From 967a0477400c05be6f6580e0c4036ca66c6d4856 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期四, 11 五月 2023 17:34:56 +0800
Subject: [PATCH] update repo
---
/dev/null | 252 --------------------------------------------------
1 files changed, 0 insertions(+), 252 deletions(-)
diff --git a/egs/aishell/data2vec_paraformer_finetune/run.bak.sh b/egs/aishell/data2vec_paraformer_finetune/run.bak.sh
deleted file mode 100755
index d033ce2..0000000
--- a/egs/aishell/data2vec_paraformer_finetune/run.bak.sh
+++ /dev/null
@@ -1,252 +0,0 @@
-#!/usr/bin/env bash
-
-. ./path.sh || exit 1;
-
-# machines configuration
-CUDA_VISIBLE_DEVICES="0,1"
-gpu_num=2
-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, for large data
-exp_dir="."
-lang=zh
-dumpdir=dump/fbank
-feats_type=fbank
-token_type=char
-scp=feats.scp
-type=kaldi_ark
-stage=0
-stop_stage=4
-
-# feature configuration
-feats_dim=80
-sample_frequency=16000
-nj=32
-speed_perturb="0.9,1.0,1.1"
-
-# data
-data_aishell=
-
-# exp tag
-tag=""
-
-model_name=damo/speech_data2vec_pretrain-zh-cn-aishell2-16k-pytorch
-init_param="$HOME/.cache/modelscope/hub/$model_name/basemodel.pb"
-
-. 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
-valid_set=dev
-test_sets="dev test"
-
-asr_config=conf/train_asr_paraformer_transformer_12e_6d_3072_768.yaml
-model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
-
-inference_config=conf/decode_asr_transformer_noctc_1best.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 0 ] && [ ${stop_stage} -ge 0 ]; then
- echo "stage 0: Data preparation"
- # Data preparation
- local/aishell_data_prep.sh ${data_aishell}/data_aishell/wav ${data_aishell}/data_aishell/transcript ${feats_dir}
- for x in train dev test; do
- cp ${feats_dir}/data/${x}/text ${feats_dir}/data/${x}/text.org
- paste -d " " <(cut -f 1 -d" " ${feats_dir}/data/${x}/text.org) <(cut -f 2- -d" " ${feats_dir}/data/${x}/text.org | tr -d " ") \
- > ${feats_dir}/data/${x}/text
- utils/text2token.py -n 1 -s 1 ${feats_dir}/data/${x}/text > ${feats_dir}/data/${x}/text.org
- mv ${feats_dir}/data/${x}/text.org ${feats_dir}/data/${x}/text
- done
-fi
-
-feat_train_dir=${feats_dir}/${dumpdir}/train; mkdir -p ${feat_train_dir}
-feat_dev_dir=${feats_dir}/${dumpdir}/dev; mkdir -p ${feat_dev_dir}
-feat_test_dir=${feats_dir}/${dumpdir}/test; mkdir -p ${feat_test_dir}
-if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
- echo "stage 1: Feature Generation"
- # compute fbank features
- fbankdir=${feats_dir}/fbank
- utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} --speed_perturb ${speed_perturb} \
- ${feats_dir}/data/train ${exp_dir}/exp/make_fbank/train ${fbankdir}/train
- utils/fix_data_feat.sh ${fbankdir}/train
- utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} \
- ${feats_dir}/data/dev ${exp_dir}/exp/make_fbank/dev ${fbankdir}/dev
- utils/fix_data_feat.sh ${fbankdir}/dev
- utils/compute_fbank.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --sample_frequency ${sample_frequency} \
- ${feats_dir}/data/test ${exp_dir}/exp/make_fbank/test ${fbankdir}/test
- utils/fix_data_feat.sh ${fbankdir}/test
-
- # compute global cmvn
- utils/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} \
- ${fbankdir}/train ${exp_dir}/exp/make_fbank/train
-
- # apply cmvn
- utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
- ${fbankdir}/train ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/train ${feat_train_dir}
- utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
- ${fbankdir}/dev ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/dev ${feat_dev_dir}
- utils/apply_cmvn.sh --cmd "$train_cmd" --nj $nj \
- ${fbankdir}/test ${fbankdir}/train/cmvn.json ${exp_dir}/exp/make_fbank/test ${feat_test_dir}
-
- cp ${fbankdir}/train/text ${fbankdir}/train/speech_shape ${fbankdir}/train/text_shape ${feat_train_dir}
