Moonshine Streaming Tiny — Arabic

Arabic streaming speech recognition, 27.0M parameters. Same architecture as moonshine-ai/moonshine-streaming-tiny, trained for Arabic with a 12,288-entry Arabic tokenizer.

Moonshine Streaming pairs a 50 Hz time-domain audio frontend with a sliding-window Transformer encoder, so it transcribes incrementally rather than waiting for an utterance to finish. It is intended for on-device use on edge-class hardware.

Checkpoint identity

This repository is a conversion of one specific training checkpoint, recorded here because the weights behind a language move as later stages win:

If you need reproducibility, pin the revision of this repository rather than tracking main.

Usage

pip install --upgrade transformers datasets[audio]

from transformers import MoonshineStreamingForConditionalGeneration, AutoProcessor
import torch

model = MoonshineStreamingForConditionalGeneration.from_pretrained(
    "moonshine-ai/moonshine-streaming-tiny-ar"
).eval()
processor = AutoProcessor.from_pretrained("moonshine-ai/moonshine-streaming-tiny-ar")

inputs = processor(audio, return_tensors="pt", sampling_rate=16000)

# Cap the output length. Like other seq2seq ASR models this one can fall into a
# repetition loop, and short or noisy clips are where it happens.
seq_lens = inputs.attention_mask.sum(dim=-1)
max_new_tokens = int((seq_lens * 6.5 / 16000).max().item()) + 2

generated = model.generate(**inputs, max_new_tokens=max_new_tokens)
print(processor.batch_decode(generated, skip_special_tokens=True)[0])

Pass the attention_mask. The encoder applies its per-layer sliding windows only when it is given one; called without a mask it attends over the whole utterance instead, which is a different model from the one that was trained. The processor returns the mask, so the snippet above is the safe form. The processor also pads audio to a whole number of 80-sample frames, which the frontend requires.

Architecture

The lookahead layers give roughly 80 ms of lookahead; the intermediate layers have none.

Training data

Trained on a large-scale automatically labeled Arabic corpus:

• Podcast crawl, roughly 30,800 hours, pseudo-labeled.
• YouTube crawl, roughly 49,800 hours, pseudo-labeled.
• Crawled corpus, roughly 10,000 hours, pseudo-labeled and unaudited.

The crawled transcripts are pseudo-labels: they were produced by running a Whisper-family teacher model over crawled audio, not by human transcription. The model therefore inherits the teacher's error modes, including its handling of proper nouns, numerals and code-switching. No human-verified transcript was used for the bulk of training.

Evaluation

Arabic is scored on word error rate (WER), after the usual case and punctuation normalization. Mandarin and Japanese in this model family are instead scored on no-space CER, because they are written without spaces; every other language, this one included, uses WER.

suite_ar is Common Voice Arabic and FLEURS Arabic. The FLEURS panel is Egyptian Arabic; Common Voice is broader but dominated by very short clips. Modern Standard Arabic and the regional dialects are not measured separately, and no dialect other than Egyptian is represented in the read-speech panel.

Do not quote a full-panel Arabic number from this card. The figures here are a seeded 400-clip sample. A wider Arabic measurement in our own notes reads 15.205, but it was taken on a 2,500-row draw of the 10,480-row Common Voice panel, so it is not a full-coverage result either. The numbers below are sound as relative measurements between these three builds, which is what they are for.

Seeded 400-utterance sample, batch 1

Batch 1 is the honest number for deployment. Batched evaluation zero-pads short clips up to the longest in the batch, and that trailing silence flatters the model.

This repository against the training checkpoint

These weights were converted from the neo training checkpoint, and the conversion was checked by measurement rather than inspection: same seeded sample, same batch size, same normalizer. A conversion that loads and emits plausible text can still have a permuted weight mapping, which only a score catches.

399/400 and 400/400 transcripts are byte-identical.

The quantized build we ship

The .ort package served to the Moonshine deployment library is quantized to int8 from these same weights, and scores 15.533 against 15.231 for the float checkpoint on the same sample under the same stopping rule -- a cost of +0.302 WER. That build is a different artifact from this repository, which is float32.

Limitations

• Machine-labeled training data. See above; the model reproduces its teacher's mistakes as well as its strengths.
• Repetition loops on short clips. Like other seq2seq ASR models this one can fall into a repetition loop, and short or noisy clips are where it happens. Cap the output length, as the usage snippet does.
• Evaluated on 2 panels only. No evaluation of telephony, children's speech, heavy dialect, or noisy far-field conditions.
• Short-clip sensitivity. The Common Voice panel is 93% short clips, and short clips are where this architecture's stopping decision is weakest; int8 quantization costs four times as much on that panel as on FLEURS.

Out-of-scope use

Not intended for non-consensual surveillance, speaker identification, or high-stakes decisions.

License

MIT.