| ## Utilities to Convert Models to Tensorflow2 | |
| Here there are experimental utilities to convert trained Torch models to Tensorflow (2.2>=). | |
| Converting Torch models to TF enables all the TF toolkit to be used for better deployment and device specific optimizations. | |
| Note that we do not plan to share training scripts for Tensorflow in near future. But any contribution in that direction would be more than welcome. | |
| To see how you can use TF model at inference, check the notebook. | |
| This is an experimental release. If you encounter an error, please put an issue or in the best send a PR but you are mostly on your own. | |
| ### Converting a Model | |
| - Run ```convert_tacotron2_torch_to_tf.py --torch_model_path /path/to/torch/model.pth.tar --config_path /path/to/model/config.json --output_path /path/to/output/tf/model``` with the right arguments. | |
| ### Known issues ans limitations | |
| - We use a custom model load/save mechanism which enables us to store model related information with models weights. (Similar to Torch). However, it is prone to random errors. | |
| - Current TF model implementation is slightly slower than Torch model. Hopefully, it'll get better with improving TF support for eager mode and ```tf.function```. | |
| - TF implementation of Tacotron2 only supports regular Tacotron2 as in the paper. | |
| - You can only convert models trained after TF model implementation since model layers has been updated in Torch model. | |