- cp ${fbankdir}/dev/text ${fbankdir}/dev/speech_shape ${fbankdir}/dev/text_shape ${feat_dev_dir}
- cp ${fbankdir}/test/text ${fbankdir}/test/speech_shape ${fbankdir}/test/text_shape ${feat_test_dir}
-
- utils/fix_data_feat.sh ${feat_train_dir}
- utils/fix_data_feat.sh ${feat_dev_dir}
- utils/fix_data_feat.sh ${feat_test_dir}
-
- #generate ark list
- utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_train_dir} ${fbankdir}/train ${feat_train_dir}
- utils/gen_ark_list.sh --cmd "$train_cmd" --nj $nj ${feat_dev_dir} ${fbankdir}/dev ${feat_dev_dir}
-fi
-
-token_list=${feats_dir}/data/${lang}_token_list/char/tokens.txt
-echo "dictionary: ${token_list}"
-if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
- echo "stage 2: Dictionary Preparation"
- mkdir -p ${feats_dir}/data/${lang}_token_list/char/
-
- echo "make a dictionary"
- echo "<blank>" > ${token_list}
- echo "<s>" >> ${token_list}
- echo "</s>" >> ${token_list}
- utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/data/train/text | cut -f 2- -d" " | tr " " "\n" \
- | sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
- num_token=$(cat ${token_list} | wc -l)
- echo "<unk>" >> ${token_list}
- vocab_size=$(cat ${token_list} | wc -l)
- awk -v v=,${vocab_size} '{print $0v}' ${feat_train_dir}/text_shape > ${feat_train_dir}/text_shape.char
- awk -v v=,${vocab_size} '{print $0v}' ${feat_dev_dir}/text_shape > ${feat_dev_dir}/text_shape.char
- mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/train
- mkdir -p ${feats_dir}/asr_stats_fbank_zh_char/dev
- cp ${feat_train_dir}/speech_shape ${feat_train_dir}/text_shape ${feat_train_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/train
- cp ${feat_dev_dir}/speech_shape ${feat_dev_dir}/text_shape ${feat_dev_dir}/text_shape.char ${feats_dir}/asr_stats_fbank_zh_char/dev
-fi
-
-# Training Stage
-world_size=$gpu_num # run on one machine
-if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
- echo "stage 3: Training"
- python utils/download_model.py --model_name ${model_name} # download pretrained model on ModelScope
- 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])
- asr_train_paraformer.py \
- --gpu_id $gpu_id \
- --use_preprocessor true \
- --token_type char \
- --token_list $token_list \
- --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/${scp},speech,${type} \
- --train_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${train_set}/text,text,text \
- --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/speech_shape \
- --train_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${train_set}/text_shape.char \
- --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/${scp},speech,${type} \
- --valid_data_path_and_name_and_type ${feats_dir}/${dumpdir}/${valid_set}/text,text,text \
- --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/speech_shape \
- --valid_shape_file ${feats_dir}/asr_stats_fbank_zh_char/${valid_set}/text_shape.char \
- --init_param ${init_param} \
- --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 \
- --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 4 ] && [ ${stop_stage} -ge 4 ]; then
- echo "stage 4: 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}/${dumpdir}/${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}" \
- --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 paraformer \
- ${_opts}
-
- for f in token token_int score text; do
- if [ -f "${_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/proce_text.py ${_dir}/text ${_dir}/text.proc
- python utils/proce_text.py ${_data}/text ${_data}/text.proc
- python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
- tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
- cat ${_dir}/text.cer.txt
- done
-fi
--
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