diff --git a/cli/__init__.py b/cli/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/runtime/triton_trtllm/client_grpc_streaming.py b/runtime/triton_trtllm/client_grpc_streaming.py
index 60c2510..36a9ed9 100644
--- a/runtime/triton_trtllm/client_grpc_streaming.py
+++ b/runtime/triton_trtllm/client_grpc_streaming.py
@@ -85,10 +85,11 @@ def tts(
         if i == 0:
             new_audio = audio[:-cross_fade_samples]
         else:
-            cross_faded_overlap = audio[:cross_fade_samples] * fade_in + audios[i - 1][-cross_fade_samples:] * fade_out
+            cross_faded_overlap = audios[i - 1][-cross_fade_samples:] * fade_out + audio[:cross_fade_samples] * fade_in
             new_audio = np.concatenate([new_audio, cross_faded_overlap, audio[cross_fade_samples:-cross_fade_samples]])
     new_audio = np.concatenate([new_audio, audio[-cross_fade_samples:]])
 
+    #new_audio = np.hstack(audios)
     sf.write(output_audio, new_audio, 16000, "PCM_16")
     print(f"save audio to {output_audio}")
 
diff --git a/sparktts/models/bicodec.py b/sparktts/models/bicodec.py
index 8cab2f0..e4a4202 100644
--- a/sparktts/models/bicodec.py
+++ b/sparktts/models/bicodec.py
@@ -228,11 +228,16 @@ def _remove_weight_norm(m):
     model = BiCodec.load_from_checkpoint(
         model_dir="pretrained_models/SparkTTS-0.5B/BiCodec",
     )
+    device = "cpu" if not torch.cuda.is_available() else "cuda"
+    print(model)
+    model_million_params = sum(p.numel() for p in model.parameters()) / 1e6
+    print(f"{model_million_params}M parameters")
+    model.to(device)
 
     # Generate random inputs for testing
     duration = 0.96
-    x = torch.randn(20, 1, int(duration * 16000))
-    feat = torch.randn(20, int(duration * 50), 1024)
+    x = torch.randn(20, 1, int(duration * 16000)).to(device)
+    feat = torch.randn(20, int(duration * 50), 1024).to(device)
     inputs = {"feat": feat, "wav": x, "ref_wav": x}
 
     # Forward pass
@@ -241,7 +246,8 @@ def _remove_weight_norm(m):
     wav_recon = model.detokenize(semantic_tokens, global_tokens)
 
     # Verify if the reconstruction matches
-    if torch.allclose(outputs["recons"].detach(), wav_recon):
+    if torch.allclose(outputs["recons"].detach(), wav_recon, rtol=1e-3, atol=1e-5):
+    #if torch.allclose(outputs["recons"].detach(), wav_recon):
         print("Test successful")
     else:
         print("Test failed")
diff --git a/sparktts/utils/token_parser.py b/sparktts/utils/token_parser.py
index cc43782..9dbd13c 100644
--- a/sparktts/utils/token_parser.py
+++ b/sparktts/utils/token_parser.py
@@ -156,10 +156,11 @@ def emotion(emotion: str):
 
 # test
 if __name__ == "__main__":
+    import os
     from transformers import AutoTokenizer
 
     tokenizer = AutoTokenizer.from_pretrained(
-        "/aifs4su/xinshengwang/code/StyleCraft/tokenizer/stylecraft-bicodec-pitch-loudness-speed-emotion-tokenizer"
+        os.getenv("TOKENIZER_PATH", "/aifs4su/xinshengwang/code/StyleCraft/tokenizer/stylecraft-bicodec-pitch-loudness-speed-emotion-tokenizer")
     )
 
     tasks = ["tts", "tts", "understand", "controllable_tts", "prompt_tts"]
@@ -183,5 +184,7 @@ def emotion(emotion: str):
         inputs = [task, age, gender, mel, mel_level, loudness, loudness_level, emotion]
         inputs = "".join(inputs)
         ids = tokenizer.encode(inputs, add_special_tokens=False)
-        print(ids)
-        print("decode", tokenizer.decode(ids))
+        print("tokenized ids",ids)
+        tokens = tokenizer.decode(ids)
+        print("decoded tokens", tokens)
+        assert tokens == inputs