Speech Encoder Decoder Models
Source: transformers

# Speech Encoder Decoder ModelsThe [`SpeechEncoderDecoderModel`] can be used to initialize a speech-to-text modelwith 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 speechrecognition and speech translation has *e.g.* been shown in [Large-Scale Self- and Semi-Supervised Learning for SpeechTranslation](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 encoderand 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", "google-bert/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.```## TrainingOnce 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 thespeech inputs) and `labels` (which are the `input_ids` of the encoded target sequence).```python>>> from transformers import AutoTokenizer, AutoFeatureExtractor, SpeechEncoderDecoderModel>>> from datasets import load_dataset>>> encoder_id = "facebook/wav2vec2-base-960h"  # acoustic model encoder>>> decoder_id = "google-bert/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