- 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.