diff --git a/egs/tokenizer/CODEC/encodec/train.py b/egs/tokenizer/CODEC/encodec/train.py
index ccc4c4cb3..f74be7d4c 100755
--- a/egs/tokenizer/CODEC/encodec/train.py
+++ b/egs/tokenizer/CODEC/encodec/train.py
@@ -170,6 +170,12 @@ def get_parser():
         help="The chunk size of the biomarker (in second).",
     )
 
+    parser.add_argument(
+        "--n-q",
+        type=int,
+        help="The number of quantization levels.",
+    )
+
     return parser
 
 
@@ -300,7 +306,7 @@ def get_model(params: AttributeDict) -> nn.Module:
     generator_params = {
         "generator_n_filters": 32,
         "dimension": 512,
-        "ratios": [8, 5, 4, 2],
+        "ratios": [8, 6, 4, 2],
         "target_bandwidths": [1.5, 3, 6, 12, 24],
         "bins": 1024,
     }
@@ -315,6 +321,9 @@ def get_model(params: AttributeDict) -> nn.Module:
         "target_bw": 6,
     }
 
+    logging.info(f"Generator params: {generator_params}")
+    logging.info(f"Discriminator params: {discriminator_params}")
+    logging.info(f"Inference params: {inference_params}")
     params.update(generator_params)
     params.update(discriminator_params)
     params.update(inference_params)
@@ -324,7 +333,7 @@ def get_model(params: AttributeDict) -> nn.Module:
         1000
         * params.target_bandwidths[-1]
         // (math.ceil(params.sampling_rate / hop_length) * 10)
-    )
+    ) if params.n_q is None else params.n_q
 
     encoder = SEANetEncoder(
         n_filters=params.generator_n_filters,