README.md

---
license: mit
library_name: transformers.js
pipeline_tag: automatic-speech-recognition
base_model: UsefulSensors/moonshine-streaming-small
language:
- en
tags:
- onnx
- moonshine
- speech-recognition
- browser
---

# moonshine-streaming-small-ONNX

ONNX export of [UsefulSensors/moonshine-streaming-small](https://huggingface.co/UsefulSensors/moonshine-streaming-small) (Moonshine v2, the `moonshine_streaming` architecture) for in-browser speech-to-text with [transformers.js](https://github.com/huggingface/transformers.js). No official ONNX export of this architecture exists upstream; this one was built and validated by Workmind. The export is batch/full-utterance (encoder + merged decoder with past-KV), not chunked-streaming.

## Usage (transformers.js)

```js
import { pipeline } from '@huggingface/transformers';

const transcriber = await pipeline(
  'automatic-speech-recognition',
  'Workmind/moonshine-streaming-small-ONNX',
  { dtype: { encoder_model: 'q8', decoder_model_merged: 'q8' } },
);

const { text } = await transcriber(audio); // Float32Array, 16 kHz mono
```

**Input length caveat:** the v2 audio frontend reshapes input into 5 ms frames (80 samples @ 16 kHz) and fails on partial frames. transformers.js's generic feature extractor does not pad for you, so zero-pad each utterance to a multiple of 80 samples before calling the pipeline.

## Compatibility note (why `config.json` says `moonshine`)

transformers.js has no `moonshine_streaming` registration yet, so the published `config.json` intentionally declares `model_type: "moonshine"` with v1-shaped normalized-config keys. The stock transformers.js bundle then loads this model through its registered Moonshine v1 path; all v2 architectural differences (including the sliding-window encoder attention mask, synthesized all-ones inside the graph) are baked into the ONNX graphs, which are v1-compatible in I/O. Once native `moonshine_streaming` support ships upstream, a new revision of this repo will carry the real config.

## Files

| Variant | Encoder | Decoder (merged) | Notes |
|---|---|---|---|
| fp32 | `encoder_model.onnx` (196 MB) | `decoder_model_merged.onnx` (340 MB) | reference |
| q8 | `encoder_model_quantized.onnx` (71 MB) | `decoder_model_merged_quantized.onnx` (87 MB) | recommended; transcript-lossless vs fp32 |
| q4 | `encoder_model_q4.onnx` (56 MB) | `decoder_model_merged_q4.onnx` (116 MB) | transcript-lossless vs fp32 |

The q8 quantization keeps the causal-Conv audio frontend weights in fp32 (int8 Conv quantization silently produces empty transcripts).

## Validation

fp32 greedy decode via onnxruntime matches the PyTorch reference token-for-token at the pinned revisions. In-browser (transformers.js 4.2.0 / onnxruntime-web WASM, M-series MacBook, 11 s clip): q8/q8 loads in ~2.3 s and transcribes in ~0.9 s with an exact transcript match against the Python reference.

## Provenance

Built from `UsefulSensors/moonshine-streaming-small` at revision `2c036506f23a09c18df5a50057599ba6d9280999` with transformers 5.14.1 and a patched optimum-onnx export config. The full reproducible pipeline (export, quantization, parity checks, config rewrite) lives at [WorkmindAI/stt-models](https://github.com/WorkmindAI/stt-models).

## License and attribution

MIT, same as the upstream [Moonshine](https://github.com/moonshine-ai/moonshine) weights by UsefulSensors / Moonshine AI. This repo is a converted/quantized redistribution of those weights with a compatibility-rewritten config; all credit for the model itself goes to the original authors.