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badrex/w2v-bert-2.0-zulu-asr

sourceHugging Facecc-by-4.0updated 11mo agoView on Hugging Face
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Model Card

<div align="center" style="line-height: 1;"> <h1>Automatic Speech Recognition for Zulu</h1> <a href="https://huggingface.co/datasets/dsfsi-anv/za-african-next-voices" target="blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-ffc107?color=ffca28&logoColor=white" style="display: inline-block; vertical-align: middle;"/> <a href="https://huggingface.co/spaces/badrex/Zulu-ASR" target="blank" style="margin: 2px;"> <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Space-ffc107?color=c62828&logoColor=white" style="display: inline-block; vertical-align: middle;"/> <a href="https://creativecommons.org/licenses/by/4.0/deed.en" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg" style="display: inline-block; vertical-align: middle;"/> </a> </div>

Model Description 🍋‍🟩

This model is a fine-tuned version of Wav2Vec2-BERT 2.0 for Zulu automatic speech recognition (ASR). It was trained on the 250 hours of transcribed Zulu speech. The ASR model is robust and the in-domain WER is below 16.3%.

  • —Developed by: Badr al-Absi
  • —Model type: Speech Recognition (ASR)
  • —Language: Zulu (zu)
  • —License: CC-BY-4.0
  • —Finetuned from: facebook/w2v-bert-2.0

Examples 🚀

AudioHuman TranscriptionASR Transcription
1<audio controls src="https://huggingface.co/badrex/w2v-bert-2.0-zulu-asr/resolve/main/examples/example_2.wav"></audio>Yenza isinqumo ngezilimo uzozitshala kumaphi amasimu uphinde idwebe imephu njengereferensi yakho.yenza isinqumo ngezilimo ozozitshala kumaphi amasimu uphinde igwebe imephu njengereference yakho
2<audio controls src="https://huggingface.co/badrex/w2v-bert-2.0-zulu-asr/resolve/main/examples/example_3.wav"></audio>Emdlalweni wokugcina ngokumelene IFrance, wayengumuntu ongasetshenziswanga esikhundleni njengoba i-Argentina inqobe ngo-4-2 nge-penalty ukuze ithole isiqu sayo sesithathu seNdebe Yomhlaba.emdlalweni wokugqina ngokumelene i-france wayengumuntu ongasetshenziswanga esikhundleni njengoba i-argentina incobe ngo-4-2 ngephelnathi ukuze ithole isiqu sayo sesithathu sendebe yomhlaba
3<audio controls src="https://huggingface.co/badrex/w2v-bert-2.0-zulu-asr/resolve/main/examples/example_1.wav"></audio>Amadolobhana angaphandle angaphezu kwamamitha ambalwa, Reneging cishe 140m, amamitha angu-459.3, ngaphezu kogu lolwandle. Le ndawo iningi emahlathini ama-dune asogwini, ikakhulukazi eceleni kwe-zindunduma zasogwini nasedolobheni lase-Meerensee.amadolobhana angaphandle angaphezu kwamamitha ambalwa reneging cishe 140m amamitha angu 4593 ngaphezu kogulolwandle le ndawo iningi emahlabathini amedum esogwini ikakhulukazi eceleni kwezindunduma zasogwini nasedolobheni lasemerins

Direct Use

The model can be used directly for automatic speech recognition of Zulu audio:

python
from transformers import Wav2Vec2BertProcessor, Wav2Vec2BertForCTC
import torch
import torchaudio

# load model and processor
processor = Wav2Vec2BertProcessor.from_pretrained("badrex/w2v-bert-2.0-zulu-asr")
model = Wav2Vec2BertForCTC.from_pretrained("badrex/w2v-bert-2.0-zulu-asr")

# load audio
audio_input, sample_rate = torchaudio.load("path/to/audio.wav")

# preprocess
inputs = processor(audio_input.squeeze(), sampling_rate=sample_rate, return_tensors="pt")

# inference
with torch.no_grad():
    logits = model(**inputs).logits

# decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
print(transcription)

Downstream Use

This model can be used as a foundation for:

  • —building voice assistants for Zulu speakers
  • —transcription services for Zulu content
  • —accessibility tools for Zulu-speaking communities
  • —research in low-resource speech recognition

Model Architecture

  • —Base model: Wav2Vec2-BERT 2.0
  • —Architecture: transformer-based with convolutional feature extractor
  • —Parameters: ~600M (inherited from base model)
  • —Objective: connectionist temporal classification (CTC)

Funding

The development of this model was supported by CLEAR Global and Gates Foundation.

Citation

bibtex
@misc{w2v_bert_zulu_asr,
  author = {Badr M. Abdullah},
  title = {Adapting Wav2Vec2-BERT 2.0 for Zulu ASR},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/badrex/w2v-bert-2.0-zulu-asr}
}

Model Card Contact

For questions or issues, please contact via the Hugging Face model repository in the community discussion section.