Automatic Speech Recognition - CKB
This model is trained on the 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 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
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