run.sh

#!/bin/bash

# Copyright 2019 Mobvoi Inc. All Rights Reserved.
. ./path.sh || exit 1;

# Use this to control how many gpu you use, It's 1-gpu training if you specify
# just 1gpu, otherwise it's is multiple gpu training based on DDP in pytorch
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
# The NCCL_SOCKET_IFNAME variable specifies which IP interface to use for nccl
# communication. More details can be found in
# https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/env.html
# export NCCL_SOCKET_IFNAME=ens4f1
export NCCL_DEBUG=INFO
stage=0 # start from 0 if you need to start from data preparation
stop_stage=6
# The num of nodes or machines used for multi-machine training
# Default 1 for single machine/node
# NFS will be needed if you want run multi-machine training
num_nodes=1
# The rank of each node or machine, range from 0 to num_nodes -1
# The first node/machine sets node_rank 0, the second one sets node_rank 1
# the third one set node_rank 2, and so on. Default 0
node_rank=0
num_utts_per_shard=1000
data_url=https://www.openslr.org/resources/111
data_source=/home/work_nfs5_ssd/yhliang/data/aishell4
# modify this to your AISHELL-4 data path

nj=16
dict=data/dict/lang_char.txt

train_set=aishell4_train
dev_set=aishell4_test
test_sets=aishell4_test

train_config=conf/train_conformer.yaml
cmvn=true
dir=exp/conformer
checkpoint=

# use average_checkpoint will get better result
average_checkpoint=true
decode_checkpoint=$dir/final.pt
average_num=30
decode_modes="attention_rescoring"

. tools/parse_options.sh || exit 1;

if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
  echo "stage -1: Data Download"
  local/download_and_untar.sh ${data_source} ${data_url} train_L
  local/download_and_untar.sh ${data_source} ${data_url} train_M
  local/download_and_untar.sh ${data_source} ${data_url} train_S
  local/download_and_untar.sh ${data_source} ${data_url} test
fi

if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
  # Data preparation
  local/prepare_data.sh ${data_source} || exit 1;
fi

if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
  # remove the space between the text labels for Mandarin dataset
  for x in ${train_set} ${test_sets}; do
    cp data/${x}/text data/${x}/text.org
    paste -d " " <(cut -d " " -f 1 data/${x}/text.org) <(cut -d " " -f 2 data/${x}/text.org \
      | tr 'a-z' 'A-Z' | sed 's/\([A-Z]\) \([A-Z]\)/\1▁\2/g' | tr -d " ") > data/${x}/text
    rm data/${x}/text.org
  done

  tools/compute_cmvn_stats.py --num_workers 32 --train_config $train_config \
    --in_scp data/${train_set}/wav.scp \
    --out_cmvn data/$train_set/global_cmvn

fi

if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
  # Make train dict
  echo "Make a dictionary"
  mkdir -p $(dirname $dict)
  echo "<blank> 0" > ${dict} # 0 will be used for "blank" in CTC
  echo "<unk> 1" >> ${dict} # <unk> must be 1
  tools/text2token.py -s 1 -n 1 data/${train_set}/text | cut -f 2- -d" " | tr " " "\n" \
    | sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0 " " NR+1}' >> ${dict}
  num_token=$(cat $dict | wc -l)
  echo "<sos/eos> $num_token" >> $dict # <eos>
fi

if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
  # Prepare wenet required data
  echo "Prepare data, prepare required format"
  for x in $train_set ${test_sets}; do
    tools/make_shard_list.py --num_utts_per_shard $num_utts_per_shard \
      --num_threads 32 --segments data/$x/segments \
      data/$x/wav.scp data/$x/text $(realpath data/$x/shards) data/$x/data.list
  done
fi

if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
  # Training
  mkdir -p $dir
  INIT_FILE=$dir/ddp_init
  # You had better rm it manually before you start run.sh on first node.
  # rm -f $INIT_FILE # delete old one before starting
  init_method=file://$(readlink -f $INIT_FILE)
  echo "$0: init method is $init_method"
  # The number of gpus runing on each node/machine
  num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
  # Use "nccl" if it works, otherwise use "gloo"
  dist_backend="gloo"
  # The total number of processes/gpus, so that the master knows
  # how many workers to wait for.
  # More details about ddp can be found in
  # https://pytorch.org/tutorials/intermediate/dist_tuto.html
  world_size=`expr $num_gpus \* $num_nodes`
  echo "total gpus is: $world_size"
  cmvn_opts=
  $cmvn && cp data/${train_set}/global_cmvn $dir
  $cmvn && cmvn_opts="--cmvn ${dir}/global_cmvn"
  # train.py will write $train_config to $dir/train.yaml with model input
  # and output dimension, train.yaml will be used for inference or model
  # export later
  for ((i = 0; i < $num_gpus; ++i)); do
  {
    gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
    # Rank of each gpu/process used for knowing whether it is
    # the master of a worker.
    rank=`expr $node_rank \* $num_gpus + $i`
    python wenet/bin/train.py --gpu $gpu_id \
      --config $train_config \
      --data_type shard \
      --symbol_table $dict \
      --train_data data/$train_set/data.list \
      --cv_data data/${dev_set}/data.list \
      ${checkpoint:+--checkpoint $checkpoint} \
      --model_dir $dir \
      --ddp.init_method $init_method \
      --ddp.world_size $world_size \
      --ddp.rank $rank \
      --ddp.dist_backend $dist_backend \
      --num_workers 1 \
      $cmvn_opts
  }
  done
  wait
fi

if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
  # Test model, please specify the model you want to test by --checkpoint
  if [ ${average_checkpoint} == true ]; then
    decode_checkpoint=$dir/avg_${average_num}.pt
    echo "do model average and final checkpoint is $decode_checkpoint"
    python wenet/bin/average_model.py \
      --dst_model $decode_checkpoint \
      --src_path $dir  \
      --num ${average_num}
  fi
  # Specify decoding_chunk_size if it's a unified dynamic chunk trained model
  # -1 for full chunk
  decoding_chunk_size=
  ctc_weight=0.5
  for mode in ${decode_modes}; do
  {
    for test_set in ${test_sets}; do
    {
      test_dir=$dir/test_${mode}
      mkdir -p $test_dir
      python wenet/bin/recognize.py --gpu $(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f1) \
        --mode $mode \
        --config $dir/train.yaml \
        --data_type shard \
        --test_data data/${test_set}/data.list \
        --checkpoint $decode_checkpoint \
        --beam_size 10 \
        --batch_size 1 \
        --penalty 0.0 \
        --dict $dict \
        --ctc_weight $ctc_weight \
        --result_file $test_dir/text \
        ${decoding_chunk_size:+--decoding_chunk_size $decoding_chunk_size}
      python tools/compute-wer.py --char=1 --v=1 \
        data/${test_set}/text $test_dir/text > $test_dir/wer
    } &
    done
  }
  done
  wait

fi

if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
  # Export the best model you want
  python wenet/bin/export_jit.py \
    --config $dir/train.yaml \
    --checkpoint $dir/avg_${average_num}.pt \
    --output_file $dir/final.zip \
    --output_quant_file $dir/final_quant.zip
fi