[EncoderDecoder] Improve docs (#18271)

* Improve docs

* Improve docs of speech one as well

* Apply suggestions from code review

Co-authored-by: Niels Rogge <nielsrogge@Nielss-MacBook-Pro.local>

docs/source/en/model_doc/speech-encoder-decoder.mdx

@@ -12,7 +12,7 @@ specific language governing permissions and limitations under the License.
 
 # Speech Encoder Decoder Models
 
-The [`SpeechEncoderDecoderModel`] can be used to initialize a speech-sequence-to-text-sequence model
+The [`SpeechEncoderDecoderModel`] can be used to initialize a speech-to-text model
 with any pretrained speech autoencoding model as the encoder (*e.g.* [Wav2Vec2](wav2vec2), [Hubert](hubert)) and any pretrained autoregressive model as the decoder.
 
 The effectiveness of initializing speech-sequence-to-text-sequence models with pretrained checkpoints for speech
@@ -20,9 +20,95 @@ recognition and speech translation has *e.g.* been shown in [Large-Scale Self- a
 Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli,
 Alexis Conneau.
 
-An example of how to use a [`SpeechEncoderDecoderModel`] for inference can be seen in
-[Speech2Text2](speech_to_text_2).
+An example of how to use a [`SpeechEncoderDecoderModel`] for inference can be seen in [Speech2Text2](speech_to_text_2).
 
+## Randomly initializing `SpeechEncoderDecoderModel` from model configurations.
+
+[`SpeechEncoderDecoderModel`] can be randomly initialized from an encoder and a decoder config. In the following example, we show how to do this using the default [`Wav2Vec2Model`] configuration for the encoder
+and the default [`BertForCausalLM`] configuration for the decoder.
+
+```python
+>>> from transformers import BertConfig, Wav2Vec2Config, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel
+
+>>> config_encoder = Wav2Vec2Config()
+>>> config_decoder = BertConfig()
+
+>>> config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
+>>> model = SpeechEncoderDecoderModel(config=config)
+```
+
+## Initialising `SpeechEncoderDecoderModel` from a pretrained encoder and a pretrained decoder.
+
+[`SpeechEncoderDecoderModel`] can be initialized from a pretrained encoder checkpoint and a pretrained decoder checkpoint. Note that any pretrained Transformer-based speech model, *e.g.* [Wav2Vec2](wav2vec2), [Hubert](hubert) can serve as the encoder and both pretrained auto-encoding models, *e.g.* BERT, pretrained causal language models, *e.g.* GPT2, as well as the pretrained decoder part of sequence-to-sequence models, *e.g.* decoder of BART, can be used as the decoder.
+Depending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized.
+Initializing [`SpeechEncoderDecoderModel`] from a pretrained encoder and decoder checkpoint requires the model to be fine-tuned on a downstream task, as has been shown in [the *Warm-starting-encoder-decoder blog post*](https://huggingface.co/blog/warm-starting-encoder-decoder).
+To do so, the `SpeechEncoderDecoderModel` class provides a [`SpeechEncoderDecoderModel.from_encoder_decoder_pretrained`] method.
+
+```python
+>>> from transformers import SpeechEncoderDecoderModel
+
+>>> model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
+...     "facebook/hubert-large-ll60k", "bert-base-uncased"
+... )
+```
+
+## Loading an existing `SpeechEncoderDecoderModel` checkpoint and perform inference.
+
+To load fine-tuned checkpoints of the `SpeechEncoderDecoderModel` class, [`SpeechEncoderDecoderModel`] provides the `from_pretrained(...)` method just like any other model architecture in Transformers.
+
+To perform inference, one uses the [`generate`] method, which allows to autoregressively generate text. This method supports various forms of decoding, such as greedy, beam search and multinomial sampling.
+
+```python
+>>> from transformers import Wav2Vec2Processor, SpeechEncoderDecoderModel
+>>> from datasets import load_dataset
+>>> import torch
+
+>>> # load a fine-tuned speech translation model and corresponding processor
+>>> model = SpeechEncoderDecoderModel.from_pretrained("facebook/wav2vec2-xls-r-300m-en-to-15")
+>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xls-r-300m-en-to-15")
+
+>>> # let's perform inference on a piece of English speech (which we'll translate to German)
+>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+>>> input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values
+
+>>> # autoregressively generate transcription (uses greedy decoding by default)
+>>> generated_ids = model.generate(input_values)
+>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+>>> print(generated_text)
+Mr. Quilter ist der Apostel der Mittelschicht und wir freuen uns, sein Evangelium willkommen heißen zu können.
+```
+
+## Training
+
+Once the model is created, it can be fine-tuned similar to BART, T5 or any other encoder-decoder model on a dataset of (speech, text) pairs.
+As you can see, only 2 inputs are required for the model in order to compute a loss: `input_values` (which are the
+speech inputs) and `labels` (which are the `input_ids` of the encoded target sequence).
+
+```python
+>>> from transformers import Wav2Vec2Processor, SpeechEncoderDecoderModel
+>>> from datasets import load_dataset
+
+>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
+>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+>>> model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
+...     "facebook/wav2vec2-base-960h", "bert-base-uncased"
+... )
+
+>>> model.config.decoder_start_token_id = processor.tokenizer.cls_token_id
+>>> model.config.pad_token_id = processor.tokenizer.pad_token_id
+
+>>> # load a speech input
+>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+>>> input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values
+
+>>> # load its corresponding transcription
+>>> with processor.as_target_processor():
+...     labels = processor(ds[0]["text"], return_tensors="pt").input_ids
+
+>>> # the forward function automatically creates the correct decoder_input_ids
+>>> loss = model(input_values, labels=labels).loss
+>>> loss.backward()
+```
 
 ## SpeechEncoderDecoderConfig