Fixing some issues with chime7-task1 baseline

egs2/chime7_task1/asr1/local/data.sh

@@ -15,6 +15,7 @@ gss_dsets=
 manifests_root=
 gss_dump_root=
 augm_num_data_reps=4
+decode_only=0
 foreground_snrs="20:10:15:5:0"
 background_snrs="20:10:15:5:0"
 
@@ -25,6 +26,11 @@ background_snrs="20:10:15:5:0"
 gss_dsets=$(echo $gss_dsets | tr "," " ") # split by commas
 
 
+if [ $decode_only == 1 ]; then
+  # stop after gss
+  stop_stage=1
+fi
+
 if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
   log "Dumping all lhotse manifests to kaldi manifests and merging everything for dev set close mics,
   you may want these for validation."
@@ -43,7 +49,49 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
 fi
 
 
-if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
+if [ ${stage} -le 1 ] && [ $stop_stage -ge 1 ]; then
+    # Preparing ASR training and validation data;
+    log "Parsing the GSS output to Kaldi manifests"
+    cv_kaldi_manifests_gss=()
+    tr_kaldi_manifests_gss=() # if gss is used also for training
+    for dset in $gss_dsets; do
+      # for each dataset get the name and part (dev or train)
+      dset_name="$(cut -d'_' -f1 <<<${dset})"
+      dset_part="$(cut -d'_' -f2 <<<${dset})"
+      python local/gss2lhotse.py -i ${gss_dump_root}/${dset_name}/${dset_part} \
+        -o $manifests_root/gss/${dset_name}/${dset_part}/${dset_name}_${dset_part}_gss
+
+      lhotse kaldi export -p $manifests_root/gss/${dset_name}/${dset_part}/${dset_name}_${dset_part}_gss_recordings.jsonl.gz  \
+          $manifests_root/gss/${dset_name}/${dset_part}/${dset_name}_${dset_part}_gss_supervisions.jsonl.gz \
+          data/kaldi/${dset_name}/${dset_part}/gss
+
+      ./utils/utt2spk_to_spk2utt.pl data/kaldi/${dset_name}/${dset_part}/gss/utt2spk > data/kaldi/${dset_name}/${dset_part}/gss/spk2utt
+      ./utils/fix_data_dir.sh data/kaldi/${dset_name}/${dset_part}/gss
+
+      if [ $dset_part == train ]; then
+         tr_kaldi_manifests_gss+=( "data/kaldi/${dset}/${dset_part}/gss")
+      fi
+
+      if [ $dset_part == dev ]; then
+         cv_kaldi_manifests_gss+=( "data/kaldi/${dset}/${dset_part}/gss")
+      fi
+    done
+
+    if (( ${#tr_kaldi_manifests_gss[@]} )); then
+    # possibly combine with all training data the gss training data
+      tr_kaldi_manifests_gss+=( data/kaldi/train_all_mdm_ihm_rvb)
+      ./utils/combine_data.sh data/kaldi/train_all_mdm_ihm_rvb_gss  "${tr_kaldi_manifests_gss[@]}"
+      ./utils/fix_data_dir.sh data/kaldi/train_all_mdm_ihm_rvb_gss
+    fi
+
+    if (( ${#cv_kaldi_manifests_gss[@]} )); then # concatenate all gss data to use for validation
+      ./utils/combine_data.sh data/kaldi/dev_all_gss  "${cv_kaldi_manifests_gss[@]}"
+      ./utils/fix_data_dir.sh data/kaldi/dev_all_gss
+    fi
+fi
+
+
+if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
   all_tr_manifests=()
   all_tr_manifests_ihm=()
   log "Dumping all lhotse manifests to kaldi manifests and merging everything for training set."
@@ -78,7 +126,7 @@ if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
 fi
 
 
-if [ $stage -le 2 ] && [ $stop_stage -ge 3 ]; then
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
   log "Augmenting close-talk data with MUSAN and CHiME-6 extracted noises."
   local/extract_noises.py ${chime6_root}/audio/train ${chime6_root}/transcriptions/train \
     local/distant_audio_list distant_noises
@@ -116,47 +164,6 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 3 ]; then
 fi
 
