---
language:
- xh
pipeline_tag: automatic-speech-recognition
tags:
- audio
- automatic-speech-recognition
datasets:
- Beijuka/xhosa_parakeet_50hr
- wjbmattingly/xhosa_merged_audio
metrics:
- wer
base_model: openai/whisper-small
license: apache-2.0
---

# Whisper-Small Fine-tuned for isiXhosa ASR

## Model Description
This model is a fine-tuned version of OpenAI's Whisper-small, optimized for isiXhosa Automatic Speech Recognition (ASR). It has been trained on the NCHLT isiXhosa Speech Corpus to improve its performance on isiXhosa speech transcription tasks.

## Performance
- Word Error Rate (WER): 29.73%

## Base Model
- Name: openai/whisper-small
- Type: Automatic Speech Recognition (ASR)
- Original language: Multilingual

## Usage
To use this model for inference:

```python
from transformers import WhisperForConditionalGeneration, WhisperProcessor
import torch

# Load model and processor
model = WhisperForConditionalGeneration.from_pretrained("TheirStory-Inc/whisper-small-xhosa")
processor = WhisperProcessor.from_pretrained("TheirStory-Inc/whisper-small-xhosa")

# Prepare your audio file (16kHz sampling rate)
audio_input = ...  # Load your audio file here

# Process the audio
input_features = processor(audio_input, sampling_rate=16000, return_tensors="pt").input_features

# Generate token ids
predicted_ids = model.generate(input_features)

# Decode the token ids to text
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

print(transcription)
```

## Fine-tuning Dataset
- Name: NCHLT isiXhosa Speech Corpus
- Size: Approximately 56 hours of transcribed speech
- Speakers: 209 (106 female, 103 male)
- Content: Prompted speech (3-5 word utterances read from a smartphone screen)
- Source: Audio recordings smartphone-collected in non-studio environment
- License: Creative Commons Attribution 3.0 Unported License (CC BY 3.0)

### Citation

```tex
De Vries, N.J., Davel, M.H., Badenhorst, J., Basson, W.D., de Wet, F., Barnard, E. and de Waal, A. (2014). A smartphone-based ASR data collection tool for under-resourced languages. Speech Communication, 56, 119-131. https://hdl.handle.net/20.500.12185/279
```