dicksonsarpong9/qwen3-asr-akuapem-twi

<qwen3-twi-asr>

Author: Dickson Sarpong

Overview

Qwen3-ASR-Akuapem-Twi is an automatic speech recognition (ASR) model fine-tuned to transcribe Akuapem Twi speech into text. It adapts the Qwen3-ASR-1.7B foundation model for Ghanaian Twi speech recognition using the Akuapem Twi subset of the Ghana Speech datase#

Training data

Trained on the Ghana Speech dataset (audio + text, 42 Ghanaian language subsets), licensed CC BY-NC 4.0.

Intended use & license

Non-commercial use only (CC BY-NC 4.0, inherited from the training data).

How to use

import torch from qwen_asr import Qwen3ASRModel

model = Qwen3ASRModel.frompretrained( "dicksonsarpong9/qwen3-asr-akuapem-twi", dtype=torch.bfloat16, devicemap="cuda:0", )

result = model.transcribe( audio="sample.wav" )

print(result[0].language) print(result[0].text)

## Training details
- Base model / architecture: Qwen3-ASR-1.7B
- Task: Automatic Speech Recognition (ASR)
- Language subset(s): Akuapem Twi
- Training examples:
   Train: 51,597 samples
   Validation: 1,053 samples
- Hardware: NVIDIA H200 (Ghana NLP)
- Training duration: ~4 hours
- Epochs: 3
- Batch size: 8
- Gradient accumulation: 8
- Learning rate: 2e-5
- Final training loss: 2.64
- Inference RTF: 0.275

## Acknowledgements
Compute resources provided by **AI Skills and Compute Africa (AISCA)**.
Trained on the Ghana NLP H200 GPU. Please keep derivatives non-commercial and
share improvements back with the Ghana NLP community (`ghananlpcommunity`).