From 4ebde3c4ac27c15ff39ffbd5aa601035d189497a Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 18:42:35 +0800
Subject: [PATCH] aishell example
---
examples/aishell/paraformer/run.sh | 175 ++++++++++++++++++++++++++-------------------------------
1 files changed, 80 insertions(+), 95 deletions(-)
diff --git a/examples/aishell/paraformer/run.sh b/examples/aishell/paraformer/run.sh
index 3f485c2..410751a 100755
--- a/examples/aishell/paraformer/run.sh
+++ b/examples/aishell/paraformer/run.sh
@@ -39,23 +39,14 @@
valid_set=dev
test_sets="dev test"
-asr_config=train_asr_paraformer_conformer_12e_6d_2048_256.yaml
-model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
+config=train_asr_paraformer_conformer_12e_6d_2048_256.yaml
+model_dir="baseline_$(basename "${config}" .yaml)_${lang}_${token_type}_${tag}"
-#inference_config=conf/decode_asr_transformer_noctc_1best.yaml
-#inference_asr_model=valid.acc.ave_10best.pb
+inference_device="cuda" #"cpu"
+inference_checkpoint="model.pt"
+inference_scp="wav.scp"
-## 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"
@@ -85,10 +76,10 @@
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
+# utils/compute_cmvn.sh --fbankdir ${feats_dir}/data/${train_set} --cmd "$train_cmd" --nj $nj --feats_dim ${feats_dim} --config_file "$config" --scale 1.0
python ../../../funasr/bin/compute_audio_cmvn.py \
--config-path "${workspace}" \
- --config-name "${asr_config}" \
+ --config-name "${config}" \
++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
++cmvn_file="${feats_dir}/data/${train_set}/cmvn.json" \
++dataset_conf.num_workers=$nj
@@ -116,90 +107,84 @@
# ASR Training Stage
if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
-echo "stage 4: ASR Training"
+ echo "stage 4: ASR Training"
+ log_file="${exp_dir}/exp/${model_dir}/train.log.txt"
+ echo "log_file: ${log_file}"
torchrun \
--nnodes 1 \
--nproc_per_node ${gpu_num} \
../../../funasr/bin/train.py \
--config-path "${workspace}" \
- --config-name "${asr_config}" \
+ --config-name "${config}" \
++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
- ++cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
- ++token_list="${token_list}" \
- ++output_dir="${exp_dir}/exp/${model_dir}"
+ ++tokenizer_conf.token_list="${token_list}" \
+ ++frontend_conf.cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
+ ++output_dir="${exp_dir}/exp/${model_dir}" &> ${log_file}
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 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
-#
-## Prepare files for ModelScope fine-tuning and inference
-#if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
-# echo "stage 6: ModelScope Preparation"
-# cp ${feats_dir}/data/${train_set}/cmvn/am.mvn ${exp_dir}/exp/${model_dir}/am.mvn
-# vocab_size=$(cat ${token_list} | wc -l)
-# python utils/gen_modelscope_configuration.py \
-# --am_model_name $inference_asr_model \
-# --mode paraformer \
-# --model_name paraformer \
-# --dataset aishell \
-# --output_dir $exp_dir/exp/$model_dir \
-# --vocab_size $vocab_size \
-# --nat _nat \
-# --tag $tag
-#fi
\ No newline at end of file
+
+
+# Testing Stage
+if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
+ echo "stage 5: Inference"
+
+ if ${inference_device} == "cuda"; then
+ nj=$(echo CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
+ else
+ nj=$njob
+ batch_size=1
+ gpuid_list=""
+ for JOB in $(seq ${nj}); do
+ gpuid_list=CUDA_VISIBLE_DEVICES"-1,"
+ done
+ fi
+
+ for dset in ${test_sets}; do
+
+ inference_dir="${asr_exp}/${inference_checkpoint}/${dset}"
+ _logdir="${inference_dir}/logdir"
+
+ mkdir -p "${_logdir}"
+ data_dir="${feats_dir}/data/${dset}"
+ key_file=${data_dir}/${inference_scp}
+
+ split_scps=
+ for JOB in $(seq "${nj}"); do
+ split_scps+=" ${_logdir}/keys.${JOB}.scp"
+ done
+ utils/split_scp.pl "${key_file}" ${split_scps}
+
+ for JOB in $(seq ${nj}); do
+ {
+ python ../../../funasr/bin/inference.py \
+ --config-path="${exp_dir}/exp/${model_dir}" \
+ --config-name="config.yaml" \
+ ++init_param="${exp_dir}/exp/${model_dir}/${inference_checkpoint}" \
+ ++tokenizer_conf.token_list="${token_list}" \
+ ++frontend_conf.cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
+ ++input="${_logdir}/keys.${JOB}.scp" \
+ ++output_dir="${inference_dir}/${JOB}" \
+ ++device="${inference_device}"
+ }&
+
+ done
+ wait
+
+ mkdir -p ${inference_dir}/1best_recog
+ for f in token score text; do
+ if [ -f "${inference_dir}/${JOB}/1best_recog/${f}" ]; then
+ for JOB in $(seq "${nj}"); do
+ cat "${inference_dir}/${JOB}/1best_recog/${f}"
+ done | sort -k1 >"${inference_dir}/1best_recog/${f}"
+ fi
+ done
+
+ echo "Computing WER ..."
+ cp ${inference_dir}/1best_recog/text ${inference_dir}/1best_recog/text.proc
+ cp ${data_dir}/text ${inference_dir}/1best_recog/text.ref
+ python utils/compute_wer.py ${inference_dir}/1best_recog/text.ref ${inference_dir}/1best_recog/text.proc ${inference_dir}/1best_recog/text.cer
+ tail -n 3 ${inference_dir}/1best_recog/text.cer
+ done
+
+fi
--
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