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https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
aishell example
This commit is contained in:
游雁
parent
6a9c21a408
commit
4ebde3c4ac
@ -39,23 +39,14 @@ train_set=train
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valid_set=dev
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test_sets="dev test"
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asr_config=train_asr_paraformer_conformer_12e_6d_2048_256.yaml
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model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
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config=train_asr_paraformer_conformer_12e_6d_2048_256.yaml
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model_dir="baseline_$(basename "${config}" .yaml)_${lang}_${token_type}_${tag}"
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inference_device="cuda" #"cpu"
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inference_checkpoint="model.pt"
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inference_scp="wav.scp"
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#inference_config=conf/decode_asr_transformer_noctc_1best.yaml
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#inference_asr_model=valid.acc.ave_10best.pb
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## you can set gpu num for decoding here
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#gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default
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#ngpu=$(echo $gpuid_list | awk -F "," '{print NF}')
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#
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#if ${gpu_inference}; then
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# inference_nj=$[${ngpu}*${njob}]
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# _ngpu=1
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#else
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# inference_nj=$njob
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# _ngpu=0
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#fi
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if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
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echo "stage -1: Data Download"
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@ -85,10 +76,10 @@ fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Feature and CMVN Generation"
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# 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
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# 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
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python ../../../funasr/bin/compute_audio_cmvn.py \
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--config-path "${workspace}" \
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--config-name "${asr_config}" \
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--config-name "${config}" \
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++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
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++cmvn_file="${feats_dir}/data/${train_set}/cmvn.json" \
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++dataset_conf.num_workers=$nj
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@ -116,90 +107,84 @@ fi
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# ASR Training Stage
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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echo "stage 4: ASR Training"
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echo "stage 4: ASR Training"
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log_file="${exp_dir}/exp/${model_dir}/train.log.txt"
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echo "log_file: ${log_file}"
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torchrun \
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--nnodes 1 \
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--nproc_per_node ${gpu_num} \
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../../../funasr/bin/train.py \
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--config-path "${workspace}" \
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--config-name "${asr_config}" \
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--config-name "${config}" \
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++train_data_set_list="${feats_dir}/data/${train_set}/audio_datasets.jsonl" \
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++cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
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++token_list="${token_list}" \
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++output_dir="${exp_dir}/exp/${model_dir}"
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++tokenizer_conf.token_list="${token_list}" \
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++frontend_conf.cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
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++output_dir="${exp_dir}/exp/${model_dir}" &> ${log_file}
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fi
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#
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## Testing Stage
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#if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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# echo "stage 5: Inference"
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# for dset in ${test_sets}; do
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# asr_exp=${exp_dir}/exp/${model_dir}
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# inference_tag="$(basename "${inference_config}" .yaml)"
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# _dir="${asr_exp}/${inference_tag}/${inference_asr_model}/${dset}"
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# _logdir="${_dir}/logdir"
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# if [ -d ${_dir} ]; then
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# echo "${_dir} is already exists. if you want to decode again, please delete this dir first."
