update repo

egs/aishell2/data2vec_pretrain/conf/train_pretrain_transformer.yaml

@@ -2,47 +2,52 @@
 # 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
+    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
+    mask_prob: 0.65
+    mask_length: 10
 
-  loss_beta: 0
-  loss_scale: null
+    loss_beta: 0
+    loss_scale: null
 
-  instance_norm_target_layer: true
-  average_top_k_layers: 8
+    instance_norm_target_layer: true
+    average_top_k_layers: 8
 
-  pos_conv_depth: 5
-  conv_pos: 95
+    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
+    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
+    require_same_masks: true
+    mask_dropout: 0
 
-log_interval: 50
-normalize: None
+# frontend related
+frontend: wav_frontend
+frontend_conf:
+    fs: 16000
+    window: hamming
+    n_mels: 80
+    frame_length: 25
+    frame_shift: 10
+    lfr_m: 1
+    lfr_n: 1
 
-# minibatch related
-batch_type: length
-batch_bins: 64000
-num_workers: 16
+model: data2vec
 
 # optimization related
 accum_grad: 1
 grad_clip: 5
 patience: none
-max_epoch: 600
+max_epoch: 1800
 val_scheduler_criterion:
     - valid
     - acc
@@ -68,7 +73,7 @@ scheduler_conf:
 dataset_conf:
     batch_mode: clipping
     data_names: speech,none
-    data_types: kaldi_ark,none
+    data_types: sound,none
     shuffle: true
     shuffle_conf:
         shuffle_size: 12800
@@ -76,4 +81,7 @@ dataset_conf:
     batch_conf:
         batch_type: token
         batch_size: 64000
-    num_workers: 8
+    num_workers: 8
+
+log_interval: 50
+normalize: None

egs/aishell2/data2vec_pretrain/run.sh

@@ -7,28 +7,25 @@ CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
 gpu_num=8
 count=1
 
-train_cmd=tools/run.pl
+train_cmd=utils/run.pl
 
 # general configuration
 feats_dir="../DATA" #feature output dictionary
 exp_dir="."
 lang=zh
-dumpdir=dump/fbank
-feats_type=fbank
 token_type=char
+speed_perturb="0.9 1.0 1.1"
 dataset_type=large
-stage=0
-stop_stage=4
+stage=3
+stop_stage=3
 
 # feature configuration
 feats_dim=80
-sample_frequency=16000
-nj=100
-speed_perturb="0.9,1.0,1.1"
+nj=64
 
 # data
-tr_dir=
-dev_tst_dir=
+tr_dir=/nfs/wangjiaming.wjm/asr_data/aishell2/AISHELL-2/iOS/data
+dev_tst_dir=/nfs/wangjiaming.wjm/asr_data/aishell2/AISHELL-DEV-TEST-SET
 
 # exp tag
 tag="exp1"
@@ -45,68 +42,31 @@ train_set=train
 valid_set=dev_ios
 
 asr_config=conf/train_pretrain_transformer.yaml
-model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
+model_dir="baseline_$(basename "${asr_config}" .yaml) _${lang}_${token_type}_${tag}"
 
 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
+    for x in iOS; do
         local/prepare_data.sh ${dev_tst_dir}/${x}/dev ${feats_dir}/data/local/dev_${x,,} ${feats_dir}/data/dev_${x,,} || exit 1;
         local/prepare_data.sh ${dev_tst_dir}/${x}/test ${feats_dir}/data/local/test_${x,,} ${feats_dir}/data/test_${x,,} || exit 1;
-    done 
+    done
     # Normalize text to capital letters
-    for x in train dev_android dev_ios dev_mic test_android test_ios test_mic; do
+    for x in train dev_ios test_ios; do
         mv ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
         paste -d " " <(cut -f 1 ${feats_dir}/data/${x}/text.org) <(cut -f 2- ${feats_dir}/data/${x}/text.org \
              | tr 'A-Z' 'a-z' | tr -d " ") \
             > ${feats_dir}/data/${x}/text
-        tools/text2token.py -n 1 -s 1 ${feats_dir}/data/${x}/text > ${feats_dir}/data/${x}/text.org
+        utils/text2token.py -n 1 -s 1 ${feats_dir}/data/${x}/text > ${feats_dir}/data/${x}/text.org
         mv ${feats_dir}/data/${x}/text.org ${feats_dir}/data/${x}/text
     done
 fi
 
-feat_train_dir=${feats_dir}/${dumpdir}/${train_set}; mkdir -p ${feat_train_dir}
-feat_dev_dir=${feats_dir}/${dumpdir}/${valid_set}; mkdir -p ${feat_dev_dir}
 if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
-    echo "stage 1: Feature Generation"
-    # compute fbank features
-    fbankdir=${feats_dir}/fbank
-    steps/compute_fbank.sh --cmd "$train_cmd" --nj $nj --speed_perturb ${speed_perturb} \
-        ${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_${x}
-    done
+    echo "stage 1: Feature and CMVN Generation"
+    utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} ${feats_dir}/data/${train_set}
 fi
 
 token_list=${feats_dir}/data/${lang}_token_list/char/tokens.txt
@@ -114,22 +74,59 @@ 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" \
+    utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/${train_set}/text | cut -f 2- -d" " | tr " " "\n" \
         | sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
-    num_token=$(cat ${token_list} | wc -l)
     echo "<unk>" >> ${token_list}
-    vocab_size=$(cat ${token_list} | wc -l)
-    awk -v v=,${vocab_size} '{print $0v}' ${feat_train_dir}/text_shape > ${feat_train_dir}/text_shape.char
-    awk -v v=,${vocab_size} '{print $0v}' ${feat_dev_dir}/text_shape > ${feat_dev_dir}/text_shape.char
     mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/${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])
+            train.py \
+                --task_name asr \
+                --gpu_id $gpu_id \
+                --use_preprocessor true \
+                --token_type char \
+                --token_list $token_list \
+                --data_dir ${feats_dir}/data \
+                --train_set ${train_set} \
+                --valid_set ${valid_set} \
+                --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
+                --speed_perturb ${speed_perturb} \
+                --dataset_type $dataset_type \
+                --resume true \
+                --output_dir ${exp_dir}/exp/${model_dir} \
+                --config $asr_config \
+                --ngpu $gpu_num \
+                --num_worker_count $count \
+                --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
 
 # Training Stage
@@ -149,12 +146,16 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
             rank=$i
             local_rank=$i
             gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
-            data2vec_train.py \
+            train.py \
+                --task_name pretrain \
                 --gpu_id $gpu_id \
                 --use_preprocessor true \
+                --data_dir ${feats_dir}/data \
+                --train_set ${train_set} \
+                --valid_set ${valid_set} \
+                --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
+                --speed_perturb ${speed_perturb} \
                 --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 \

funasr/build_utils/build_pretrain_model.py

@@ -89,7 +89,7 @@ def build_pretrain_model(args):
         **args.encoder_conf,
     )
 
-    if args.model_name == "data2vec":
+    if args.model == "data2vec":
         model_class = model_choices.get_class("data2vec")
         model = model_class(
             frontend=frontend,