---
library_name: transformers
license: cc-by-4.0
datasets:
- badrex/shona-speech
language:
- sn
metrics:
- wer
- cer
base_model:
- facebook/w2v-bert-2.0
pipeline_tag: automatic-speech-recognition
---


<div align="center" style="line-height: 1;">
  <h1>Automatic Speech Recognition for Shona</h1>
  <a href="https://huggingface.co/datasets/badrex/shona-speech" 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/Shona-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 Shona automatic speech recognition (ASR). It was trained on 72 hours of transcribed Shona speech. The ASR model is robust and the in-domain WER is below 23%. 

- **Developed by:** Badr al-Absi
- **Model type:** Speech Recognition (ASR)
- **Language:** Shona (sn)
- **License:** CC-BY-4.0
- **Finetuned from:** facebook/w2v-bert-2.0

<!-- ### Examples 🚀
|  | Audio | Human Transcription |  ASR 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 a Shona audio:

```python
from transformers import Wav2Vec2BertProcessor, Wav2Vec2BertForCTC
import torch
import torchaudio

# load model and processor
processor = Wav2Vec2BertProcessor.from_pretrained("badrex/w2v-bert-2.0-shona-asr")
model = Wav2Vec2BertForCTC.from_pretrained("badrex/w2v-bert-2.0-shona-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 Shona speakers
- transcription services for Shona content
- accessibility tools for Shona-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](https://clearglobal.org/) and [Gates Foundation](https://www.gatesfoundation.org/). 




### Citation

```bibtex
@misc{w2v_bert_shona_asr,
  author = {Badr M. Abdullah},
  title = {Adapting Wav2Vec2-BERT 2.0 for Shona ASR},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/badrex/w2v-bert-2.0-shona-asr}
}

```

### Model Card Contact

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