Dev aky2 (#588)

* support resume model from pai

* add padding for streaming rnnt conv input

* fix large dataset training bug

* bug fix

* modify aishell rnnt egs to support wav input

* add libri_100 rnnt recipe

* bug fix

* add librispeech rnnt recipe

* add librispeech README

* update rnnt results

* bug fix

---------

Co-authored-by: aky15 <ankeyu.aky@11.17.44.249>

egs/aishell/rnnt/README.md

@@ -5,14 +5,14 @@
 - 8 gpu(Tesla V100)
 - Feature info: using 80 dims fbank, global cmvn, speed perturb(0.9, 1.0, 1.1), specaugment
 - Train config: conf/train_conformer_rnnt_unified.yaml
-- chunk config: chunk size 16, full left chunk
+- chunk config: chunk size 16, 1 left chunk
 - LM config: LM was not used
 - Model size: 90M
 
 ## Results (CER)
-- Decode config: conf/train_conformer_rnnt_unified.yaml
+- Decode config: conf/decode_rnnt_conformer_streaming.yaml
 
-|   testset   | CER(%)  |
+|   testset   |  CER(%) |
 |:-----------:|:-------:|
-|     dev     |  5.53   |
-|    test     |  6.24   |
+|     dev     |  5.43   |
+|    test     |  6.04   |

egs/aishell/rnnt/run.sh

@@ -4,7 +4,7 @@
 
 # machines configuration
 CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
-gpu_num=2
+gpu_num=8
 count=1
 gpu_inference=true  # Whether to perform gpu decoding, set false for cpu decoding
 # for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob

egs/librispeech/rnnt/README.md

@@ -0,0 +1,18 @@
+
+# Streaming RNN-T Result
+
+## Training Config
+- 8 gpu(Tesla V100)
+- Feature info: using 80 dims fbank, global cmvn, speed perturb(0.9, 1.0, 1.1), specaugment
+- Train config: conf/train_conformer_rnnt_unified.yaml
+- chunk config: chunk size 16, 1 left chunk
+- LM config: LM was not used
+- Model size: 90M
+
+## Results (CER)
+- Decode config: conf/decode_rnnt_conformer_streaming.yaml
+
+|      testset   |  WER(%) |
+|:--------------:|:-------:|
+|    test_clean  |   3.58  |
+|    test_other  |   9.27  |

egs/librispeech/rnnt/conf/decode_rnnt_conformer_streaming.yaml

@@ -0,0 +1,8 @@
+# The conformer transducer decoding configuration from @jeon30c
+beam_size: 10
+simu_streaming: false
+streaming: true
+chunk_size: 16
+left_context: 16
+right_context: 0
+

egs/librispeech/rnnt/conf/train_conformer_rnnt_unified.yaml

@@ -0,0 +1,98 @@
+encoder: chunk_conformer
+encoder_conf:
+      activation_type: swish
+      time_reduction_factor: 2
+      unified_model_training: true
+      default_chunk_size: 16
+      jitter_range: 4
+      left_chunk_size: 1
+      embed_vgg_like: false
+      subsampling_factor: 4
+      linear_units: 2048
+      output_size: 512
+      attention_heads: 8
+      dropout_rate: 0.5
+      positional_dropout_rate: 0.5
+      attention_dropout_rate: 0.5
+      cnn_module_kernel: 15
+      num_blocks: 12    
+
+# decoder related
+rnnt_decoder: rnnt
+rnnt_decoder_conf:
+    embed_size: 512
+    hidden_size: 512
+    embed_dropout_rate: 0.5
+    dropout_rate: 0.5
+    use_embed_mask: true
+
+joint_network_conf:
+    joint_space_size: 512
+
+# 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
+
+# Auxiliary CTC
+model: rnnt_unified
+model_conf:
+    auxiliary_ctc_weight: 0.0
+
+# minibatch related
+use_amp: true
+
+# optimization related
+accum_grad: 4
+grad_clip: 5
+max_epoch: 100
+val_scheduler_criterion:
+    - valid
+    - loss
+best_model_criterion:
+-   - valid
+    - cer_transducer_chunk
+    - min
+keep_nbest_models: 10
+
+optim: adam
+optim_conf:
+   lr: 0.001
+scheduler: warmuplr
+scheduler_conf:
+   warmup_steps: 25000
+
+specaug: specaug
+specaug_conf:
+    apply_time_warp: true
+    time_warp_window: 5
+    time_warp_mode: bicubic
+    apply_freq_mask: true
+    freq_mask_width_range:
+    - 0
+    - 40
+    num_freq_mask: 2
+    apply_time_mask: true
+    time_mask_width_range:
+    - 0
+    - 50
+    num_time_mask: 5
+
+dataset_conf:
+    shuffle: True
+    shuffle_conf:
+        shuffle_size: 1024
+        sort_size: 500
+    batch_conf:
+        batch_type: token
+        batch_size: 10000
+    num_workers: 8
+
+log_interval: 50
+normalize: None

