diff --git a/cosyvoice/cli/cosyvoice.py b/cosyvoice/cli/cosyvoice.py
index 48babf3..be82f0c 100644
--- a/cosyvoice/cli/cosyvoice.py
+++ b/cosyvoice/cli/cosyvoice.py
@@ -67,6 +67,8 @@ class CosyVoice:
     def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, stream=False, speed=1.0):
         prompt_text = self.frontend.text_normalize(prompt_text, split=False)
         for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
+            if len(i) < 0.5 * len(prompt_text):
+                logging.warning('synthesis text {} too short than prompt text {}, this may lead to bad performance'.format(i, prompt_text))
             model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k)
             start_time = time.time()
             logging.info('synthesis text {}'.format(i))
diff --git a/cosyvoice/llm/llm.py b/cosyvoice/llm/llm.py
index 00e4af0..cf9c231 100644
--- a/cosyvoice/llm/llm.py
+++ b/cosyvoice/llm/llm.py
@@ -202,6 +202,9 @@ class TransformerLM(torch.nn.Module):
                                                                   att_mask=torch.tril(torch.ones((1, lm_input.shape[1], lm_input.shape[1]),
                                                                                                  device=lm_input.device)).to(torch.bool))
             logp = self.llm_decoder(y_pred[:, -1]).log_softmax(dim=-1)
+            # force continue decode first token
+            if i == 0:
+                logp[:, self.speech_token_size] = -float('inf')
             top_ids = self.sampling_ids(logp.squeeze(dim=0), out_tokens, sampling, ignore_eos=True if i < min_len else False).item()
             if top_ids == self.speech_token_size:
                 break