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*This model was released on 2021-06-14 and added to Hugging Face Transformers on 2021-06-16 and contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).*

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# Hubert

[Hubert](https://huggingface.co/papers/2106.07447) addresses challenges in self-supervised speech representation learning by introducing Hidden-Unit BERT (HuBERT). HuBERT employs an offline clustering step to generate aligned target labels for a BERT-like prediction loss, focusing on masked regions to learn a combined acoustic and language model. Using k-means clustering with two iterations, HuBERT matches or surpasses wav2vec 2.0 on Librispeech and Libri-light benchmarks across various fine-tuning subsets. A 1B parameter HuBERT model achieves significant relative WER reductions on dev-other and test-other subsets.

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

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="automatic-speech-recognition", model="facebook/hubert-large-ls960-ft", 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("facebook/hubert-base-ls960")
model = AutoModelForCTC.from_pretrained("facebook/hubert-base-ls960", 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>

## HubertConfig

[[autodoc]] HubertConfig

## HubertModel

[[autodoc]] HubertModel
    - forward

## HubertForCTC

[[autodoc]] HubertForCTC
    - forward

## HubertForSequenceClassification

[[autodoc]] HubertForSequenceClassification
    - forward

