README.md

---
title: Nawah ASR 50M
emoji: 🎧
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 7860
pinned: false
short_description: تفريغ صوتي عربي بموديل Emhotob 50M
---

# Nawah-ASR-50M

Arabic ASR where a **50M Arabic LLM writes the transcript**. A frozen Whisper-small encoder feeds
a 0.39M projector; [`oddadmix/50M-2048-Emhotob`](https://huggingface.co/oddadmix/50M-2048-Emhotob)
does the generating.

Record or upload a clip (up to 30s). The demo reports the **audio token count**, which tracks the
clip's real duration at 25 tokens/sec -- a 3-second clip costs 75 tokens, not the 375 a fixed
30-second window would spend on silence.

Scored on a 3,165-clip video-disjoint MASC split (same clips, same normalizer for every row):

| model | decoder | WER | CER |
|---|---|---|---|
| `openai/whisper-small` (zero-shot) | Whisper 153M | 0.5739 | 0.2062 |
| `whisper-small-arabic-dialectal` | Whisper 153M | 0.6775 | 0.2244 |
| `Nawah-ASR-50M-v1` (60 h) | Emhotob 50M | 0.6145 | 0.2936 |
| **`Nawah-ASR-50M-v1.1` (300 h)** | **Emhotob 50M** | **0.4109** | **0.1815** |

With the encoder fixed, swapping Whisper's 153M decoder for Emhotob's 50M takes WER 0.5739 ->
0.4109. Caveat: this is MASC's home turf and both Whisper checkpoints are out-of-domain here --
the tell is that the dialect-tuned one scores *worse* than the base model on this MSA-leaning data.

Model: [`oddadmix/Nawah-ASR-50M-v1.1`](https://huggingface.co/oddadmix/Nawah-ASR-50M-v1.1)