#!/usr/bin/env bash
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. ./path.sh || exit 1;
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# machines configuration
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CUDA_VISIBLE_DEVICES="6,7"
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gpu_num=2
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count=1
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gpu_inference=true # Whether to perform gpu decoding, set false for cpu decoding
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# for gpu decoding, inference_nj=ngpu*njob; for cpu decoding, inference_nj=njob
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njob=8
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train_cmd=utils/run.pl
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infer_cmd=utils/run.pl
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# general configuration
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feats_dir="data" #feature output dictionary
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exp_dir="exp"
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lang=zh
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token_type=char
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type=sound
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scp=wav.scp
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speed_perturb="1.0"
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min_wav_duration=0.1
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max_wav_duration=20
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profile_modes="cluster oracle"
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stage=7
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stop_stage=7
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# feature configuration
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feats_dim=80
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nj=32
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# data
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raw_data=
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data_url=
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# exp tag
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tag=""
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. utils/parse_options.sh || exit 1;
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# Set bash to 'debug' mode, it will exit on :
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# -e 'error', -u 'undefined variable', -o ... 'error in pipeline', -x 'print commands',
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set -e
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set -u
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set -o pipefail
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train_set=Train_Ali_far
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valid_set=Eval_Ali_far
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test_sets="Test_Ali_far Eval_Ali_far"
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test_2023="Test_2023_Ali_far_release"
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asr_config=conf/train_asr_conformer.yaml
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sa_asr_config=conf/train_sa_asr_conformer.yaml
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asr_model_dir="baseline_$(basename "${asr_config}" .yaml)_${lang}_${token_type}_${tag}"
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sa_asr_model_dir="baseline_$(basename "${sa_asr_config}" .yaml)_${lang}_${token_type}_${tag}"
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inference_config=conf/decode_asr_rnn.yaml
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inference_sa_asr_model=valid.acc_spk.ave.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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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 0 ] && [ ${stop_stage} -ge 0 ]; then
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echo "stage 0: Data preparation"
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# Data preparation
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./local/alimeeting_data_prep.sh --tgt Test --min_wav_duration $min_wav_duration --max_wav_duration $max_wav_duration
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./local/alimeeting_data_prep.sh --tgt Eval --min_wav_duration $min_wav_duration --max_wav_duration $max_wav_duration
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./local/alimeeting_data_prep.sh --tgt Train --min_wav_duration $min_wav_duration --max_wav_duration $max_wav_duration
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# remove long/short data
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for x in ${train_set} ${valid_set} ${test_sets}; do
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cp -r ${feats_dir}/org/${x} ${feats_dir}/${x}
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rm ${feats_dir}/"${x}"/wav.scp ${feats_dir}/"${x}"/segments
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local/format_wav_scp.sh --nj "${nj}" --cmd "${train_cmd}" \
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--audio-format wav --segments ${feats_dir}/org/${x}/segments \
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"${feats_dir}/org/${x}/${scp}" "${feats_dir}/${x}"
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_min_length=$(python3 -c "print(int(${min_wav_duration} * 16000))")
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_max_length=$(python3 -c "print(int(${max_wav_duration} * 16000))")
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<"${feats_dir}/${x}/utt2num_samples" \
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awk '{if($2 > '$_min_length' && $2 < '$_max_length')print $0;}' \
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>"${feats_dir}/${x}/utt2num_samples_rmls"
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mv ${feats_dir}/${x}/utt2num_samples_rmls ${feats_dir}/${x}/utt2num_samples
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<"${feats_dir}/${x}/wav.scp" \
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utils/filter_scp.pl "${feats_dir}/${x}/utt2num_samples" \
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>"${feats_dir}/${x}/wav.scp_rmls"
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mv ${feats_dir}/${x}/wav.scp_rmls ${feats_dir}/${x}/wav.scp
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<"${feats_dir}/${x}/text" \
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awk '{ if( NF != 1 ) print $0; }' >"${feats_dir}/${x}/text_rmblank"
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mv ${feats_dir}/${x}/text_rmblank ${feats_dir}/${x}/text
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local/fix_${feats_dir}_dir.sh "${feats_dir}/${x}"
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<"${feats_dir}/${x}/utt2spk_all_fifo" \
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utils/filter_scp.pl "${feats_dir}/${x}/text" \
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>"${feats_dir}/${x}/utt2spk_all_fifo_rmls"
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mv "${feats_dir}/${x}/utt2spk_all_fifo_rmls" "${feats_dir}/${x}/utt2spk_all_fifo"
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done
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for x in ${test_2023}; do
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local/format_wav_scp.sh --nj "${nj}" --cmd "${train_cmd}" \
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--audio-format wav --segments ${feats_dir}/org/${x}/segments \
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"${feats_dir}/org/${x}/${scp}" "${feats_dir}/${x}"
