README.md

metadata

license: cc-by-4.0
library_name: onnx
pipeline_tag: automatic-speech-recognition
language:
  - ar
tags:
  - quran
  - quranic-arabic
  - fastconformer
  - onnx
  - int8
  - streaming
  - speech-recognition
base_model: mohammed/fastconformer-quran-ar

FastConformer Quran — int8 ONNX (stateless export)

A mirror of mohammed/fastconformer-quran-ar-onnx-int8 (CC-BY-4.0), packaged for the on-device recitation engine of the Voidwave Quran web app (runs fully in the browser via onnxruntime-web).

The base model is a Qatar Computing-style FastConformer fine-tuned on Quranic recitations (NVIDIA NeMo, tarteel-ai/everyayah). This export is fully causal [128, 0] — no caches in or out — so a plain stateless loop decodes it one token per encoder frame (greedy).

Files

file	size	what it is
encoder.int8.onnx	~126 MB	streaming encoder: audio_signal [1, 80, T] + length [1] → [1, 512, T/8] + encoded_lengths
decoder.int8.onnx	~5 MB	fused decoder_joint: encoder_outputs [1, 512, 1], targets [1, 1], target_length [1], input_states_1/2 [1, 1, 640] → joint logits [1, 1, 1, 1025] + output states
tokens.txt	~13 KB	1025 entries: 1024 BPE tokens (with diacritics) + <blk> at id 1024
meta.json	<1 KB	upstream metadata

Notes:

• The upstream joiner.int8.onnx is a byte-identical duplicate of decoder.int8.onnx and is omitted here.
• Runs on onnxruntime-web ≥ 1.30 (wasm kernels; earlier runtimes lack the quantized ConvInteger op).
• Input features must be NeMo-style log-mel: 80 filters, 25 ms window / 10 ms hop, n_fft 512, slaney mel, log(x + 2^-24), and per-feature normalization is required — despite meta.json claiming normalize_type: "" (upstream metadata is wrong on this point; without normalization the decoder emits an empty result).

Usage (browser, onnxruntime-web)

const ort = await import('https://cdn.jsdelivr.net/npm/onnxruntime-web@1.30.0/dist/ort.min.js');
const encoder = await ort.InferenceSession.create('encoder.int8.onnx', { executionProviders: ['wasm'] });
const decoder = await ort.InferenceSession.create('decoder.int8.onnx', { executionProviders: ['wasm'] });
// feature extraction: NeMo-compatible mel + per-feature normalization (see notes above)
// decoding: encoder → per-frame greedy join with a single re-primed predictor state

Attribution

• Base model: mohammed/fastconformer-quran-ar (NVIDIA NeMo FastConformer, fine-tuned on tarteel-ai/everyayah)
• Original ONNX export: mohammed/fastconformer-quran-ar-onnx-int8
• License: CC-BY-4.0 (see upstream) — please keep this attribution when redistributing.