egs/librispeech/rnnt/local/data_prep.sh

@@ -0,0 +1,58 @@
+#!/usr/bin/env bash
+
+# Copyright 2014  Vassil Panayotov
+#           2014  Johns Hopkins University (author: Daniel Povey)
+# Apache 2.0
+
+if [ "$#" -ne 2 ]; then
+  echo "Usage: $0 <src-dir> <dst-dir>"
+  echo "e.g.: $0 /export/a15/vpanayotov/data/LibriSpeech/dev-clean data/dev-clean"
+  exit 1
+fi
+
+src=$1
+dst=$2
+
+# all utterances are FLAC compressed
+if ! which flac >&/dev/null; then
+   echo "Please install 'flac' on ALL worker nodes!"
+   exit 1
+fi
+
+spk_file=$src/../SPEAKERS.TXT
+
+mkdir -p $dst || exit 1
+
+[ ! -d $src ] && echo "$0: no such directory $src" && exit 1
+[ ! -f $spk_file ] && echo "$0: expected file $spk_file to exist" && exit 1
+
+
+wav_scp=$dst/wav.scp; [[ -f "$wav_scp" ]] && rm $wav_scp
+trans=$dst/text; [[ -f "$trans" ]] && rm $trans
+
+for reader_dir in $(find -L $src -mindepth 1 -maxdepth 1 -type d | sort); do
+  reader=$(basename $reader_dir)
+  if ! [ $reader -eq $reader ]; then  # not integer.
+    echo "$0: unexpected subdirectory name $reader"
+    exit 1
+  fi
+
+  for chapter_dir in $(find -L $reader_dir/ -mindepth 1 -maxdepth 1 -type d | sort); do
+    chapter=$(basename $chapter_dir)
+    if ! [ "$chapter" -eq "$chapter" ]; then
+      echo "$0: unexpected chapter-subdirectory name $chapter"
+      exit 1
+    fi
+
+    find -L $chapter_dir/ -iname "*.flac" | sort | xargs -I% basename % .flac | \
+      awk -v "dir=$chapter_dir" '{printf "%s %s/%s.flac \n", $0, dir, $0}' >>$wav_scp|| exit 1
+
+    chapter_trans=$chapter_dir/${reader}-${chapter}.trans.txt
+    [ ! -f  $chapter_trans ] && echo "$0: expected file $chapter_trans to exist" && exit 1
+    cat $chapter_trans >>$trans
+  done
+done
+
+echo "$0: successfully prepared data in $dst"
+
+exit 0