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cut -d " " -f1 ${feats_dir}/${x}/wav.scp > ${feats_dir}/${x}/uttid
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paste -d " " ${feats_dir}/${x}/uttid ${feats_dir}/${x}/uttid > ${feats_dir}/${x}/utt2spk
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cp ${feats_dir}/${x}/utt2spk ${feats_dir}/${x}/spk2utt
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done
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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echo "stage 1: Speaker profile and CMVN Generation"
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mkdir -p "profile_log"
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for x in "${train_set}" "${valid_set}" "${test_sets}"; do
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# generate text_id spk2id
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python local/process_sot_fifo_textchar2spk.py --path ${feats_dir}/${x}
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echo "Successfully generate ${feats_dir}/${x}/text_id ${feats_dir}/${x}/spk2id"
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# generate text_id_train for sot
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python local/process_text_id.py ${feats_dir}/${x}
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echo "Successfully generate ${feats_dir}/${x}/text_id_train"
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# generate oracle_embedding from single-speaker audio segment
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echo "oracle_embedding is being generated in the background, and the log is profile_log/gen_oracle_embedding_${x}.log"
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python local/gen_oracle_embedding.py "${feats_dir}/${x}" "data/org/${x}_single_speaker" &> "profile_log/gen_oracle_embedding_${x}.log"
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echo "Successfully generate oracle embedding for ${x} (${feats_dir}/${x}/oracle_embedding.scp)"
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# generate oracle_profile and cluster_profile from oracle_embedding and cluster_embedding (padding the speaker during training)
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if [ "${x}" = "${train_set}" ]; then
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python local/gen_oracle_profile_padding.py ${feats_dir}/${x}
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echo "Successfully generate oracle profile for ${x} (${feats_dir}/${x}/oracle_profile_padding.scp)"
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else
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python local/gen_oracle_profile_nopadding.py ${feats_dir}/${x}
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echo "Successfully generate oracle profile for ${x} (${feats_dir}/${x}/oracle_profile_nopadding.scp)"
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fi
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# generate cluster_profile with spectral-cluster directly (for infering and without oracle information)
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if [ "${x}" = "${valid_set}" ] || [ "${x}" = "${test_sets}" ]; then
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echo "cluster_profile is being generated in the background, and the log is profile_log/gen_cluster_profile_infer_${x}.log"
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python local/gen_cluster_profile_infer.py "${feats_dir}/${x}" "${feats_dir}/org/${x}" 0.996 0.815 &> "profile_log/gen_cluster_profile_infer_${x}.log"
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echo "Successfully generate cluster profile for ${x} (${feats_dir}/${x}/cluster_profile_infer.scp)"
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fi
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# compute CMVN
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if [ "${x}" = "${train_set}" ]; then
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local/compute_cmvn.sh --cmd "$train_cmd" --nj $nj --fbankdir ${feats_dir}/${train_set} --feats_dim ${feats_dim} --config_file "$asr_config" --scale 1.0
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fi
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done
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for x in "${test_2023}"; do
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# generate cluster_profile with spectral-cluster directly (for infering and without oracle information)
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python local/gen_cluster_profile_infer.py "${feats_dir}/${x}" "${feats_dir}/org/${x}" 0.996 0.815 &> "profile_log/gen_cluster_profile_infer_${x}.log"
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echo "Successfully generate cluster profile for ${x} (${feats_dir}/${x}/cluster_profile_infer.scp)"
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done
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fi
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token_list=${feats_dir}/${lang}_token_list/char/tokens.txt
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echo "dictionary: ${token_list}"
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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echo "stage 2: Dictionary Preparation"
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mkdir -p ${feats_dir}/${lang}_token_list/char/
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echo "make a dictionary"
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echo "<blank>" > ${token_list}
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echo "<s>" >> ${token_list}
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echo "</s>" >> ${token_list}
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utils/text2token.py -s 1 -n 1 --space "" ${feats_dir}/$train_set/text | cut -f 2- -d" " | tr " " "\n" \
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| sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
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echo "<unk>" >> ${token_list}
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fi
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# LM Training Stage
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world_size=$gpu_num # run on one machine
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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echo "stage 3: LM Training"
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fi
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# ASR Training Stage
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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echo "Stage 4: ASR Training"
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asr_exp=${exp_dir}/${asr_model_dir}
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mkdir -p ${asr_exp}
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mkdir -p ${asr_exp}/log
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INIT_FILE=${asr_exp}/ddp_init
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if [ -f $INIT_FILE ];then
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rm -f $INIT_FILE
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fi
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init_method=file://$(readlink -f $INIT_FILE)
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echo "$0: init method is $init_method"
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for ((i = 0; i < $ngpu; ++i)); do
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{
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# i=0
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rank=$i
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local_rank=$i
