
||||
sample_frequency=16000
|
||||
nj=100
|
||||
speed_perturb="0.9,1.0,1.1"
|
||||
|
||||
# data
|
||||
data_librispeech=
|
||||
|
||||
# bpe model
|
||||
nbpe=5000
|
||||
bpemode=unigram
|
||||
|
||||
# exp tag
|
||||
tag=""
|
||||
|
||||
. utils/parse_options.sh || exit 1;
|
||||
|
||||
# Set bash to 'debug' mode, it will exit on :
|
||||
# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
|
||||
set -e
|
||||
set -u
|
||||
set -o pipefail
|
||||
|
||||
train_set=train_960
|
||||
valid_set=dev
|
||||
test_sets="test_clean test_other dev_clean dev_other"
|
||||
|
||||
asr_config=conf/train_asr_conformer.yaml
|
||||
#asr_config=conf/train_asr_conformer_uttnorm.yaml
|
||||
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
|
||||
|
||||
inference_config=conf/decode_asr_transformer.yaml
|
||||
#inference_config=conf/decode_asr_transformer_beam60_ctc0.3.yaml
|
||||
inference_asr_model=valid.acc.ave_10best.pth
|
||||
|
||||
# you can set gpu num for decoding here
|
||||
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default
|
||||
ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
|
||||
|
||||
if ${gpu_inference}; then
|
||||
inference_nj=$[${ngpu}*${njob}]
|
||||
_ngpu=1
|
||||
else
|
||||
inference_nj=$njob
|
||||
_ngpu=0
|
||||
fi
|
||||
|
||||
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
|
||||
echo "stage 0: Data preparation"
|
||||
# Data preparation
|
||||
for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
|
||||
local/data_prep_librispeech.sh ${data_librispeech}/LibriSpeech/${x} ${feats_dir}/data/${x//-/_}
|
||||
done
|
||||
fi
|
||||
|
||||
feat_train_dir=${feats_dir}/${dumpdir}/$train_set; mkdir -p ${feat_train_dir}
|
||||
feat_dev_clean_dir=${feats_dir}/${dumpdir}/dev_clean; mkdir -p ${feat_dev_clean_dir}
|
||||
feat_dev_other_dir=${feats_dir}/${dumpdir}/dev_other; mkdir -p ${feat_dev_other_dir}
|
||||
feat_test_clean_dir=${feats_dir}/${dumpdir}/test_clean; mkdir -p ${feat_test_clean_dir}
|
||||
feat_test_other_dir=${feats_dir}/${dumpdir}/test_other; mkdir -p ${feat_test_other_dir}
|
||||
feat_dev_dir=${feats_dir}/${dumpdir}/$valid_set; mkdir -p ${feat_dev_dir}
|
||||
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
|
||||
echo "stage 1: Feature Generation"
|
||||
# compute fbank features
|
||||
fbankdir=${feats_dir}/fbank
|
||||
for x in dev_clean dev_other test_clean test_other; do
|
||||
utils/compute_fbank.sh --cmd "$train_cmd" --nj 1 --max_lengths 3000 --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} \
|
||||
${feats_dir}/data/${x} ${exp_dir}/exp/make_fbank/${x} ${fbankdir}/${x}
|
||||
utils/fix_data_feat.sh ${fbankdir}/${x}
|
||||
done
|
||||
|
||||
mkdir ${feats_dir}/data/$train_set
|
||||
train_sets="train_clean_100 train_clean_360 train_other_500"
|
||||
for file in wav.scp text; do
|
||||
( for f in $train_sets; do cat $feats_dir/data/$f/$file; done ) | sort -k1 > $feats_dir/data/$train_set/$file || exit 1;
|
||||
done
|
||||
utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --max_lengths 3000 --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} --speed_perturb ${speed_perturb} \
|
||||
${feats_dir}/data/$train_set ${exp_dir}/exp/make_fbank/$train_set ${fbankdir}/$train_set
|
||||
utils/fix_data_feat.sh ${fbankdir}/$train_set
|
||||
|
||||
# compute global cmvn
|
||||
utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} \
|
||||
${fbankdir}/$train_set ${exp_dir}/exp/make_fbank/$train_set
|
||||
|
||||
# apply cmvn
|
||||
utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
