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*This model was released on 2021-09-14 and added to Hugging Face Transformers on 2021-10-15 and contributed by [anton-l](https://huggingface.co/anton-l).*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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# SEW

[SEW](https://huggingface.co/papers/2109.06870) introduces a pre-trained model architecture with significant improvements in both performance and efficiency for automatic speech recognition. Building on wav2vec 2.0, SEW achieves a 1.9x inference speedup and a 13.5% relative reduction in word error rate under a 100h-960h semi-supervised setup on LibriSpeech. Additionally, SEW reduces word error rate by 25-50% across different model sizes while maintaining similar inference times.

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

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="automatic-speech-recognition", model="asapp/sew-tiny-100k", dtype="auto")
pipeline("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from datasets import load_dataset
from transformers import AutoProcessor, AutoModelForCTC

dataset = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation").sort("id")
sampling_rate = dataset.features["audio"].sampling_rate

processor = AutoProcessor.from_pretrained("asapp/sew-tiny-100k")
model = AutoModelForCTC.from_pretrained("asapp/sew-tiny-100k", dtype="auto")

inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
predicted_ids = torch.argmax(logits, dim=-1)
print(f"Transcription: {processor.batch_decode(predicted_ids)[0]}")
```

</hfoption>
</hfoptions>

## SEWConfig

[[autodoc]] SEWConfig

## SEWModel

[[autodoc]] SEWModel
    - forward

## SEWForCTC

[[autodoc]] SEWForCTC
    - forward

## SEWForSequenceClassification

[[autodoc]] SEWForSequenceClassification
    - forward

