speech_recognition/FunASR
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嘉渊 2023-05-11 16:30:20 +08:00
parent c2bf708f87
commit 08b3c31d26
4 changed files with 61 additions and 97 deletions
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2
.github/workflows/UnitTest.yml vendored
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@ -6,7 +6,7 @@ on:
- main
push:
branches:
- dev_wjm2
- dev_wjm
- dev_jy
jobs:

14
egs/librispeech/conformer/conf/train_asr_conformer.yaml
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@ -27,13 +27,25 @@ decoder_conf:
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
# frontend related
frontend: wav_frontend
frontend_conf:
fs: 16000
window: hamming
n_mels: 80
frame_length: 25
frame_shift: 10
lfr_m: 1
lfr_n: 1
# hybrid CTC/attention
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
accum_grad: 2
max_epoch: 50
max_epoch: 150
patience: none
init: none
best_model_criterion:

140
egs/librispeech/conformer/run.sh
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@ -16,30 +16,26 @@ infer_cmd=utils/run.pl
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
type=sound
scp=wav.scp
stage=0
stop_stage=2
# feature configuration
feats_dim=80
sample_frequency=16000
nj=100
speed_perturb="0.9,1.0,1.1"
nj=64
# data
data_librispeech=
raw_data=
data_url=www.openslr.org/resources/12
# bpe model
nbpe=5000
bpemode=unigram
# exp tag
tag=""
tag="exp1"
. utils/parse_options.sh || exit 1;
@ -54,8 +50,7 @@ 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}"
model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer.yaml
#inference_config=conf/decode_asr_transformer_beam60_ctc0.3.yaml
@ -73,97 +68,53 @@ else
_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//-/_}
if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
echo "stage -1: Data Download"
for part in dev-clean test-clean dev-other test-other train-clean-100 train-clean-360 train-other-500; do
local/download_and_untar.sh ${raw_data} ${data_url} ${part}
done
fi
feat_train_dir=${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}
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
echo "stage 0: Data preparation"
# Data preparation
for x in dev-clean dev-other test-clean test-other train-clean-100 train-clean-360 train-other-500; do
local/data_prep.sh ${raw_data}/LibriSpeech/${x} ${feats_dir}/data/${x//-/_}
done
mkdir ${feats_dir}/data/$train_set
mkdir $feats_dir/data/$valid_set
dev_sets="dev_clean dev_other"
for file in wav.scp text; do
( for f in $dev_sets; do cat $feats_dir/data/$f/$file; done ) | sort -k1 > $feats_dir/data/$valid_set/$file || exit 1;
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
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "stage 1: Feature and CMVN Generation"
utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} ${feats_dir}/data/${train_set}
fi
token_list=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
bpemodel=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}
echo "dictionary: ${dict}"
echo "dictionary: ${token_list}"
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}
echo "<blank>" > ${token_list}
echo "<s>" >> ${token_list}
echo "</s>" >> ${token_list}
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
local/spm_train.py --input=${feats_dir}/data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000
local/spm_encode.py --model=${bpemodel}.model --output_format=piece < ${feats_dir}/data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0}' >> ${token_list}
echo "<unk>" >> ${token_list}
fi
# Training Stage
world_size=$gpu_num # run on one machine
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
@ -181,20 +132,20 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
rank=$i
local_rank=$i
gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
asr_train.py \
train.py \
--task_name asr \
--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 \
--token_list $token_list \
--data_dir ${feats_dir}/data \
--train_set ${train_set} \
--valid_set ${valid_set} \
--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 \
@ -220,7 +171,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
exit 0
fi
mkdir -p "${_logdir}"
_data="${feats_dir}/${dumpdir}/${dset}"
_data="${feats_dir}/data/${dset}"
key_file=${_data}/${scp}
num_scp_file="$(<${key_file} wc -l)"
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
@ -241,6 +192,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
--njob ${njob} \
--gpuid_list ${gpuid_list} \
--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
--cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
--key_file "${_logdir}"/keys.JOB.scp \
--asr_train_config "${asr_exp}"/config.yaml \
--asr_model_file "${asr_exp}"/"${inference_asr_model}" \

2
egs/librispeech_100h/conformer/run.sh
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@ -166,7 +166,7 @@ if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
exit 0
fi
mkdir -p "${_logdir}"
_data="${feats_dir}/${dumpdir}/${dset}"
_data="${feats_dir}/data/${dset}"
key_file=${_data}/${scp}
num_scp_file="$(<${key_file} wc -l)"
_nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")