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@@ -54,7 +54,7 @@ To see all available training options, run `python train.py --help`. Note that t
 
 **Note:**
 - Our journal preprint [2] uses `--backbone ncsnpp`.
-- For the 48 kHz model [3], use `--backbone ncsnpp_48k --spec_factor 0.065 --spec_abs_exponent 0.667 --sigma-min 0.1 --sigma-max 1.0 --theta 2.0`
+- For the 48 kHz model [3], use `--backbone ncsnpp_48k --n_fft 1534 --hop_length 384 --spec_factor 0.065 --spec_abs_exponent 0.667 --sigma-min 0.1 --sigma-max 1.0 --theta 2.0`
 - Our Interspeech paper [1] uses `--backbone dcunet`. You need to pass `--n_fft 512` to make it work.
     - Also note that the default parameters for the spectrogram transformation in this repository are slightly different from the ones listed in the first (Interspeech) paper (`--spec_factor 0.15` rather than `--spec_factor 0.333`), but we've found the value in this repository to generally perform better for both models [1] and [2].