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*This model was released on 2021-04-14 and added to Hugging Face Transformers on 2021-09-01.*

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# Speech Encoder Decoder Models

SpeechEncoderDecoderModel creates speech-to-text models by combining pretrained speech encoders with autoregressive decoders. This approach works well for speech recognition and translation tasks, as shown in [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://huggingface.co/papers/2104.06678).

<hfoptions id="usage">
<hfoption id="SpeechEncoderDecoderModel">

```py
import torch
from transformers import Wav2Vec2Processor, SpeechEncoderDecoderModel
from datasets import load_dataset

model = SpeechEncoderDecoderModel.from_pretrained("facebook/wav2vec2-xls-r-300m-en-to-15", dtype="auto")
processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xls-r-300m-en-to-15")

ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values

generated_ids = model.generate(input_values)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(generated_text)
```

</hfoption>
</hfoptions>

## SpeechEncoderDecoderConfig

[[autodoc]] SpeechEncoderDecoderConfig

## SpeechEncoderDecoderModel

[[autodoc]] SpeechEncoderDecoderModel
    - forward
    - from_encoder_decoder_pretrained