Update app.py

app.py

@@ -43,8 +43,9 @@ def inference(text):
   torchaudio.save("output_waveglow.wav", waveforms[0:1].cpu(), sample_rate=22050)
   return "output_waveglow.wav"
   
-title="TACOTRON 2"
-description="Gradio demo for TACOTRON 2: The Tacotron 2 model for generating mel spectrograms from text. To use it, simply add you text or click on one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1712.05884' target='_blank'>Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions</a> | <a href='https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2' target='_blank'>Github Repo</a></p>"
+title="WAVEGLOW"
+description="Gradio demo for WAVEGLOW: WaveGlow model for generating speech from mel spectrograms (generated by Tacotron2). To use it, simply add you text or click on one of the examples to load them. Read more at the links below."
+
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/1811.00002' target='_blank'>WaveGlow: A Flow-based Generative Network for Speech Synthesis</a> | <a href='https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/SpeechSynthesis/Tacotron2' target='_blank'>Github Repo</a></p>"
 examples=[["life is like a box of chocolates"]]
 gr.Interface(inference,"text",gr.outputs.Audio(type="file"),title=title,description=description,article=article,examples=examples).launch(enable_queue=True)