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@@ -107,8 +107,8 @@ inference_asr_model=valid.acc.ave.pb # ASR model path for decoding.
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# inference_asr_model=valid.acc.best.pth
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# inference_asr_model=valid.loss.ave.pth
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inference_sa_asr_model=valid.acc_spk.ave.pb
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-download_model= # Download a model from Model Zoo and use it for decoding.
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-
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+infer_with_pretrained_model=false # Use pretrained model for decoding
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+download_sa_asr_model= # Download the SA-ASR model from ModelScope and use it for decoding.
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# [Task dependent] Set the datadir name created by local/data.sh
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train_set= # Name of training set.
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valid_set= # Name of validation set used for monitoring/tuning network training.
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@@ -203,7 +203,8 @@ Options:
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# Note that it will overwrite args in inference config.
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--inference_lm # Language modle path for decoding (default="${inference_lm}").
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--inference_asr_model # ASR model path for decoding (default="${inference_asr_model}").
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- --download_model # Download a model from Model Zoo and use it for decoding (default="${download_model}").
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+ --infer_with_pretrained_model # Use pretrained model for decoding (default="${infer_with_pretrained_model}").
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+ --download_sa_asr_model= # Download the SA-ASR model from ModelScope and use it for decoding(default="${download_sa_asr_model}").
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# [Task dependent] Set the datadir name created by local/data.sh
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--train_set # Name of training set (required).
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@@ -304,6 +305,9 @@ else
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lm_token_type="${token_type}"
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fi
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+if ${infer_with_pretrained_model}; then
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+ skip_train=true
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+fi
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# Set tag for naming of model directory
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if [ -z "${asr_tag}" ]; then
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@@ -1220,119 +1224,20 @@ else
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log "Skip the training stages"
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fi
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+if ${infer_with_pretrained_model}; then
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+ log "Use ${download_sa_asr_model} for decoding and evaluation"
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-if ! "${skip_eval}"; then
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- if [ ${stage} -le 13 ] && [ ${stop_stage} -ge 13 ]; then
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- log "Stage 13: Decoding multi-talker ASR: training_dir=${asr_exp}"
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-
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- if ${gpu_inference}; then
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- _cmd="${cuda_cmd}"
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- inference_nj=$[${ngpu}*${njob_infer}]
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- _ngpu=1
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-
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- else
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- _cmd="${decode_cmd}"
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- inference_nj=$inference_nj
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- _ngpu=0
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- fi
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-
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- _opts=
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- if [ -n "${inference_config}" ]; then
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- _opts+="--config ${inference_config} "
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- fi
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- if "${use_lm}"; then
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- if "${use_word_lm}"; then
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- _opts+="--word_lm_train_config ${lm_exp}/config.yaml "
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- _opts+="--word_lm_file ${lm_exp}/${inference_lm} "
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- else
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- _opts+="--lm_train_config ${lm_exp}/config.yaml "
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- _opts+="--lm_file ${lm_exp}/${inference_lm} "
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- fi
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- fi
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-
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- # 2. Generate run.sh
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- log "Generate '${asr_exp}/${inference_tag}/run.sh'. You can resume the process from stage 13 using this script"
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- mkdir -p "${asr_exp}/${inference_tag}"; echo "${run_args} --stage 13 \"\$@\"; exit \$?" > "${asr_exp}/${inference_tag}/run.sh"; chmod +x "${asr_exp}/${inference_tag}/run.sh"
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-
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- for dset in ${test_sets}; do
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- _data="${data_feats}/${dset}"
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- _dir="${asr_exp}/${inference_tag}/${dset}"
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- _logdir="${_dir}/logdir"
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- mkdir -p "${_logdir}"
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-
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- _feats_type="$(<${_data}/feats_type)"
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- if [ "${_feats_type}" = raw ]; then
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- _scp=wav.scp
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- if [[ "${audio_format}" == *ark* ]]; then
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- _type=kaldi_ark
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- else
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- _type=sound
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- fi
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- else
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- _scp=feats.scp
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- _type=kaldi_ark
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- fi
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-