 
-if [ ${stage} -le 3 ] && [ $stop_stage -ge 3 ]; then
-    # Preparing ASR training and validation data;
-    log "Parsing the GSS output to Kaldi manifests"
-    cv_kaldi_manifests_gss=()
-    tr_kaldi_manifests_gss=() # if gss is used also for training
-    for dset in $gss_dsets; do
-      # for each dataset get the name and part (dev or train)
-      dset_name="$(cut -d'_' -f1 <<<${dset})"
-      dset_part="$(cut -d'_' -f2 <<<${dset})"
-      python local/gss2lhotse.py -i ${gss_dump_root}/${dset_name}/${dset_part} \
-        -o $manifests_root/gss/${dset_name}/${dset_part}/${dset_name}_${dset_part}_gss
-
-      lhotse kaldi export -p $manifests_root/gss/${dset_name}/${dset_part}/${dset_name}_${dset_part}_gss_recordings.jsonl.gz  \
-          $manifests_root/gss/${dset_name}/${dset_part}/${dset_name}_${dset_part}_gss_supervisions.jsonl.gz \
-          data/kaldi/${dset_name}/${dset_part}/gss
-
-      ./utils/utt2spk_to_spk2utt.pl data/kaldi/${dset_name}/${dset_part}/gss/utt2spk > data/kaldi/${dset_name}/${dset_part}/gss/spk2utt
-      ./utils/fix_data_dir.sh data/kaldi/${dset_name}/${dset_part}/gss
-
-      if [ $dset_part == train ]; then
-         tr_kaldi_manifests_gss+=( "data/kaldi/${dset}/${dset_part}/gss")
-      fi
-
-      if [ $dset_part == dev ]; then
-         cv_kaldi_manifests_gss+=( "data/kaldi/${dset}/${dset_part}/gss")
-      fi
-    done
-
-    if (( ${#tr_kaldi_manifests_gss[@]} )); then
-    # possibly combine with all training data the gss training data
-      tr_kaldi_manifests_gss+=( data/kaldi/train_all_mdm_ihm_rvb)
-      ./utils/combine_data.sh data/kaldi/train_all_mdm_ihm_rvb_gss  "${tr_kaldi_manifests_gss[@]}"
-      ./utils/fix_data_dir.sh data/kaldi/train_all_mdm_ihm_rvb_gss
-    fi
-
-    if (( ${#cv_kaldi_manifests_gss[@]} )); then # concatenate all gss data to use for validation
-      ./utils/combine_data.sh data/kaldi/dev_all_gss  "${cv_kaldi_manifests_gss[@]}"
-      ./utils/fix_data_dir.sh data/kaldi/dev_all_gss
-    fi
-fi
-
 
 if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
     log "stage 2: Create non linguistic symbols: ${nlsyms_file}"

egs2/chime7_task1/asr1/local/gen_task1_data.py

@@ -280,7 +280,11 @@ def _get_time(x):
         sess2audio = {}
         for x in audio_files:
             session_name = Path(x).stem.split("_")[0]
-            if Path(x).stem.split("_")[-1].startswith("P") and eval_opt != 2:
+            if (
+                split == "eval"
+                and Path(x).stem.split("_")[-1].startswith("P")
+                and eval_opt != 2
+            ):
                 continue
             if session_name not in sess2audio:
                 sess2audio[session_name] = [x]

egs2/chime7_task1/asr1/local/install_dependencies.sh

@@ -2,6 +2,12 @@
 set -euo pipefail
 [ -f ./path.sh ] && . ./path.sh
 
+
+if ! command conda  &>/dev/null; then
+  echo "Conda command not found, please follow the instructions on
+  this recipe README.md on how to install ESPNet with conda as the venv."
+fi
+
 # install lhotse from master, we need the most up-to-date one
 pip install git+https://github.com/lhotse-speech/lhotse
 
@@ -13,11 +19,11 @@ if ! command -v wav-reverberate &>/dev/null; then
 fi
 
 # install s3prl
-./tools/installers/install_s3prl.sh
+${MAIN_ROOT}/tools/installers/install_s3prl.sh
 
 if ! command -v gss &>/dev/null; then
   conda install -yc conda-forge cupy=10.2
-  ./${MAIN_ROOT}/tools/install_gss.sh
+  ${MAIN_ROOT}/tools/installers/install_gss.sh.
 fi
 
 sox_conda=`command -v ../../../tools/venv/bin/sox 2>/dev/null`

egs2/chime7_task1/asr1/local/run_gss.sh

@@ -46,9 +46,9 @@ fi
 if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
   echo "Stage 4: Enhance segments using GSS"
 
-  affix=()
+  affix=
   if ! [ $channels == all  ]; then
-    affix+=("--channels=$channels")
+    affix+="--channels=$channels"
   fi
 
   $cmd JOB=1:$nj  ${exp_dir}/${dset_name}/${dset_part}/log/enhance.JOB.log \
@@ -66,5 +66,5 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
       --num-workers 4 \
       --force-overwrite \
       --duration-tolerance 3.0 \
-       "${affix[@]}" || exit 1
+       ${affix} || exit 1
 fi

egs2/chime7_task1/asr1/run.sh

@@ -9,7 +9,7 @@ log() {
     echo -e "$(date '+%Y-%m-%dT%H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
 }
 ######################################################################################
-# CHiME-7 Task 1 SUB-TASK 1 baseline system script: GSS + ASR using oracle diarization
+# CHiME-7 Task 1 SUB-TASK baseline system script: GSS + ASR using oracle diarization
 ######################################################################################
 
 stage=3
@@ -19,7 +19,7 @@ stop_stage=100
 chime7_root=${PWD}/chime7_task1
 chime5_root= # you can leave it empty if you have already generated CHiME-6 data
 chime6_root=/raid/users/popcornell/CHiME6/espnet/egs2/chime6/asr1/CHiME6 # will be created automatically from chime5
-# but if you have it already it will be skipped
+# but if you have it already it will be skipped, please put your own path
 dipco_root=${PWD}/../../chime7/task1/datasets/dipco # this will be automatically downloaded
 mixer6_root=/raid/users/popcornell/mixer6/
 
@@ -38,6 +38,7 @@ train_max_segment_length=20  # also reduce if you get OOM, here A100 40GB
 gss_max_batch_dur=360 # set accordingly to your GPU VRAM, here A100 40GB
 cmd_gss=run.pl # change to suit your needs e.g. slurm !
 gss_dsets="chime6_train,chime6_dev,dipco_dev,mixer6_dev" # no mixer6 train in baseline
+# but you can try to add it.
 
