133 lines
6.9 KiB
Markdown
133 lines
6.9 KiB
Markdown
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# Speech Encoder Decoder Models
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The [`SpeechEncoderDecoderModel`] can be used to initialize a speech-to-text model
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with any pretrained speech autoencoding model as the encoder (*e.g.* [Wav2Vec2](wav2vec2), [Hubert](hubert)) and any pretrained autoregressive model as the decoder.
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The effectiveness of initializing speech-sequence-to-text-sequence models with pretrained checkpoints for speech
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recognition and speech translation has *e.g.* been shown in [Large-Scale Self- and Semi-Supervised Learning for Speech
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Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli,
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Alexis Conneau.
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An example of how to use a [`SpeechEncoderDecoderModel`] for inference can be seen in [Speech2Text2](speech_to_text_2).
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## Randomly initializing `SpeechEncoderDecoderModel` from model configurations.
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[`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
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and the default [`BertForCausalLM`] configuration for the decoder.
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```python
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>>> from transformers import BertConfig, Wav2Vec2Config, SpeechEncoderDecoderConfig, SpeechEncoderDecoderModel
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>>> config_encoder = Wav2Vec2Config()
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>>> config_decoder = BertConfig()
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>>> config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
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>>> model = SpeechEncoderDecoderModel(config=config)
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```
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## Initialising `SpeechEncoderDecoderModel` from a pretrained encoder and a pretrained decoder.
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[`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.
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Depending on which architecture you choose as the decoder, the cross-attention layers might be randomly initialized.
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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).
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To do so, the `SpeechEncoderDecoderModel` class provides a [`SpeechEncoderDecoderModel.from_encoder_decoder_pretrained`] method.
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```python
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>>> from transformers import SpeechEncoderDecoderModel
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>>> model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(
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... "facebook/hubert-large-ll60k", "google-bert/bert-base-uncased"
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... )
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```
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## Loading an existing `SpeechEncoderDecoderModel` checkpoint and perform inference.
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To load fine-tuned checkpoints of the `SpeechEncoderDecoderModel` class, [`SpeechEncoderDecoderModel`] provides the `from_pretrained(...)` method just like any other model architecture in Transformers.
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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.
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```python
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>>> from transformers import Wav2Vec2Processor, SpeechEncoderDecoderModel
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>>> from datasets import load_dataset
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>>> import torch
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>>> # load a fine-tuned speech translation model and corresponding processor
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>>> model = SpeechEncoderDecoderModel.from_pretrained("facebook/wav2vec2-xls-r-300m-en-to-15")
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>>> processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xls-r-300m-en-to-15")
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>>> # let's perform inference on a piece of English speech (which we'll translate to German)
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>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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>>> input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values
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>>> # autoregressively generate transcription (uses greedy decoding by default)
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>>> generated_ids = model.generate(input_values)
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>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
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>>> print(generated_text)
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Mr. Quilter ist der Apostel der Mittelschicht und wir freuen uns, sein Evangelium willkommen heißen zu können.
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```
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## Training
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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.
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As you can see, only 2 inputs are required for the model in order to compute a loss: `input_values` (which are the
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speech inputs) and `labels` (which are the `input_ids` of the encoded target sequence).
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```python
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>>> from transformers import AutoTokenizer, AutoFeatureExtractor, SpeechEncoderDecoderModel
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>>> from datasets import load_dataset
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>>> encoder_id = "facebook/wav2vec2-base-960h" # acoustic model encoder
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>>> decoder_id = "google-bert/bert-base-uncased" # text decoder
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>>> feature_extractor = AutoFeatureExtractor.from_pretrained(encoder_id)
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>>> tokenizer = AutoTokenizer.from_pretrained(decoder_id)
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>>> # Combine pre-trained encoder and pre-trained decoder to form a Seq2Seq model
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>>> model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_id, decoder_id)
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>>> model.config.decoder_start_token_id = tokenizer.cls_token_id
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>>> model.config.pad_token_id = tokenizer.pad_token_id
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>>> # load an audio input and pre-process (normalise mean/std to 0/1)
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>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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>>> input_values = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt").input_values
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>>> # load its corresponding transcription and tokenize to generate labels
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>>> labels = tokenizer(ds[0]["text"], return_tensors="pt").input_ids
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>>> # the forward function automatically creates the correct decoder_input_ids
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>>> loss = model(input_values=input_values, labels=labels).loss
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>>> loss.backward()
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```
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## SpeechEncoderDecoderConfig
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[[autodoc]] SpeechEncoderDecoderConfig
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## SpeechEncoderDecoderModel
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[[autodoc]] SpeechEncoderDecoderModel
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- forward
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- from_encoder_decoder_pretrained
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## FlaxSpeechEncoderDecoderModel
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[[autodoc]] FlaxSpeechEncoderDecoderModel
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- __call__
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- from_encoder_decoder_pretrained
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