update eval data preparation

egs/icmcasr/ASR/local/compute_fbank_icmcasr.py

@@ -58,14 +58,14 @@ def compute_fbank_icmcasr(num_mel_bins: int = 80, perturb_speed: bool = False):
         suffix="jsonl.gz",
     )
     manifests_sdm = read_manifests_if_cached(
-        dataset_parts=["train", "dev"],
+        dataset_parts=["train", "dev", "eval_track1"],
         output_dir=src_dir,
         prefix="icmcasr-sdm",
         suffix="jsonl.gz",
     )
     # For GSS we already have cuts so we read them directly.
     manifests_gss = read_manifests_if_cached(
-        dataset_parts=["train", "dev"],
+        dataset_parts=["train", "dev", "eval_track1"],
         output_dir=src_dir,
         prefix="icmcasr-gss",
         suffix="jsonl.gz",
@@ -96,7 +96,7 @@ def compute_fbank_icmcasr(num_mel_bins: int = 80, perturb_speed: bool = False):
             storage_path=storage_path,
             manifest_path=manifest_path,
             batch_duration=5000,
-            num_workers=8,
+            num_workers=4,
             storage_type=LilcomChunkyWriter,
         )
 
@@ -153,7 +153,7 @@ def compute_fbank_icmcasr(num_mel_bins: int = 80, perturb_speed: bool = False):
     )
 
     logging.info("Preparing test cuts: IHM, SDM, GSS (optional)")
-    for split in ["dev"]:
+    for split in ["dev", "eval_track1"]:
         logging.info(f"Processing {split} IHM")
         cuts_ihm = (
             CutSet.from_manifests(**manifests_ihm[split])

egs/icmcasr/ASR/local/prepare_icmc_enhanced.py

@@ -92,7 +92,7 @@ def main(args):
 
     # Load manifests from cache if they exist (saves time)
     manifests = read_manifests_if_cached(
-        dataset_parts=["train", "dev"],
+        dataset_parts=["train", "dev", "eval_track1"],
         output_dir=manifests_dir,
         prefix="icmcasr-sdm",
         suffix="jsonl.gz",
@@ -103,7 +103,7 @@ def main(args):
         )
 
     with ThreadPoolExecutor(args.num_jobs) as ex:
-        for part in ["train", "dev",]:
+        for part in ["train", "dev", "eval_track1"]:
             logging.info(f"Processing {part}...")
             supervisions_orig = manifests[part]["supervisions"].filter(
                 lambda s: s.duration >= args.min_segment_duration

egs/icmcasr/ASR/local/prepare_icmc_gss.sh

@@ -30,7 +30,7 @@ log() {
 
 if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
   log "Stage 1: Prepare cut sets"
-  for part in train dev; do
+  for part in train dev eval_track1; do
     lhotse cut simple \
       -r $DATA_DIR/icmcasr-mdm_recordings_${part}.jsonl.gz \
       -s $DATA_DIR/icmcasr-mdm_supervisions_${part}.jsonl.gz \
@@ -40,7 +40,7 @@ fi
 
 if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
   log "Stage 2: Trim cuts to supervisions (1 cut per supervision segment)"
-  for part in train dev; do
+  for part in train dev eval_track1; do
     lhotse cut trim-to-supervisions --discard-overlapping \
         $EXP_DIR/cuts_${part}.jsonl.gz $EXP_DIR/cuts_per_segment_${part}.jsonl.gz
   done
@@ -48,7 +48,7 @@ fi
 
 if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
   log "Stage 3: Split manifests for multi-GPU processing (optional)"
-  for part in train dev; do
+  for part in train dev eval_track1; do
     gss utils split $nj $EXP_DIR/cuts_per_segment_${part}.jsonl.gz \
       $EXP_DIR/cuts_per_segment_${part}_split$nj
   done
@@ -75,7 +75,7 @@ fi
 if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
   log "Stage 5: Enhance eval/test segments using GSS (using GPU)"
   # for eval/test, we use larger context and smaller batches to get better quality
-  for part in dev; do
+  for part in dev eval_track1; do
     for JOB in $(seq $nj); do
       gss enhance cuts $EXP_DIR/cuts_${part}.jsonl.gz \
       $EXP_DIR/cuts_per_segment_${part}_split$nj/cuts_per_segment_${part}.$JOB.jsonl.gz \