egs/librispeech/rnnt/local/download_and_untar.sh

@@ -0,0 +1,97 @@
+#!/usr/bin/env bash
+
+# Copyright   2014  Johns Hopkins University (author: Daniel Povey)
+# Apache 2.0
+
+remove_archive=false
+
+if [ "$1" == --remove-archive ]; then
+  remove_archive=true
+  shift
+fi
+
+if [ $# -ne 3 ]; then
+  echo "Usage: $0 [--remove-archive] <data-base> <url-base> <corpus-part>"
+  echo "e.g.: $0 /export/a15/vpanayotov/data www.openslr.org/resources/11 dev-clean"
+  echo "With --remove-archive it will remove the archive after successfully un-tarring it."
+  echo "<corpus-part> can be one of: dev-clean, test-clean, dev-other, test-other,"
+  echo "          train-clean-100, train-clean-360, train-other-500."
+  exit 1
+fi
+
+data=$1
+url=$2
+part=$3
+
+if [ ! -d "$data" ]; then
+  echo "$0: no such directory $data"
+  exit 1
+fi
+
+part_ok=false
+list="dev-clean test-clean dev-other test-other train-clean-100 train-clean-360 train-other-500"
+for x in $list; do
+  if [ "$part" == $x ]; then part_ok=true; fi
+done
+if ! $part_ok; then
+  echo "$0: expected <corpus-part> to be one of $list, but got '$part'"
+  exit 1
+fi
+
+if [ -z "$url" ]; then
+  echo "$0: empty URL base."
+  exit 1
+fi
+
+if [ -f $data/LibriSpeech/$part/.complete ]; then
+  echo "$0: data part $part was already successfully extracted, nothing to do."
+  exit 0
+fi
+
+
+# sizes of the archive files in bytes.  This is some older versions.
+sizes_old="371012589 347390293 379743611 361838298 6420417880 23082659865 30626749128"
+# sizes_new is the archive file sizes of the final release.  Some of these sizes are of
+# things we probably won't download.
+sizes_new="337926286 314305928 695964615 297279345 87960560420 33373768 346663984 328757843 6387309499 23049477885 30593501606"
+
+if [ -f $data/$part.tar.gz ]; then
+  size=$(/bin/ls -l $data/$part.tar.gz | awk '{print $5}')
+  size_ok=false
+  for s in $sizes_old $sizes_new; do if [ $s == $size ]; then size_ok=true; fi; done
+  if ! $size_ok; then
+    echo "$0: removing existing file $data/$part.tar.gz because its size in bytes $size"
+    echo "does not equal the size of one of the archives."
+    rm $data/$part.tar.gz
+  else
+    echo "$data/$part.tar.gz exists and appears to be complete."
+  fi
+fi
+
+if [ ! -f $data/$part.tar.gz ]; then
+  if ! which wget >/dev/null; then
+    echo "$0: wget is not installed."
+    exit 1
+  fi
+  full_url=$url/$part.tar.gz
+  echo "$0: downloading data from $full_url.  This may take some time, please be patient."
+
+  if ! wget -P $data --no-check-certificate $full_url; then
+    echo "$0: error executing wget $full_url"
+    exit 1
+  fi
+fi
+
+if ! tar -C $data -xvzf $data/$part.tar.gz; then
+  echo "$0: error un-tarring archive $data/$part.tar.gz"
+  exit 1
+fi
+
+touch $data/LibriSpeech/$part/.complete
+
+echo "$0: Successfully downloaded and un-tarred $data/$part.tar.gz"
+
+if $remove_archive; then
+  echo "$0: removing $data/$part.tar.gz file since --remove-archive option was supplied."
+  rm $data/$part.tar.gz
+fi

egs/librispeech/rnnt/local/spm_encode.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in
+# https://github.com/pytorch/fairseq/blob/master/LICENSE
+
+
+import argparse
+import contextlib
+import sys
+
+import sentencepiece as spm
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model", required=True,
+                        help="sentencepiece model to use for encoding")
+    parser.add_argument("--inputs", nargs="+", default=['-'],
+                        help="input files to filter/encode")
+    parser.add_argument("--outputs", nargs="+", default=['-'],
+                        help="path to save encoded outputs")
+    parser.add_argument("--output_format", choices=["piece", "id"], default="piece")
+    parser.add_argument("--min-len", type=int, metavar="N",
+                        help="filter sentence pairs with fewer than N tokens")
+    parser.add_argument("--max-len", type=int, metavar="N",
+                        help="filter sentence pairs with more than N tokens")
+    args = parser.parse_args()
+
+    assert len(args.inputs) == len(args.outputs), \
+        "number of input and output paths should match"
+
+    sp = spm.SentencePieceProcessor()
+    sp.Load(args.model)
+
+    if args.output_format == "piece":
+        def encode(l):
+            return sp.EncodeAsPieces(l)
+    elif args.output_format == "id":
+        def encode(l):
+            return list(map(str, sp.EncodeAsIds(l)))
+    else:
+        raise NotImplementedError
+
+    if args.min_len is not None or args.max_len is not None:
+        def valid(line):
+            return (
+                (args.min_len is None or len(line) >= args.min_len) and
+                (args.max_len is None or len(line) <= args.max_len)
+            )
+    else:
+        def valid(lines):
+            return True
+
+    with contextlib.ExitStack() as stack:
+        inputs = [
+            stack.enter_context(open(input, "r", encoding="utf-8"))
+            if input != "-" else sys.stdin
+            for input in args.inputs
+        ]
+        outputs = [
+            stack.enter_context(open(output, "w", encoding="utf-8"))
+            if output != "-" else sys.stdout
+            for output in args.outputs
+        ]
+
+        stats = {
+            "num_empty": 0,
+            "num_filtered": 0,
+        }
+
+        def encode_line(line):
+            line = line.strip()
+            if len(line) > 0:
+                line = encode(line)
+                if valid(line):
+                    return line
+                else:
+                    stats["num_filtered"] += 1
+            else:
+                stats["num_empty"] += 1
+            return None
+
+        for i, lines in enumerate(zip(*inputs), start=1):
+            enc_lines = list(map(encode_line, lines))
+            if not any(enc_line is None for enc_line in enc_lines):
+                for enc_line, output_h in zip(enc_lines, outputs):
+                    print(" ".join(enc_line), file=output_h)
+            if i % 10000 == 0:
+                print("processed {} lines".format(i), file=sys.stderr)
+
+        print("skipped {} empty lines".format(stats["num_empty"]), file=sys.stderr)
+        print("filtered {} lines".format(stats["num_filtered"]), file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()

