add aishell2 feat

egs/aishell2/ASR/local/compute_fbank_aishell2.py

@@ -29,7 +29,7 @@ import os
 from pathlib import Path
 
 import torch
-from lhotse import CutSet, Fbank, FbankConfig, LilcomChunkyWriter
+from lhotse import CutSet, WhisperFbank, WhisperFbankConfig, Fbank, FbankConfig, LilcomChunkyWriter
 from lhotse.recipes.utils import read_manifests_if_cached
 
 from icefall.utils import get_executor, str2bool
@@ -42,7 +42,7 @@ torch.set_num_threads(1)
 torch.set_num_interop_threads(1)
 
 
-def compute_fbank_aishell2(num_mel_bins: int = 80, perturb_speed: bool = False):
+def compute_fbank_aishell2(num_mel_bins: int = 80, perturb_speed: bool = False, whisper_fbank: bool = False):
     src_dir = Path("data/manifests")
     output_dir = Path("data/fbank")
     num_jobs = min(15, os.cpu_count())
@@ -68,8 +68,10 @@ def compute_fbank_aishell2(num_mel_bins: int = 80, perturb_speed: bool = False):
         list(manifests.keys()),
         dataset_parts,
     )
-
-    extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
+    if whisper_fbank:
+        extractor = WhisperFbank(WhisperFbankConfig(num_filters=num_mel_bins, device='cuda'))
+    else:
+        extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
 
     with get_executor() as ex:  # Initialize the executor only once.
         for partition, m in manifests.items():
@@ -111,7 +113,12 @@ def get_args():
         default=False,
         help="Enable 0.9 and 1.1 speed perturbation for data augmentation. Default: False.",
     )
-
+    parser.add_argument(
+        "--whisper-fbank",
+        type=str2bool,
+        default=False,
+        help="Use WhisperFbank instead of Fbank. Default: False.",
+    )
     return parser.parse_args()
 
 
@@ -122,5 +129,5 @@ if __name__ == "__main__":
 
     args = get_args()
     compute_fbank_aishell2(
-        num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed
+        num_mel_bins=args.num_mel_bins, perturb_speed=args.perturb_speed, whisper_fbank=args.whisper_fbank
     )

egs/aishell2/ASR/prepare.sh

@@ -6,8 +6,8 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
 set -eou pipefail
 
 nj=30
-stage=0
-stop_stage=7
+stage=1
+stop_stage=1
 perturb_speed=true
 
 
@@ -108,6 +108,16 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
   fi
 fi
 
+whisper_mel_bins=80
+if [ $stage -le 30 ] && [ $stop_stage -ge 30 ]; then
+  log "Stage 30: Compute whisper fbank for aishell2"
+  if [ ! -f data/fbank/.aishell2.whisper.done ]; then
+    mkdir -p data/fbank
+    ./local/compute_fbank_aishell2.py --perturb-speed ${perturb_speed} --num-mel-bins ${whisper_mel_bins} --whisper-fbank true
+    touch data/fbank/.aishell2.whisper.done
+  fi
+fi
+
 if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
   log "Stage 4: Compute fbank for musan"
   if [ ! -f data/fbank/.msuan.done ]; then

egs/wenetspeech/ASR/prepare.sh

@@ -6,8 +6,8 @@ export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
 set -eou pipefail
 
 nj=15
-stage=130
-stop_stage=130
+stage=131
+stop_stage=131
 
 # Split L subset to this number of pieces
 # This is to avoid OOM during feature extraction.
@@ -198,7 +198,7 @@ if [ $stage -le 130 ] && [ $stop_stage -ge 130 ]; then
 
   python3 ./local/compute_fbank_wenetspeech_splits.py \
     --training-subset L \
-    --num-workers 40 \
+    --num-workers 8 \
     --batch-duration 1600 \
     --start 0 \
     --num-mel-bins ${whisper_mel_bins} --whisper-fbank true \
@@ -215,9 +215,9 @@ if [ $stage -le 131 ] && [ $stop_stage -ge 131 ]; then
 
   python3 ./local/compute_fbank_wenetspeech_splits.py \
     --training-subset L \
-    --num-workers 40 \
+    --num-workers 8 \
     --batch-duration 1600 \
-    --start 99 \
+    --start 98 \
     --num-mel-bins ${whisper_mel_bins} --whisper-fbank false \
     --num-splits $num_splits