From 2db4a207d11bb4b5269def967f29f9aed1fe3ba7 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期一, 15 五月 2023 18:26:27 +0800
Subject: [PATCH] update repo
---
egs/aishell2/paraformerbert/run.sh | 110 ++++++++++++++++---------------------------------------
1 files changed, 32 insertions(+), 78 deletions(-)
diff --git a/egs/aishell2/paraformerbert/run.sh b/egs/aishell2/paraformerbert/run.sh
index 239a7e3..3b42e34 100755
--- a/egs/aishell2/paraformerbert/run.sh
+++ b/egs/aishell2/paraformerbert/run.sh
@@ -8,36 +8,32 @@
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=tools/run.pl
+njob=1
+train_cmd=utils/run.pl
infer_cmd=utils/run.pl
# general configuration
-feats_dir="../DATA" #feature output dictionary, for large data
+feats_dir="../DATA" #feature output dictionary
exp_dir="."
lang=zh
-dumpdir=dump/fbank
-feats_type=fbank
token_type=char
+type=sound
+scp=wav.scp
+speed_perturb="0.9 1.0 1.1"
dataset_type=large
-scp=feats.scp
-type=kaldi_ark
-stage=0
-stop_stage=5
+stage=3
+stop_stage=4
skip_extract_embed=false
-bert_model_root="../../huggingface_models"
bert_model_name="bert-base-chinese"
# feature configuration
feats_dim=80
-sample_frequency=16000
-nj=100
-speed_perturb="0.9,1.0,1.1"
+nj=64
# data
-tr_dir=
-dev_tst_dir=
+tr_dir=/nfs/wangjiaming.wjm/asr_data/aishell2/AISHELL-2/iOS/data
+dev_tst_dir=/nfs/wangjiaming.wjm/asr_data/aishell2/AISHELL-DEV-TEST-SET
# exp tag
tag="exp1"
@@ -55,7 +51,7 @@
test_sets="dev_ios test_ios"
asr_config=conf/train_asr_paraformerbert_conformer_20e_6d_1280_320.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_noctc_1best.yaml
inference_asr_model=valid.acc.ave_10best.pb
@@ -75,86 +71,44 @@
if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
echo "stage 0: Data preparation"
# For training set
- local/prepare_data.sh ${tr_dir} data/local/train data/train || exit 1;
+ local/prepare_data.sh ${tr_dir} ${feats_dir}/data/local/train ${feats_dir}/data/train || exit 1;
# # For dev and test set
- for x in Android iOS Mic; do
- local/prepare_data.sh ${dev_tst_dir}/${x}/dev data/local/dev_${x,,} data/dev_${x,,} || exit 1;
- local/prepare_data.sh ${dev_tst_dir}/${x}/test data/local/test_${x,,} data/test_${x,,} || exit 1;
- done
+ for x in iOS; do
+ local/prepare_data.sh ${dev_tst_dir}/${x}/dev ${feats_dir}/data/local/dev_${x,,} ${feats_dir}/data/dev_${x,,} || exit 1;
+ local/prepare_data.sh ${dev_tst_dir}/${x}/test ${feats_dir}/data/local/test_${x,,} ${feats_dir}/data/test_${x,,} || exit 1;
+ done
# Normalize text to capital letters
- for x in train dev_android dev_ios dev_mic test_android test_ios test_mic; do
- mv data/${x}/text data/${x}/text.org
- paste <(cut -f 1 data/${x}/text.org) <(cut -f 2 data/${x}/text.org | tr '[:lower:]' '[:upper:]') \
- > data/${x}/text
- tools/text2token.py -n 1 -s 1 data/${x}/text > data/${x}/text.org
- mv data/${x}/text.org data/${x}/text
+ for x in train dev_ios test_ios; do
+ mv ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
+ paste -d " " <(cut -f 1 ${feats_dir}/data/${x}/text.org) <(cut -f 2- ${feats_dir}/data/${x}/text.org \
+ | tr 'A-Z' 'a-z' | tr -d " ") \
+ > ${feats_dir}/data/${x}/text
+ utils/text2token.py -n 1 -s 1 ${feats_dir}/data/${x}/text > ${feats_dir}/data/${x}/text.org
+ mv ${feats_dir}/data/${x}/text.org ${feats_dir}/data/${x}/text
done
fi
-feat_train_dir=${feats_dir}/${dumpdir}/${train_set}; mkdir -p ${feat_train_dir}
