diff --git a/docs/source/released_model.md b/docs/source/released_model.md
index 74435ae1..19a47e6c 100644
--- a/docs/source/released_model.md
+++ b/docs/source/released_model.md
@@ -88,11 +88,3 @@ PANN | VoxCeleb| [voxceleb_ecapatdnn](https://github.com/PaddlePaddle/PaddleSpee
 Model Type | Dataset| Example Link | Pretrained Models
 :-------------:| :------------:| :-----: | :-----:
 Ernie Linear | IWLST2012_zh |[iwslt2012_punc0](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/examples/iwslt2012/punc0)|[ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip](https://paddlespeech.bj.bcebos.com/text/ernie_linear_p3_iwslt2012_zh_ckpt_0.1.1.zip)
-
-## Speech Recognition Model  from paddle 1.8
-
-| Acoustic Model |Training Data| Token-based | Size | Descriptions | CER | WER | Hours of speech |
-| :-----:| :-----:  |  :-----:  |  :-----:  | :-----:  |  :-----: | :-----:  | :-----: |
-| [Ds2 Offline Aishell model](https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_v1.8_to_v2.x.tar.gz) |        Aishell Dataset  | Char-based  | 234 MB | 2 Conv + 3 bidirectional GRU layers  | 0.0804 | —  | 151 h  |
-| [Ds2 Offline Librispeech model](https://deepspeech.bj.bcebos.com/eng_models/librispeech_v1.8_to_v2.x.tar.gz) |      Librispeech Dataset | Word-based  | 307 MB | 2 Conv + 3 bidirectional sharing weight RNN layers | —  | 0.0685 | 960 h |
-| [Ds2 Offline Baidu en8k model](https://deepspeech.bj.bcebos.com/eng_models/baidu_en8k_v1.8_to_v2.x.tar.gz) | Baidu Internal English Dataset | Word-based  | 273 MB | 2 Conv + 3 bidirectional GRU layers   |—  | 0.0541 | 8628 h|
diff --git a/examples/other/1xt2x/.gitignore b/examples/other/1xt2x/.gitignore
deleted file mode 100644
index a9a5aecf..00000000
--- a/examples/other/1xt2x/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-tmp
diff --git a/examples/other/1xt2x/README.md b/examples/other/1xt2x/README.md
deleted file mode 100644
index 49f850d2..00000000
--- a/examples/other/1xt2x/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# 1xt2x
-
-Convert Deepspeech 1.8 released model to 2.x.
-
-## Model source directory
-* Deepspeech2x
-
-## Expriment directory
-* aishell
-* librispeech
-* baidu_en8k
-
-# The released model
-
-Acoustic Model |  Training Data | Hours of Speech | Token-based | CER | WER
-:-------------:| :------------:| :---------------: | :---------: | :---: | :----:
-Ds2 Offline Aishell 1xt2x model| Aishell Dataset | 151 h | Char-based | 0.080447 |
-Ds2 Offline Librispeech 1xt2x model | Librispeech Dataset | 960 h | Word-based | | 0.068548
-Ds2 Offline Baidu en8k 1x2x model | Baidu Internal English Dataset | 8628 h |Word-based | | 0.054112
diff --git a/examples/other/1xt2x/aishell/.gitignore b/examples/other/1xt2x/aishell/.gitignore
deleted file mode 100644
index 3631e544..00000000
--- a/examples/other/1xt2x/aishell/.gitignore
+++ /dev/null
@@ -1,5 +0,0 @@
-exp
-data
-*log
-tmp
-nohup*
diff --git a/examples/other/1xt2x/aishell/conf/augmentation.json b/examples/other/1xt2x/aishell/conf/augmentation.json
deleted file mode 100644
index fe51488c..00000000
--- a/examples/other/1xt2x/aishell/conf/augmentation.json
+++ /dev/null
@@ -1 +0,0 @@
-[]
diff --git a/examples/other/1xt2x/aishell/conf/deepspeech2.yaml b/examples/other/1xt2x/aishell/conf/deepspeech2.yaml
deleted file mode 100644
index c2db2c7c..00000000
--- a/examples/other/1xt2x/aishell/conf/deepspeech2.yaml
+++ /dev/null
@@ -1,65 +0,0 @@
-# https://yaml.org/type/float.html
-###########################################
-#                   Data                  #
-###########################################
-train_manifest: data/manifest.train
-dev_manifest: data/manifest.dev
-test_manifest: data/manifest.test
-min_input_len: 0.0
-max_input_len: 27.0 # second
-min_output_len: 0.0
-max_output_len: .inf
-min_output_input_ratio: 0.00
-max_output_input_ratio: .inf
-
-###########################################
-#              Dataloader                 #
-###########################################
-batch_size: 64 # one gpu
-mean_std_filepath: data/mean_std.npz
-unit_type: char
-vocab_filepath: data/vocab.txt 
-augmentation_config: conf/augmentation.json
-random_seed: 0
-spm_model_prefix: 
-spectrum_type: linear
-feat_dim: 
-delta_delta: False
-stride_ms: 10.0
-window_ms: 20.0
-n_fft: None
-max_freq: None
-target_sample_rate: 16000
-use_dB_normalization: True
-target_dB: -20
-dither: 1.0
-keep_transcription_text: False
-sortagrad: True
-shuffle_method: batch_shuffle
-num_workers: 2
-
-############################################
-#           Network Architecture           #
-############################################
-num_conv_layers: 2
-num_rnn_layers: 3
-rnn_layer_size: 1024
-use_gru: True 
-share_rnn_weights: False
-blank_id: 4333
-
-###########################################
-#                Training                 #
-###########################################
-n_epoch: 80
-accum_grad: 1
-lr: 2e-3
-lr_decay: 0.83
-weight_decay: 1e-06
-global_grad_clip: 3.0
-log_interval: 100
-checkpoint:
-  kbest_n: 50
-  latest_n: 5
-  
-  
diff --git a/examples/other/1xt2x/aishell/conf/tuning/decode.yaml b/examples/other/1xt2x/aishell/conf/tuning/decode.yaml
deleted file mode 100644
index b5283a93..00000000
--- a/examples/other/1xt2x/aishell/conf/tuning/decode.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-decode_batch_size: 32
-error_rate_type: cer 
-decoding_method: ctc_beam_search
-lang_model_path: data/lm/zh_giga.no_cna_cmn.prune01244.klm
-alpha: 2.6
-beta: 5.0
-beam_size: 300
-cutoff_prob: 0.99
-cutoff_top_n: 40
-num_proc_bsearch: 8
\ No newline at end of file
diff --git a/examples/other/1xt2x/aishell/local/data.sh b/examples/other/1xt2x/aishell/local/data.sh
deleted file mode 100755
index a9d5b141..00000000
--- a/examples/other/1xt2x/aishell/local/data.sh
+++ /dev/null
@@ -1,69 +0,0 @@
-#!/bin/bash
-if [ $# != 1 ];then
-    echo "usage: ${0} ckpt_dir"
-    exit -1
-fi
-
-ckpt_dir=$1
-
-stage=-1
-stop_stage=100
-
-source ${MAIN_ROOT}/utils/parse_options.sh
-
-mkdir -p data
-TARGET_DIR=${MAIN_ROOT}/dataset
-mkdir -p ${TARGET_DIR}
-
-bash local/download_model.sh ${ckpt_dir}
-if [ $? -ne 0 ]; then
-   exit 1
-fi
-
-cd ${ckpt_dir}
-tar xzvf aishell_model_v1.8_to_v2.x.tar.gz
-cd -
-mv ${ckpt_dir}/mean_std.npz data/
-mv ${ckpt_dir}/vocab.txt data/
-
-
-if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
-    # download data, generate manifests
-    python3 ${TARGET_DIR}/aishell/aishell.py \
-    --manifest_prefix="data/manifest" \
-    --target_dir="${TARGET_DIR}/aishell"
-
-    if [ $? -ne 0 ]; then
-        echo "Prepare Aishell failed. Terminated."
-        exit 1
-    fi
-
-    for dataset in train dev test; do
-        mv data/manifest.${dataset} data/manifest.${dataset}.raw
-    done
-fi
-
-
-
-if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
-    # format manifest with tokenids, vocab size
-    for dataset in train dev test; do
-    {
-        python3 ${MAIN_ROOT}/utils/format_data.py \
-                --cmvn_path "data/mean_std.npz" \
-                --unit_type "char" \
-                --vocab_path="data/vocab.txt" \
-                --manifest_path="data/manifest.${dataset}.raw" \
-                --output_path="data/manifest.${dataset}"
-
-        if [ $? -ne 0 ]; then
-                echo "Formt mnaifest failed. Terminated."
-                exit 1
-        fi
-    } &
-    done
-    wait
-fi
-
-echo "Aishell data preparation done."
-exit 0
diff --git a/examples/other/1xt2x/aishell/local/download_lm_ch.sh b/examples/other/1xt2x/aishell/local/download_lm_ch.sh
deleted file mode 100755
index 47153f4b..00000000
--- a/examples/other/1xt2x/aishell/local/download_lm_ch.sh
+++ /dev/null
@@ -1,23 +0,0 @@
-#!/bin/bash
-
-. ${MAIN_ROOT}/utils/utility.sh
-
-DIR=data/lm
-mkdir -p ${DIR}
-
-URL='https://deepspeech.bj.bcebos.com/zh_lm/zh_giga.no_cna_cmn.prune01244.klm'
-MD5="29e02312deb2e59b3c8686c7966d4fe3"
-TARGET=${DIR}/zh_giga.no_cna_cmn.prune01244.klm
-
-
-echo "Start downloading the language model. The language model is large, please wait for a moment ..."
-download $URL $MD5 $TARGET > /dev/null 2>&1
-if [ $? -ne 0 ]; then
-    echo "Fail to download the language model!"
-    exit 1
-else
-    echo "Download the language model sucessfully"
-fi
-
-
-exit 0
diff --git a/examples/other/1xt2x/aishell/local/download_model.sh b/examples/other/1xt2x/aishell/local/download_model.sh
deleted file mode 100644
index ffa2f810..00000000
--- a/examples/other/1xt2x/aishell/local/download_model.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#! /usr/bin/env bash
-
-if [ $# != 1 ];then
-    echo "usage: ${0} ckpt_dir"
-    exit -1
-fi
-
-ckpt_dir=$1
-
-. ${MAIN_ROOT}/utils/utility.sh
-
-URL='https://deepspeech.bj.bcebos.com/mandarin_models/aishell_model_v1.8_to_v2.x.tar.gz'
-MD5=87e7577d4bea737dbf3e8daab37aa808
-TARGET=${ckpt_dir}/aishell_model_v1.8_to_v2.x.tar.gz
-
-
-echo "Download Aishell model ..."
-download $URL $MD5 $TARGET
-if [ $? -ne 0 ]; then
-    echo "Fail to download Aishell model!"
-    exit 1
-fi
-
-
-exit 0
diff --git a/examples/other/1xt2x/aishell/local/test.sh b/examples/other/1xt2x/aishell/local/test.sh
deleted file mode 100755
index 463593ef..00000000
--- a/examples/other/1xt2x/aishell/local/test.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/bin/bash
-
-if [ $# != 4 ];then
-    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
-    exit -1
-fi
-
-ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
-echo "using $ngpu gpus..."
