Merge pull request #807 from alibaba-damo-academy/dev_wjm

jmwang66

2023-08-07 cf8e000a84e888495dcf30c4dbfecea1ee7ab4e2

Merge pull request #807 from alibaba-damo-academy/dev_wjm

update e_branchformer

 egs/aishell2/data2vec_pretrain/run.sh

@@ -20,7 +20,6 @@
stop_stage=3

# feature configuration
feats_dim=80
nj=64

# data
@@ -42,7 +41,7 @@
valid_set=dev_ios

asr_config=conf/train_pretrain_transformer.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml) _${lang}_${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"
@@ -115,7 +114,6 @@
                --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 \

 egs/librispeech/branchformer/conf/decode_asr_transformer_beam10_ctc0.3.yaml

New file
@@ -0,0 +1,6 @@
beam_size: 10
penalty: 0.0
maxlenratio: 0.0
minlenratio: 0.0
ctc_weight: 0.3
lm_weight: 0.0

 egs/librispeech/branchformer/conf/train_asr_branchformer.yaml

New file
@@ -0,0 +1,104 @@
# network architecture
# encoder related
encoder: branchformer
encoder_conf:
    output_size: 512
    use_attn: true
    attention_heads: 8
    attention_layer_type: rel_selfattn
    pos_enc_layer_type: rel_pos
    rel_pos_type: latest
    use_cgmlp: true
    cgmlp_linear_units: 3072
    cgmlp_conv_kernel: 31
    use_linear_after_conv: false
    gate_activation: identity
    merge_method: concat
    cgmlp_weight: 0.5               # used only if merge_method is "fixed_ave"
    attn_branch_drop_rate: 0.0      # used only if merge_method is "learned_ave"
    num_blocks: 18
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.1
    input_layer: conv2d
    stochastic_depth_rate: 0.0

# decoder related
decoder: transformer
decoder_conf:
    attention_heads: 8
    linear_units: 2048
    num_blocks: 6
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.1
    src_attention_dropout_rate: 0.1

# 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

# hybrid CTC/attention
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1     # label smoothing option
    length_normalized_loss: false

# optimization related
accum_grad: 2
grad_clip: 5
max_epoch: 210
val_scheduler_criterion:
    - valid
    - acc
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 10

optim: adam
optim_conf:
    lr: 0.0025
    weight_decay: 0.000001
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 40000

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
    - 27
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_ratio_range:
    - 0.
    - 0.05
    num_time_mask: 10

dataset_conf:
    data_names: speech,text
    data_types: sound,text
    shuffle: True
    shuffle_conf:
        shuffle_size: 2048
        sort_size: 500
    batch_conf:
        batch_type: token
        batch_size: 30000
    num_workers: 8

log_interval: 50
normalize: None

 egs/librispeech/branchformer/local/data_prep.sh

New file
@@ -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/branchformer/local/download_and_untar.sh

New file
@@ -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/branchformer/local/spm_encode.py

New file
@@ -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/branchformer/local/spm_train.py

New file
@@ -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/branchformer/path.sh

New file
@@ -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/branchformer/run.sh

New file
@@ -0,0 +1,223 @@
#!/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="../DATA" #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_asr_branchformer.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"

inference_config=conf/decode_asr_transformer_beam10_ctc0.3.yaml
inference_asr_model=valid.acc.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 --fbankdir ${feats_dir}/data/${train_set} --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --config_file "$asr_config" --scale 1.0
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=${exp_dir}/exp/${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 \
                --split_with_space false \
                --bpemodel ${bpemodel}.model \
                --token_type $token_type \
                --token_list $token_list \
                --dataset_type large \
                --data_dir ${feats_dir}/data \
                --train_set ${train_set} \
                --valid_set ${valid_set} \
                --cmvn_file ${feats_dir}/data/${train_set}/cmvn/am.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/am.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/branchformer/utils

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

 egs/librispeech/e_branchformer/conf/decode_asr_transformer_beam10_ctc0.3.yaml

New file
@@ -0,0 +1,6 @@
beam_size: 10
penalty: 0.0
maxlenratio: 0.0
minlenratio: 0.0
ctc_weight: 0.3
lm_weight: 0.0

 egs/librispeech/e_branchformer/conf/train_asr_e_branchformer.yaml

New file
@@ -0,0 +1,105 @@
# network architecture
# encoder related
encoder: e_branchformer
encoder_conf:
    output_size: 512
    attention_heads: 8
    attention_layer_type: rel_selfattn
    pos_enc_layer_type: rel_pos
    rel_pos_type: latest
    cgmlp_linear_units: 3072
    cgmlp_conv_kernel: 31
    use_linear_after_conv: false
    gate_activation: identity
    num_blocks: 17
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.1
    input_layer: conv2d
    layer_drop_rate: 0.1
    linear_units: 1024
    positionwise_layer_type: linear
    macaron_ffn: true
    use_ffn: true
    merge_conv_kernel: 31