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# exit 0
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# fi
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# mkdir -p "${_logdir}"
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# _data="${feats_dir}/data/${dset}"
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# key_file=${_data}/${scp}
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# num_scp_file="$(<${key_file} wc -l)"
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# _nj=$([ $inference_nj -le $num_scp_file ] && echo "$inference_nj" || echo "$num_scp_file")
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# split_scps=
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# for n in $(seq "${_nj}"); do
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# split_scps+=" ${_logdir}/keys.${n}.scp"
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# done
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# # shellcheck disable=SC2086
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# utils/split_scp.pl "${key_file}" ${split_scps}
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# _opts=
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# if [ -n "${inference_config}" ]; then
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# _opts+="--config ${inference_config} "
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# fi
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# ${infer_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
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# python -m funasr.bin.asr_inference_launch \
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# --batch_size 1 \
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# --ngpu "${_ngpu}" \
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# --njob ${njob} \
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# --gpuid_list ${gpuid_list} \
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# --data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
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# --cmvn_file ${feats_dir}/data/${train_set}/cmvn/am.mvn \
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# --key_file "${_logdir}"/keys.JOB.scp \
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# --asr_train_config "${asr_exp}"/config.yaml \
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# --asr_model_file "${asr_exp}"/"${inference_asr_model}" \
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# --output_dir "${_logdir}"/output.JOB \
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# --mode paraformer \
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# ${_opts}
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#
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# for f in token token_int score text; do
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# if [ -f "${_logdir}/output.1/1best_recog/${f}" ]; then
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# for i in $(seq "${_nj}"); do
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# cat "${_logdir}/output.${i}/1best_recog/${f}"
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# done | sort -k1 >"${_dir}/${f}"
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# fi
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# done
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# python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
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# python utils/proce_text.py ${_data}/text ${_data}/text.proc
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# python utils/compute_wer.py ${_data}/text.proc ${_dir}/text.proc ${_dir}/text.cer
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# tail -n 3 ${_dir}/text.cer > ${_dir}/text.cer.txt
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# cat ${_dir}/text.cer.txt
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# done
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#fi
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#
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## Prepare files for ModelScope fine-tuning and inference
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#if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
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# echo "stage 6: ModelScope Preparation"
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# cp ${feats_dir}/data/${train_set}/cmvn/am.mvn ${exp_dir}/exp/${model_dir}/am.mvn
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# vocab_size=$(cat ${token_list} | wc -l)
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# python utils/gen_modelscope_configuration.py \
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# --am_model_name $inference_asr_model \
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# --mode paraformer \
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# --model_name paraformer \
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# --dataset aishell \
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# --output_dir $exp_dir/exp/$model_dir \
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# --vocab_size $vocab_size \
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# --nat _nat \
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# --tag $tag
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#fi
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# Testing Stage
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if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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echo "stage 5: Inference"
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if ${inference_device} == "cuda"; then
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nj=$(echo CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
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else
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nj=$njob
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batch_size=1
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gpuid_list=""
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for JOB in $(seq ${nj}); do
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gpuid_list=CUDA_VISIBLE_DEVICES"-1,"
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done
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fi
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for dset in ${test_sets}; do
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inference_dir="${asr_exp}/${inference_checkpoint}/${dset}"
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_logdir="${inference_dir}/logdir"
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mkdir -p "${_logdir}"
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data_dir="${feats_dir}/data/${dset}"
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key_file=${data_dir}/${inference_scp}
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split_scps=
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for JOB in $(seq "${nj}"); do
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split_scps+=" ${_logdir}/keys.${JOB}.scp"
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done
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utils/split_scp.pl "${key_file}" ${split_scps}
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for JOB in $(seq ${nj}); do
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{
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python ../../../funasr/bin/inference.py \
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--config-path="${exp_dir}/exp/${model_dir}" \
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--config-name="config.yaml" \
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++init_param="${exp_dir}/exp/${model_dir}/${inference_checkpoint}" \
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++tokenizer_conf.token_list="${token_list}" \
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++frontend_conf.cmvn_file="${feats_dir}/data/${train_set}/am.mvn" \
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++input="${_logdir}/keys.${JOB}.scp" \
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++output_dir="${inference_dir}/${JOB}" \
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++device="${inference_device}"
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}&
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done
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wait
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mkdir -p ${inference_dir}/1best_recog
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for f in token score text; do
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if [ -f "${inference_dir}/${JOB}/1best_recog/${f}" ]; then
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for JOB in $(seq "${nj}"); do
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cat "${inference_dir}/${JOB}/1best_recog/${f}"
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done | sort -k1 >"${inference_dir}/1best_recog/${f}"
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fi
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done
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echo "Computing WER ..."
