diff --git a/examples/whisper/README.md b/examples/whisper/README.md
index 6766607f0..44be097d2 100755
--- a/examples/whisper/README.md
+++ b/examples/whisper/README.md
@@ -127,7 +127,8 @@ python3 convert_checkpoint.py \
                 --use_weight_only \
                 --weight_only_precision $WEIGHT_ONLY_PRECISION \
                 --output_dir $checkpoint_dir \
-                --model_name distil-medium.en
+                --model_name distil-medium.en \
+                --chunk_length 15
 ```
 
 <details><summary> Now, we can build and run the model like before: </summary><p>
diff --git a/examples/whisper/convert_checkpoint.py b/examples/whisper/convert_checkpoint.py
index 9e5ab01bf..1dae0e33b 100644
--- a/examples/whisper/convert_checkpoint.py
+++ b/examples/whisper/convert_checkpoint.py
@@ -25,6 +25,7 @@
 from tensorrt_llm.functional import LayerNormPositionType, LayerNormType
 from tensorrt_llm.models.convert_utils import weight_only_quantize_dict
 from tensorrt_llm.quantization import QuantAlgo
+from whisper_utils import SAMPLE_RATE, HOP_LENGTH
 
 
 def parse_arguments():
@@ -100,7 +101,7 @@ def get_encoder_config(model_metadata: dict, dtype: str, chunk_length: int,
         'hidden_size': model_metadata['n_audio_state'],
         'n_mels': model_metadata['n_mels'],
         'n_audio_ctx': model_metadata['n_audio_ctx'],
-        'chunk_length': chunk_length * 100,
+        'chunk_length': chunk_length * SAMPLE_RATE / HOP_LENGTH,
         'vocab_size': model_metadata['n_vocab'],
         'hidden_act': "gelu",
         'num_languages': num_languages,