diff --git a/cosyvoice/flow/flow_matching.py b/cosyvoice/flow/flow_matching.py
index d3beb9ec2..fe6a63b31 100644
--- a/cosyvoice/flow/flow_matching.py
+++ b/cosyvoice/flow/flow_matching.py
@@ -92,20 +92,23 @@ def solve_euler(self, x, t_span, mu, mask, spks, cond, streaming=False):
 
         # Do not use concat, it may cause memory format changed and trt infer with wrong results!
         # NOTE when flow run in amp mode, x.dtype is float32, which cause nan in trt fp16 inference, so set dtype=spks.dtype
-        x_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype)
-        mask_in = torch.zeros([2, 1, x.size(2)], device=x.device, dtype=spks.dtype)
-        mu_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype)
-        t_in = torch.zeros([2], device=x.device, dtype=spks.dtype)
-        spks_in = torch.zeros([2, 80], device=x.device, dtype=spks.dtype)
-        cond_in = torch.zeros([2, 80, x.size(2)], device=x.device, dtype=spks.dtype)
+        bsz = x.size(0)
+        x_in = torch.zeros([2 * bsz, 80, x.size(2)], device=x.device, dtype=spks.dtype)
+        mask_in = torch.zeros([2 * bsz, 1, x.size(2)], device=x.device, dtype=spks.dtype)
+        mu_in = torch.zeros([2 * bsz, 80, x.size(2)], device=x.device, dtype=spks.dtype)
+        t_in = torch.zeros([2 * bsz], device=x.device, dtype=spks.dtype)
+        spks_in = torch.zeros([2 * bsz, 80], device=x.device, dtype=spks.dtype)
+        cond_in = torch.zeros([2 * bsz, 80, x.size(2)], device=x.device, dtype=spks.dtype)
         for step in range(1, len(t_span)):
             # Classifier-Free Guidance inference introduced in VoiceBox
-            x_in[:] = x
-            mask_in[:] = mask
-            mu_in[0] = mu
+            x_in[:bsz] = x
+            x_in[bsz:] = x
+            mask_in[:bsz] = mask
+            mask_in[bsz:] = mask
+            mu_in[:bsz] = mu
             t_in[:] = t.unsqueeze(0)
-            spks_in[0] = spks
-            cond_in[0] = cond
+            spks_in[:bsz] = spks
+            cond_in[:bsz] = cond
             dphi_dt = self.forward_estimator(
                 x_in, mask_in,
                 mu_in, t_in,