-
-config_path=$1
-decode_config_path=$2
-ckpt_prefix=$3
-model_type=$4
-
-# download language model
-bash local/download_lm_ch.sh
-if [ $? -ne 0 ]; then
-   exit 1
-fi
-
-python3 -u ${BIN_DIR}/test.py \
---ngpu ${ngpu} \
---config ${config_path} \
---decode_cfg ${decode_config_path} \
---result_file ${ckpt_prefix}.rsl \
---checkpoint_path ${ckpt_prefix} \
---model_type ${model_type}
-
-if [ $? -ne 0 ]; then
-    echo "Failed in evaluation!"
-    exit 1
-fi
-
-
-exit 0
diff --git a/examples/other/1xt2x/aishell/path.sh b/examples/other/1xt2x/aishell/path.sh
deleted file mode 100644
index ce44e65c..00000000
--- a/examples/other/1xt2x/aishell/path.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-export MAIN_ROOT=`realpath ${PWD}/../../../../`
-export LOCAL_DEEPSPEECH2=`realpath ${PWD}/../`
-
-export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
-export LC_ALL=C
-
-export PYTHONDONTWRITEBYTECODE=1
-# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
-export PYTHONIOENCODING=UTF-8
-export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
-export PYTHONPATH=${LOCAL_DEEPSPEECH2}:${PYTHONPATH}
-
-export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
-
-MODEL=deepspeech2
-export BIN_DIR=${LOCAL_DEEPSPEECH2}/src_deepspeech2x/bin
-echo "BIN_DIR "${BIN_DIR}
diff --git a/examples/other/1xt2x/aishell/run.sh b/examples/other/1xt2x/aishell/run.sh
deleted file mode 100755
index 89a63411..00000000
--- a/examples/other/1xt2x/aishell/run.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-set -e
-source path.sh
-
-stage=0
-stop_stage=100
-conf_path=conf/deepspeech2.yaml
-decode_conf_path=conf/tuning/decode.yaml
-avg_num=1
-model_type=offline
-gpus=2
-
-source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
-
-v18_ckpt=aishell_v1.8
-ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
-echo "checkpoint name ${ckpt}"
-
-if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
-    # prepare data
-    mkdir -p exp/${ckpt}/checkpoints
-    bash ./local/data.sh exp/${ckpt}/checkpoints || exit -1
-fi
-
-if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
-fi
-
diff --git a/examples/other/1xt2x/baidu_en8k/.gitignore b/examples/other/1xt2x/baidu_en8k/.gitignore
deleted file mode 100644
index 3631e544..00000000
--- a/examples/other/1xt2x/baidu_en8k/.gitignore
+++ /dev/null
@@ -1,5 +0,0 @@
-exp
-data
-*log
-tmp
-nohup*
diff --git a/examples/other/1xt2x/baidu_en8k/conf/augmentation.json b/examples/other/1xt2x/baidu_en8k/conf/augmentation.json
deleted file mode 100644
index fe51488c..00000000
--- a/examples/other/1xt2x/baidu_en8k/conf/augmentation.json
+++ /dev/null
@@ -1 +0,0 @@
-[]
diff --git a/examples/other/1xt2x/baidu_en8k/conf/deepspeech2.yaml b/examples/other/1xt2x/baidu_en8k/conf/deepspeech2.yaml
deleted file mode 100644
index 0c08fbc6..00000000
--- a/examples/other/1xt2x/baidu_en8k/conf/deepspeech2.yaml
+++ /dev/null
@@ -1,64 +0,0 @@
-# https://yaml.org/type/float.html
-###########################################
-#                   Data                  #
-###########################################
-train_manifest: data/manifest.train
-dev_manifest: data/manifest.dev
-test_manifest: data/manifest.test-clean
-min_input_len: 0.0
-max_input_len: .inf # second
-min_output_len: 0.0
-max_output_len: .inf
-min_output_input_ratio: 0.00
-max_output_input_ratio: .inf
-
-###########################################
-#              Dataloader                 #
-###########################################
-batch_size: 64 # one gpu
-mean_std_filepath: data/mean_std.npz
-unit_type: char
-vocab_filepath: data/vocab.txt 
-augmentation_config: conf/augmentation.json
-random_seed: 0
-spm_model_prefix: 
-spectrum_type: linear
-feat_dim: 
-delta_delta: False
-stride_ms: 10.0
-window_ms: 20.0
-n_fft: None
-max_freq: None
-target_sample_rate: 16000
-use_dB_normalization: True
-target_dB: -20
-dither: 1.0
-keep_transcription_text: False
-sortagrad: True
-shuffle_method: batch_shuffle
-num_workers: 2
-
-############################################
-#           Network Architecture           #
-############################################
-num_conv_layers: 2
-num_rnn_layers: 3
-rnn_layer_size: 1024
-use_gru: True
-share_rnn_weights: False
-blank_id: 28
-
-###########################################
-#                Training                 #
-###########################################
-n_epoch: 80
-accum_grad: 1
-lr: 2e-3
-lr_decay: 0.83
-weight_decay: 1e-06
-global_grad_clip: 3.0
-log_interval: 100
-checkpoint:
-  kbest_n: 50
-  latest_n: 5
-
diff --git a/examples/other/1xt2x/baidu_en8k/conf/tuning/decode.yaml b/examples/other/1xt2x/baidu_en8k/conf/tuning/decode.yaml
deleted file mode 100644
index f52dde32..00000000
--- a/examples/other/1xt2x/baidu_en8k/conf/tuning/decode.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-decode_batch_size: 32
-error_rate_type: wer 
-decoding_method: ctc_beam_search
-lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
-alpha: 1.4
-beta: 0.35
-beam_size: 500
-cutoff_prob: 1.0
-cutoff_top_n: 40
-num_proc_bsearch: 8
\ No newline at end of file
diff --git a/examples/other/1xt2x/baidu_en8k/local/data.sh b/examples/other/1xt2x/baidu_en8k/local/data.sh
deleted file mode 100755
index 9b017324..00000000
--- a/examples/other/1xt2x/baidu_en8k/local/data.sh
+++ /dev/null
@@ -1,85 +0,0 @@
-#!/bin/bash
-if [ $# != 1 ];then
-    echo "usage: ${0} ckpt_dir"
-    exit -1
-fi
-
-ckpt_dir=$1
-
-stage=-1
-stop_stage=100
-unit_type=char
-
-source ${MAIN_ROOT}/utils/parse_options.sh
-
-mkdir -p data
-TARGET_DIR=${MAIN_ROOT}/dataset
-mkdir -p ${TARGET_DIR}
-
-
-bash local/download_model.sh ${ckpt_dir}
-if [ $? -ne 0 ]; then
-   exit 1
-fi
-
-cd ${ckpt_dir}
-tar xzvf baidu_en8k_v1.8_to_v2.x.tar.gz
-cd -
-mv ${ckpt_dir}/mean_std.npz data/
-mv ${ckpt_dir}/vocab.txt data/
-
-
-if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
-    # download data, generate manifests
-    python3 ${TARGET_DIR}/librispeech/librispeech.py \
-    --manifest_prefix="data/manifest" \
-    --target_dir="${TARGET_DIR}/librispeech" \
-    --full_download="True"
-
-    if [ $? -ne 0 ]; then
-        echo "Prepare LibriSpeech failed. Terminated."
-        exit 1
-    fi
-
-    for set in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
-        mv data/manifest.${set} data/manifest.${set}.raw
-    done
-
-    rm -rf data/manifest.train.raw data/manifest.dev.raw  data/manifest.test.raw
-    for set in train-clean-100 train-clean-360 train-other-500; do
-        cat data/manifest.${set}.raw >> data/manifest.train.raw
-    done
-
-    for set in dev-clean dev-other; do
-        cat data/manifest.${set}.raw >> data/manifest.dev.raw
-    done
-
-    for set in test-clean test-other; do
-        cat data/manifest.${set}.raw >> data/manifest.test.raw
-    done
-fi
-
-
-if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
-    # format manifest with tokenids, vocab size
-    for set in train dev test dev-clean dev-other test-clean test-other; do
-    {
-        python3 ${MAIN_ROOT}/utils/format_data.py \
-        --cmvn_path "data/mean_std.npz" \
-        --unit_type ${unit_type} \
-        --vocab_path="data/vocab.txt" \
-        --manifest_path="data/manifest.${set}.raw" \
-        --output_path="data/manifest.${set}"
-
-        if [ $? -ne 0 ]; then
-            echo "Formt mnaifest.${set} failed. Terminated."
-            exit 1
-        fi
-    }&
-    done
-    wait
-fi
-
-echo "LibriSpeech Data preparation done."
-exit 0
-
diff --git a/examples/other/1xt2x/baidu_en8k/local/download_lm_en.sh b/examples/other/1xt2x/baidu_en8k/local/download_lm_en.sh
deleted file mode 100755
index 390fffc9..00000000
--- a/examples/other/1xt2x/baidu_en8k/local/download_lm_en.sh
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/bin/bash
-
-. ${MAIN_ROOT}/utils/utility.sh
-
-DIR=data/lm
-mkdir -p ${DIR}
-
-URL=https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm
-MD5="099a601759d467cd0a8523ff939819c5"
-TARGET=${DIR}/common_crawl_00.prune01111.trie.klm
-
-echo "Start downloading the language model. The language model is large, please wait for a moment ..."
-download $URL $MD5 $TARGET > /dev/null 2>&1
-if [ $? -ne 0 ]; then
-    echo "Fail to download the language model!"
-    exit 1
-else
-    echo "Download the language model sucessfully"
-fi
-
-
-exit 0
diff --git a/examples/other/1xt2x/baidu_en8k/local/download_model.sh b/examples/other/1xt2x/baidu_en8k/local/download_model.sh
deleted file mode 100644
index a8fbc31e..00000000
--- a/examples/other/1xt2x/baidu_en8k/local/download_model.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#! /usr/bin/env bash
-if [ $# != 1 ];then
-    echo "usage: ${0} ckpt_dir"
-    exit -1
-fi
-
-ckpt_dir=$1
-
-
-. ${MAIN_ROOT}/utils/utility.sh
-
-URL='https://deepspeech.bj.bcebos.com/eng_models/baidu_en8k_v1.8_to_v2.x.tar.gz'
-MD5=c1676be8505cee436e6f312823e9008c
-TARGET=${ckpt_dir}/baidu_en8k_v1.8_to_v2.x.tar.gz
-
-
-echo "Download BaiduEn8k model ..."
-download $URL $MD5 $TARGET
-if [ $? -ne 0 ]; then
-    echo "Fail to download BaiduEn8k model!"
-    exit 1
-fi
-
-
-exit 0
diff --git a/examples/other/1xt2x/baidu_en8k/local/test.sh b/examples/other/1xt2x/baidu_en8k/local/test.sh
deleted file mode 100755
index ea40046b..00000000
--- a/examples/other/1xt2x/baidu_en8k/local/test.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/bin/bash
-
-if [ $# != 4 ];then
-    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
-    exit -1
-fi
-
-ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
-echo "using $ngpu gpus..."
