sulaimank
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w2v-bert-2.0-swahili-400hr

Automatic Speech Recognition

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w2v-bert-2.0 Swahili (400h)

A facebook/w2v-bert-2.0 model fine-tuned for Swahili automatic speech recognition (CTC) on ~400 hours of Swahili speech.

Training data

Fine-tuned on a combined ~400h Swahili corpus from four public datasets:

Common Voice (CV) — crowd-sourced read speech
FLEURS — Google FLEURS read speech
AMMI — African Masters in Machine Intelligence Swahili speech
ALFFA — African Languages in the Field: speech Fundamentals and Automation

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Safetensors

Model size

0.6B params

Tensor type

F32

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Base model

facebook/w2v-bert-2.0

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