-feat_dev_dir=${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
- steps/compute_fbank.sh --cmd "$train_cmd" --nj $nj --speed_perturb ${speed_perturb} \
- data/train exp/make_fbank/train ${fbankdir}/train
- tools/fix_data_feat.sh ${fbankdir}/train
- for x in android ios mic; do
- steps/compute_fbank.sh --cmd "$train_cmd" --nj $nj \
- data/dev_${x} exp/make_fbank/dev_${x} ${fbankdir}/dev_${x}
- tools/fix_data_feat.sh ${fbankdir}/dev_${x}
- steps/compute_fbank.sh --cmd "$train_cmd" --nj $nj \
- data/test_${x} exp/make_fbank/test_${x} ${fbankdir}/test_${x}
- tools/fix_data_feat.sh ${fbankdir}/test_${x}
- done
-
- # compute global cmvn
- steps/compute_cmvn.sh --cmd "$train_cmd" --nj $nj \
- ${fbankdir}/train exp/make_fbank/train
-
- # apply cmvn
- steps/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
- ${fbankdir}/${train_set} ${fbankdir}/train/cmvn.json exp/make_fbank/${train_set} ${feat_train_dir}
- steps/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
- ${fbankdir}/${valid_set} ${fbankdir}/train/cmvn.json exp/make_fbank/${valid_set} ${feat_dev_dir}
- for x in android ios mic; do
- steps/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
- ${fbankdir}/test_${x} ${fbankdir}/train/cmvn.json exp/make_fbank/test_${x} ${feats_dir}/${dumpdir}/test_${x}
- done
-
- cp ${fbankdir}/${train_set}/text ${fbankdir}/${train_set}/speech_shape ${fbankdir}/${train_set}/text_shape ${feat_train_dir}
- tools/fix_data_feat.sh ${feat_train_dir}
- cp ${fbankdir}/${valid_set}/text ${fbankdir}/${valid_set}/speech_shape ${fbankdir}/${valid_set}/text_shape ${feat_dev_dir}
- tools/fix_data_feat.sh ${feat_dev_dir}
- for x in android ios mic; do
- cp ${fbankdir}/test_${x}/text ${fbankdir}/test_${x}/speech_shape ${fbankdir}/test_${x}/text_shape ${feats_dir}/${dumpdir}/test_${x}
- tools/fix_data_feat.sh ${feats_dir}/${dumpdir}/test_${x}
- done
+ echo "stage 1: Feature and CMVN Generation"
+ utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} ${feats_dir}/data/${train_set}
fi
token_list=${feats_dir}/data/${lang}_token_list/char/tokens.txt
echo "dictionary: ${token_list}"
if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
echo "stage 2: Dictionary Preparation"
- mkdir -p data/${lang}_token_list/char/
-
+ mkdir -p ${feats_dir}/data/${lang}_token_list/char/
+
echo "make a dictionary"
echo "<blank>" > ${token_list}
echo "<s>" >> ${token_list}
echo "</s>" >> ${token_list}
- tools/text2token.py -s 1 -n 1 --space "" data/${train_set}/text | cut -f 2- -d" " | tr " " "\n" \
+ utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/${train_set}/text | 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 ${token_list} | wc -l)
- awk -v v=,${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 > ${feat_dev_dir}/text_shape.char
- mkdir -p asr_stats_fbank_zh_char/${train_set}
- mkdir -p asr_stats_fbank_zh_char/${valid_set}
- cp ${feat_train_dir}/speech_shape ${feat_train_dir}/text_shape ${feat_train_dir}/text_shape.char asr_stats_fbank_zh_char/${train_set}
- cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char asr_stats_fbank_zh_char/${valid_set}
-fi
+ mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${train_set}
+ mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}
+ fi
# Training Stage
world_size=$gpu_num # run on one machine
--
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