-
-config_path=$1
-decode_config_path=$2
-ckpt_prefix=$3
-model_type=$4
-
-# download language model
-bash local/download_lm_en.sh
-if [ $? -ne 0 ]; then
-   exit 1
-fi
-
-python3 -u ${BIN_DIR}/test.py \
---ngpu ${ngpu} \
---config ${config_path} \
---decode_cfg ${decode_config_path} \
---result_file ${ckpt_prefix}.rsl \
---checkpoint_path ${ckpt_prefix} \
---model_type ${model_type}
-
-if [ $? -ne 0 ]; then
-    echo "Failed in evaluation!"
-    exit 1
-fi
-
-
-exit 0
diff --git a/examples/other/1xt2x/baidu_en8k/path.sh b/examples/other/1xt2x/baidu_en8k/path.sh
deleted file mode 100644
index ce44e65c..00000000
--- a/examples/other/1xt2x/baidu_en8k/path.sh
+++ /dev/null
@@ -1,17 +0,0 @@
-export MAIN_ROOT=`realpath ${PWD}/../../../../`
-export LOCAL_DEEPSPEECH2=`realpath ${PWD}/../`
-
-export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
-export LC_ALL=C
-
-export PYTHONDONTWRITEBYTECODE=1
-# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
-export PYTHONIOENCODING=UTF-8
-export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
-export PYTHONPATH=${LOCAL_DEEPSPEECH2}:${PYTHONPATH}
-
-export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
-
-MODEL=deepspeech2
-export BIN_DIR=${LOCAL_DEEPSPEECH2}/src_deepspeech2x/bin
-echo "BIN_DIR "${BIN_DIR}
diff --git a/examples/other/1xt2x/baidu_en8k/run.sh b/examples/other/1xt2x/baidu_en8k/run.sh
deleted file mode 100755
index 82de56b0..00000000
--- a/examples/other/1xt2x/baidu_en8k/run.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash
-set -e
-source path.sh
-
-stage=0
-stop_stage=100
-conf_path=conf/deepspeech2.yaml
-decode_conf_path=conf/tuning/decode.yaml
-avg_num=1
-model_type=offline
-gpus=0
-
-source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
-
-v18_ckpt=baidu_en8k_v1.8
-ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
-echo "checkpoint name ${ckpt}"
-
-if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
-    # prepare data
-    mkdir -p exp/${ckpt}/checkpoints
-    bash ./local/data.sh exp/${ckpt}/checkpoints || exit -1
-fi
-
-if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
-fi
-
diff --git a/examples/other/1xt2x/librispeech/.gitignore b/examples/other/1xt2x/librispeech/.gitignore
deleted file mode 100644
index 3631e544..00000000
--- a/examples/other/1xt2x/librispeech/.gitignore
+++ /dev/null
@@ -1,5 +0,0 @@
-exp
-data
-*log
-tmp
-nohup*
diff --git a/examples/other/1xt2x/librispeech/conf/augmentation.json b/examples/other/1xt2x/librispeech/conf/augmentation.json
deleted file mode 100644
index fe51488c..00000000
--- a/examples/other/1xt2x/librispeech/conf/augmentation.json
+++ /dev/null
@@ -1 +0,0 @@
-[]
diff --git a/examples/other/1xt2x/librispeech/conf/deepspeech2.yaml b/examples/other/1xt2x/librispeech/conf/deepspeech2.yaml
deleted file mode 100644
index a2a5649b..00000000
--- a/examples/other/1xt2x/librispeech/conf/deepspeech2.yaml
+++ /dev/null
@@ -1,64 +0,0 @@
-# https://yaml.org/type/float.html
-###########################################
-#                   Data                  #
-###########################################
-train_manifest: data/manifest.train
-dev_manifest: data/manifest.dev
-test_manifest: data/manifest.test-clean
-min_input_len: 0.0
-max_input_len: 1000.0 # second
-min_output_len: 0.0
-max_output_len: .inf
-min_output_input_ratio: 0.00
-max_output_input_ratio: .inf
-
-###########################################
-#              Dataloader                 #
-###########################################
-batch_size: 64 # one gpu
-mean_std_filepath: data/mean_std.npz
-unit_type: char
-vocab_filepath: data/vocab.txt 
-augmentation_config: conf/augmentation.json
-random_seed: 0
-spm_model_prefix: 
-spectrum_type: linear
-feat_dim: 
-delta_delta: False
-stride_ms: 10.0
-window_ms: 20.0
-n_fft: None
-max_freq: None
-target_sample_rate: 16000
-use_dB_normalization: True
-target_dB: -20
-dither: 1.0
-keep_transcription_text: False
-sortagrad: True
-shuffle_method: batch_shuffle
-num_workers: 2
-
-############################################
-#           Network Architecture           #
-############################################
-num_conv_layers: 2
-num_rnn_layers: 3
-rnn_layer_size: 2048
-use_gru: False
-share_rnn_weights: True
-blank_id: 28
-
-###########################################
-#                Training                 #
-###########################################
-n_epoch: 80
-accum_grad: 1
-lr: 2e-3
-lr_decay: 0.83
-weight_decay: 1e-06
-global_grad_clip: 3.0
-log_interval: 100
-checkpoint:
-  kbest_n: 50
-  latest_n: 5
-
diff --git a/examples/other/1xt2x/librispeech/conf/tuning/decode.yaml b/examples/other/1xt2x/librispeech/conf/tuning/decode.yaml
deleted file mode 100644
index f3b51def..00000000
--- a/examples/other/1xt2x/librispeech/conf/tuning/decode.yaml
+++ /dev/null
@@ -1,10 +0,0 @@
-decode_batch_size: 32
-error_rate_type: wer 
-decoding_method: ctc_beam_search
-lang_model_path: data/lm/common_crawl_00.prune01111.trie.klm
-alpha: 2.5
-beta: 0.3
-beam_size: 500
-cutoff_prob: 1.0
-cutoff_top_n: 40
-num_proc_bsearch: 8
\ No newline at end of file
diff --git a/examples/other/1xt2x/librispeech/local/data.sh b/examples/other/1xt2x/librispeech/local/data.sh
deleted file mode 100755
index 43b5426d..00000000
--- a/examples/other/1xt2x/librispeech/local/data.sh
+++ /dev/null
@@ -1,83 +0,0 @@
-#!/bin/bash
-
-if [ $# != 1 ];then
-    echo "usage: ${0} ckpt_dir"
-    exit -1
-fi
-
-ckpt_dir=$1
-
-stage=-1
-stop_stage=100
-unit_type=char
-
-source ${MAIN_ROOT}/utils/parse_options.sh
-
-mkdir -p data
-TARGET_DIR=${MAIN_ROOT}/dataset
-mkdir -p ${TARGET_DIR}
-
-bash local/download_model.sh ${ckpt_dir}
-if [ $? -ne 0 ]; then
-   exit 1
-fi
-
-cd ${ckpt_dir}
-tar xzvf librispeech_v1.8_to_v2.x.tar.gz
-cd -
-mv ${ckpt_dir}/mean_std.npz data/
-mv ${ckpt_dir}/vocab.txt data/
-
-if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
-    # download data, generate manifests
-    python3 ${TARGET_DIR}/librispeech/librispeech.py \
-    --manifest_prefix="data/manifest" \
-    --target_dir="${TARGET_DIR}/librispeech" \
-    --full_download="True"
-
-    if [ $? -ne 0 ]; then
-        echo "Prepare LibriSpeech failed. Terminated."
-        exit 1
-    fi
-
-    for set in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
-        mv data/manifest.${set} data/manifest.${set}.raw
-    done
-
-    rm -rf data/manifest.train.raw data/manifest.dev.raw  data/manifest.test.raw
-    for set in train-clean-100 train-clean-360 train-other-500; do
-        cat data/manifest.${set}.raw >> data/manifest.train.raw
-    done
-
-    for set in dev-clean dev-other; do
-        cat data/manifest.${set}.raw >> data/manifest.dev.raw
-    done
-
-    for set in test-clean test-other; do
-        cat data/manifest.${set}.raw >> data/manifest.test.raw
-    done
-fi
-
-if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
-    # format manifest with tokenids, vocab size
-    for set in train dev test dev-clean dev-other test-clean test-other; do
-    {
-        python3 ${MAIN_ROOT}/utils/format_data.py \
-        --cmvn_path "data/mean_std.npz" \
-        --unit_type ${unit_type} \
-        --vocab_path="data/vocab.txt" \
-        --manifest_path="data/manifest.${set}.raw" \
-        --output_path="data/manifest.${set}"
-
-        if [ $? -ne 0 ]; then
-            echo "Formt mnaifest.${set} failed. Terminated."
-            exit 1
-        fi
-    }&
-    done
-    wait
-fi
-
-echo "LibriSpeech Data preparation done."
-exit 0
-
diff --git a/examples/other/1xt2x/librispeech/local/download_lm_en.sh b/examples/other/1xt2x/librispeech/local/download_lm_en.sh
deleted file mode 100755
index 390fffc9..00000000
--- a/examples/other/1xt2x/librispeech/local/download_lm_en.sh
+++ /dev/null
@@ -1,22 +0,0 @@
-#!/bin/bash
-
-. ${MAIN_ROOT}/utils/utility.sh
-
-DIR=data/lm
-mkdir -p ${DIR}
-
-URL=https://deepspeech.bj.bcebos.com/en_lm/common_crawl_00.prune01111.trie.klm
-MD5="099a601759d467cd0a8523ff939819c5"
-TARGET=${DIR}/common_crawl_00.prune01111.trie.klm
-
-echo "Start downloading the language model. The language model is large, please wait for a moment ..."
-download $URL $MD5 $TARGET > /dev/null 2>&1
-if [ $? -ne 0 ]; then
-    echo "Fail to download the language model!"
-    exit 1
-else
-    echo "Download the language model sucessfully"
-fi
-
-
-exit 0
diff --git a/examples/other/1xt2x/librispeech/local/download_model.sh b/examples/other/1xt2x/librispeech/local/download_model.sh
deleted file mode 100644
index 375d6640..00000000
--- a/examples/other/1xt2x/librispeech/local/download_model.sh
+++ /dev/null
@@ -1,25 +0,0 @@
-#! /usr/bin/env bash
-
-if [ $# != 1 ];then
-    echo "usage: ${0} ckpt_dir"
-    exit -1
-fi
-
-ckpt_dir=$1
-
-. ${MAIN_ROOT}/utils/utility.sh
-
-URL='https://deepspeech.bj.bcebos.com/eng_models/librispeech_v1.8_to_v2.x.tar.gz'
-MD5=a06d9aadb560ea113984dc98d67232c8
-TARGET=${ckpt_dir}/librispeech_v1.8_to_v2.x.tar.gz
-
-
-echo "Download LibriSpeech model ..."
-download $URL $MD5 $TARGET
-if [ $? -ne 0 ]; then
-    echo "Fail to download LibriSpeech model!"
-    exit 1
-fi
-
-
-exit 0
diff --git a/examples/other/1xt2x/librispeech/local/test.sh b/examples/other/1xt2x/librispeech/local/test.sh
deleted file mode 100755
index ea40046b..00000000
--- a/examples/other/1xt2x/librispeech/local/test.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/bin/bash
-
-if [ $# != 4 ];then
-    echo "usage: ${0} config_path decode_config_path ckpt_path_prefix model_type"
-    exit -1
-fi
-
-ngpu=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
-echo "using $ngpu gpus..."