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cp ${inference_dir}/1best_recog/text ${inference_dir}/1best_recog/text.proc
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cp ${data_dir}/text ${inference_dir}/1best_recog/text.ref
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python utils/compute_wer.py ${inference_dir}/1best_recog/text.ref ${inference_dir}/1best_recog/text.proc ${inference_dir}/1best_recog/text.cer
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tail -n 3 ${inference_dir}/1best_recog/text.cer
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done
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fi
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157
examples/aishell/paraformer/utils/compute_wer.py
Executable file
157
examples/aishell/paraformer/utils/compute_wer.py
Executable file
@ -0,0 +1,157 @@
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import os
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import numpy as np
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import sys
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def compute_wer(ref_file,
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hyp_file,
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cer_detail_file):
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rst = {
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'Wrd': 0,
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'Corr': 0,
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'Ins': 0,
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'Del': 0,
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'Sub': 0,
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'Snt': 0,
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'Err': 0.0,
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'S.Err': 0.0,
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'wrong_words': 0,
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'wrong_sentences': 0
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}
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hyp_dict = {}
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ref_dict = {}
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with open(hyp_file, 'r') as hyp_reader:
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for line in hyp_reader:
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key = line.strip().split()[0]
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value = line.strip().split()[1:]
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hyp_dict[key] = value
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with open(ref_file, 'r') as ref_reader:
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for line in ref_reader:
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key = line.strip().split()[0]
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value = line.strip().split()[1:]
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ref_dict[key] = value
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cer_detail_writer = open(cer_detail_file, 'w')
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for hyp_key in hyp_dict:
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if hyp_key in ref_dict:
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out_item = compute_wer_by_line(hyp_dict[hyp_key], ref_dict[hyp_key])
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rst['Wrd'] += out_item['nwords']
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rst['Corr'] += out_item['cor']
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rst['wrong_words'] += out_item['wrong']
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rst['Ins'] += out_item['ins']
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rst['Del'] += out_item['del']
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rst['Sub'] += out_item['sub']
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rst['Snt'] += 1
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if out_item['wrong'] > 0:
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rst['wrong_sentences'] += 1
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cer_detail_writer.write(hyp_key + print_cer_detail(out_item) + '\n')
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cer_detail_writer.write("ref:" + '\t' + " ".join(list(map(lambda x: x.lower(), ref_dict[hyp_key]))) + '\n')
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cer_detail_writer.write("hyp:" + '\t' + " ".join(list(map(lambda x: x.lower(), hyp_dict[hyp_key]))) + '\n')
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if rst['Wrd'] > 0:
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rst['Err'] = round(rst['wrong_words'] * 100 / rst['Wrd'], 2)
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if rst['Snt'] > 0:
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rst['S.Err'] = round(rst['wrong_sentences'] * 100 / rst['Snt'], 2)
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cer_detail_writer.write('\n')
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cer_detail_writer.write("%WER " + str(rst['Err']) + " [ " + str(rst['wrong_words'])+ " / " + str(rst['Wrd']) +
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", " + str(rst['Ins']) + " ins, " + str(rst['Del']) + " del, " + str(rst['Sub']) + " sub ]" + '\n')
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cer_detail_writer.write("%SER " + str(rst['S.Err']) + " [ " + str(rst['wrong_sentences']) + " / " + str(rst['Snt']) + " ]" + '\n')
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cer_detail_writer.write("Scored " + str(len(hyp_dict)) + " sentences, " + str(len(hyp_dict) - rst['Snt']) + " not present in hyp." + '\n')
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def compute_wer_by_line(hyp,
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ref):
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hyp = list(map(lambda x: x.lower(), hyp))
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ref = list(map(lambda x: x.lower(), ref))
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len_hyp = len(hyp)
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len_ref = len(ref)
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cost_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int16)
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ops_matrix = np.zeros((len_hyp + 1, len_ref + 1), dtype=np.int8)
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for i in range(len_hyp + 1):
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cost_matrix[i][0] = i
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for j in range(len_ref + 1):
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cost_matrix[0][j] = j
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for i in range(1, len_hyp + 1):
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for j in range(1, len_ref + 1):
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if hyp[i - 1] == ref[j - 1]:
