add data2vec pretrain

egs/aishell2/data2vec_pretrain/conf/train_pretrain_transformer.yaml

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+# network architecture
+# encoder related
+encoder: data2vec_encoder
+encoder_conf:
+  extractor_mode: layer_norm
+  encoder_layerdrop: 0.05
+  dropout_input: 0.0
+  dropout_features: 0.0
+  feature_grad_mult: 1.0
+  encoder_embed_dim: 768
+
+  mask_prob: 0.65
+  mask_length: 10
+
+  loss_beta: 0
+  loss_scale: null
+
+  instance_norm_target_layer: true
+  average_top_k_layers: 8
+
+  pos_conv_depth: 5
+  conv_pos: 95
+
+  ema_decay: 0.999
+  ema_end_decay: 0.9999
+  ema_anneal_end_step: 30000
+  ema_transformer_only: true
+  ema_layers_only: true
+
+  require_same_masks: true
+  mask_dropout: 0
+
+log_interval: 50
+normalize: None
+
+# minibatch related
+batch_type: length
+batch_bins: 64000
+num_workers: 16
+
+# optimization related
+accum_grad: 1
+grad_clip: 5
+patience: none
+max_epoch: 600
+val_scheduler_criterion:
+    - valid
+    - acc
+best_model_criterion:
+-   - valid
+    - loss
+    - min
+keep_nbest_models: 50
+unused_parameters: true
+
+optim: fairseq_adam
+optim_conf:
+    lr: 0.0005
+    adam_betas: [0.9,0.98]
+    adam_eps: 1.0e-06
+    weight_decay: 0.01
+
+scheduler: tri_stage
+scheduler_conf:
+    phase_ratio: [0.03,0.9,0.07]

egs/aishell2/data2vec_pretrain/local/prepare_data.sh

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+#!/usr/bin/env bash
+# Copyright 2018 AIShell-Foundation(Authors:Jiayu DU, Xingyu NA, Bengu WU, Hao ZHENG)
+#           2018 Beijing Shell Shell Tech. Co. Ltd. (Author: Hui BU)
+# Apache 2.0
+
+# transform raw AISHELL-2 data to kaldi format
+
+. ./path.sh || exit 1;
+
+tmp=
+dir=
+
+if [ $# != 3 ]; then
+  echo "Usage: $0 <corpus-data-dir> <tmp-dir> <output-dir>"
+  echo " $0 /export/AISHELL-2/iOS/train data/local/train data/train"
+  exit 1;
+fi
+
+corpus=$1
+tmp=$2
+dir=$3
+
+echo "prepare_data.sh: Preparing data in $corpus"
+
+mkdir -p $tmp
+mkdir -p $dir
+
+# corpus check
+if [ ! -d $corpus ] || [ ! -f $corpus/wav.scp ] || [ ! -f $corpus/trans.txt ]; then
+  echo "Error: $0 requires wav.scp and trans.txt under $corpus directory."
+  exit 1;
+fi
+
+# validate utt-key list, IC0803W0380 is a bad utterance
+awk '{print $1}' $corpus/wav.scp | grep -v 'IC0803W0380' > $tmp/wav_utt.list
+awk '{print $1}' $corpus/trans.txt > $tmp/trans_utt.list
+tools/filter_scp.pl -f 1 $tmp/wav_utt.list $tmp/trans_utt.list > $tmp/utt.list
+
+# wav.scp
+awk -F'\t' -v path_prefix=$corpus '{printf("%s\t%s/%s\n",$1,path_prefix,$2)}' $corpus/wav.scp > $tmp/tmp_wav.scp
+tools/filter_scp.pl -f 1 $tmp/utt.list $tmp/tmp_wav.scp | sort -k 1 | uniq > $tmp/wav.scp
+
+# text
+tools/filter_scp.pl -f 1 $tmp/utt.list $corpus/trans.txt | sort -k 1 | uniq > $tmp/text
+
+# copy prepared resources from tmp_dir to target dir
+mkdir -p $dir
+for f in wav.scp text; do
+  cp $tmp/$f $dir/$f || exit 1;
+done
+
+echo "local/prepare_data.sh succeeded"
+exit 0;