egs/librispeech/rnnt/local/spm_train.py

@@ -0,0 +1,12 @@
+#!/usr/bin/env python3
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# https://github.com/pytorch/fairseq/blob/master/LICENSE
+import sys
+
+import sentencepiece as spm
+
+if __name__ == "__main__":
+    spm.SentencePieceTrainer.Train(" ".join(sys.argv[1:]))

egs/librispeech/rnnt/path.sh

@@ -0,0 +1,5 @@
+export FUNASR_DIR=$PWD/../../..
+
+# NOTE(kan-bayashi): Use UTF-8 in Python to avoid UnicodeDecodeError when LC_ALL=C
+export PYTHONIOENCODING=UTF-8
+export PATH=$FUNASR_DIR/funasr/bin:$PATH

egs/librispeech/rnnt/run.sh

@@ -0,0 +1,222 @@
+#!/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
+gpu_inference=true  # Whether to perform gpu decoding, set false for cpu decoding
+# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
+njob=5
+train_cmd=utils/run.pl
+infer_cmd=utils/run.pl
+
+# general configuration
+feats_dir="" #feature output dictionary
+exp_dir=""
+lang=en
+token_type=bpe
+type=sound
+scp=wav.scp
+speed_perturb="0.9 1.0 1.1"
+stage=0
+stop_stage=5
+
+# feature configuration
+feats_dim=80
+nj=64
+
+# data
+raw_data=
+data_url=www.openslr.org/resources/12
+
+# bpe model
+nbpe=5000
+bpemode=unigram
+
+# 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_960
+valid_set=dev
+test_sets="test_clean test_other dev_clean dev_other"
+
+asr_config=conf/train_conformer_rnnt_unified.yaml
+model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
+
+inference_config=conf/decode_rnnt_conformer_streaming.yaml
+inference_asr_model=valid.cer_transducer_chunk.ave_10best.pb
+
+# you can set gpu num for decoding here
+gpuid_list=$CUDA_VISIBLE_DEVICES  # set gpus for decoding, the same as training stage by default
+ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
+
+if ${gpu_inference}; then
+    inference_nj=$[${ngpu}*${njob}]
+    _ngpu=1
+else
+    inference_nj=$njob
+    _ngpu=0
+fi
+
+
+if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
+    echo "stage -1: Data Download"
+    for part in dev-clean test-clean dev-other test-other train-clean-100 train-clean-360 train-other-500; do
+        local/download_and_untar.sh ${raw_data} ${data_url} ${part}
+    done
+fi
+
+if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
+    echo "stage 0: Data preparation"
+    # Data preparation
+    for x in dev-clean dev-other test-clean test-other train-clean-100 train-clean-360 train-other-500; do
+        local/data_prep.sh ${raw_data}/LibriSpeech/${x} ${feats_dir}/data/${x//-/_}
+    done
+    mkdir $feats_dir/data/$valid_set
+    dev_sets="dev_clean dev_other"
+    for file in wav.scp text; do
+        ( for f in $dev_sets; do cat $feats_dir/data/$f/$file; done ) | sort -k1 > $feats_dir/data/$valid_set/$file || exit 1;
+    done
+    mkdir $feats_dir/data/$train_set
+    train_sets="train_clean_100 train_clean_360 train_other_500"
+    for file in wav.scp text; do
+        ( for f in $train_sets; do cat $feats_dir/data/$f/$file; done ) | sort -k1 > $feats_dir/data/$train_set/$file || exit 1;
+    done
+fi
+
+if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+    echo "stage 1: Feature and CMVN Generation"
+    utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} ${feats_dir}/data/${train_set}
+fi
+
+token_list=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}_units.txt
+bpemodel=${feats_dir}/data/lang_char/${train_set}_${bpemode}${nbpe}
+echo "dictionary: ${token_list}"
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
+    ### Task dependent. You have to check non-linguistic symbols used in the corpus.
+    echo "stage 2: Dictionary and Json Data Preparation"
+    mkdir -p ${feats_dir}/data/lang_char/
+    echo "<blank>" > ${token_list}
+    echo "<s>" >> ${token_list}
+    echo "</s>" >> ${token_list}
+    cut -f 2- -d" " ${feats_dir}/data/${train_set}/text > ${feats_dir}/data/lang_char/input.txt
