README.md

metadata

title: ScriptFlow Hausa Inference
emoji: 🎬
colorFrom: indigo
colorTo: purple
sdk: gradio
sdk_version: 6.24.0
python_version: '3.12'
app_file: app.py
pinned: false
license: other
short_description: Hausa ASR + MT for ScriptFlow

ScriptFlow inference service

Hausa ASR + Hausa→English MT, served over HTTP so the Render backend does not have to hold ~2GB of model weights in a 512MB process.

• ASR — NCAIR1/Hausa-ASR (Whisper-small fine-tune)
• MT — Helsinki-NLP/opus-mt-ha-en

Deploying

1. Create a Space: New Space → Gradio → Blank, hardware CPU basic (free). Gradio rather than Docker because Docker Spaces need PRO. It costs nothing here: Gradio is FastAPI underneath, so /asr and /translate sit at the root exactly as they would have, with a status UI at /ui. Dockerfile is kept for anyone who does have PRO — switch sdk: back to docker to use it.
2. Push the contents of this directory to it. packages.txt installs ffmpeg.
3. On the model page for NCAIR1/Hausa-ASR, accept the licence — the model is gated (gated: auto), and without acceptance the download 403s.
4. In Space → Settings → Secrets, set:
   • HF_TOKEN — a read token from the account that accepted the licence
   • SERVICE_TOKEN — any random string; the backend must send the same value

Gradio serves its status page at /. The first request downloads weights and can take several minutes. GET / answers immediately throughout and reports load state, so you can watch it come up without holding a request open.

API

POST /asr — raw audio bytes as the body, X-Service-Token header.

{
  "text": "...",
  "duration": 41.2,
  "wordLevel": true,
  "words": [{"word": "sannu", "start": 0.4, "end": 0.9, "speaker": null}]
}

Times are relative to the audio posted; the backend adds each chunk's offset. end may be null — that is meaningful, and the backend infers a real end from the following word rather than inventing a duration here.

POST /translate — {"texts": [...]}, returns {"translations": [...]} with one entry per input, same order.

Notes

• Free Spaces sleep after inactivity; the first call after a sleep pays the cold start again.
• CPU inference on Whisper-small runs roughly 1–3× realtime, so a 20-minute chunk is minutes of compute, not seconds.
• NCAIR1/Hausa-ASR is licensed with a 1000 active end-user cap for non-commercial use. Check that against how ScriptFlow ships.