Re-export moonshine-streaming-tiny (loom-exporter 34483dc)

.gitattributes

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README.md

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+---
+license: mit
+language:
+- en
+base_model:
+- moonshine-ai/moonshine-streaming-tiny
+pipeline_tag: automatic-speech-recognition
+tags:
+- loom
+- automatic-speech-recognition
+library_name: loom-py-rt
+---
+# Moonshine Streaming Tiny
+
+Useful Sensors' Moonshine Streaming tiny (34M) English speech recognizer -- a sliding-window encoder over the raw waveform and an autoregressive decoder -- exported for loom.cpp.
+
+This is a [loom.cpp](https://github.com/loom-ai-org/loom.cpp) export: a single self-describing GGUF
+that carries its own graph topologies, tokenizer (if any) and driver script, produced by
+[loom-exporter](https://github.com/loom-ai-org/loom-exporter).
+
+## Original model
+
+Exported from [`moonshine-ai/moonshine-streaming-tiny`](https://huggingface.co/moonshine-ai/moonshine-streaming-tiny). Weights are unmodified; this repo packages the same parameters into
+loom.cpp's GGUF format.
+
+## License
+
+`mit`, inherited from the base model above.
+
+## Language(s)
+
+`en`
+
+## Usage
+
+Run it with [loom-py](https://github.com/loom-ai-org/loom-py) -- `loom-py-rt` on PyPI:
+
+```sh
+pip install -U "loom-py-rt[hub]"
+```
+
+```python
+import loom
+
+model = loom.Model.from_pretrained("loom-ai-org/moonshine-streaming-tiny-loom")
+
+# Audio is a mono float list at 16 kHz. This model decodes in the one language it was trained for and
+# takes no `language=` argument -- passing one warns and is ignored, because nothing in its decode
+# could act on it.
+result = model.speech2text.infer(audio, timestamps=True)
+print(result.text)
+
+# It emits no timestamp tokens, so `segments` is one span covering the whole clip and
+# `result.timestamped` is False. Check that before treating a start/end as a boundary the model chose.
+for segment in result.segments:
+    print(segment.start, segment.end, segment.text)
+```
+
+### The layer underneath
+
+The call above is the high-level door: one per task, named for the modality pair it maps between, with
+the windowing, sampling and assembly this model needs already applied. Under it, `model.infer(...)`
+passes your arguments straight to the driver this GGUF embeds -- which is where you go for a knob the
+door does not name.
+
+`model.driver_source` prints that driver, including a header comment documenting every argument it
+accepts for this model, and is the authority on it. See [loom-py](https://github.com/loom-ai-org/loom-py) for the API and
+[loom.cpp](https://github.com/loom-ai-org/loom.cpp) for what the engine does between the two.
+
+## Known limitations
+
+**At most 81.9 s per call.** The decoder's position table has 4096 encoder rows (50 per second); a longer clip is refused with an error rather than truncated. Split long audio at its pauses -- a VAD such as `silero-vad-loom` finds them -- and transcribe each part.
+
+Decoding is the model card's own: greedy, and capped at 6.5 tokens per second of audio "to avoid hallucination loops". A clip shorter than about 0.3 s therefore returns no text. Like other encoder-decoder recognizers it can still repeat or invent words on noisy or very short audio.
+
+The whole clip is one pass, every encoder layer attending through the sliding window the model was trained with -- what the model card's usage computes. This export does not run the encoder incrementally (live streaming); English only, mono 16 kHz.
+
+## Files
+
+- `moonshine-streaming-tiny.gguf` -- the model, exported with loom-exporter.

moonshine-streaming-tiny.gguf

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