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- # 1. Split the key file
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- key_file=${_data}/${_scp}
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- split_scps=""
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- _nj=$(min "${inference_nj}" "$(<${key_file} wc -l)")
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- echo $_nj
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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}" ${split_scps}
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-
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- # 2. Submit decoding jobs
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- log "Decoding started... log: '${_logdir}/asr_inference.*.log'"
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-
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- ${_cmd} --gpu "${_ngpu}" --max-jobs-run "${_nj}" JOB=1:"${_nj}" "${_logdir}"/asr_inference.JOB.log \
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- python -m funasr.bin.asr_inference_launch \
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- --batch_size 1 \
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- --mc True \
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- --nbest 1 \
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- --ngpu "${_ngpu}" \
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- --njob ${njob_infer} \
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- --gpuid_list ${device} \
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- --data_path_and_name_and_type "${_data}/${_scp},speech,${_type}" \
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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 asr \
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- ${_opts}
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-
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- # 3. Concatenates the output files from each jobs
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- for f in token token_int score text; do
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- for i in $(seq "${_nj}"); do
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- cat "${_logdir}/output.${i}/1best_recog/${f}"
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- done | LC_ALL=C sort -k1 >"${_dir}/${f}"
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- done
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- done
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- fi
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-
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-
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- if [ ${stage} -le 14 ] && [ ${stop_stage} -ge 14 ]; then
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- log "Stage 14: Scoring multi-talker ASR"
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-
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- for dset in ${test_sets}; do
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- _data="${data_feats}/${dset}"
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- _dir="${asr_exp}/${inference_tag}/${dset}"
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-
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- python utils/proce_text.py ${_data}/text ${_data}/text.proc
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- python utils/proce_text.py ${_dir}/text ${_dir}/text.proc
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-
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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 ${_dir}/text.cer.txt
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-
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- done
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+ sa_asr_exp="${expdir}/${download_sa_asr_model}"
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+ mkdir -p "${sa_asr_exp}"
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- fi
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+ python local/download_pretrained_model_from_modelscope.py $download_sa_asr_model ${expdir}
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+ inference_sa_asr_model="model.pb"
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+ inference_config=${sa_asr_exp}/decoding.yaml
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+fi
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- if [ ${stage} -le 15 ] && [ ${stop_stage} -ge 15 ]; then
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- log "Stage 15: Decoding SA-ASR (oracle profile): training_dir=${sa_asr_exp}"
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+if ! "${skip_eval}"; then
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+ if [ ${stage} -le 13 ] && [ ${stop_stage} -ge 13 ]; then
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+ log "Stage 13: Decoding SA-ASR (oracle profile): training_dir=${sa_asr_exp}"
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if ${gpu_inference}; then
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_cmd="${cuda_cmd}"
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@@ -1423,8 +1328,8 @@ if ! "${skip_eval}"; then
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done
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fi
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- if [ ${stage} -le 16 ] && [ ${stop_stage} -ge 16 ]; then
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- log "Stage 16: Scoring SA-ASR (oracle profile)"
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+ if [ ${stage} -le 14 ] && [ ${stop_stage} -ge 14 ]; then
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+ log "Stage 14: Scoring SA-ASR (oracle profile)"
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for dset in ${test_sets}; do
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_data="${data_feats}/${dset}"
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@@ -1448,8 +1353,8 @@ if ! "${skip_eval}"; then
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fi
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- if [ ${stage} -le 17 ] && [ ${stop_stage} -ge 17 ]; then
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- log "Stage 17: Decoding SA-ASR (cluster profile): training_dir=${sa_asr_exp}"
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+ if [ ${stage} -le 15 ] && [ ${stop_stage} -ge 15 ]; then
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+ log "Stage 15: Decoding SA-ASR (cluster profile): training_dir=${sa_asr_exp}"
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if ${gpu_inference}; then
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_cmd="${cuda_cmd}"
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@@ -1539,8 +1444,8 @@ if ! "${skip_eval}"; then
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done
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fi
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- if [ ${stage} -le 18 ] && [ ${stop_stage} -ge 18 ]; then
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- log "Stage 18: Scoring SA-ASR (cluster profile)"
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+ if [ ${stage} -le 16 ] && [ ${stop_stage} -ge 16 ]; then
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+ log "Stage 16: Scoring SA-ASR (cluster profile)"
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for dset in ${test_sets}; do
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_data="${data_feats}/${dset}"
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yhliang
