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Merge pull request #297 from GrzegorzKarchNV/tacotron2-readme-update

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updated tacotron2 trtis readme
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PyTorch/SpeechSynthesis/Tacotron2/notebooks/trtis/README.md
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@ -19,7 +19,7 @@ below to learn how to train, export --- or simply download the pretrained models
You can either download the pretrained checkpoints or train the models yourself. You can either download the pretrained checkpoints or train the models yourself.
#### Download pretrained checkpoints. #### (Option 1) Download pretrained checkpoints.
If you want to use a pretrained checkpoints, download them from [NGC](https://ngc.nvidia.com/catalog/models): If you want to use a pretrained checkpoints, download them from [NGC](https://ngc.nvidia.com/catalog/models):
@ -27,7 +27,7 @@ If you want to use a pretrained checkpoints, download them from [NGC](https://ng
- [WaveGlow checkpoint](https://ngc.nvidia.com/models/nvidia:waveglow256pyt_fp16) - [WaveGlow checkpoint](https://ngc.nvidia.com/models/nvidia:waveglow256pyt_fp16)
#### Train Tacotron 2 and WaveGlow models. #### (Option 2) Train Tacotron 2 and WaveGlow models.
In order to train the models, follow the QuickStart section in the `Tacotron2/README.md` In order to train the models, follow the QuickStart section in the `Tacotron2/README.md`
file by executing points 1-5. You have to train WaveGlow in a different way than described there. Use file by executing points 1-5. You have to train WaveGlow in a different way than described there. Use
@ -52,25 +52,25 @@ python export_tacotron2_ts_config.py --amp-run
``` ```
This will export the folder structure of the TRTIS repository and the config file of Tacotron 2. This will export the folder structure of the TRTIS repository and the config file of Tacotron 2.
By default, it will be found in the `trtis_repo/tacotron` folder. By default, it will be found in the `trtis_repo/tacotron2` folder.
Now there are two ways to proceed. Now there are two ways to proceed.
#### Download the Tacotron 2 TorchScript model. #### (Option 1) Download the Tacotron 2 TorchScript model.
Download the Tacotron 2 TorchScript model from: Download the Tacotron 2 TorchScript model from:
- [Tacotron2 TorchScript](https://ngc.nvidia.com/models/nvidia:tacotron2pyt_jit_fp16) - [Tacotron2 TorchScript](https://ngc.nvidia.com/models/nvidia:tacotron2pyt_jit_fp16)
Move the downloaded model to `trtis_repo/tacotron2/1/model.pt` Move the downloaded model to `trtis_repo/tacotron2/1/model.pt`
#### Export the Tacotron 2 model using TorchScript. #### (Option 2) Export the Tacotron 2 model using TorchScript.
To export the Tacotron 2 model using TorchScript, type: To export the Tacotron 2 model using TorchScript, type:
```bash ```bash
python export_tacotron2_ts.py --tacotron2 <tacotron2_checkpoint> -o trtis_repo/tacotron2/1/model.pt --amp-run python export_tacotron2_ts.py --tacotron2 <tacotron2_checkpoint> -o trtis_repo/tacotron2/1/model.pt --amp-run
``` ```
This will save the model as ``trtis_repo/tacotron/1/model.pt``. This will save the model as ``trtis_repo/tacotron2/1/model.pt``.
### Setup WaveGlow TRT engine. ### Setup WaveGlow TRT engine.
@ -86,14 +86,14 @@ By default, it will be found in the `trtis_repo/waveglow` folder.
There are two ways to proceed. There are two ways to proceed.
#### Download the WaveGlow TRT engine. #### (Option 1) Download the WaveGlow TRT engine.
Download the WaveGlow TRT engine from: Download the WaveGlow TRT engine from:
- [WaveGlow TRT engine](https://ngc.nvidia.com/models/nvidia:waveglow256pyt_trt_fp16) - [WaveGlow TRT engine](https://ngc.nvidia.com/models/nvidia:waveglow256pyt_trt_fp16)
Move the downloaded model to `trtis_repo/waveglow/1/model.plan` Move the downloaded model to `trtis_repo/waveglow/1/model.plan`
#### Export the WaveGlow model to TRT. #### (Option 2) Export the WaveGlow model to TRT.
Before exporting the model, you need to install onnx-tensorrt by typing: Before exporting the model, you need to install onnx-tensorrt by typing:
```bash ```bash