Fix handling of output embeddings in TFSpeech2Text

This resolves an issue of output embeddings in
`TFSpeech2TextModelTest.test_save_load_after_resize_token_embeddings`
where resizing token embeddings caused the following error:

```
ValueError: Attempt to convert a value (None) with an unsupported type
(<class 'NoneType'>) to a Tensor.
```

src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py

@@ -1431,8 +1431,12 @@ def resize_token_embeddings(self, new_num_tokens: int) -> tf.Variable:
     def get_output_embeddings(self):
         return self.lm_head
 
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
+    def set_output_embeddings(self, value):
+        self.lm_head = keras.layers.Dense(shape_list(value)[0], use_bias=False, name="lm_head")
+        # in a dense layer the kernel has a shape (last_dim, units), for us (dim, num_tokens)
+        # value has a shape (num_tokens, dim) then needs to be transposed
+        transposed_value = tf.transpose(value)
+        self.lm_head.kernel = tf.Variable(transposed_value)
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(SPEECH_TO_TEXT_INPUTS_DOCSTRING)