text_to_speech_synthesizer

Text to speech synthesis using WaveGlow & Tacotron2 model.

This example works only on NVIDIA CUDA device and not on CPU

We have used the following Waveglow/Tacotron2 model for this example:

https://pytorch.org/hub/nvidia_deeplearningexamples_waveglow/

We have copied WaveGlow's model file from following github repo: https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/waveglow/model.py

Install pip dependencies using following commands

pip install numpy scipy unidecode inflect
pip install librosa --user

Serve the WaveGlow speech synthesis model on TorchServe

• Generate the model archive for waveglow speech synthesis model using following command

  ./create_mar.sh

• Register the model on TorchServe using the above model archive file

  mkdir model_store
  mv waveglow_synthesizer.mar model_store/
  torchserve --start --model-store model_store --models waveglow_synthesizer.mar

• Run inference and download audio output using curl command :

  curl http://127.0.0.1:8080/predictions/waveglow_synthesizer -T sample_text.txt -o audio.wav

• Run inference and download audio output using python script :

  import requests
  
  files = {'data': open('sample_text.txt','rb')}
  response = requests.post('http://localhost:8080/predictions/waveglow_synthesizer', files=files)
  data = response.content
  
  with open('audio.wav', 'wb') as audio_file:
      audio_file.write(data)

• Change the host and port in above samples as per your server configuration.

• Response : An audio.wav file gets downloaded.

Note : The above example works only for smaller text size. Refer following NVidia/DeepLearningExamples ticket for more details : NVIDIA/DeepLearningExamples#497