diff --git a/examples/waveflow/README.md b/examples/waveflow/README.md
index 4e1ee99..b9d8523 100644
--- a/examples/waveflow/README.md
+++ b/examples/waveflow/README.md
@@ -24,7 +24,6 @@ There are many hyperparameters to be tuned depending on the specification of mod
 We provide `wavenet_ljspeech.yaml` as a hyperparameter set that works well on the LJSpeech dataset.
 Note that we use [convolutional queue](https://arxiv.org/abs/1611.09482) at audio synthesis to cache the intermediate hidden states, which will speed up the autoregressive inference over the height dimension. Current implementation only supports height dimension equals 8 or 16, i.e., where there is no dilation on the height dimension. Therefore, you can only set value of `n_group` key in the yaml config file to be either 8 or 16.
 
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 Also note that `train.py`, `synthesis.py`, and `benchmark.py` all accept a `--config` parameter. To ensure consistency, you should use the same config yaml file for both training, synthesizing and benchmarking. You can also overwrite these preset hyperparameters with command line by updating parameters after `--config`.
 For example `--config=${yaml} --batch_size=8` can overwrite the corresponding hyperparameters in the `${yaml}` config file. For more details about these hyperparameters, check `utils.add_config_options_to_parser`.