# decoder related
decoder: transformer
decoder_conf:
    attention_heads: 8
    linear_units: 2048
    num_blocks: 6
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.1
    src_attention_dropout_rate: 0.1
    layer_drop_rate: 0.2

# 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

# hybrid CTC/attention
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1     # label smoothing option
    length_normalized_loss: false

# optimization related
accum_grad: 2
grad_clip: 5
max_epoch: 240
val_scheduler_criterion:
    - valid
    - acc
best_model_criterion:
-   - valid
    - acc
    - max
keep_nbest_models: 10

optim: adam
optim_conf:
    lr: 0.002
    weight_decay: 0.000001
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 40000

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
    - 27
    num_freq_mask: 2
    apply_time_mask: true
    time_mask_width_ratio_range:
    - 0.
    - 0.05
    num_time_mask: 10

dataset_conf:
    data_names: speech,text
    data_types: sound,text
    shuffle: True
    shuffle_conf:
        shuffle_size: 2048
        sort_size: 500
    batch_conf:
        batch_type: token
        batch_size: 30000
    num_workers: 8

log_interval: 50
normalize: None

 egs/librispeech/e_branchformer/local/data_prep.sh

New file
@@ -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/e_branchformer/local/download_and_untar.sh

New file
@@ -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/e_branchformer/local/spm_encode.py

New file
@@ -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/e_branchformer/local/spm_train.py

New file
@@ -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/e_branchformer/path.sh

New file
@@ -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/e_branchformer/run.sh

New file
@@ -0,0 +1,223 @@
#!/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="../DATA" #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_asr_e_branchformer.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"

inference_config=conf/decode_asr_transformer_beam10_ctc0.3.yaml
inference_asr_model=valid.acc.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 --fbankdir ${feats_dir}/data/${train_set} --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --config_file "$asr_config" --scale 1.0
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=${exp_dir}/exp/${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 \
                --split_with_space false \
                --bpemodel ${bpemodel}.model \
                --token_type $token_type \
                --token_list $token_list \
                --dataset_type large \
                --data_dir ${feats_dir}/data \
                --train_set ${train_set} \
                --valid_set ${valid_set} \
                --cmvn_file ${feats_dir}/data/${train_set}/cmvn/am.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/am.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/e_branchformer/utils

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

 funasr/build_utils/build_args.py

@@ -54,6 +54,12 @@
            default=None,
            help="The number of input dimension of the feature",
        )
        task_parser.add_argument(
            "--cmvn_file",
            type=str_or_none,
            default=None,
            help="The path of cmvn file.",
        )

    elif args.task_name == "lm":
        from funasr.build_utils.build_lm_model import class_choices_list

 funasr/build_utils/build_pretrain_model.py

@@ -4,13 +4,18 @@
from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
from funasr.models.frontend.default import DefaultFrontend
from funasr.models.frontend.windowing import SlidingWindow
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.models.specaug.specaug import SpecAug
from funasr.torch_utils.initialize import initialize
from funasr.train.class_choices import ClassChoices

frontend_choices = ClassChoices(
    name="frontend",
    classes=dict(default=DefaultFrontend, sliding_window=SlidingWindow),
    classes=dict(
        default=DefaultFrontend,
        sliding_window=SlidingWindow,
        wav_frontend=WavFrontend,
    ),
    default="default",
)
specaug_choices = ClassChoices(

 funasr/models/data2vec.py

@@ -12,12 +12,12 @@
import torch

from funasr.layers.abs_normalize import AbsNormalize
from funasr.models.base_model import FunASRModel
from funasr.models.encoder.abs_encoder import AbsEncoder
from funasr.models.frontend.abs_frontend import AbsFrontend
from funasr.models.preencoder.abs_preencoder import AbsPreEncoder
from funasr.models.specaug.abs_specaug import AbsSpecAug
from funasr.torch_utils.device_funcs import force_gatherable
from funasr.models.base_model import FunASRModel

if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
    from torch.cuda.amp import autocast
@@ -36,8 +36,8 @@
            frontend: Optional[AbsFrontend],
            specaug: Optional[AbsSpecAug],
            normalize: Optional[AbsNormalize],
            preencoder: Optional[AbsPreEncoder],
            encoder: AbsEncoder,
            preencoder: Optional[AbsPreEncoder] = None,
    ):

        super().__init__()