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*This model was released on 2021-10-12 and added to Hugging Face Transformers on 2021-10-26 and contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten).*

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# UniSpeech-SAT

[UniSpeech-SAT](https://huggingface.co/papers/2110.05752) enhances self-supervised learning for speaker representation by integrating multi-task learning with utterance-wise contrastive loss and an utterance mixing strategy for data augmentation. These methods are incorporated into the HuBERT framework, achieving state-of-the-art performance on the SUPERB benchmark, particularly for speaker identification tasks. Scaling up the training dataset to 94 thousand hours of public audio data further improves performance across all SUPERB tasks.

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

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="automatic-speech-recognition", model="microsoft/unispeech-sat-base-100h-libri-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("microsoft/unispeech-sat-base-100h-libri-ft")
model = AutoModelForCTC.from_pretrained("microsoft/unispeech-sat-base-100h-libri-ft", 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>

## UniSpeechSatConfig

[[autodoc]] UniSpeechSatConfig

## UniSpeechSat specific outputs

[[autodoc]] models.unispeech_sat.modeling_unispeech_sat.UniSpeechSatForPreTrainingOutput

## UniSpeechSatModel

[[autodoc]] UniSpeechSatModel
    - forward

## UniSpeechSatForCTC

[[autodoc]] UniSpeechSatForCTC
    - forward

## UniSpeechSatForSequenceClassification

[[autodoc]] UniSpeechSatForSequenceClassification
    - forward

## UniSpeechSatForAudioFrameClassification

[[autodoc]] UniSpeechSatForAudioFrameClassification
    - forward

## UniSpeechSatForXVector

[[autodoc]] UniSpeechSatForXVector
    - forward

## UniSpeechSatForPreTraining

[[autodoc]] UniSpeechSatForPreTraining
    - forward

