run_validate.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"
# 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=5 # start from 0 if you need to start from data preparation
stop_stage=5

# The num of machines(nodes) for multi-machine training, 1 is for one machine.
# NFS is required if num_nodes > 1.
num_nodes=1

# The rank of each node or machine, which ranges from 0 to `num_nodes - 1`.
# You should set the node_rank=0 on the first machine, set the node_rank=1
# on the second machine, and so on.
node_rank=0
# The aishell dataset location, please change this to your own path
# make sure of using absolute path. DO-NOT-USE relatvie path!
data=/data/conformer/train/
data_url=www.openslr.org/resources/33

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

# 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_u2++_conformer.yaml: U2++ conformer
# 6. conf/train_u2++_transformer.yaml: U2++ transformer
train_config=conf/train_conformer.yaml
cmvn=true
dir=exp/conformer
checkpoint=

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

. tools/parse_options.sh || exit 1;

if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
  # 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}
    
    # The code of computing script below has been executed in the wenet/bin/train.py 
    #python tools/compute-wer.py --char=1 --v=1 \
    #  data/test/text $test_dir/text > $test_dir/wer
  } &
  done
  wait
fi