 
 # ASR CONFIG
@@ -52,26 +53,31 @@ use_lm=false
 use_word_lm=false
 word_vocab_size=65000
 nbpe=500
-
-
-# and not contribute much (but you may use all)
 asr_max_epochs=8
-# ESPNet does not scale parameters with num of GPUs by default, doing it
-# here for you
+# put popcornell/chime7_task1_asr1_baseline if you want to test with pretrained model
+use_pretrained=
+decode_only=0
 
 . ./path.sh
 . ./cmd.sh
 . ./utils/parse_options.sh
 
+
+# ESPNet does not scale parameters with num of GPUs by default, doing it
+# here for you
 asr_batch_size=$(calc_int 128*$ngpu) # reduce 128 bsz if you get OOMs errors
 asr_max_lr=$(calc_float $ngpu/10000.0)
 asr_warmup=$(calc_int 40000.0/$ngpu)
 
+if [ $decode_only == 1 ]; then
+  # apply gss only on dev
+  gss_dsets="chime6_dev,dipco_dev,mixer6_dev"
+fi
 
 if [ ${stage} -le 0 ] && [ $stop_stage -ge 0 ]; then
   # this script creates the task1 dataset
   local/gen_task1_data.sh --chime6-root $chime6_root --stage $dprep_stage  --chime7-root $chime7_root \
-    --chime5_root $chime5_root \
+    --chime5_root "$chime5_root" \
 	  --dipco-root $dipco_root \
 	  --mixer6-root $mixer6_root \
 	  --stage $dprep_stage \
@@ -86,6 +92,11 @@ if [ ${stage} -le 1 ] && [ $stop_stage -ge 1 ]; then
       if [ $dset == dipco ] && [ $dset_part == train ]; then
           continue # dipco has no train set
       fi
+
+      if [ $decode_only == 1 ] && [ $dset_part == train ]; then
+        continue
+      fi
+
       log "Creating lhotse manifests for ${dset} in $manifests_root/${dset}"
       python local/get_lhotse_manifests.py -c $chime7_root \
            -d $dset \
@@ -117,7 +128,7 @@ if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
 
     if [ ${dset_name} == dipco ]; then
       channels=2,5,9,12,16,19,23,26,30,33 # in dipco only using opposite mics on each array, works better
-    elif [ ${dset_name} == chime6 ] && [ ${dset_part} == dev ]; then
+    elif [ ${dset_name} == chime6 ] && [ ${dset_part} == dev ]; then # use only outer mics
       channels=0,3,4,7,8,11,12,15,16,19
     fi
 
@@ -140,17 +151,28 @@ fi
 
 
 if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
+
+  # ASR training and inference on dev set
   asr_train_set=kaldi/train_all_mdm_ihm_rvb_gss
   asr_cv_set=kaldi/chime6/dev/gss # use chime only for validation
   # Decoding on dev set because test is blind for now
   asr_tt_set="kaldi/chime6/dev/gss/ kaldi/dipco/dev/gss/ kaldi/mixer6/dev/gss/"
   # these are args to ASR data prep, done in local/data.sh
   data_opts="--stage $asr_dprep_stage --chime6-root ${chime6_root} --train-set ${asr_train_set}"
   data_opts+=" --manifests-root $manifests_root --gss_dsets $gss_dsets --gss-dump-root $gss_dump_root"
+  data_opts+=" --decode-only $decode_only"
   # override ASR conf/tuning to scale automatically with num of GPUs
   asr_args="--batch_size ${asr_batch_size} --scheduler_conf warmup_steps=${asr_warmup}"
   asr_args+=" --max_epoch=${asr_max_epochs} --optim_conf lr=${asr_max_lr}"
 
+  pretrained_affix=
+  if [ -z "$use_pretrained" ]; then
+    pretrained_affix+="--skip_data_prep true --skip_train true "
+    pretrained_affix+="--download_model ${use_pretrained}"
+  fi
+
+
+
   ./asr.sh \
     --lang en \
     --local_data_opts "${data_opts}" \
@@ -177,5 +199,5 @@ if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
     --valid_set "${asr_cv_set}" \
     --test_sets "${asr_tt_set}" \
     --bpe_train_text "data/${asr_train_set}/text" \
-    --lm_train_text "data/${asr_train_set}/text" "$@"
+    --lm_train_text "data/${asr_train_set}/text" ${pretrained_affix}
 fi