-
-config_path=$1
-decode_config_path=$2
-ckpt_prefix=$3
-model_type=$4
-
-# download language model
-bash local/download_lm_en.sh
-if [ $? -ne 0 ]; then
-   exit 1
-fi
-
-python3 -u ${BIN_DIR}/test.py \
---ngpu ${ngpu} \
---config ${config_path} \
---decode_cfg ${decode_config_path} \
---result_file ${ckpt_prefix}.rsl \
---checkpoint_path ${ckpt_prefix} \
---model_type ${model_type}
-
-if [ $? -ne 0 ]; then
-    echo "Failed in evaluation!"
-    exit 1
-fi
-
-
-exit 0
diff --git a/examples/other/1xt2x/librispeech/path.sh b/examples/other/1xt2x/librispeech/path.sh
deleted file mode 100644
index e3696ddd..00000000
--- a/examples/other/1xt2x/librispeech/path.sh
+++ /dev/null
@@ -1,16 +0,0 @@
-export MAIN_ROOT=`realpath ${PWD}/../../../../`
-export LOCAL_DEEPSPEECH2=`realpath ${PWD}/../`
-
-export PATH=${MAIN_ROOT}:${MAIN_ROOT}/utils:${PATH}
-export LC_ALL=C
-
-export PYTHONDONTWRITEBYTECODE=1
-# Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
-export PYTHONIOENCODING=UTF-8
-export PYTHONPATH=${MAIN_ROOT}:${PYTHONPATH}
-export PYTHONPATH=${LOCAL_DEEPSPEECH2}:${PYTHONPATH}
-
-export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib/
-
-MODEL=deepspeech2
-export BIN_DIR=${LOCAL_DEEPSPEECH2}/src_deepspeech2x/bin
diff --git a/examples/other/1xt2x/librispeech/run.sh b/examples/other/1xt2x/librispeech/run.sh
deleted file mode 100755
index 8b614bbb..00000000
--- a/examples/other/1xt2x/librispeech/run.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/bin/bash
-set -e
-source path.sh
-
-stage=0
-stop_stage=100
-conf_path=conf/deepspeech2.yaml
-decode_conf_path=conf/tuning/decode.yaml
-avg_num=1
-model_type=offline
-gpus=1
-
-source ${MAIN_ROOT}/utils/parse_options.sh || exit 1;
-
-v18_ckpt=librispeech_v1.8
-ckpt=$(basename ${conf_path} | awk -F'.' '{print $1}')
-echo "checkpoint name ${ckpt}"
-
-if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
-    # prepare data
-    mkdir -p exp/${ckpt}/checkpoints
-    bash ./local/data.sh exp/${ckpt}/checkpoints || exit -1
-fi
-
-if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    # test ckpt avg_n
-    CUDA_VISIBLE_DEVICES=${gpus} ./local/test.sh ${conf_path} ${decode_conf_path} exp/${ckpt}/checkpoints/${v18_ckpt} ${model_type}|| exit -1
-fi
diff --git a/examples/other/1xt2x/src_deepspeech2x/__init__.py b/examples/other/1xt2x/src_deepspeech2x/__init__.py
deleted file mode 100644
index 74be4a25..00000000
--- a/examples/other/1xt2x/src_deepspeech2x/__init__.py
+++ /dev/null
@@ -1,370 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from typing import Any
-from typing import List
-from typing import Tuple
-from typing import Union
-
-import paddle
-from paddle import nn
-from paddle.fluid import core
-from paddle.nn import functional as F
-
-from paddlespeech.s2t.utils.log import Log
-
-#TODO(Hui Zhang): remove  fluid import
-logger = Log(__name__).getlog()
-
-########### hack logging #############
-logger.warn = logger.warning
-
-########### hack paddle #############
-paddle.half = 'float16'
-paddle.float = 'float32'
-paddle.double = 'float64'
-paddle.short = 'int16'
-paddle.int = 'int32'
-paddle.long = 'int64'
-paddle.uint16 = 'uint16'
-paddle.cdouble = 'complex128'
-
-
-def convert_dtype_to_string(tensor_dtype):
-    """
-    Convert the data type in numpy to the data type in Paddle
-    Args:
-        tensor_dtype(core.VarDesc.VarType): the data type in numpy.
-    Returns:
-        core.VarDesc.VarType: the data type in Paddle.
-    """
-    dtype = tensor_dtype
-    if dtype == core.VarDesc.VarType.FP32:
-        return paddle.float32
-    elif dtype == core.VarDesc.VarType.FP64:
-        return paddle.float64
-    elif dtype == core.VarDesc.VarType.FP16:
-        return paddle.float16
-    elif dtype == core.VarDesc.VarType.INT32:
-        return paddle.int32
-    elif dtype == core.VarDesc.VarType.INT16:
-        return paddle.int16
-    elif dtype == core.VarDesc.VarType.INT64:
-        return paddle.int64
-    elif dtype == core.VarDesc.VarType.BOOL:
-        return paddle.bool
-    elif dtype == core.VarDesc.VarType.BF16:
-        # since there is still no support for bfloat16 in NumPy,
-        # uint16 is used for casting bfloat16
-        return paddle.uint16
-    elif dtype == core.VarDesc.VarType.UINT8:
-        return paddle.uint8
-    elif dtype == core.VarDesc.VarType.INT8:
-        return paddle.int8
-    elif dtype == core.VarDesc.VarType.COMPLEX64:
-        return paddle.complex64
-    elif dtype == core.VarDesc.VarType.COMPLEX128:
-        return paddle.complex128
-    else:
-        raise ValueError("Not supported tensor dtype %s" % dtype)
-
-
-if not hasattr(paddle, 'softmax'):
-    logger.warn("register user softmax to paddle, remove this when fixed!")
-    setattr(paddle, 'softmax', paddle.nn.functional.softmax)
-
-if not hasattr(paddle, 'log_softmax'):
-    logger.warn("register user log_softmax to paddle, remove this when fixed!")
-    setattr(paddle, 'log_softmax', paddle.nn.functional.log_softmax)
-
-if not hasattr(paddle, 'sigmoid'):
-    logger.warn("register user sigmoid to paddle, remove this when fixed!")
-    setattr(paddle, 'sigmoid', paddle.nn.functional.sigmoid)
-
-if not hasattr(paddle, 'log_sigmoid'):
-    logger.warn("register user log_sigmoid to paddle, remove this when fixed!")
-    setattr(paddle, 'log_sigmoid', paddle.nn.functional.log_sigmoid)
-
-if not hasattr(paddle, 'relu'):
-    logger.warn("register user relu to paddle, remove this when fixed!")
-    setattr(paddle, 'relu', paddle.nn.functional.relu)
-
-
-def cat(xs, dim=0):
-    return paddle.concat(xs, axis=dim)
-
-
-if not hasattr(paddle, 'cat'):
-    logger.warn(
-        "override cat of paddle if exists or register, remove this when fixed!")
-    paddle.cat = cat
-
-
-########### hack paddle.Tensor #############
-def item(x: paddle.Tensor):
-    return x.numpy().item()
-
-
-if not hasattr(paddle.Tensor, 'item'):
-    logger.warn(
-        "override item of paddle.Tensor if exists or register, remove this when fixed!"
-    )
-    paddle.Tensor.item = item
-
-
-def func_long(x: paddle.Tensor):
-    return paddle.cast(x, paddle.long)
-
-
-if not hasattr(paddle.Tensor, 'long'):
-    logger.warn(
-        "override long of paddle.Tensor if exists or register, remove this when fixed!"
-    )
-    paddle.Tensor.long = func_long
-
-if not hasattr(paddle.Tensor, 'numel'):
-    logger.warn(
-        "override numel of paddle.Tensor if exists or register, remove this when fixed!"
-    )
-    paddle.Tensor.numel = paddle.numel
-
-
-def new_full(x: paddle.Tensor,
-             size: Union[List[int], Tuple[int], paddle.Tensor],
-             fill_value: Union[float, int, bool, paddle.Tensor],
-             dtype=None):
-    return paddle.full(size, fill_value, dtype=x.dtype)
-
-
-if not hasattr(paddle.Tensor, 'new_full'):
-    logger.warn(
-        "override new_full of paddle.Tensor if exists or register, remove this when fixed!"
-    )
-    paddle.Tensor.new_full = new_full
-
-
-def eq(xs: paddle.Tensor, ys: Union[paddle.Tensor, float]) -> paddle.Tensor:
-    if convert_dtype_to_string(xs.dtype) == paddle.bool:
-        xs = xs.astype(paddle.int)
-    return xs.equal(
-        paddle.to_tensor(
-            ys, dtype=convert_dtype_to_string(xs.dtype), place=xs.place))
-
-
-if not hasattr(paddle.Tensor, 'eq'):
-    logger.warn(
-        "override eq of paddle.Tensor if exists or register, remove this when fixed!"
-    )
-    paddle.Tensor.eq = eq
-
-if not hasattr(paddle, 'eq'):
-    logger.warn(
-        "override eq of paddle if exists or register, remove this when fixed!")
-    paddle.eq = eq
-
-
-def contiguous(xs: paddle.Tensor) -> paddle.Tensor:
-    return xs
-
-
-if not hasattr(paddle.Tensor, 'contiguous'):
-    logger.warn(
-        "override contiguous of paddle.Tensor if exists or register, remove this when fixed!"
-    )
-    paddle.Tensor.contiguous = contiguous
-
-
-def size(xs: paddle.Tensor, *args: int) -> paddle.Tensor:
-    nargs = len(args)
-    assert (nargs <= 1)
-    s = paddle.shape(xs)
-    if nargs == 1:
-        return s[args[0]]
-    else:
-        return s
-
-
-#`to_static` do not process `size` property, maybe some `paddle` api dependent on it.
-logger.warn(
-    "override size of paddle.Tensor "
-    "(`to_static` do not process `size` property, maybe some `paddle` api dependent on it), remove this when fixed!"
-)
-paddle.Tensor.size = size
-
-
-def view(xs: paddle.Tensor, *args: int) -> paddle.Tensor:
-    return xs.reshape(args)
-
-
-if not hasattr(paddle.Tensor, 'view'):
-    logger.warn("register user view to paddle.Tensor, remove this when fixed!")
-    paddle.Tensor.view = view
-
-
-def view_as(xs: paddle.Tensor, ys: paddle.Tensor) -> paddle.Tensor:
-    return xs.reshape(ys.size())
-
-
-if not hasattr(paddle.Tensor, 'view_as'):
-    logger.warn(
-        "register user view_as to paddle.Tensor, remove this when fixed!")
