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This example was implemented by @NandanApoorv. Let's take a look at the model architecture.
It starts by defining two embedding layers: a positional embedding for text tokens, and an embedding for speech features, that uses 1D convolutions with strides for downsampling.pic.twitter.com/7bj9QtZSyV
Then it defines a Transformer encoder, which is your usual Transformer block, as well as a Transformer decoder, which is also your usual Transformer block, but with causal attention to prevent later timesteps to influence the decoding of earlier timesteps.pic.twitter.com/Ige93alEwK
Finally, it combines the encoder and decoder in a Model subclass, where training logic is packaged in the `train_step()` method (this enables training via `fit()`, which gives you callbacks and distribution support for free). Also note the `generate()` method for inference!pic.twitter.com/cG6KkgUNNO
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