From 81a5b29804800a4edd76c8dda2727d6fdf4b5643 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期四, 11 五月 2023 17:35:49 +0800
Subject: [PATCH] update repo
---
egs/librispeech_100h/conformer/run.sh | 114 ++++++++++++++------------------------------------------
1 files changed, 29 insertions(+), 85 deletions(-)
diff --git a/egs/librispeech_100h/conformer/run.sh b/egs/librispeech_100h/conformer/run.sh
index a400788..4959ba0 100755
--- a/egs/librispeech_100h/conformer/run.sh
+++ b/egs/librispeech_100h/conformer/run.sh
@@ -19,8 +19,8 @@
token_type=bpe
type=sound
scp=wav.scp
-stage=0
-stop_stage=0
+stage=3
+stop_stage=4
# feature configuration
feats_dim=80
@@ -45,7 +45,7 @@
set -u
set -o pipefail
-train_set=train_960
+train_set=train_clean_100
valid_set=dev
test_sets="test_clean test_other dev_clean dev_other"
@@ -79,93 +79,36 @@
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 ${raw_data}/LibriSpeech/${x} ${feats_dir}/data/${x//-/_}
+ for x in dev-clean dev-other test-clean test-other train-clean-100; do
+ local/data_prep.sh ${raw_data}/LibriSpeech/${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}
-
+ mkdir $feats_dir/data/$valid_set
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;
+ 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/$file || exit 1;
done
-
- #generate ark list
- utils/gen_ark_list.sh --cmd "$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
@@ -184,20 +127,20 @@
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 $count \
--multiprocessing_distributed true \
@@ -223,7 +166,7 @@
exit 0
fi
mkdir -p "${_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")
@@ -244,6 +187,7 @@
--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}" \
--
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