egs/aishell2/data2vec_pretrain/path.sh

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+export FUNASR_DIR=$PWD/../../..
+
+# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8
+export PYTHONPATH=../../../:$PYTHONPATH
+export PATH=$FUNASR_DIR/funasr/bin:$PATH

egs/aishell2/data2vec_pretrain/run.sh

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+#!/usr/bin/env bash
+
+. ./path.sh || exit 1;
+
+# machines configuration
+CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
+gpu_num=8
+count=1
+
+train_cmd=tools/run.pl
+
+# general configuration
+feats_dir="../DATA" #feature output dictionary
+exp_dir="."
+lang=zh
+dumpdir=dump/fbank
+feats_type=fbank
+token_type=char
+dataset_type=large
+stage=0
+stop_stage=4
+
+# feature configuration
+feats_dim=80
+sample_frequency=16000
+nj=100
+speed_perturb="0.9,1.0,1.1"
+
+# data
+tr_dir=
+dev_tst_dir=
+
+# exp tag
+tag="exp1"
+
+. utils/parse_options.sh || exit 1;
+
+# Set bash to 'debug' mode, it will exit on :
+# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
+set -e
+set -u
+set -o pipefail
+
+train_set=train
+valid_set=dev_ios
+
+asr_config=conf/train_asr_paraformer_conformer_20e_1280_320_6d_1280_320.yaml
+model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    echo "stage 0: Data preparation"
+    # For training set
+    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 ${feats_dir}/data/local/dev_${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 ${feats_dir}/data/${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
+        tools/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} \
+        ${feats_dir}/data/train ${exp_dir}/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 \
+            ${feats_dir}/data/dev_${x} ${exp_dir}/exp/make_fbank/dev_${x} ${fbankdir}/dev_${x}
+        tools/fix_data_feat.sh ${fbankdir}/dev_${x}
+        steps/compute_fbank.sh --cmd "$train_cmd" --nj $nj \
+            ${feats_dir}/data/test_${x} ${exp_dir}/exp/make_fbank/test_${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 ${exp_dir}/exp/make_fbank/train
+
+    # apply cmvn 
+    steps/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
+        ${fbankdir}/${train_set} ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/${train_set} ${feat_train_dir}
+    steps/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
+        ${fbankdir}/${valid_set} ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/${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 ${exp_dir}/exp/make_fbank/test_${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_${x}
+    done
+fi
+
+token_list=${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 ${feats_dir}/data/${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 "" ${feats_dir}/data/${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 ${feats_dir}/asr_stats_fbank_zh_char/${train_set}
+    mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}
+    cp ${feat_train_dir}/speech_shape ${feat_train_dir}/text_shape ${feat_train_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/${train_set} 
+    cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}
+fi
+
+# Training Stage
+world_size=$gpu_num  # run on one machine
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    echo "stage 3: Training"
+    mkdir -p ${exp_dir}/exp/${model_dir}
+    mkdir -p ${exp_dir}/exp/${model_dir}/log
+    INIT_FILE=${exp_dir}/exp/${model_dir}/ddp_init
+    if [ -f $INIT_FILE ];then
+        rm -f $INIT_FILE
+    fi
+    init_method=file://$(readlink -f $INIT_FILE)
+    echo "$0: init method is $init_method"
+    for ((i = 0; i < $gpu_num; ++i)); do
+        {
+            rank=$i
+            local_rank=$i
+            gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
+            data2vec_train.py \
+                --gpu_id $gpu_id \
+                --use_preprocessor true \
+                --dataset_type $dataset_type \
+                --train_data_file $feats_dir/$dumpdir/${train_set}/data.list \
+                --valid_data_file $feats_dir/$dumpdir/${valid_set}/data.list \
+                --resume true \
+                --output_dir ${exp_dir}/exp/${model_dir} \
+                --config $asr_config \
+                --input_size $feats_dim \
+                --ngpu $gpu_num \
+                --num_worker_count $count \
+                --multiprocessing_distributed true \
+                --dist_init_method $init_method \
+                --dist_world_size $world_size \
+                --dist_rank $rank \
+                --local_rank $local_rank 1> ${exp_dir}/exp/${model_dir}/log/train.log.$i 2>&1
+        } &
+      done
+      wait
+fi

egs/aishell2/data2vec_pretrain/utils

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+../../aishell/transformer/utils