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"
# 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=4
# 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
# data
timit_data=/home/Liangcd/data/timit
# path to save preproecssed data
# export data=data


nj=16

# data_type can be `raw` or `shard`. Typically, raw is used for small dataset,
# `shard` is used for large dataset which is over 1k hours, and `shard` is
# faster on reading data and training.
data_type=raw
num_utts_per_shard=1000

train_set=train
# Optional train_config
# 1. conf/train_transformer.yaml: Standard transformer
# 2. conf/train_conformer.yaml: Standard conformer
# 3. conf/train_unified_conformer.yaml: Unified dynamic chunk causal conformer
# 4. conf/train_unified_transformer.yaml: Unified dynamic chunk transformer
# 5. conf/train_conformer_no_pos.yaml: Conformer without relative positional encoding
# 6. conf/train_u2++_conformer.yaml: U2++ conformer
# 7. conf/train_u2++_transformer.yaml: U2++ transformer
train_config=conf/train_transformer.yaml
cmvn=true
dir=exp/transformer_phn_5k_acc4_bs16
checkpoint=


# use average_checkpoint will get better result
average_checkpoint=true
decode_checkpoint=$dir/final.pt
average_num=20
decode_modes="ctc_greedy_search ctc_prefix_beam_search attention attention_rescoring"
# choose in [phn]
trans_type=phn

dict=data/dict/${trans_type}_units.txt


. tools/parse_options.sh || exit 1;

if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then

    echo "stage 0: Data preparation"
    echo "preparing data for TIMIT for ${trans_type} level transcripts"
    local/timit_data_prep.sh ${timit_data} ${trans_type} || exit 1;
    local/timit_format_data.sh
    echo "Finish stage 0"
fi

if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
    echo "stage 1: compute global cmvn"
    # compute cmvn
    tools/compute_cmvn_stats.py --num_workers 16 --train_config $train_config \
        --in_scp data/${train_set}/wav.scp \
        --out_cmvn data/${train_set}/global_cmvn
    echo "Finish stage 1"
fi



if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
    echo "stage 2: make train dict"
    # 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 --space sil --trans_type ${trans_type} data/${train_set}/text  \
      | cut -f 2- -d" " | tr " " "\n" | sort | uniq | grep -v -e '^\s*$' | \
      awk '{print $0 " " NR+1}' >> ${dict}
    wc -l ${dict}
    num_token=$(cat $dict | wc -l)
    echo "<sos/eos> $num_token" >> $dict # <eos>
    echo "Finish stage 2"
fi

if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
  echo "stage 3: Prepare data, prepare required format"
  for x in dev test ${train_set}; do
    if [ $data_type == "shard" ]; then
      tools/make_shard_list.py --num_utts_per_shard $num_utts_per_shard \
        --num_threads 16 data/$x/wav.scp data/$x/text \
        $(realpath data/$x/shards) data/$x/data.list
    else
      tools/make_raw_list.py data/$x/wav.scp data/$x/text \
        data/$x/data.list
    fi
  done
  echo "Finish stage 3"
fi

if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
  mkdir -p $dir
  # You have to rm `INIT_FILE` manually when you resume or restart a
  # multi-machine training.
  INIT_FILE=$dir/ddp_init
  init_method=file://$(readlink -f $INIT_FILE)
  echo "$0: init method is $init_method"
  num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
  # Use "nccl" if it works, otherwise use "gloo"
  dist_backend="gloo"
  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 rewrite $train_config to $dir/train.yaml with model input
  # and output dimension, and $dir/train.yaml will be used for inference
  # and export.
  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 $data_type \
      --symbol_table $dict \
      --train_data data/$train_set/data.list \
      --cv_data data/dev/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 \
      --pin_memory
  } &
  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} \
      --val_best
  fi
  # Please specify decoding_chunk_size for unified streaming and
  # non-streaming model. The default value is -1, which is full chunk
  # for non-streaming inference.
  decoding_chunk_size=
  ctc_weight=0.5
  reverse_weight=0.0
  for mode in ${decode_modes}; do
  {
    test_dir=$dir/test_${mode}
    mkdir -p $test_dir
    python wenet/bin/recognize.py --gpu 0 \
      --mode $mode \
      --config $dir/train.yaml \
      --data_type $data_type \
      --test_data data/test/data.list \
      --checkpoint $decode_checkpoint \
      --beam_size 10 \
      --batch_size 1 \
      --penalty 0.0 \
      --dict $dict \
      --ctc_weight $ctc_weight \
      --reverse_weight $reverse_weight \
      --result_file $test_dir/text \
      ${decoding_chunk_size:+--decoding_chunk_size $decoding_chunk_size} \
      --connect_symbol ▁
    python tools/compute-wer.py --char=1 --v=1 \
      data/test/text $test_dir/text > $test_dir/wer
  } &
  done
  wait
fi

if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
  # compute wer
  for mode in ${decode_modes}; do
    for test_set in test; do
     test_dir=$dir/test_${mode}
     sed 's:▁: :g' $test_dir/text > $test_dir/text.norm
     python tools/compute-wer.py --char=1 --v=1 \
       data/$test_set/text $test_dir/text.norm > $test_dir/wer
    done
  done
fi