enginex-mlu370-any2any/transformers/docs/source/en/model_doc/moonshine.md

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*This model was released on 2024-10-21 and added 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 command. 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">

```py
import torch
from transformers import pipeline

pipeline = pipeline(
    task="automatic-speech-recognition",
    model="UsefulSensors/moonshine-base",
    dtype=torch.float16,
    device=0
)
pipeline("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
```

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

```py
# pip install datasets
import torch
from datasets import load_dataset
from transformers import AutoProcessor, MoonshineForConditionalGeneration

processor = AutoProcessor.from_pretrained(
    "UsefulSensors/moonshine-base",
)
model = MoonshineForConditionalGeneration.from_pretrained(
    "UsefulSensors/moonshine-base",
    dtype=torch.float16,
    device_map="auto",
    attn_implementation="sdpa"
)

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=torch.float16)

predicted_ids = model.generate(**input_features, cache_implementation="static")
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
transcription[0]
```

</hfoption>
</hfoptions>

## MoonshineConfig

[[autodoc]] MoonshineConfig

## MoonshineModel

[[autodoc]] MoonshineModel
    - forward
    - _mask_input_features

## MoonshineForConditionalGeneration

[[autodoc]] MoonshineForConditionalGeneration
    - forward
    - generate