-    paddle.Tensor.view_as = view_as
-
-
-def is_broadcastable(shp1, shp2):
-    for a, b in zip(shp1[::-1], shp2[::-1]):
-        if a == 1 or b == 1 or a == b:
-            pass
-        else:
-            return False
-    return True
-
-
-def masked_fill(xs: paddle.Tensor,
-                mask: paddle.Tensor,
-                value: Union[float, int]):
-    assert is_broadcastable(xs.shape, mask.shape) is True
-    bshape = paddle.broadcast_shape(xs.shape, mask.shape)
-    mask = mask.broadcast_to(bshape)
-    trues = paddle.ones_like(xs) * value
-    xs = paddle.where(mask, trues, xs)
-    return xs
-
-
-if not hasattr(paddle.Tensor, 'masked_fill'):
-    logger.warn(
-        "register user masked_fill to paddle.Tensor, remove this when fixed!")
-    paddle.Tensor.masked_fill = masked_fill
-
-
-def masked_fill_(xs: paddle.Tensor,
-                 mask: paddle.Tensor,
-                 value: Union[float, int]) -> paddle.Tensor:
-    assert is_broadcastable(xs.shape, mask.shape) is True
-    bshape = paddle.broadcast_shape(xs.shape, mask.shape)
-    mask = mask.broadcast_to(bshape)
-    trues = paddle.ones_like(xs) * value
-    ret = paddle.where(mask, trues, xs)
-    paddle.assign(ret.detach(), output=xs)
-    return xs
-
-
-if not hasattr(paddle.Tensor, 'masked_fill_'):
-    logger.warn(
-        "register user masked_fill_ to paddle.Tensor, remove this when fixed!")
-    paddle.Tensor.masked_fill_ = masked_fill_
-
-
-def fill_(xs: paddle.Tensor, value: Union[float, int]) -> paddle.Tensor:
-    val = paddle.full_like(xs, value)
-    paddle.assign(val.detach(), output=xs)
-    return xs
-
-
-if not hasattr(paddle.Tensor, 'fill_'):
-    logger.warn("register user fill_ to paddle.Tensor, remove this when fixed!")
-    paddle.Tensor.fill_ = fill_
-
-
-def repeat(xs: paddle.Tensor, *size: Any) -> paddle.Tensor:
-    return paddle.tile(xs, size)
-
-
-if not hasattr(paddle.Tensor, 'repeat'):
-    logger.warn(
-        "register user repeat to paddle.Tensor, remove this when fixed!")
-    paddle.Tensor.repeat = repeat
-
-if not hasattr(paddle.Tensor, 'softmax'):
-    logger.warn(
-        "register user softmax to paddle.Tensor, remove this when fixed!")
-    setattr(paddle.Tensor, 'softmax', paddle.nn.functional.softmax)
-
-if not hasattr(paddle.Tensor, 'sigmoid'):
-    logger.warn(
-        "register user sigmoid to paddle.Tensor, remove this when fixed!")
-    setattr(paddle.Tensor, 'sigmoid', paddle.nn.functional.sigmoid)
-
-if not hasattr(paddle.Tensor, 'relu'):
-    logger.warn("register user relu to paddle.Tensor, remove this when fixed!")
-    setattr(paddle.Tensor, 'relu', paddle.nn.functional.relu)
-
-
-def type_as(x: paddle.Tensor, other: paddle.Tensor) -> paddle.Tensor:
-    return x.astype(other.dtype)
-
-
-if not hasattr(paddle.Tensor, 'type_as'):
-    logger.warn(
-        "register user type_as to paddle.Tensor, remove this when fixed!")
-    setattr(paddle.Tensor, 'type_as', type_as)
-
-
-def to(x: paddle.Tensor, *args, **kwargs) -> paddle.Tensor:
-    assert len(args) == 1
-    if isinstance(args[0], str):  # dtype
-        return x.astype(args[0])
-    elif isinstance(args[0], paddle.Tensor):  #Tensor
-        return x.astype(args[0].dtype)
-    else:  # Device
-        return x
-
-
-if not hasattr(paddle.Tensor, 'to'):
-    logger.warn("register user to to paddle.Tensor, remove this when fixed!")
-    setattr(paddle.Tensor, 'to', to)
-
-
-def func_float(x: paddle.Tensor) -> paddle.Tensor:
-    return x.astype(paddle.float)
-
-
-if not hasattr(paddle.Tensor, 'float'):
-    logger.warn("register user float to paddle.Tensor, remove this when fixed!")
-    setattr(paddle.Tensor, 'float', func_float)
-
-
-def func_int(x: paddle.Tensor) -> paddle.Tensor:
-    return x.astype(paddle.int)
-
-
-if not hasattr(paddle.Tensor, 'int'):
-    logger.warn("register user int to paddle.Tensor, remove this when fixed!")
-    setattr(paddle.Tensor, 'int', func_int)
-
-
-def tolist(x: paddle.Tensor) -> List[Any]:
-    return x.numpy().tolist()
-
-
-if not hasattr(paddle.Tensor, 'tolist'):
-    logger.warn(
-        "register user tolist to paddle.Tensor, remove this when fixed!")
-    setattr(paddle.Tensor, 'tolist', tolist)
-
-
-########### hack paddle.nn #############
-class GLU(nn.Layer):
-    """Gated Linear Units (GLU) Layer"""
-
-    def __init__(self, dim: int=-1):
-        super().__init__()
-        self.dim = dim
-
-    def forward(self, xs):
-        return F.glu(xs, axis=self.dim)
-
-
-if not hasattr(paddle.nn, 'GLU'):
-    logger.warn("register user GLU to paddle.nn, remove this when fixed!")
-    setattr(paddle.nn, 'GLU', GLU)
diff --git a/examples/other/1xt2x/src_deepspeech2x/bin/test.py b/examples/other/1xt2x/src_deepspeech2x/bin/test.py
deleted file mode 100644
index 88a13fdc..00000000
--- a/examples/other/1xt2x/src_deepspeech2x/bin/test.py
+++ /dev/null
@@ -1,59 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Evaluation for DeepSpeech2 model."""
-from src_deepspeech2x.test_model import DeepSpeech2Tester as Tester
-from yacs.config import CfgNode
-
-from paddlespeech.s2t.training.cli import default_argument_parser
-from paddlespeech.s2t.utils.utility import print_arguments
-
-
-def main_sp(config, args):
-    exp = Tester(config, args)
-    exp.setup()
-    exp.run_test()
-
-
-def main(config, args):
-    main_sp(config, args)
-
-
-if __name__ == "__main__":
-    parser = default_argument_parser()
-    parser.add_argument(
-        "--model_type", type=str, default='offline', help='offline/online')
-    # save asr result to
-    parser.add_argument(
-        "--result_file", type=str, help="path of save the asr result")
-    args = parser.parse_args()
-    print_arguments(args, globals())
-    print("model_type:{}".format(args.model_type))
-
-    # https://yaml.org/type/float.html
-    config = CfgNode(new_allowed=True)
-    if args.config:
-        config.merge_from_file(args.config)
-    if args.decode_cfg:
-        decode_confs = CfgNode(new_allowed=True)
-        decode_confs.merge_from_file(args.decode_cfg)
-        config.decode = decode_confs
-    if args.opts:
-        config.merge_from_list(args.opts)
-    config.freeze()
-    print(config)
-    if args.dump_config:
-        with open(args.dump_config, 'w') as f:
-            print(config, file=f)
-
-    main(config, args)
diff --git a/examples/other/1xt2x/src_deepspeech2x/models/__init__.py b/examples/other/1xt2x/src_deepspeech2x/models/__init__.py
deleted file mode 100644
index 185a92b8..00000000
--- a/examples/other/1xt2x/src_deepspeech2x/models/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
diff --git a/examples/other/1xt2x/src_deepspeech2x/models/ds2/__init__.py b/examples/other/1xt2x/src_deepspeech2x/models/ds2/__init__.py
deleted file mode 100644
index 39bea5bf..00000000
--- a/examples/other/1xt2x/src_deepspeech2x/models/ds2/__init__.py
+++ /dev/null
@@ -1,17 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from .deepspeech2 import DeepSpeech2InferModel
-from .deepspeech2 import DeepSpeech2Model
-
-__all__ = ['DeepSpeech2Model', 'DeepSpeech2InferModel']
diff --git a/examples/other/1xt2x/src_deepspeech2x/models/ds2/deepspeech2.py b/examples/other/1xt2x/src_deepspeech2x/models/ds2/deepspeech2.py
deleted file mode 100644
index f6e185ff..00000000
--- a/examples/other/1xt2x/src_deepspeech2x/models/ds2/deepspeech2.py
+++ /dev/null
@@ -1,275 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Deepspeech2 ASR Model"""
-import paddle
-from paddle import nn
-from src_deepspeech2x.models.ds2.rnn import RNNStack
-
-from paddlespeech.s2t.models.ds2.conv import ConvStack
-from paddlespeech.s2t.modules.ctc import CTCDecoder
-from paddlespeech.s2t.utils import layer_tools
-from paddlespeech.s2t.utils.checkpoint import Checkpoint
-from paddlespeech.s2t.utils.log import Log
-logger = Log(__name__).getlog()
-
-__all__ = ['DeepSpeech2Model', 'DeepSpeech2InferModel']
-
-
-class CRNNEncoder(nn.Layer):
-    def __init__(self,
-                 feat_size,
-                 dict_size,
-                 num_conv_layers=2,
-                 num_rnn_layers=3,
-                 rnn_size=1024,
-                 use_gru=False,
-                 share_rnn_weights=True):
-        super().__init__()
-        self.rnn_size = rnn_size
-        self.feat_size = feat_size  # 161 for linear
-        self.dict_size = dict_size
-
-        self.conv = ConvStack(feat_size, num_conv_layers)
-
-        i_size = self.conv.output_height  # H after conv stack
-        self.rnn = RNNStack(
-            i_size=i_size,
-            h_size=rnn_size,
-            num_stacks=num_rnn_layers,
-            use_gru=use_gru,
-            share_rnn_weights=share_rnn_weights)
-
-    @property
-    def output_size(self):
-        return self.rnn_size * 2
-
-    def forward(self, audio, audio_len):
-        """Compute Encoder outputs
-
-        Args:
-            audio (Tensor): [B, Tmax, D]
-            text (Tensor): [B, Umax]
-            audio_len (Tensor): [B]
-            text_len (Tensor): [B]
-        Returns:
-            x (Tensor): encoder outputs, [B, T, D]
-            x_lens (Tensor): encoder length, [B]
-        """
-        # [B, T, D]  -> [B, D, T]
-        audio = audio.transpose([0, 2, 1])
-        # [B, D, T] -> [B, C=1, D, T]
-        x = audio.unsqueeze(1)
-        x_lens = audio_len
-
-        # convolution group
-        x, x_lens = self.conv(x, x_lens)
-        x_val = x.numpy()
-
-        # convert data from convolution feature map to sequence of vectors
-        #B, C, D, T = paddle.shape(x)  # not work under jit
-        x = x.transpose([0, 3, 1, 2])  #[B, T, C, D]
-        #x = x.reshape([B, T, C * D])  #[B, T, C*D]  # not work under jit
-        x = x.reshape([0, 0, -1])  #[B, T, C*D]
-
-        # remove padding part
-        x, x_lens = self.rnn(x, x_lens)  #[B, T, D]
-        return x, x_lens
-
-
-class DeepSpeech2Model(nn.Layer):
-    """The DeepSpeech2 network structure.
-
-    :param audio_data: Audio spectrogram data layer.