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cost_matrix[i][j] = cost_matrix[i - 1][j - 1]
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else:
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substitution = cost_matrix[i - 1][j - 1] + 1
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insertion = cost_matrix[i - 1][j] + 1
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deletion = cost_matrix[i][j - 1] + 1
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compare_val = [substitution, insertion, deletion]
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min_val = min(compare_val)
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operation_idx = compare_val.index(min_val) + 1
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cost_matrix[i][j] = min_val
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ops_matrix[i][j] = operation_idx
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match_idx = []
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i = len_hyp
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j = len_ref
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rst = {
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'nwords': len_ref,
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'cor': 0,
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'wrong': 0,
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'ins': 0,
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'del': 0,
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'sub': 0
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}
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while i >= 0 or j >= 0:
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i_idx = max(0, i)
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j_idx = max(0, j)
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if ops_matrix[i_idx][j_idx] == 0: # correct
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if i - 1 >= 0 and j - 1 >= 0:
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match_idx.append((j - 1, i - 1))
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rst['cor'] += 1
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i -= 1
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j -= 1
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elif ops_matrix[i_idx][j_idx] == 2: # insert
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i -= 1
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rst['ins'] += 1
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elif ops_matrix[i_idx][j_idx] == 3: # delete
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j -= 1
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rst['del'] += 1
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elif ops_matrix[i_idx][j_idx] == 1: # substitute
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i -= 1
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j -= 1
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rst['sub'] += 1
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if i < 0 and j >= 0:
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rst['del'] += 1
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elif j < 0 and i >= 0:
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rst['ins'] += 1
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match_idx.reverse()
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wrong_cnt = cost_matrix[len_hyp][len_ref]
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rst['wrong'] = wrong_cnt
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return rst
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def print_cer_detail(rst):
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return ("(" + "nwords=" + str(rst['nwords']) + ",cor=" + str(rst['cor'])
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+ ",ins=" + str(rst['ins']) + ",del=" + str(rst['del']) + ",sub="
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+ str(rst['sub']) + ") corr:" + '{:.2%}'.format(rst['cor']/rst['nwords'])
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+ ",cer:" + '{:.2%}'.format(rst['wrong']/rst['nwords']))
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if __name__ == '__main__':
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if len(sys.argv) != 4:
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print("usage : python compute-wer.py test.ref test.hyp test.wer")
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sys.exit(0)
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ref_file = sys.argv[1]
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hyp_file = sys.argv[2]
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cer_detail_file = sys.argv[3]
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compute_wer(ref_file, hyp_file, cer_detail_file)
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@ -6,10 +6,10 @@
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#git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
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## generate jsonl from wav.scp and text.txt
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python funasr/datasets/audio_datasets/scp2jsonl.py \
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++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
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++data_type_list='["source", "target"]' \
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++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl
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#python funasr/datasets/audio_datasets/scp2jsonl.py \
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#++scp_file_list='["/Users/zhifu/funasr1.0/test_local/wav.scp", "/Users/zhifu/funasr1.0/test_local/text.txt"]' \
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#++data_type_list='["source", "target"]' \
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#++jsonl_file_out=/Users/zhifu/funasr1.0/test_local/audio_datasets.jsonl
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# torchrun \
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@ -24,5 +24,4 @@ python funasr/bin/train.py \
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++dataset_conf.batch_type="example" \
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++train_conf.max_epoch=2 \
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++dataset_conf.num_workers=4 \
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+output_dir="outputs/debug/ckpt/funasr2/exp2" \
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+debug="true"
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+output_dir="outputs/debug/ckpt/funasr2/exp2"
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@ -9,3 +9,6 @@ python funasr/bin/inference.py \
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+output_dir="./outputs/debug" \
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+device="cpu" \
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Reference in New Issue
Block a user