first commit

docs/source/en/model_doc/moonshine_streaming.md

@@ -0,0 +1,104 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was published in HF papers on 2024-10-21 and contributed to Hugging Face Transformers on 2026-02-04.*
+
+<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">
+          <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# Moonshine Streaming
+
+Moonshine Streaming is a streaming variant of the [Moonshine](https://huggingface.co/papers/2410.15608) speech recognition model, optimized for real-time transcription with low latency. Like the original Moonshine, it is an encoder-decoder model that uses Rotary Position Embedding (RoPE) for handling variable-length speech efficiently. The streaming architecture includes sliding window attention in the encoder and a context adapter that enables incremental processing of audio chunks.
+
+Moonshine Streaming is available in three sizes: tiny, small, and medium, offering a trade-off between speed and accuracy. It is particularly well-suited for on-device streaming transcription and voice command applications.
+
+You can find all the original Moonshine Streaming checkpoints under the [Useful Sensors](https://huggingface.co/UsefulSensors) organization.
+
+> [!TIP]
+> Moonshine Streaming processes raw audio waveforms directly without requiring mel-spectrogram preprocessing, making it efficient for real-time applications.
+
+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
+
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model="UsefulSensors/moonshine-streaming-tiny",
+    device=0
+)
+pipe("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, MoonshineStreamingForConditionalGeneration
+
+
+processor = AutoProcessor.from_pretrained("UsefulSensors/moonshine-streaming-tiny")
+model = MoonshineStreamingForConditionalGeneration.from_pretrained(
+    "UsefulSensors/moonshine-streaming-tiny",
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+
+ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+audio_sample = ds[0]["audio"]
+
+inputs = processor(audio_sample["array"], return_tensors="pt").to(model.device)
+inputs = inputs.to(model.device)
+
+generated_ids = model.generate(**inputs, max_new_tokens=100)
+transcription = processor.decode(generated_ids[0], skip_special_tokens=True)
+transcription
+```
+
+</hfoption>
+</hfoptions>
+
+## MoonshineStreamingProcessor
+
+[[autodoc]] MoonshineStreamingProcessor
+
+## MoonshineStreamingEncoderConfig
+
+[[autodoc]] MoonshineStreamingEncoderConfig
+
+## MoonshineStreamingConfig
+
+[[autodoc]] MoonshineStreamingConfig
+
+## MoonshineStreamingModel
+
+[[autodoc]] MoonshineStreamingModel
+    - forward
+
+## MoonshineStreamingForConditionalGeneration
+
+[[autodoc]] MoonshineStreamingForConditionalGeneration
+    - forward
+    - generate