# 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.

thon

>>> 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.

thon

>>> 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.

thon

>>> 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).

thon

>>> from transformers import AutoTokenizer, AutoFeatureExtractor, SpeechEncoderDecoderModel

>>> from datasets import load_dataset

>>> encoder_id = "facebook/wav2vec2-base-960h"  # acoustic model encoder

>>> decoder_id = "bert-base-uncased"  # text decoder

>>> feature_extractor = AutoFeatureExtractor.from_pretrained(encoder_id)

>>> tokenizer = AutoTokenizer.from_pretrained(decoder_id)

>>> # Combine pre-trained encoder and pre-trained decoder to form a Seq2Seq model

>>> model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_id, decoder_id)

>>> model.config.decoder_start_token_id = tokenizer.cls_token_id

>>> model.config.pad_token_id = tokenizer.pad_token_id

>>> # load an audio input and pre-process (normalise mean/std to 0/1)

>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")

>>> input_values = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt").input_values

>>> # load its corresponding transcription and tokenize to generate labels

>>> labels = tokenizer(ds[0]["text"], return_tensors="pt").input_ids

>>> # the forward function automatically creates the correct decoder_input_ids

>>> loss = model(input_values=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