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gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
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train.py \
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--task_name asr \
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--model asr \
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--gpu_id $gpu_id \
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--use_preprocessor true \
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--split_with_space false \
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--token_type char \
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--token_list $token_list \
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--data_dir ${feats_dir} \
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--train_set ${train_set} \
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--valid_set ${valid_set} \
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--data_file_names "wav.scp,text" \
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--cmvn_file ${feats_dir}/${train_set}/cmvn/cmvn.mvn \
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--speed_perturb ${speed_perturb} \
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--resume true \
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--output_dir ${exp_dir}/${asr_model_dir} \
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--config $asr_config \
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--ngpu $gpu_num \
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--num_worker_count $count \
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--dist_init_method $init_method \
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--dist_world_size $world_size \
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--dist_rank $rank \
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--local_rank $local_rank 1> ${exp_dir}/${asr_model_dir}/log/train.log.$i 2>&1
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} &
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done
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wait
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fi
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if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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echo "SA-ASR training"
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asr_exp=${exp_dir}/${asr_model_dir}
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sa_asr_exp=${exp_dir}/${sa_asr_model_dir}
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mkdir -p ${sa_asr_exp}
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mkdir -p ${sa_asr_exp}/log
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INIT_FILE=${sa_asr_exp}/ddp_init
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if [ ! -L ${feats_dir}/${train_set}/profile.scp ]; then
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ln -sr ${feats_dir}/${train_set}/oracle_profile_padding.scp ${feats_dir}/${train_set}/profile.scp
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ln -sr ${feats_dir}/${valid_set}/oracle_profile_nopadding.scp ${feats_dir}/${valid_set}/profile.scp
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fi
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if [ ! -f "${exp_dir}/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.pth" ]; then
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# download xvector extractor model file
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python local/download_xvector_model.py ${exp_dir}
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echo "Successfully download the pretrained xvector extractor to exp/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.pth"
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fi
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if [ -f $INIT_FILE ];then
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rm -f $INIT_FILE
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fi
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init_method=file://$(readlink -f $INIT_FILE)
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echo "$0: init method is $init_method"
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for ((i = 0; i < $ngpu; ++i)); do
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{
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rank=$i
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local_rank=$i
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gpu_id=$(echo $CUDA_VISIBLE_DEVICES | cut -d',' -f$[$i+1])
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train.py \
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--task_name asr \
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--model sa_asr \
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--gpu_id $gpu_id \
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--use_preprocessor true \
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--split_with_space false \
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--unused_parameters true \
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--token_type char \
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--resume true \
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--token_list $token_list \
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--data_dir ${feats_dir} \
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--train_set ${train_set} \
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--valid_set ${valid_set} \
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--data_file_names "wav.scp,text,profile.scp,text_id_train" \
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--cmvn_file ${feats_dir}/${train_set}/cmvn/cmvn.mvn \
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--speed_perturb ${speed_perturb} \
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--init_param "${asr_exp}/valid.acc.ave.pb:encoder:asr_encoder" \
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--init_param "${asr_exp}/valid.acc.ave.pb:ctc:ctc" \
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--init_param "${asr_exp}/valid.acc.ave.pb:decoder.embed:decoder.embed" \
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--init_param "${asr_exp}/valid.acc.ave.pb:decoder.output_layer:decoder.asr_output_layer" \
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--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.0.self_attn:decoder.decoder1.self_attn" \
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--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.0.src_attn:decoder.decoder3.src_attn" \
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--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.0.feed_forward:decoder.decoder3.feed_forward" \
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--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.1:decoder.decoder4.0" \
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--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.2:decoder.decoder4.1" \
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--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.3:decoder.decoder4.2" \
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--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.4:decoder.decoder4.3" \
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--init_param "${asr_exp}/valid.acc.ave.pb:decoder.decoders.5:decoder.decoder4.4" \
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--init_param "exp/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.pth:encoder:spk_encoder" \
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--init_param "exp/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.pth:decoder:spk_encoder:decoder.output_dense" \
