A re-host, not a new model. These are the unmodified v0.2.0 release assets of yazinsai/tilawa, mirrored here for one reason: GitHub Release downloads are served without an Access-Control-Allow-Origin header, so a browser cannot fetch() them. Hugging Face model repositories are CORS-enabled, which makes in-browser inference possible without any server of our own.

Used by the in-browser build of Shazam for Quran, which identifies a recited verse fully client-side.

The model is a CTC encoder with no autoregressive decoder. That matters for Quranic audio specifically: Whisper-family models are prone to repetition loops on repetitive, melodic, elongated speech — exactly the acoustic profile of recitation — because each token is predicted from the last. A CTC model emits per-frame probabilities independently and structurally cannot loop.

Verse identification is not plain transcription. Tilawa decodes the audio, then re-ranks all 6,236 verses by CTC log-probability, so the search is constrained to text that actually exists in the Quran rather than matching a free-form transcript.

Licensed CC-BY-4.0, inherited from the NVIDIA base model. No weights were retrained, quantized, or altered — this repository only changes where the bytes are served from.