-    :type audio_data: Variable
-    :param text_data: Transcription text data layer.
-    :type text_data: Variable
-    :param audio_len: Valid sequence length data layer.
-    :type audio_len: Variable
-    :param masks: Masks data layer to reset padding.
-    :type masks: Variable
-    :param dict_size: Dictionary size for tokenized transcription.
-    :type dict_size: int
-    :param num_conv_layers: Number of stacking convolution layers.
-    :type num_conv_layers: int
-    :param num_rnn_layers: Number of stacking RNN layers.
-    :type num_rnn_layers: int
-    :param rnn_size: RNN layer size (dimension of RNN cells).
-    :type rnn_size: int
-    :param use_gru: Use gru if set True. Use simple rnn if set False.
-    :type use_gru: bool
-    :param share_rnn_weights: Whether to share input-hidden weights between
-                              forward and backward direction RNNs.
-                              It is only available when use_gru=False.
-    :type share_weights: bool
-    :return: A tuple of an output unnormalized log probability layer (
-             before softmax) and a ctc cost layer.
-    :rtype: tuple of LayerOutput
-    """
-
-    def __init__(self,
-                 feat_size,
-                 dict_size,
-                 num_conv_layers=2,
-                 num_rnn_layers=3,
-                 rnn_size=1024,
-                 use_gru=False,
-                 share_rnn_weights=True,
-                 blank_id=0):
-        super().__init__()
-        self.encoder = CRNNEncoder(
-            feat_size=feat_size,
-            dict_size=dict_size,
-            num_conv_layers=num_conv_layers,
-            num_rnn_layers=num_rnn_layers,
-            rnn_size=rnn_size,
-            use_gru=use_gru,
-            share_rnn_weights=share_rnn_weights)
-        assert (self.encoder.output_size == rnn_size * 2)
-
-        self.decoder = CTCDecoder(
-            odim=dict_size,  # <blank> is in  vocab
-            enc_n_units=self.encoder.output_size,
-            blank_id=blank_id,  # first token is <blank>
-            dropout_rate=0.0,
-            reduction=True,  # sum
-            batch_average=True)  # sum / batch_size
-
-    def forward(self, audio, audio_len, text, text_len):
-        """Compute Model loss
-
-        Args:
-            audio (Tensor): [B, T, D]
-            audio_len (Tensor): [B]
-            text (Tensor): [B, U]
-            text_len (Tensor): [B]
-
-        Returns:
-            loss (Tensor): [1]
-        """
-        eouts, eouts_len = self.encoder(audio, audio_len)
-        loss = self.decoder(eouts, eouts_len, text, text_len)
-        return loss
-
-    @paddle.no_grad()
-    def decode(self, audio, audio_len):
-        # decoders only accept string encoded in utf-8
-
-        # Make sure the decoder has been initialized
-        eouts, eouts_len = self.encoder(audio, audio_len)
-        probs = self.decoder.softmax(eouts)
-        batch_size = probs.shape[0]
-        self.decoder.reset_decoder(batch_size=batch_size)
-        self.decoder.next(probs, eouts_len)
-        trans_best, trans_beam = self.decoder.decode()
-        return trans_best
-
-    @classmethod
-    def from_pretrained(cls, dataloader, config, checkpoint_path):
-        """Build a DeepSpeech2Model model from a pretrained model.
-        Parameters
-        ----------
-        dataloader: paddle.io.DataLoader
-
-        config: yacs.config.CfgNode
-            model configs
-
-        checkpoint_path: Path or str
-            the path of pretrained model checkpoint, without extension name
-
-        Returns
-        -------
-        DeepSpeech2Model
-            The model built from pretrained result.
-        """
-        model = cls(feat_size=dataloader.collate_fn.feature_size,
-                    dict_size=len(dataloader.collate_fn.vocab_list),
-                    num_conv_layers=config.num_conv_layers,
-                    num_rnn_layers=config.num_rnn_layers,
-                    rnn_size=config.rnn_layer_size,
-                    use_gru=config.use_gru,
-                    share_rnn_weights=config.share_rnn_weights)
-        infos = Checkpoint().load_parameters(
-            model, checkpoint_path=checkpoint_path)
-        logger.info(f"checkpoint info: {infos}")
-        layer_tools.summary(model)
-        return model
-
-    @classmethod
-    def from_config(cls, config):
-        """Build a DeepSpeec2Model from config
-        Parameters
-
-        config: yacs.config.CfgNode
-            config
-        Returns
-        -------
-        DeepSpeech2Model
-            The model built from config.
-        """
-        model = cls(feat_size=config.feat_size,
-                    dict_size=config.dict_size,
-                    num_conv_layers=config.num_conv_layers,
-                    num_rnn_layers=config.num_rnn_layers,
-                    rnn_size=config.rnn_layer_size,
-                    use_gru=config.use_gru,
-                    share_rnn_weights=config.share_rnn_weights,
-                    blank_id=config.blank_id)
-        return model
-
-
-class DeepSpeech2InferModel(DeepSpeech2Model):
-    def __init__(self,
-                 feat_size,
-                 dict_size,
-                 num_conv_layers=2,
-                 num_rnn_layers=3,
-                 rnn_size=1024,
-                 use_gru=False,
-                 share_rnn_weights=True,
-                 blank_id=0):
-        super().__init__(
-            feat_size=feat_size,
-            dict_size=dict_size,
-            num_conv_layers=num_conv_layers,
-            num_rnn_layers=num_rnn_layers,
-            rnn_size=rnn_size,
-            use_gru=use_gru,
-            share_rnn_weights=share_rnn_weights,
-            blank_id=blank_id)
-
-    def forward(self, audio, audio_len):
-        """export model function
-
-        Args:
-            audio (Tensor): [B, T, D]
-            audio_len (Tensor): [B]
-
-        Returns:
-            probs: probs after softmax
-        """
-        eouts, eouts_len = self.encoder(audio, audio_len)
-        probs = self.decoder.softmax(eouts)
-        return probs, eouts_len
-
-    def export(self):
-        static_model = paddle.jit.to_static(
-            self,
-            input_spec=[
-                paddle.static.InputSpec(
-                    shape=[None, None, self.encoder.feat_size],
-                    dtype='float32'),  # audio, [B,T,D]
-                paddle.static.InputSpec(shape=[None],
-                                        dtype='int64'),  # audio_length, [B]
-            ])
-        return static_model
diff --git a/examples/other/1xt2x/src_deepspeech2x/models/ds2/rnn.py b/examples/other/1xt2x/src_deepspeech2x/models/ds2/rnn.py
deleted file mode 100644
index 383a0746..00000000
--- a/examples/other/1xt2x/src_deepspeech2x/models/ds2/rnn.py
+++ /dev/null
@@ -1,334 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-import math
-
-import paddle
-from paddle import nn
-from paddle.nn import functional as F
-from paddle.nn import initializer as I
-
-from paddlespeech.s2t.modules.activation import brelu
-from paddlespeech.s2t.modules.mask import make_non_pad_mask
-from paddlespeech.s2t.utils.log import Log
-logger = Log(__name__).getlog()
-
-__all__ = ['RNNStack']
-
-
-class RNNCell(nn.RNNCellBase):
-    r"""
-    Elman RNN (SimpleRNN) cell. Given the inputs and previous states, it
-    computes the outputs and updates states.
-    The formula used is as follows:
-    .. math::
-        h_{t} & = act(x_{t} + b_{ih} + W_{hh}h_{t-1} + b_{hh})
-        y_{t} & = h_{t}
-
-    where :math:`act` is for :attr:`activation`.
-    """
-
-    def __init__(self,
-                 hidden_size: int,
-                 activation="tanh",
-                 weight_ih_attr=None,
-                 weight_hh_attr=None,
-                 bias_ih_attr=None,
-                 bias_hh_attr=None,
-                 name=None):
-        super().__init__()
-        std = 1.0 / math.sqrt(hidden_size)
-        self.weight_hh = self.create_parameter(
-            (hidden_size, hidden_size),
-            weight_hh_attr,
-            default_initializer=I.Uniform(-std, std))
-        self.bias_ih = None
-        self.bias_hh = self.create_parameter(
-            (hidden_size, ),
-            bias_hh_attr,
-            is_bias=True,
-            default_initializer=I.Uniform(-std, std))
-
-        self.hidden_size = hidden_size
-        if activation not in ["tanh", "relu", "brelu"]:
-            raise ValueError(
-                "activation for SimpleRNNCell should be tanh or relu, "
-                "but get {}".format(activation))
-        self.activation = activation
-        self._activation_fn = paddle.tanh \
-            if activation == "tanh" \
-            else F.relu
-        if activation == 'brelu':
-            self._activation_fn = brelu
-
-    def forward(self, inputs, states=None):
-        if states is None:
-            states = self.get_initial_states(inputs, self.state_shape)
-        pre_h = states
-        i2h = inputs
-        if self.bias_ih is not None:
-            i2h += self.bias_ih
-        h2h = paddle.matmul(pre_h, self.weight_hh, transpose_y=True)
-        if self.bias_hh is not None:
-            h2h += self.bias_hh
-        h = self._activation_fn(i2h + h2h)
-        return h, h
-
-    @property
-    def state_shape(self):
-        return (self.hidden_size, )
-
-
-class GRUCell(nn.RNNCellBase):
-    r"""
-    Gated Recurrent Unit (GRU) RNN cell. Given the inputs and previous states,
-    it computes the outputs and updates states.
-    The formula for GRU used is as follows:
-    ..  math::
-        r_{t} & = \sigma(W_{ir}x_{t} + b_{ir} + W_{hr}h_{t-1} + b_{hr})
-        z_{t} & = \sigma(W_{iz}x_{t} + b_{iz} + W_{hz}h_{t-1} + b_{hz})
-        \widetilde{h}_{t} & = \tanh(W_{ic}x_{t} + b_{ic} + r_{t} * (W_{hc}h_{t-1} + b_{hc}))
-        h_{t} & = z_{t} * h_{t-1} + (1 - z_{t}) * \widetilde{h}_{t}
-        y_{t} & = h_{t}
-
-    where :math:`\sigma` is the sigmoid fucntion, and * is the elemetwise
-    multiplication operator.