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--output_dir ${exp_dir}/${sa_asr_model_dir} \
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--config $sa_asr_config \
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--ngpu $gpu_num \
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--num_worker_count $count \
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--dist_init_method $init_method \
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--dist_world_size $world_size \
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--dist_rank $rank \
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--local_rank $local_rank 1> ${exp_dir}/${sa_asr_model_dir}/log/train.log.$i 2>&1
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} &
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done
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wait
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fi
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if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
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echo "stage 6: Inference test sets"
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for x in ${test_sets}; do
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for profile_mode in ${profile_modes}; do
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echo "decoding ${x} with ${profile_mode} profile"
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sa_asr_exp=${exp_dir}/${sa_asr_model_dir}
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inference_tag="$(basename "${inference_config}" .yaml)"
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_dir="${sa_asr_exp}/${inference_tag}_${profile_mode}/${inference_sa_asr_model}/${x}"
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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}/${x}"
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key_file=${_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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if [ $profile_mode = "oracle" ]; then
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profile_scp=${profile_mode}_profile_nopadding.scp
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else
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profile_scp=${profile_mode}_profile_infer.scp
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fi
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${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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--mc True \
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--ngpu "${_ngpu}" \
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--njob ${njob} \
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--nbest 1 \
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--gpuid_list ${gpuid_list} \
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--allow_variable_data_keys true \
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--cmvn_file ${feats_dir}/${train_set}/cmvn/cmvn.mvn \
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--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
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--data_path_and_name_and_type "${_data}/$profile_scp,profile,npy" \
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--key_file "${_logdir}"/keys.JOB.scp \
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--asr_train_config "${sa_asr_exp}"/config.yaml \
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--asr_model_file "${sa_asr_exp}"/"${inference_sa_asr_model}" \
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--output_dir "${_logdir}"/output.JOB \
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--mode sa_asr \
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${_opts}
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for f in token token_int score text text_id; do
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if [ -f "${_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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sed 's/\$//g' ${_data}/text > ${_data}/text_nosrc
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sed 's/\$//g' ${_dir}/text > ${_dir}/text_nosrc
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python utils/proce_text.py ${_data}/text_nosrc ${_data}/text.proc
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python utils/proce_text.py ${_dir}/text_nosrc ${_dir}/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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python local/process_text_spk_merge.py ${_dir}
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python local/process_text_spk_merge.py ${_data}
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python local/compute_cpcer.py ${_data}/text_spk_merge ${_dir}/text_spk_merge ${_dir}/text.cpcer
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tail -n 1 ${_dir}/text.cpcer > ${_dir}/text.cpcer.txt
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cat ${_dir}/text.cpcer.txt
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done
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done
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fi
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if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
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echo "stage 7: Inference test 2023"
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for x in ${test_2023}; do
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sa_asr_exp=${exp_dir}/${sa_asr_model_dir}
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inference_tag="$(basename "${inference_config}" .yaml)"
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_dir="${sa_asr_exp}/${inference_tag}_cluster/${inference_sa_asr_model}/${x}"
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_logdir="${_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}/${x}"
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key_file=${_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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--mc True \
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--ngpu "${_ngpu}" \
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--njob ${njob} \
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--nbest 1 \
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--gpuid_list ${gpuid_list} \
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--allow_variable_data_keys true \
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--data_path_and_name_and_type "${_data}/${scp},speech,${type}" \
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--data_path_and_name_and_type "${_data}/cluster_profile_infer.scp,profile,npy" \
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--cmvn_file ${feats_dir}/${train_set}/cmvn/cmvn.mvn \
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--key_file "${_logdir}"/keys.JOB.scp \
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--asr_train_config "${sa_asr_exp}"/config.yaml \
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--asr_model_file "${sa_asr_exp}"/"${inference_sa_asr_model}" \
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--output_dir "${_logdir}"/output.JOB \
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--mode sa_asr \
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${_opts}
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for f in token token_int score text text_id; 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 local/process_text_spk_merge.py ${_dir}
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done
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fi
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