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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, 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 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.

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.

>>> 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, 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. To do so, the SpeechEncoderDecoderModel class provides a [SpeechEncoderDecoderModel.from_encoder_decoder_pretrained] method.

>>> 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.

>>> 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).

>>> 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