Explicit conversion

nvidia_deeplearningexamples_tacotron2.md

@@ -28,7 +28,7 @@ This implementation of Tacotron 2 model differs from the model described in the
 
 In the example below:
 - pretrained Tacotron2 and Waveglow models are loaded from torch.hub
-- Tacotron2 generates mel spectrogram given tensor represantation of an input text ("Hello world, I missed you so much")
+- Given a tensor representation of the input text ("Hello world, I missed you so much"), Tacotron2 generates a Mel spectrogram as shown on the illustration
 - Waveglow generates sound given the mel spectrogram
 - the output sound is saved in an 'audio.wav' file
 

nvidia_deeplearningexamples_waveglow.md

@@ -26,7 +26,7 @@ The Tacotron 2 and WaveGlow model form a text-to-speech system that enables user
 
 In the example below:
 - pretrained Tacotron2 and Waveglow models are loaded from torch.hub
-- Tacotron2 generates mel spectrogram given tensor represantation of an input text ("Hello world, I missed you so much")
+- Given a tensor representation of the input text ("Hello world, I missed you so much"), Tacotron2 generates a Mel spectrogram as shown on the illustration
 - Waveglow generates sound given the mel spectrogram
 - the output sound is saved in an 'audio.wav' file
 
@@ -98,4 +98,4 @@ For detailed information on model input and output, training recipies, inference
  - [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://arxiv.org/abs/1712.05884)
  - [WaveGlow: A Flow-based Generative Network for Speech Synthesis](https://arxiv.org/abs/1811.00002)
  - [Tacotron2 and WaveGlow on NGC](https://ngc.nvidia.com/catalog/resources/nvidia:tacotron_2_and_waveglow_for_pytorch)
- - [Tacotron2 and Waveglow on github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2)
+ - [Tacotron2 and Waveglow on github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2)