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* Map model_type and doc pages names * Add script * Fix typo * Quality * Manual check for Auto Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
123 lines
4.4 KiB
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123 lines
4.4 KiB
Plaintext
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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# Speech2Text2
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## Overview
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The Speech2Text2 model is used together with [Wav2Vec2](wav2vec2) for Speech Translation models proposed in
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[Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by
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Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
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Speech2Text2 is a *decoder-only* transformer model that can be used with any speech *encoder-only*, such as
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[Wav2Vec2](wav2vec2) or [HuBERT](hubert) for Speech-to-Text tasks. Please refer to the
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[SpeechEncoderDecoder](speech-encoder-decoder) class on how to combine Speech2Text2 with any speech *encoder-only*
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model.
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This model was contributed by [Patrick von Platen](https://huggingface.co/patrickvonplaten).
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The original code can be found [here](https://github.com/pytorch/fairseq/blob/1f7ef9ed1e1061f8c7f88f8b94c7186834398690/fairseq/models/wav2vec/wav2vec2_asr.py#L266).
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Tips:
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- Speech2Text2 achieves state-of-the-art results on the CoVoST Speech Translation dataset. For more information, see
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the [official models](https://huggingface.co/models?other=speech2text2) .
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- Speech2Text2 is always used within the [SpeechEncoderDecoder](speech-encoder-decoder) framework.
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- Speech2Text2's tokenizer is based on [fastBPE](https://github.com/glample/fastBPE).
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## Inference
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Speech2Text2's [`SpeechEncoderDecoderModel`] model accepts raw waveform input values from speech and
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makes use of [`~generation_utils.GenerationMixin.generate`] to translate the input speech
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autoregressively to the target language.
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The [`Wav2Vec2FeatureExtractor`] class is responsible for preprocessing the input speech and
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[`Speech2Text2Tokenizer`] decodes the generated target tokens to the target string. The
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[`Speech2Text2Processor`] wraps [`Wav2Vec2FeatureExtractor`] and
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[`Speech2Text2Tokenizer`] into a single instance to both extract the input features and decode the
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predicted token ids.
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- Step-by-step Speech Translation
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```python
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>>> import torch
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>>> from transformers import Speech2Text2Processor, SpeechEncoderDecoderModel
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>>> from datasets import load_dataset
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>>> import soundfile as sf
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>>> model = SpeechEncoderDecoderModel.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
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>>> processor = Speech2Text2Processor.from_pretrained("facebook/s2t-wav2vec2-large-en-de")
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>>> def map_to_array(batch):
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... speech, _ = sf.read(batch["file"])
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... batch["speech"] = speech
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... return batch
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>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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>>> ds = ds.map(map_to_array)
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>>> inputs = processor(ds["speech"][0], sampling_rate=16_000, return_tensors="pt")
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>>> generated_ids = model.generate(input_ids=inputs["input_values"], attention_mask=inputs["attention_mask"])
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>>> transcription = processor.batch_decode(generated_ids)
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```
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- Speech Translation via Pipelines
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The automatic speech recognition pipeline can also be used to translate speech in just a couple lines of code
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```python
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>>> from datasets import load_dataset
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>>> from transformers import pipeline
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>>> librispeech_en = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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>>> asr = pipeline(
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... "automatic-speech-recognition",
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... model="facebook/s2t-wav2vec2-large-en-de",
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... feature_extractor="facebook/s2t-wav2vec2-large-en-de",
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... )
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>>> translation_de = asr(librispeech_en[0]["file"])
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```
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See [model hub](https://huggingface.co/models?filter=speech2text2) to look for Speech2Text2 checkpoints.
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## Speech2Text2Config
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[[autodoc]] Speech2Text2Config
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## Speech2TextTokenizer
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[[autodoc]] Speech2Text2Tokenizer
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- batch_decode
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- decode
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- save_vocabulary
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## Speech2Text2Processor
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[[autodoc]] Speech2Text2Processor
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- __call__
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- from_pretrained
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- save_pretrained
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- batch_decode
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- decode
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- as_target_processor
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## Speech2Text2ForCausalLM
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[[autodoc]] Speech2Text2ForCausalLM
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- forward
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