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AhmadMbow/wolof-tts-harness

Domaine:

natural language processing

Type de record:

softwaretools
Créateur:
Ahm
Hôte:
Local evaluation harness for GalsenAI's Wolof TTS model — measuring whether Wolof dubbing is viable # Wolof TTS — local evaluation harness A small harness to answer one question: **is Wolof speech synthesis good enough, and fast enough, to dub video with?** I'm building SankofaDrama, a vertical mini-drama platform for African audiences. Its bet is one-click dubbing into African languages — so before designing anything around that bet, I wanted numbers rather than a vendor's demo reel. Commercial providers (ElevenLabs and friends) cover Wolof poorly or not at all, which leaves open models. This is the rig I used to try one. ## Attribution — read this first The model is **not mine**. It is `galsenai/parler-tts-mini-v1-wolof`, fine-tuned and released by the **GalsenAI** community in Senegal, on top of Parler-TTS by Hugging Face. Their training work lives at Galsenaicommunity/Wolof-TTS. This repository contains only the evaluation code I wrote around their model. All credit for making Wolof synthesis exist goes to them. ## What's here | File | What it is | |---|---| | `test_tts.py` | Smoke test. One fixed Wolof sentence → `test_wolof.wav`, prints generation time. The first thing to run: it proves the model loads and speaks. | | `app.py` | Gradio UI — voice-style presets, temperature and length sliders, sample sentences, timing on every generation. | | `server.py` | The same thing with **zero web dependencies** — Python's `http.server` only. Serves a browser page and a `POST /tts` endpoint returning WAV with an `X-Audio-Duration` header. | `server.py` exists because `app.py` didn't survive contact with dependency resolution. Gradio drags in a large tree that conflicted with the pinned `transformers` / `numpy` versions Parler-TTS needs. Rewriting the UI against the standard library removed the whole class of problem: the only dependencies left are the ones the model itself requires. On a machine where you are already fighting `scipy` and `numpy` pins to get inference running, a UI that cannot break is worth more than a UI with sliders. ## What it measures Ever …

Visit

github.com

Tasks

text to speechspeech processing

Languages

Wolof

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