Teacher forcing on TIMIT and GRID dataset

[hjzzju]

Hi, I want to know how to set teacher forcing in GRID and TCDTIMIT dataset. The same as lip2wav dataset? teacher forcing decay from 29000 steps?

[Rudrabha]

You can decay it earlier. Start from 1000 steps or something similar. You may decay within 10,000 steps and then let it train without teacher forcing for sometime.

[Domhnall-Liopa]

Hi,

With tacotron_teacher_forcing_mode="constant" during training, the teacher forcing ratio is never decayed and always stays at 1. Then in synthesizer/models/helpers.py the following code is used to select the groundtruth or output of the previous time-step:

next_inputs = tf.cond(
      tf.less(tf.random_uniform([], minval=0, maxval=1, dtype=tf.float32), self._ratio),
      lambda: self._targets[:, time, :],
      lambda: outputs[:,-self._output_dim:])

Since the ratio is always 1 and never decayed, the decoder is just passed in the groundtruth of the previous time-step for the entire training. Is this expected? Should there be a switch at some point so the outputs of the previous time-step are passed during training?