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# Speech Encoder Decoder Models

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
recognition and speech translation has *e.g.* been shown in [Large-Scale Self- and Semi-Supervised Learning for Speech
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).

## 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

[[autodoc]] SpeechEncoderDecoderConfig

## SpeechEncoderDecoderModel

[[autodoc]] SpeechEncoderDecoderModel
    - forward
    - from_encoder_decoder_pretrained

## FlaxSpeechEncoderDecoderModel

[[autodoc]] FlaxSpeechEncoderDecoderModel
    - __call__
    - from_encoder_decoder_pretrained