|
||||
${fbankdir}/$train_set ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/$train_set ${feat_train_dir}
|
||||
utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1 \
|
||||
${fbankdir}/dev_clean ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/dev_clean ${feat_dev_clean_dir}
|
||||
utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1\
|
||||
${fbankdir}/dev_other ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/dev_other ${feat_dev_other_dir}
|
||||
utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1 \
|
||||
${fbankdir}/test_clean ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/test_clean ${feat_test_clean_dir}
|
||||
utils/apply_cmvn.sh --cmd "$train_cmd" --nj 1 \
|
||||
${fbankdir}/test_other ${fbankdir}/$train_set/cmvn.json ${exp_dir}/exp/make_fbank/test_other ${feat_test_other_dir}
|
||||
|
||||
cp ${fbankdir}/$train_set/text ${fbankdir}/$train_set/speech_shape ${fbankdir}/$train_set/text_shape ${feat_train_dir}
|
||||
cp ${fbankdir}/dev_clean/text ${fbankdir}/dev_clean/speech_shape ${fbankdir}/dev_clean/text_shape ${feat_dev_clean_dir}
|
||||
cp ${fbankdir}/dev_other/text ${fbankdir}/dev_other/speech_shape ${fbankdir}/dev_other/text_shape ${feat_dev_other_dir}
|
||||
cp ${fbankdir}/test_clean/text ${fbankdir}/test_clean/speech_shape ${fbankdir}/test_clean/text_shape ${feat_test_clean_dir}
|
||||
cp ${fbankdir}/test_other/text ${fbankdir}/test_other/speech_shape ${fbankdir}/test_other/text_shape ${feat_test_other_dir}
|
||||
|
||||
dev_sets="dev_clean dev_other"
|
||||
for file in feats.scp text speech_shape text_shape; do
|
||||
( for f in $dev_sets; do cat $feats_dir/${dumpdir}/$f/$file; done ) | sort -k1 > $feat_dev_dir/$file || exit 1;
|
||||
done
|
||||
|
||||
#generate ark list
|
||||
utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_train_dir} ${fbankdir}/${train_set} ${feat_train_dir}
|
||||
utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_dev_dir} ${fbankdir}/${valid_set} ${feat_dev_dir}
|
||||
fi
|
||||
|
||||
dict=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
|
||||
bpemodel=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}
|
||||
echo "dictionary: ${dict}"
|
||||
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
|
||||
### Task dependent. You have to check non-linguistic symbols used in the corpus.
|
||||
echo "stage 2: Dictionary and Json Data Preparation"
|
||||
mkdir -p ${feats_dir}/data/lang_char/
|
||||
echo "<blank>" > ${dict}
|
||||
echo "<s>" >> ${dict}
|
||||
echo "</s>" >> ${dict}
|
||||
cut -f 2- -d" " ${feats_dir}/data/${train_set}/text > ${feats_dir}/data/lang_char/input.txt
|
||||
spm_train --input=${feats_dir}/data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000
|
||||
spm_encode --model=${bpemodel}.model --output_format=piece < ${feats_dir}/data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0}' >> ${dict}
|
||||
echo "<unk>" >> ${dict}
|
||||
wc -l ${dict}
|
||||
|
||||
vocab_size=$(cat ${dict} | wc -l)
|
||||
awk -v v=,${vocab_size} '{print $0v}' ${feat_train_dir}/text_shape > ${feat_train_dir}/text_shape.char
|
||||
awk -v v=,${vocab_size} '{print $0v}' ${feat_dev_dir}/text_shape > ${feat_dev_dir}/text_shape.char
|
||||
mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/$train_set
|
||||
mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/$valid_set
|
||||
cp ${feat_train_dir}/speech_shape ${feat_train_dir}/text_shape ${feat_train_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/$train_set
|
||||
cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/$valid_set
|
||||
fi
|
||||
|
||||
|
||||
# Training Stage
|
||||
world_size=$gpu_num # run on one machine
|
||||
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
|
||||
echo "stage 3: Training"
|
||||
mkdir -p ${exp_dir}/exp/${model_dir}
|
||||
mkdir -p ${exp_dir}/exp/${model_dir}/log
|
||||
INIT_FILE=${exp_dir}/exp/${model_dir}/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 < $gpu_num; ++i)); do
|
||||
{
|
||||
rank=$i
|
||||
local_rank=$i
|
||||
gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
|
||||
asr_train.py \
|
||||
--gpu_id $gpu_id \
|
||||
--use_preprocessor true \
|
||||
--split_with_space false \
|
||||
--bpemodel ${bpemodel}.model \
|
||||
--token_type $token_type \
|
||||
--dataset_type $dataset_type \
|
||||
--token_list $dict \
|
||||
--train_data_file $feats_dir/$dumpdir/${train_set}/ark_txt.scp \
|
||||
--valid_data_file $feats_dir/$dumpdir/${valid_set}/ark_txt.scp \
|
||||
--resume true \
|
||||
--output_dir ${exp_dir}/exp/${model_dir} \
|
||||
--config $asr_config \
|
||||
--input_size $feats_dim \
|
||||
--ngpu $gpu_num \
|
||||
--num_worker_count $count \
|
||||
--multiprocessing_distributed true \
|
||||
--dist_init_method $init_method \
|
||||
--dist_world_size $world_size \
|
||||
--dist_rank $rank \
|
||||
--local_rank $local_rank 1> ${exp_dir}/exp/${model_dir}/log/train.log.$i 2>&1
|
||||
} &
|
||||
done
|
||||
wait
|
||||
fi
|
||||
|
||||
# Testing Stage
|
||||
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
|
||||
echo "stage 4: Inference"
|
||||
for dset in ${test_sets}; do
|
||||
asr_exp=${exp_dir}/exp/${model_dir}
|
||||
inference_tag="$(basename "${inference_config}" .yaml)"
|
||||
_dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
|
||||
_logdir="${_dir}/logdir"
|
||||
if [ -d ${_dir} ]; then
|
||||
echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
|
||||
exit 0
|
||||
fi
|
||||
mkdir -p "${_logdir}"
|
||||
_data="${feats_dir}/${dumpdir}/${dset}"
|
||||
key_file=${_data}/${scp}
|
||||
num_scp_file="$(<${key_file} wc -l)"
|
||||
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
|
||||
split_scps=
|
||||
for n in $(seq "${_nj}"); do
|
||||
split_scps+=" ${_logdir}/keys.${n}.scp"
|
||||
done
|
||||
# shellcheck disable=SC2086
|
||||
utils/split_scp.pl "${key_file}" ${split_scps}
|
||||
_opts=
|
||||
if [ -n "${inference_config}" ]; then
|
||||
_opts+="--config ${inference_config} "
|
||||
fi
|
||||
${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
|
||||
python -m funasr.bin.asr_inference_launch \
|
||||
--batch_size 1 \
|
||||
--ngpu "${_ngpu}" \
|
||||
--njob ${njob} \
|
||||
--gpuid_list ${gpuid_list} \
|
||||
--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
|
||||
--key_file "${_logdir}"/keys.JOB.scp \
|
||||
--asr_train_config "${asr_exp}"/config.yaml \
|
||||
--asr_model_file "${asr_exp}"/"${inference_asr_model}" \
|
||||
--output_dir "${_logdir}"/output.JOB \
|
||||
--mode asr \
|
||||
${_opts}
|
||||
|
||||
for f in token token_int score text; do
|
||||
if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
|
||||
for i in $(seq "${_nj}"); do
|
||||
cat "${_logdir}/output.${i}/1best_recog/${f}"
|
||||
done | sort -k1 >"${_dir}/${f}"
|
||||
fi
|
||||
done
|
||||
python utils/compute_wer.py ${_data}/text ${_dir}/text ${_dir}/text.cer
|
||||
tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
|
||||
cat ${_dir}/text.cer.txt
|
||||
done
|
||||
fi
|
||||
Loading…
Reference in New Issue
Block a user