How does Wav2Vec 2.0 feed output from Convolutional Feature Encoder as input to the Transformer Context Network
They key is that the 768 dimensional vector of the transformer is the size of a single input... let me explain.

You start with a variable length audio input
This is passed through a temporal CNN network, which will give you outputs, called $z_1$ to $z_T$ by the paper - where T is variable across the batch and is the number of timesteps in a particular audio input (different for different audio inputs).
Each of these $z$ are passed as 768 dimensional vectors to the
transformer, i.e. there are $T$ 768-dim vectors being passed to your
transformer

As you can imagine, practically you would need to pass a mask to the transformer too, so it knows what the variable $T$ is for the different inputs in your batch.