+    local/spm_train.py --input=${feats_dir}/data/lang_char/input.txt --vocab_size=${nbpe} --model_type=${bpemode} --model_prefix=${bpemodel} --input_sentence_size=100000000
+    local/spm_encode.py --model=${bpemodel}.model --output_format=piece < ${feats_dir}/data/lang_char/input.txt | tr ' ' '\n' | sort | uniq | awk '{print $0}' >> ${token_list}
+    echo "<unk>" >> ${token_list}
+fi
+
+# LM Training Stage
+world_size=$gpu_num  # run on one machine
+if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+    echo "stage 3: LM Training"
+fi
+
+# ASR Training Stage
+world_size=$gpu_num  # run on one machine
+if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
+    echo "stage 4: ASR Training"
+    mkdir -p ${exp_dir}/exp/${model_dir}
+    mkdir -p ${exp_dir}/exp/${model_dir}/log
+    INIT_FILE=./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 \
+                --split_with_space false \
+                --bpemodel ${bpemodel}.model \
+                --token_type $token_type \
+                --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} \
+                --resume true \
+                --output_dir ${exp_dir}/exp/${model_dir} \
+                --config $asr_config \
+                --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
+
+# Testing Stage
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+    echo "stage 5: Inference"
+    for dset in ${test_sets}; do
+        asr_exp=${exp_dir}/exp/${model_dir}
+        inference_tag="$(basename "${inference_config}" .yaml)"
+        _dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
+        _logdir="${_dir}/logdir"
+        if [ -d ${_dir} ]; then
+            echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
+            exit 0
+        fi
+        mkdir -p "${_logdir}"
+        _data="${feats_dir}/data/${dset}"
+        key_file=${_data}/${scp}
+        num_scp_file="$(<${key_file} wc -l)"
+        _nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
+        split_scps=
+        for n in $(seq "${_nj}"); do
+            split_scps+=" ${_logdir}/keys.${n}.scp"
+        done
+        # shellcheck disable=SC2086
+        utils/split_scp.pl "${key_file}" ${split_scps}
+        _opts=
+        if [ -n "${inference_config}" ]; then
+            _opts+="--config ${inference_config} "
+        fi
+        ${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
+            python -m funasr.bin.asr_inference_launch \
+                --batch_size 1 \
+                --ngpu "${_ngpu}" \
+                --njob ${njob} \
+                --gpuid_list ${gpuid_list} \
+                --data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
+                --cmvn_file ${feats_dir}/data/${train_set}/cmvn/cmvn.mvn \
+                --key_file "${_logdir}"/keys.JOB.scp \
+                --asr_train_config "${asr_exp}"/config.yaml \
+                --asr_model_file "${asr_exp}"/"${inference_asr_model}" \
+                --output_dir "${_logdir}"/output.JOB \
+                --mode asr \
+                ${_opts}
+
+        for f in token token_int score text; do
+            if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
+                for i in $(seq "${_nj}"); do
+                    cat "${_logdir}/output.${i}/1best_recog/${f}"
+                done | sort -k1 >"${_dir}/${f}"
+            fi
+        done
+        python utils/compute_wer.py ${_data}/text ${_dir}/text ${_dir}/text.cer
+        tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
+        cat ${_dir}/text.cer.txt
+    done
+fi

egs/librispeech/rnnt/utils

@@ -0,0 +1 @@
+../../aishell/transformer/utils

egs/librispeech_100h/rnnt/README.md

@@ -8,9 +8,9 @@
 - Model size: 30.54M
 
 ## Results (CER)
-- Decode config: conf/decode_rnnt_transformer.yaml (ctc weight:0.5)
+- Decode config: conf/decode_rnnt_conformer.yaml
 
-|      testset   | WER(%)  |
+|      testset   |  WER(%) |
 |:--------------:|:-------:|
 |    test_clean  |  6.64   |
 |    test_other  |  17.12  |