---
language:
- ckb
tags:
- generated_from_trainer
datasets:
- PawanKrd/asr-ckb
metrics:
- wer
model-index:
- name: ASR CKB
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: PawanKrd/asr-ckb
      type: PawanKrd/asr-ckb
    metrics:
    - name: Wer
      type: wer
      value: 4.1303699778079555
---

# Automatic Speech Recognition - CKB

This model is trained on the [PawanKrd/asr-ckb](https://huggingface.co/datasets/PawanKrd/asr-ckb) dataset. This model is specifically for the Central Kurdish (Sorani) language.

## Model Performance

The model achieves the following performance on the evaluation set:
- **Loss**: 0.0048
- **Word Error Rate (WER)**: 4.1304

## Model Description

This Automatic Speech Recognition (ASR) model for Central Kurdish (Sorani) is designed to transcribe spoken Kurdish into written text. It leverages a deep learning architecture optimized for speech-to-text tasks. The model is built using the Transformers library and trained on a diverse set of Central Kurdish audio recordings.

## Intended Uses & Limitations

This model is intended for automatic transcription of Central Kurdish audio. It performs best on clear, high-quality audio recordings. Performance may degrade with noisy backgrounds, strong accents, or atypical pronunciations.

### Intended Uses
- Transcribing interviews and speeches in Central Kurdish.
- Creating subtitles for Kurdish videos.
- Assisting in the documentation and preservation of the Kurdish language.

### Limitations
- Performance may be suboptimal on audio with heavy background noise.
- Strong regional accents or non-standard pronunciations can impact accuracy.
- Not suitable for real-time transcription without further optimization.

## Training and Evaluation Data

The model was trained and evaluated using the [PawanKrd/asr-ckb](https://huggingface.co/datasets/PawanKrd/asr-ckb) dataset, which consists of diverse audio samples in Central Kurdish. The training process was designed to optimize the model's recognition accuracy for this specific language.

## Training Procedure

### Hyperparameters

- **Learning Rate**: 1e-05
- **Train Batch Size**: 32
- **Eval Batch Size**: 16
- **Seed**: 42
- **Optimizer**: Adam (betas=(0.9, 0.999), epsilon=1e-08)
- **Learning Rate Scheduler**: Linear
- **Warmup Steps**: 500
- **Epochs**: 3

### Training Results

| Training Loss | Epoch  | Step  | Validation Loss | WER     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.0966        | 0.1927 | 1000  | 0.1457          | 29.30   |
| 0.0952        | 0.3854 | 2000  | 0.0988          | 22.26   |
| 0.0582        | 0.5780 | 3000  | 0.0741          | 17.51   |
| 0.0523        | 0.7707 | 4000  | 0.0532          | 15.14   |
| 0.0164        | 0.9634 | 5000  | 0.0412          | 14.19   |
| 0.0271        | 1.1561 | 6000  | 0.0519          | 15.68   |
| 0.0358        | 1.3487 | 7000  | 0.0407          | 11.18   |
| 0.0208        | 1.5414 | 8000  | 0.0327          | 9.94    |
| 0.031         | 1.7341 | 9000  | 0.0268          | 10.86   |
| 0.033         | 1.9268 | 10000 | 0.0191          | 7.70    |
| 0.0269        | 2.1195 | 11000 | 0.0138          | 6.48    |
| 0.025         | 2.3121 | 12000 | 0.0111          | 6.83    |
| 0.003         | 2.5048 | 13000 | 0.0086          | 5.78    |
| 0.0021        | 2.6975 | 14000 | 0.0065          | 4.66    |
| 0.0031        | 2.8902 | 15000 | 0.0048          | 4.13    |

### Framework Versions

- **Transformers**: 4.41.0.dev0
- **PyTorch**: 2.3.0+cu121
- **Datasets**: 2.19.1
- **Tokenizers**: 0.19.1

## Example Usage

To use this model for transcription, you can follow the example code below:

```python
from transformers import pipeline

# Load the fine-tuned model
asr_pipeline = pipeline(model="PawanKrd/asr-large-ckb")

# Transcribe audio file
audio_file = "audio.wav"
transcription = asr_pipeline(audio_file)

# Print the transcription
print(transcription["text"])
```

This code demonstrates how to load the model and use it to transcribe an audio file in Central Kurdish.