NVIDIA · nv-kkudrynski · Sep 11, 2020 · Aug 3, 2020 · Sep 11, 2020
diff --git a/CUDA-Optimized/FastSpeech/README.md b/CUDA-Optimized/FastSpeech/README.md
@@ -146,7 +146,15 @@ To train your model using mixed precision with Tensor Cores or using FP32, perfo
    python fastspeech/dataset/ljspeech_dataset.py --dataset_path="./LJSpeech-1.1" --mels_path="./mels_ljspeech1.1"
    ```
 
-   The preprocessed mel-spectrograms are stored in the ./mels_ljspeech1.1 directory. Also, the preprocessed alignments are prepared in ./aligns_ljspeech1.1 directory. For more information, refer to the [training process section](#training-process).
+   The preprocessed mel-spectrograms are stored in the ./mels_ljspeech1.1 directory. 
+
+Next, calculate alignments on the LJSpeech dataset using a pre-trained [NVIDIA Tacotron2 checkpoint](https://drive.google.com/file/d/1c5ZTuT7J08wLUoVZ2KkUs_VdZuJ86ZqA/view). The output directory is specified with `--aligns_path`.
+   ```
+   python fastspeech/align_tacotron2.py --dataset_path="./LJSpeech-1.1" --tacotron2_path="tacotron2_statedict.pt" --aligns_path="aligns_ljspeech1.1"
+   ```
+
+   The preprocessed alignments are stored in the ./aligns_ljspeech1.1 directory. For more information, refer to the [training process section](#training-process).
+
 
    Finally, run the training script:
 
@@ -439,4 +447,4 @@ July 2020
 
 ### Known issues
 
-There are no known issues in this release.
+There are no known issues in this release.
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