diff --git a/scripts/whisper/export-onnx.py b/scripts/whisper/export-onnx.py
index 1bfe03d0f..feca2542d 100755
--- a/scripts/whisper/export-onnx.py
+++ b/scripts/whisper/export-onnx.py
@@ -63,6 +63,10 @@ def add_meta_data(filename: str, meta_data: Dict[str, Any]):
         Key-value pairs.
     """
     model = onnx.load(filename)
+
+    while len(model.metadata_props):
+        model.metadata_props.pop()
+
     for key, value in meta_data.items():
         meta = model.metadata_props.add()
         meta.key = key
@@ -422,7 +426,7 @@ def main():
 
     encoder_meta_data = {
         "model_type": f"whisper-{name}",
-        "version": "1",
+        "version": "2",
         "maintainer": "k2-fsa",
         "n_mels": model.dims.n_mels,
         "n_audio_ctx": model.dims.n_audio_ctx,
@@ -453,6 +457,7 @@ def main():
         "sot_prev": tokenizer.sot_prev,
         "sot_lm": tokenizer.sot_lm,
         "no_timestamps": tokenizer.no_timestamps,
+        "timestamp_begin": tokenizer.timestamp_begin,
     }
     print(f"encoder_meta_data: {encoder_meta_data}")
     add_meta_data(filename=encoder_filename, meta_data=encoder_meta_data)
diff --git a/scripts/whisper/test.py b/scripts/whisper/test.py
index 014a19e6a..c34c669b9 100755
--- a/scripts/whisper/test.py
+++ b/scripts/whisper/test.py
@@ -6,9 +6,10 @@
 """
 import argparse
 import base64
-from typing import Tuple
+from typing import List, Tuple
 
 import kaldi_native_fbank as knf
+import numpy as np
 import onnxruntime as ort
 import torch
 import torchaudio
@@ -86,21 +87,30 @@ def init_encoder(self, encoder: str):
         )
 
         meta = self.encoder.get_modelmeta().custom_metadata_map
+        version = int(meta["version"])
+        if version < 2:
+            raise RuntimeError(
+                "If you have exported your model before 2024-06-21, please re-export it using the latest "
+                "https://github.com/k2-fsa/sherpa-onnx/blob/master/scripts/whisper/export-onnx.py\n"
+                "See https://github.com/k2-fsa/sherpa-onnx/pull/1037 for details"
+            )
         self.n_text_layer = int(meta["n_text_layer"])
         self.n_text_ctx = int(meta["n_text_ctx"])
         self.n_text_state = int(meta["n_text_state"])
         self.sot = int(meta["sot"])
+        self.sot_lm = int(meta["sot_lm"])
+        self.sot_prev = int(meta["sot_prev"])
         self.eot = int(meta["eot"])
         self.translate = int(meta["translate"])
         self.transcribe = int(meta["transcribe"])
         self.no_timestamps = int(meta["no_timestamps"])
+        self.timestamp_begin = int(meta["timestamp_begin"])
         self.no_speech = int(meta["no_speech"])
+        self.non_speech_tokens = list(map(int, meta["non_speech_tokens"].split(",")))
         self.blank = int(meta["blank_id"])
 
         self.sot_sequence = list(map(int, meta["sot_sequence"].split(",")))
 
-        self.sot_sequence.append(self.no_timestamps)
-
         self.all_language_tokens = list(
             map(int, meta["all_language_tokens"].split(","))
         )
@@ -179,20 +189,62 @@ def get_self_cache(self) -> Tuple[torch.Tensor, torch.Tensor]:
         return n_layer_self_k_cache, n_layer_self_v_cache
 
     def suppress_tokens(self, logits, is_initial: bool) -> None:
+        """
+        Args:
+          logits: 1-D nd.array. Changed in-place.
+        """
         # suppress blank
         if is_initial:
             logits[self.eot] = float("-inf")
             logits[self.blank] = float("-inf")
 
-        # suppress <|notimestamps|>
-        logits[self.no_timestamps] = float("-inf")
-
         logits[self.sot] = float("-inf")
+        logits[self.sot_prev] = float("-inf")
+        logits[self.sot_lm] = float("-inf")
         logits[self.no_speech] = float("-inf")
 
-        # logits is changed in-place
+        logits[self.transcribe] = float("-inf")
         logits[self.translate] = float("-inf")
 
+        logits[self.no_timestamps] = float("-inf")
+
+        #  logits[self.all_language_tokens] = float("-inf")
+        logits[self.non_speech_tokens] = float("-inf")
+
+    def apply_timestamp_rules(self, tokens: List[int], logits: np.ndarray):
+        """
+        Args:
+          logits: 1-D nd.array. Changed in-place.
+        """
+        sample_begin = 0
+        max_initial_timestamp_index = 50
+        logits[self.no_timestamps] = float("-inf")
+
+        last_was_timestamp = len(tokens) >= 1 and tokens[-1] >= self.timestamp_begin
+        penultimate_was_timestamp = (
+            len(tokens) < 2 or tokens[-2] >= self.timestamp_begin
+        )
+
+        if last_was_timestamp:
+            if penultimate_was_timestamp:
+                logits[self.timestamp_begin :] = float("-inf")
+            else:
+                logits[: self.eot] = float("-inf")
+        sampled_tokens = torch.tensor(tokens)
+        timestamps = sampled_tokens[sampled_tokens.ge(self.timestamp_begin)]
+        if timestamps.numel() > 0:
+            logits[self.timestamp_begin : timestamps[-1].item()] = float("-inf")
+
+        if len(tokens) == 0:
+            logits[: self.timestamp_begin] = float("-inf")
+            last_allowed = self.timestamp_begin + max_initial_timestamp_index
+            logits[last_allowed + 1 :] = float("-inf")
+        logprobs = torch.as_tensor(logits).log_softmax(dim=-1)
+        timestamp_logprob = logprobs[self.timestamp_begin :].logsumexp(dim=-1)
+        max_text_token_lobprob = logprobs[: self.timestamp_begin].max()
+        if timestamp_logprob > max_text_token_lobprob:
+            logits[: self.timestamp_begin] = float("-inf")
+
     def detect_language(
         self, n_layer_cross_k: torch.Tensor, n_layer_cross_v: torch.Tensor
     ) -> int:
@@ -348,6 +400,7 @@ def main():
         offset += 1
         logits = logits[0, -1]
         model.suppress_tokens(logits, is_initial=False)
+        model.apply_timestamp_rules(tokens=results, logits=logits)
         max_token_id = logits.argmax(dim=-1)
     token_table = load_tokens(args.tokens)
     s = b""