-    """
-
-    def __init__(self,
-                 input_size: int,
-                 hidden_size: int,
-                 weight_ih_attr=None,
-                 weight_hh_attr=None,
-                 bias_ih_attr=None,
-                 bias_hh_attr=None,
-                 name=None):
-        super().__init__()
-        std = 1.0 / math.sqrt(hidden_size)
-        self.weight_hh = self.create_parameter(
-            (3 * hidden_size, hidden_size),
-            weight_hh_attr,
-            default_initializer=I.Uniform(-std, std))
-        self.bias_ih = None
-        self.bias_hh = self.create_parameter(
-            (3 * hidden_size, ),
-            bias_hh_attr,
-            is_bias=True,
-            default_initializer=I.Uniform(-std, std))
-
-        self.hidden_size = hidden_size
-        self.input_size = input_size
-        self._gate_activation = F.sigmoid
-        self._activation = paddle.relu
-
-    def forward(self, inputs, states=None):
-        if states is None:
-            states = self.get_initial_states(inputs, self.state_shape)
-
-        pre_hidden = states  # shape [batch_size, hidden_size]
-
-        x_gates = inputs
-        if self.bias_ih is not None:
-            x_gates = x_gates + self.bias_ih
-        bias_u, bias_r, bias_c = paddle.split(
-            self.bias_hh, num_or_sections=3, axis=0)
-
-        weight_hh = paddle.transpose(
-            self.weight_hh,
-            perm=[1, 0])  #weight_hh:shape[hidden_size, 3 * hidden_size]
-        w_u_r_c = paddle.flatten(weight_hh)
-        size_u_r = self.hidden_size * 2 * self.hidden_size
-        w_u_r = paddle.reshape(w_u_r_c[:size_u_r],
-                               (self.hidden_size, self.hidden_size * 2))
-        w_u, w_r = paddle.split(w_u_r, num_or_sections=2, axis=1)
-        w_c = paddle.reshape(w_u_r_c[size_u_r:],
-                             (self.hidden_size, self.hidden_size))
-
-        h_u = paddle.matmul(
-            pre_hidden, w_u,
-            transpose_y=False) + bias_u  #shape [batch_size, hidden_size]
-        h_r = paddle.matmul(
-            pre_hidden, w_r,
-            transpose_y=False) + bias_r  #shape [batch_size, hidden_size]
-
-        x_u, x_r, x_c = paddle.split(
-            x_gates, num_or_sections=3, axis=1)  #shape[batch_size, hidden_size]
-
-        u = self._gate_activation(x_u + h_u)  #shape [batch_size, hidden_size]
-        r = self._gate_activation(x_r + h_r)  #shape [batch_size, hidden_size]
-        c = self._activation(
-            x_c + paddle.matmul(r * pre_hidden, w_c, transpose_y=False) +
-            bias_c)  # [batch_size, hidden_size]
-
-        h = (1 - u) * pre_hidden + u * c
-        # https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/fluid/layers/dynamic_gru_cn.html#dynamic-gru
-        return h, h
-
-    @property
-    def state_shape(self):
-        r"""
-        The `state_shape` of GRUCell is a shape `[hidden_size]` (-1 for batch
-        size would be automatically inserted into shape). The shape corresponds
-        to the shape of :math:`h_{t-1}`.
-        """
-        return (self.hidden_size, )
-
-
-class BiRNNWithBN(nn.Layer):
-    """Bidirectonal simple rnn layer with sequence-wise batch normalization.
-    The batch normalization is only performed on input-state weights.
-
-    :param size: Dimension of RNN cells.
-    :type size: int
-    :param share_weights: Whether to share input-hidden weights between
-                          forward and backward directional RNNs.
-    :type share_weights: bool
-    :return: Bidirectional simple rnn layer.
-    :rtype: Variable
-    """
-
-    def __init__(self, i_size: int, h_size: int, share_weights: bool):
-        super().__init__()
-        self.share_weights = share_weights
-        if self.share_weights:
-            #input-hidden weights shared between bi-directional rnn.
-            self.fw_fc = nn.Linear(i_size, h_size, bias_attr=False)
-            # batch norm is only performed on input-state projection
-            self.fw_bn = nn.BatchNorm1D(
-                h_size, bias_attr=None, data_format='NLC')
-            self.bw_fc = self.fw_fc
-            self.bw_bn = self.fw_bn
-        else:
-            self.fw_fc = nn.Linear(i_size, h_size, bias_attr=False)
-            self.fw_bn = nn.BatchNorm1D(
-                h_size, bias_attr=None, data_format='NLC')
-            self.bw_fc = nn.Linear(i_size, h_size, bias_attr=False)
-            self.bw_bn = nn.BatchNorm1D(
-                h_size, bias_attr=None, data_format='NLC')
-
-        self.fw_cell = RNNCell(hidden_size=h_size, activation='brelu')
-        self.bw_cell = RNNCell(hidden_size=h_size, activation='brelu')
-        self.fw_rnn = nn.RNN(
-            self.fw_cell, is_reverse=False, time_major=False)  #[B, T, D]
-        self.bw_rnn = nn.RNN(
-            self.bw_cell, is_reverse=True, time_major=False)  #[B, T, D]
-
-    def forward(self, x: paddle.Tensor, x_len: paddle.Tensor):
-        # x, shape [B, T, D]
-        fw_x = self.fw_bn(self.fw_fc(x))
-        bw_x = self.bw_bn(self.bw_fc(x))
-        fw_x, _ = self.fw_rnn(inputs=fw_x, sequence_length=x_len)
-        bw_x, _ = self.bw_rnn(inputs=bw_x, sequence_length=x_len)
-        x = paddle.concat([fw_x, bw_x], axis=-1)
-        return x, x_len
-
-
-class BiGRUWithBN(nn.Layer):
-    """Bidirectonal gru layer with sequence-wise batch normalization.
-    The batch normalization is only performed on input-state weights.
-
-    :param name: Name of the layer.
-    :type name: string
-    :param input: Input layer.
-    :type input: Variable
-    :param size: Dimension of GRU cells.
-    :type size: int
-    :param act: Activation type.
-    :type act: string
-    :return: Bidirectional GRU layer.
-    :rtype: Variable
-    """
-
-    def __init__(self, i_size: int, h_size: int):
-        super().__init__()
-        hidden_size = h_size * 3
-
-        self.fw_fc = nn.Linear(i_size, hidden_size, bias_attr=False)
-        self.fw_bn = nn.BatchNorm1D(
-            hidden_size, bias_attr=None, data_format='NLC')
-        self.bw_fc = nn.Linear(i_size, hidden_size, bias_attr=False)
-        self.bw_bn = nn.BatchNorm1D(
-            hidden_size, bias_attr=None, data_format='NLC')
-
-        self.fw_cell = GRUCell(input_size=hidden_size, hidden_size=h_size)
-        self.bw_cell = GRUCell(input_size=hidden_size, hidden_size=h_size)
-        self.fw_rnn = nn.RNN(
-            self.fw_cell, is_reverse=False, time_major=False)  #[B, T, D]
-        self.bw_rnn = nn.RNN(
-            self.bw_cell, is_reverse=True, time_major=False)  #[B, T, D]
-
-    def forward(self, x, x_len):
-        # x, shape [B, T, D]
-        fw_x = self.fw_bn(self.fw_fc(x))
-
-        bw_x = self.bw_bn(self.bw_fc(x))
-        fw_x, _ = self.fw_rnn(inputs=fw_x, sequence_length=x_len)
-        bw_x, _ = self.bw_rnn(inputs=bw_x, sequence_length=x_len)
-        x = paddle.concat([fw_x, bw_x], axis=-1)
-        return x, x_len
-
-
-class RNNStack(nn.Layer):
-    """RNN group with stacked bidirectional simple RNN or GRU layers.
-
-    :param input: Input layer.
-    :type input: Variable
-    :param size: Dimension of RNN cells in each layer.
-    :type size: int
-    :param num_stacks: Number of stacked rnn layers.
-    :type num_stacks: int
-    :param use_gru: Use gru if set True. Use simple rnn if set False.
-    :type use_gru: bool
-    :param share_rnn_weights: Whether to share input-hidden weights between
-                              forward and backward directional RNNs.
-                              It is only available when use_gru=False.
-    :type share_weights: bool
-    :return: Output layer of the RNN group.
-    :rtype: Variable
-    """
-
-    def __init__(self,
-                 i_size: int,
-                 h_size: int,
-                 num_stacks: int,
-                 use_gru: bool,
-                 share_rnn_weights: bool):
-        super().__init__()
-        rnn_stacks = []
-        for i in range(num_stacks):
-            if use_gru:
-                #default:GRU using tanh
-                rnn_stacks.append(BiGRUWithBN(i_size=i_size, h_size=h_size))
-            else:
-                rnn_stacks.append(
-                    BiRNNWithBN(
-                        i_size=i_size,
-                        h_size=h_size,
-                        share_weights=share_rnn_weights))
-            i_size = h_size * 2
-
-        self.rnn_stacks = nn.LayerList(rnn_stacks)
-
-    def forward(self, x: paddle.Tensor, x_len: paddle.Tensor):
-        """
-        x: shape [B, T, D]
-        x_len: shpae [B]
-        """
-        for i, rnn in enumerate(self.rnn_stacks):
-            x, x_len = rnn(x, x_len)
-            masks = make_non_pad_mask(x_len)  #[B, T]
-            masks = masks.unsqueeze(-1)  # [B, T, 1]
-            # TODO(Hui Zhang): not support bool multiply
-            masks = masks.astype(x.dtype)
-            x = x.multiply(masks)
-        return x, x_len
diff --git a/examples/other/1xt2x/src_deepspeech2x/test_model.py b/examples/other/1xt2x/src_deepspeech2x/test_model.py
deleted file mode 100644
index 11b85442..00000000
--- a/examples/other/1xt2x/src_deepspeech2x/test_model.py
+++ /dev/null
@@ -1,357 +0,0 @@
-# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Contains DeepSpeech2 and DeepSpeech2Online model."""
-import time
-from collections import defaultdict
-from contextlib import nullcontext
-
-import numpy as np
-import paddle
-from paddle import distributed as dist
-from paddle.io import DataLoader
-from src_deepspeech2x.models.ds2 import DeepSpeech2InferModel
-from src_deepspeech2x.models.ds2 import DeepSpeech2Model
-
-from paddlespeech.s2t.frontend.featurizer.text_featurizer import TextFeaturizer
-from paddlespeech.s2t.io.collator import SpeechCollator
-from paddlespeech.s2t.io.dataset import ManifestDataset
-from paddlespeech.s2t.io.sampler import SortagradBatchSampler
-from paddlespeech.s2t.io.sampler import SortagradDistributedBatchSampler
-from paddlespeech.s2t.models.ds2_online import DeepSpeech2InferModelOnline
-from paddlespeech.s2t.models.ds2_online import DeepSpeech2ModelOnline
-from paddlespeech.s2t.training.gradclip import ClipGradByGlobalNormWithLog
-from paddlespeech.s2t.training.trainer import Trainer
-from paddlespeech.s2t.utils import error_rate
-from paddlespeech.s2t.utils import layer_tools
-from paddlespeech.s2t.utils import mp_tools
-from paddlespeech.s2t.utils.log import Log
-
-logger = Log(__name__).getlog()
-
-
-class DeepSpeech2Trainer(Trainer):
-    def __init__(self, config, args):
-        super().__init__(config, args)
-
-    def train_batch(self, batch_index, batch_data, msg):
-        train_conf = self.config
-        start = time.time()
-
-        # forward
-        utt, audio, audio_len, text, text_len = batch_data
-        loss = self.model(audio, audio_len, text, text_len)
-        losses_np = {
-            'train_loss': float(loss),
-        }
-
-        # loss backward
-        if (batch_index + 1) % train_conf.accum_grad != 0:
-            # Disable gradient synchronizations across DDP processes.
-            # Within this context, gradients will be accumulated on module
-            # variables, which will later be synchronized.
-            context = self.model.no_sync
-        else:
-            # Used for single gpu training and DDP gradient synchronization
-            # processes.