egs/icmcasr/ASR/prepare.sh

@@ -6,8 +6,8 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
 set -eou pipefail
 
 nj=15
-stage=3
-stop_stage=3
+stage=4
+stop_stage=4
 
 # We assume dl_dir (download dir) contains the following
 # directories and files. If not, they will be downloaded
@@ -83,7 +83,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
 fi
 
 if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
-  log "Stage 3: Compute fbank for icmcasr"
+  log "Stage 4: Compute fbank for icmcasr"
   if [ ! -f data/fbank/.icmcasr.done ]; then
     mkdir -p data/fbank
     ./local/compute_fbank_icmcasr.py --perturb-speed True
@@ -95,7 +95,7 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
 fi
 
 if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
-  log "Stage 4: Compute fbank for musan"
+  log "Stage 5: Compute fbank for musan"
   if [ ! -f data/fbank/.msuan.done ]; then
     mkdir -p data/fbank
     ./local/compute_fbank_musan.py
@@ -105,7 +105,7 @@ fi
 
 lang_phone_dir=data/lang_phone
 if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
-  log "Stage 5: Prepare phone based lang"
+  log "Stage 6: Prepare G.fst"
   mkdir -p $lang_phone_dir
 
   (echo '!SIL SIL'; echo '<SPOKEN_NOISE> SPN'; echo '<UNK> SPN'; ) |
@@ -119,57 +119,3 @@ if [ $stage -le 6 ] && [ $stop_stage -ge 6 ]; then
   fi
 fi
 
-lang_char_dir=data/lang_char
-if [ $stage -le 7 ] && [ $stop_stage -ge 7 ]; then
-  log "Stage 6: Prepare char based lang"
-  mkdir -p $lang_char_dir
-  # We reuse words.txt from phone based lexicon
-  # so that the two can share G.pt later.
-
-  # The transcripts in training set, generated in stage 5
-  cp $lang_phone_dir/transcript_words.txt $lang_char_dir/transcript_words.txt
-
-  cat $dl_dir/icmcasr/data_icmcasr/transcript/icmcasr_transcript_v0.8.txt |
-  cut -d " " -f 2- > $lang_char_dir/text
-
-  (echo '<eps> 0'; echo '!SIL 1'; echo '<SPOKEN_NOISE> 2'; echo '<UNK> 3';) \
-    > $lang_char_dir/words.txt
-
-  cat $lang_char_dir/text | sed 's/ /\n/g' | sort -u | sed '/^$/d' \
-     | awk '{print $1" "NR+3}' >> $lang_char_dir/words.txt
-
-  num_lines=$(< $lang_char_dir/words.txt wc -l)
-  (echo "#0 $num_lines"; echo "<s> $(($num_lines + 1))"; echo "</s> $(($num_lines + 2))";) \
-    >> $lang_char_dir/words.txt
-
-  if [ ! -f $lang_char_dir/L_disambig.pt ]; then
-    ./local/prepare_char.py --lang-dir $lang_char_dir
-  fi
-
-  if [ ! -f $lang_char_dir/HLG.fst ]; then
-    ./local/prepare_lang_fst.py  --lang-dir $lang_phone_dir --ngram-G ./data/lm/G_3_gram.fst.txt
-  fi
-fi
-
-if [ $stage -le 8 ] && [ $stop_stage -ge 8 ]; then
-  log "Stage 7: Prepare Byte BPE based lang"
-
-  for vocab_size in ${vocab_sizes[@]}; do
-    lang_dir=data/lang_bbpe_${vocab_size}
-    mkdir -p $lang_dir
-
-    cp $lang_char_dir/words.txt $lang_dir
-    cp $lang_char_dir/text $lang_dir
-
-    if [ ! -f $lang_dir/bbpe.model ]; then
-      ./local/train_bbpe_model.py \
-        --lang-dir $lang_dir \
-        --vocab-size $vocab_size \
-        --transcript $lang_dir/text
-    fi
-
-    if [ ! -f $lang_dir/L_disambig.pt ]; then
-      ./local/prepare_lang_bbpe.py --lang-dir $lang_dir
-    fi
-  done
-fi