Spaces:
Running on Zero
Running on Zero
| title: Arabic Letter Verifier | |
| emoji: 🗣️ | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.31.0 | |
| app_file: app.py | |
| pinned: false | |
| short_description: Free ZeroGPU verifier for Arabic letter pronunciation | |
| models: | |
| - masumtechnonext/wav2vec2-arabic-letter-verifier | |
| # আরবি হরফ উচ্চারণ যাচাইকারী · Arabic Letter Pronunciation Verifier | |
| Free Gradio demo for | |
| [`masumtechnonext/wav2vec2-arabic-letter-verifier`](https://huggingface.co/masumtechnonext/wav2vec2-arabic-letter-verifier), | |
| a fine-tuned `wav2vec2-large-xlsr-53-arabic` model that checks whether a | |
| spoken clip matches one of the 28 Arabic alphabet letters. | |
| Record or upload a short clip of a single letter, optionally pick the target | |
| letter you were aiming for, and the app returns the predicted letter, the | |
| model confidence, whether the prediction was accepted, and whether it matched | |
| your target. | |
| ## কীভাবে কাজ করে · How it works | |
| The model has **29 classes** — the 28 letters plus a trained **Unknown** class | |
| (silence, noise, time-reversed clips, cross-letter mixups), so out-of-vocabulary | |
| audio has somewhere to go instead of being forced into a real letter. A | |
| prediction is only reported when the top softmax probability clears the | |
| calibrated threshold from `calibration.json` (**0.86**); below that the app | |
| reports *Unrecognized* rather than a guess. | |
| ## ফ্রি কোটা · Free quota | |
| This Space runs on **ZeroGPU**. GPU time is deducted from the *caller's* daily | |
| quota — 2 min/day anonymous, 5 min/day for a signed-in free account, 25 min/day | |
| for PRO. One prediction costs ~0.09 GPU-seconds, so a signed-in free account | |
| gets roughly **3,500 predictions per day**. | |
| ## API | |
| Two endpoints are exposed: | |
| - `POST /gradio_api/call/verify` — returns the verdict, the detail panel and | |
| a JSON payload with every class probability. | |
| - `POST /gradio_api/call/predict` — legacy endpoint kept for backwards | |
| compatibility; returns `(prediction_text, verdict_text)`. | |
| ```python | |
| from gradio_client import Client, handle_file | |
| client = Client("masumtechnonext/host_model_gra") | |
| verdict, details, payload = client.predict( | |
| handle_file("clip.wav"), "Sheen", api_name="/verify" | |
| ) | |
| print(payload["predicted_label"], payload["confidence"], payload["is_correct"]) | |
| ``` | |
| ## সীমাবদ্ধতা · Known limitation | |
| The source recordings have no speaker ID, so the train/validation/test split is | |
| by clip (stratified per letter), not by speaker. If the same speaker appears | |
| in both train and test, held-out accuracy may be optimistic relative to a | |
| genuinely new speaker or microphone. Validate on your own held-out speakers | |
| before deploying. | |