-            context = nullcontext
-
-        with context():
-            loss.backward()
-            layer_tools.print_grads(self.model, print_func=None)
-
-        # optimizer step
-        if (batch_index + 1) % train_conf.accum_grad == 0:
-            self.optimizer.step()
-            self.optimizer.clear_grad()
-            self.iteration += 1
-
-        iteration_time = time.time() - start
-
-        msg += "train time: {:>.3f}s, ".format(iteration_time)
-        msg += "batch size: {}, ".format(self.config.batch_size)
-        msg += "accum: {}, ".format(train_conf.accum_grad)
-        msg += ', '.join('{}: {:>.6f}'.format(k, v)
-                         for k, v in losses_np.items())
-        logger.info(msg)
-
-        if dist.get_rank() == 0 and self.visualizer:
-            for k, v in losses_np.items():
-                # `step -1` since we update `step` after optimizer.step().
-                self.visualizer.add_scalar("train/{}".format(k), v,
-                                           self.iteration - 1)
-
-    @paddle.no_grad()
-    def valid(self):
-        logger.info(f"Valid Total Examples: {len(self.valid_loader.dataset)}")
-        self.model.eval()
-        valid_losses = defaultdict(list)
-        num_seen_utts = 1
-        total_loss = 0.0
-        for i, batch in enumerate(self.valid_loader):
-            utt, audio, audio_len, text, text_len = batch
-            loss = self.model(audio, audio_len, text, text_len)
-            if paddle.isfinite(loss):
-                num_utts = batch[1].shape[0]
-                num_seen_utts += num_utts
-                total_loss += float(loss) * num_utts
-                valid_losses['val_loss'].append(float(loss))
-
-            if (i + 1) % self.config.log_interval == 0:
-                valid_dump = {k: np.mean(v) for k, v in valid_losses.items()}
-                valid_dump['val_history_loss'] = total_loss / num_seen_utts
-
-                # logging
-                msg = f"Valid: Rank: {dist.get_rank()}, "
-                msg += "epoch: {}, ".format(self.epoch)
-                msg += "step: {}, ".format(self.iteration)
-                msg += "batch : {}/{}, ".format(i + 1, len(self.valid_loader))
-                msg += ', '.join('{}: {:>.6f}'.format(k, v)
-                                 for k, v in valid_dump.items())
-                logger.info(msg)
-
-        logger.info('Rank {} Val info val_loss {}'.format(
-            dist.get_rank(), total_loss / num_seen_utts))
-        return total_loss, num_seen_utts
-
-    def setup_model(self):
-        config = self.config.clone()
-        config.defrost()
-        config.feat_size = self.train_loader.collate_fn.feature_size
-        #config.dict_size = self.train_loader.collate_fn.vocab_size
-        config.dict_size = len(self.train_loader.collate_fn.vocab_list)
-        config.freeze()
-
-        if self.args.model_type == 'offline':
-            model = DeepSpeech2Model.from_config(config)
-        elif self.args.model_type == 'online':
-            model = DeepSpeech2ModelOnline.from_config(config)
-        else:
-            raise Exception("wrong model type")
-        if self.parallel:
-            model = paddle.DataParallel(model)
-
-        logger.info(f"{model}")
-        layer_tools.print_params(model, logger.info)
-
-        grad_clip = ClipGradByGlobalNormWithLog(config.global_grad_clip)
-        lr_scheduler = paddle.optimizer.lr.ExponentialDecay(
-            learning_rate=config.lr, gamma=config.lr_decay, verbose=True)
-        optimizer = paddle.optimizer.Adam(
-            learning_rate=lr_scheduler,
-            parameters=model.parameters(),
-            weight_decay=paddle.regularizer.L2Decay(config.weight_decay),
-            grad_clip=grad_clip)
-
-        self.model = model
-        self.optimizer = optimizer
-        self.lr_scheduler = lr_scheduler
-        logger.info("Setup model/optimizer/lr_scheduler!")
-
-    def setup_dataloader(self):
-        config = self.config.clone()
-        config.defrost()
-        config.keep_transcription_text = False
-
-        config.manifest = config.train_manifest
-        train_dataset = ManifestDataset.from_config(config)
-
-        config.manifest = config.dev_manifest
-        dev_dataset = ManifestDataset.from_config(config)
-
-        config.manifest = config.test_manifest
-        test_dataset = ManifestDataset.from_config(config)
-
-        if self.parallel:
-            batch_sampler = SortagradDistributedBatchSampler(
-                train_dataset,
-                batch_size=config.batch_size,
-                num_replicas=None,
-                rank=None,
-                shuffle=True,
-                drop_last=True,
-                sortagrad=config.sortagrad,
-                shuffle_method=config.shuffle_method)
-        else:
-            batch_sampler = SortagradBatchSampler(
-                train_dataset,
-                shuffle=True,
-                batch_size=config.batch_size,
-                drop_last=True,
-                sortagrad=config.sortagrad,
-                shuffle_method=config.shuffle_method)
-
-        collate_fn_train = SpeechCollator.from_config(config)
-
-        config.augmentation_config = ""
-        collate_fn_dev = SpeechCollator.from_config(config)
-
-        config.keep_transcription_text = True
-        config.augmentation_config = ""
-        collate_fn_test = SpeechCollator.from_config(config)
-
-        self.train_loader = DataLoader(
-            train_dataset,
-            batch_sampler=batch_sampler,
-            collate_fn=collate_fn_train,
-            num_workers=config.num_workers)
-        self.valid_loader = DataLoader(
-            dev_dataset,
-            batch_size=config.batch_size,
-            shuffle=False,
-            drop_last=False,
-            collate_fn=collate_fn_dev)
-        self.test_loader = DataLoader(
-            test_dataset,
-            batch_size=config.decode.decode_batch_size,
-            shuffle=False,
-            drop_last=False,
-            collate_fn=collate_fn_test)
-        if "<eos>" in self.test_loader.collate_fn.vocab_list:
-            self.test_loader.collate_fn.vocab_list.remove("<eos>")
-        if "<eos>" in self.valid_loader.collate_fn.vocab_list:
-            self.valid_loader.collate_fn.vocab_list.remove("<eos>")
-        if "<eos>" in self.train_loader.collate_fn.vocab_list:
-            self.train_loader.collate_fn.vocab_list.remove("<eos>")
-        logger.info("Setup train/valid/test  Dataloader!")
-
-
-class DeepSpeech2Tester(DeepSpeech2Trainer):
-    def __init__(self, config, args):
-
-        self._text_featurizer = TextFeaturizer(
-            unit_type=config.unit_type, vocab=None)
-        super().__init__(config, args)
-
-    def ordid2token(self, texts, texts_len):
-        """ ord() id to chr() chr """
-        trans = []
-        for text, n in zip(texts, texts_len):
-            n = n.numpy().item()
-            ids = text[:n]
-            trans.append(''.join([chr(i) for i in ids]))
-        return trans
-
-    def compute_metrics(self,
-                        utts,
-                        audio,
-                        audio_len,
-                        texts,
-                        texts_len,
-                        fout=None):
-        cfg = self.config.decode
-        errors_sum, len_refs, num_ins = 0.0, 0, 0
-        errors_func = error_rate.char_errors if cfg.error_rate_type == 'cer' else error_rate.word_errors
-        error_rate_func = error_rate.cer if cfg.error_rate_type == 'cer' else error_rate.wer
-
-        target_transcripts = self.ordid2token(texts, texts_len)
-
-        result_transcripts = self.compute_result_transcripts(audio, audio_len)
-
-        for utt, target, result in zip(utts, target_transcripts,
-                                       result_transcripts):
-            errors, len_ref = errors_func(target, result)
-            errors_sum += errors
-            len_refs += len_ref
-            num_ins += 1
-            if fout:
-                fout.write(utt + " " + result + "\n")
-            logger.info("\nTarget Transcription: %s\nOutput Transcription: %s" %
-                        (target, result))
-            logger.info("Current error rate [%s] = %f" %
-                        (cfg.error_rate_type, error_rate_func(target, result)))
-
-        return dict(
-            errors_sum=errors_sum,
-            len_refs=len_refs,
-            num_ins=num_ins,
-            error_rate=errors_sum / len_refs,
-            error_rate_type=cfg.error_rate_type)
-
-    def compute_result_transcripts(self, audio, audio_len):
-        result_transcripts = self.model.decode(audio, audio_len)
-
-        result_transcripts = [
-            self._text_featurizer.detokenize(item)
-            for item in result_transcripts
-        ]
-        return result_transcripts
-
-    @mp_tools.rank_zero_only
-    @paddle.no_grad()
-    def test(self):
-        logger.info(f"Test Total Examples: {len(self.test_loader.dataset)}")
-        self.model.eval()
-        cfg = self.config
-        error_rate_type = None
-        errors_sum, len_refs, num_ins = 0.0, 0, 0
-
-        # Initialized the decoder in model
-        decode_cfg = self.config.decode
-        vocab_list = self.test_loader.collate_fn.vocab_list
-        decode_batch_size = self.test_loader.batch_size
-        self.model.decoder.init_decoder(
-            decode_batch_size, vocab_list, decode_cfg.decoding_method,
-            decode_cfg.lang_model_path, decode_cfg.alpha, decode_cfg.beta,
-            decode_cfg.beam_size, decode_cfg.cutoff_prob,
-            decode_cfg.cutoff_top_n, decode_cfg.num_proc_bsearch)
-
-        with open(self.args.result_file, 'w') as fout:
-            for i, batch in enumerate(self.test_loader):
-                utts, audio, audio_len, texts, texts_len = batch
-                metrics = self.compute_metrics(utts, audio, audio_len, texts,
-                                               texts_len, fout)
-                errors_sum += metrics['errors_sum']
-                len_refs += metrics['len_refs']
-                num_ins += metrics['num_ins']
-                error_rate_type = metrics['error_rate_type']
-                logger.info("Error rate [%s] (%d/?) = %f" %
-                            (error_rate_type, num_ins, errors_sum / len_refs))
-
-        # logging
-        msg = "Test: "
-        msg += "epoch: {}, ".format(self.epoch)
-        msg += "step: {}, ".format(self.iteration)
-        msg += "Final error rate [%s] (%d/%d) = %f" % (
-            error_rate_type, num_ins, num_ins, errors_sum / len_refs)
-        logger.info(msg)
-        self.model.decoder.del_decoder()
-
-    def run_test(self):
-        self.resume_or_scratch()
-        try:
-            self.test()
-        except KeyboardInterrupt:
-            exit(-1)
-
-    def export(self):
-        if self.args.model_type == 'offline':
-            infer_model = DeepSpeech2InferModel.from_pretrained(
-                self.test_loader, self.config, self.args.checkpoint_path)
-        elif self.args.model_type == 'online':
-            infer_model = DeepSpeech2InferModelOnline.from_pretrained(
-                self.test_loader, self.config, self.args.checkpoint_path)
-        else:
-            raise Exception("wrong model type")
-
-        infer_model.eval()
-        feat_dim = self.test_loader.collate_fn.feature_size
-        static_model = infer_model.export()
-        logger.info(f"Export code: {static_model.forward.code}")
-        paddle.jit.save(static_model, self.args.export_path)
-
-    def run_export(self):
-        try:
-            self.export()
-        except KeyboardInterrupt:
-            exit(-1)