diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py
index 814390ad6..b83a61ccf 100644
--- a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py
+++ b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py
@@ -24,7 +24,15 @@ from pathlib import Path
 from typing import Any, Dict, Optional
 
 import torch
-from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
+from lhotse import (
+    CutSet,
+    Fbank,
+    FbankConfig,
+    load_manifest,
+    load_manifest_lazy,
+    WhisperFbank,
+    WhisperFbankConfig,
+)
 from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
     CutConcatenate,
     CutMix,
@@ -215,6 +223,20 @@ class LibriSpeechAsrDataModule:
             help="AudioSamples or PrecomputedFeatures",
         )
 
+        group.add_argument(
+            "--use-whisper-fbank",
+            type=str2bool,
+            default=False,
+            help="Use whisper fbank feature as input",
+        )
+
+        group.add_argument(
+            "--whisper-fbank-n-mels",
+            type=int,
+            default=80,
+            help="Number of mels for whisper fbank, large-v3 uses 128-mel fbank",
+        )
+
     def train_dataloaders(
         self,
         cuts_train: CutSet,
@@ -297,9 +319,15 @@ class LibriSpeechAsrDataModule:
             # to be strict (e.g. could be randomized)
             # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
             # Drop feats to be on the safe side.
+            if self.args.use_whisper_fbank:
+                extractor = WhisperFbank(
+                    WhisperFbankConfig(num_filters=self.args.whisper_fbank_n_mels),
+                )
+            else:
+                extractor = Fbank(FbankConfig(num_mel_bins=80))
             train = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(extractor),
                 input_transforms=input_transforms,
                 return_cuts=self.args.return_cuts,
             )
@@ -355,9 +383,15 @@ class LibriSpeechAsrDataModule:
 
         logging.info("About to create dev dataset")
         if self.args.on_the_fly_feats:
+            if self.args.use_whisper_fbank:
+                extractor = WhisperFbank(
+                    WhisperFbankConfig(num_filters=self.args.whisper_fbank_n_mels),
+                )
+            else:
+                extractor = Fbank(FbankConfig(num_mel_bins=80))
             validate = K2SpeechRecognitionDataset(
                 cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
+                input_strategy=OnTheFlyFeatures(extractor),
                 return_cuts=self.args.return_cuts,
             )
         else:
@@ -383,8 +417,15 @@ class LibriSpeechAsrDataModule:
 
     def test_dataloaders(self, cuts: CutSet) -> DataLoader:
         logging.debug("About to create test dataset")
+        if self.args.use_whisper_fbank:
+            extractor = WhisperFbank(
+                WhisperFbankConfig(num_filters=self.args.whisper_fbank_n_mels),
+            )
+        else:
+            extractor = Fbank(FbankConfig(num_mel_bins=80))
+
         test = K2SpeechRecognitionDataset(
-            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
+            input_strategy=OnTheFlyFeatures(extractor)
             if self.args.on_the_fly_feats
             else eval(self.args.input_strategy)(),
             return_cuts=self.args.return_cuts,