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*This model was published in HF papers on 2024-10-21 and contributed to Hugging Face Transformers on 2025-01-10.*

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# Moonshine

[Moonshine](https://huggingface.co/papers/2410.15608) is an encoder-decoder speech recognition model optimized for real-time transcription and recognizing voice commands. Instead of using traditional absolute position embeddings, Moonshine uses Rotary Position Embedding (RoPE) to handle speech with varying lengths without using padding. This improves efficiency during inference, making it ideal for resource-constrained devices.

You can find all the original Moonshine checkpoints under the [Useful Sensors](https://huggingface.co/UsefulSensors) organization.

> [!TIP]
> Click on the Moonshine models in the right sidebar for more examples of how to apply Moonshine to different speech recognition tasks.

The example below demonstrates how to transcribe speech into text with [`Pipeline`] or the [`AutoModel`] class.

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

```python
from transformers import pipeline


pipeline = pipeline(
    task="automatic-speech-recognition",
    model="UsefulSensors/moonshine-base",
    device=0
)
pipeline("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
```

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

```python
from datasets import load_dataset

from transformers import AutoProcessor, MoonshineForConditionalGeneration


processor = AutoProcessor.from_pretrained("UsefulSensors/moonshine-base")
model = MoonshineForConditionalGeneration.from_pretrained("UsefulSensors/moonshine-base", device_map="auto")

ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", split="validation")
audio_sample = ds[0]["audio"]

input_features = processor(
    audio_sample["array"],
    sampling_rate=audio_sample["sampling_rate"],
    return_tensors="pt"
)
input_features = input_features.to(model.device, dtype=model.dtype)

predicted_ids = model.generate(**input_features, cache_implementation="static")
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription)
```

</hfoption>
</hfoptions>

## MoonshineConfig

[[autodoc]] MoonshineConfig

## MoonshineModel

[[autodoc]] MoonshineModel
    - forward
    - _mask_input_features

## MoonshineForConditionalGeneration

[[autodoc]] MoonshineForConditionalGeneration
    - forward
    - generate
