add speaker-attributed ASR task for alimeeting

smohan-speech

2023-05-08 af6740a2207840a772261b8a033ab9996f862529

 egs/alimeeting/sa-asr/asr_local.sh

@@ -475,7 +475,9 @@
                fi
                local/copy_data_dir.sh --validate_opts --non-print data/"${dset}" "${data_feats}${_suf}/${dset}"
                
                cp data/"${dset}"/utt2spk_all_fifo "${data_feats}${_suf}/${dset}/"
                if [ "${dset}" = "Train_Ali_far" ] || [ "${dset}" = "Eval_Ali_far" ] || [ "${dset}" = "Test_Ali_far" ]; then
                    cp data/"${dset}"/utt2spk_all_fifo "${data_feats}${_suf}/${dset}/"
                fi

                rm -f ${data_feats}${_suf}/${dset}/{segments,wav.scp,reco2file_and_channel,reco2dur}
                _opts=
@@ -568,8 +570,11 @@

            # generate uttid
            cut -d ' ' -f 1 "${data_feats}/${dset}/wav.scp" > "${data_feats}/${dset}/uttid"
            # filter utt2spk_all_fifo
            python local/filter_utt2spk_all_fifo.py ${data_feats}/${dset}/uttid ${data_feats}/org/${dset} ${data_feats}/${dset}
            
            if [ "${dset}" = "Train_Ali_far" ] || [ "${dset}" = "Eval_Ali_far" ] || [ "${dset}" = "Test_Ali_far" ]; then
                # filter utt2spk_all_fifo
                python local/filter_utt2spk_all_fifo.py ${data_feats}/${dset}/uttid ${data_feats}/org/${dset} ${data_feats}/${dset}
            fi
        done

        # shellcheck disable=SC2002
@@ -585,7 +590,7 @@
        echo "<blank>" > ${token_list}
        echo "<s>" >> ${token_list}
        echo "</s>" >> ${token_list}
        local/text2token.py -s 1 -n 1 --space "" ${data_feats}/lm_train.txt | cut -f 2- -d" " | tr " " "\n" \
        utils/text2token.py -s 1 -n 1 --space "" ${data_feats}/lm_train.txt | cut -f 2- -d" " | tr " " "\n" \
            | sort | uniq | grep -a -v -e '^\s*$' | awk '{print $0}' >> ${token_list}
        num_token=$(cat ${token_list} | wc -l)
        echo "<unk>" >> ${token_list}
@@ -603,6 +608,7 @@
            python local/process_text_id.py ${data_feats}/${dset}
            log "Successfully generate ${data_feats}/${dset}/text_id_train"
            # generate oracle_embedding from single-speaker audio segment
            log "oracle_embedding is being generated in the background, and the log is profile_log/gen_oracle_embedding_${dset}.log"
            python local/gen_oracle_embedding.py "${data_feats}/${dset}" "data/local/${dset}_correct_single_speaker" &> "profile_log/gen_oracle_embedding_${dset}.log"
            log "Successfully generate oracle embedding for ${dset} (${data_feats}/${dset}/oracle_embedding.scp)"
            # generate oracle_profile and cluster_profile from oracle_embedding and cluster_embedding (padding the speaker during training)
@@ -615,6 +621,7 @@
            fi
            # generate cluster_profile with spectral-cluster directly (for infering and without oracle information)
            if [ "${dset}" = "${valid_set}" ] || [ "${dset}" = "${test_sets}" ]; then
                log "cluster_profile is being generated in the background, and the log is profile_log/gen_cluster_profile_infer_${dset}.log"
                python local/gen_cluster_profile_infer.py "${data_feats}/${dset}" "data/local/${dset}" 0.996 0.815 &> "profile_log/gen_cluster_profile_infer_${dset}.log"
                log "Successfully generate cluster profile for ${dset} (${data_feats}/${dset}/cluster_profile_infer.scp)"
            fi