---
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: 44.878759772094874
---

# Automatic Speech Recognition - CKB

This model is trained on the [PawanKrd/asr-ckb](https://huggingface.co/datasets/PawanKrd/asr-ckb) dataset. It achieves the following results on the evaluation set:
- Loss: 0.1310
- Wer: 44.8788

## Model description

This model is designed for automatic speech recognition (ASR) of the Central Kurdish language (Sorani dialect). It leverages a transformer-based architecture to transcribe spoken Kurdish into text. The model was trained using data that includes various speech samples representative of the language's phonetic diversity.

## Intended uses & limitations

### Intended Uses
- Transcribing spoken Kurdish into text for applications such as subtitling, voice assistants, and transcription services.
- Enhancing accessibility for Kurdish speakers by providing speech-to-text functionality in their native language.

### Limitations
- The model's performance may degrade with speakers who have strong accents or dialects not well-represented in the training data.
- It may not perform well in noisy environments or with overlapping speech.
- The Wer (Word Error Rate) of 44.8788 indicates room for improvement in accuracy.

## Training and evaluation data

The model was trained on the [PawanKrd/asr-ckb](https://huggingface.co/datasets/PawanKrd/asr-ckb) dataset, which consists of diverse speech recordings in Central Kurdish. The dataset includes a variety of speakers, both male and female, across different age groups and regions, providing a broad representation of the language.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 15000

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.253         | 0.1927 | 1000  | 0.3988          | 76.9180 |
| 0.2675        | 0.3854 | 2000  | 0.3212          | 65.9103 |
| 0.231         | 0.5780 | 3000  | 0.2816          | 61.2462 |
| 0.1703        | 0.7707 | 4000  | 0.2539          | 59.0665 |
| 0.1399        | 0.9634 | 5000  | 0.2321          | 55.0053 |
| 0.1671        | 1.1561 | 6000  | 0.2174          | 57.1154 |
| 0.1732        | 1.3487 | 7000  | 0.2026          | 54.5581 |
| 0.1258        | 1.5414 | 8000  | 0.1900          | 52.7660 |
| 0.1692        | 1.7341 | 9000  | 0.1817          | 57.1055 |
| 0.1854        | 1.9268 | 10000 | 0.1691          | 49.9702 |
| 0.2143        | 2.1195 | 11000 | 0.1588          | 48.6816 |
| 0.1562        | 2.3121 | 12000 | 0.1515          | 49.2083 |
| 0.0966        | 2.5048 | 13000 | 0.1445          | 47.7408 |
| 0.1071        | 2.6975 | 14000 | 0.1372          | 48.3868 |
| 0.0794        | 2.8902 | 15000 | 0.1310          | 44.8788 |

## Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1