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--- |
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language: |
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- ckb |
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tags: |
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- generated_from_trainer |
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datasets: |
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- PawanKrd/asr-ckb |
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metrics: |
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- wer |
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model-index: |
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- name: ASR CKB |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: PawanKrd/asr-ckb |
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type: PawanKrd/asr-ckb |
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metrics: |
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- name: Wer |
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type: wer |
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value: 44.878759772094874 |
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--- |
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# Automatic Speech Recognition - CKB |
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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: |
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- Loss: 0.1310 |
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- Wer: 44.8788 |
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## Model description |
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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. |
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## Intended uses & limitations |
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### Intended Uses |
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- Transcribing spoken Kurdish into text for applications such as subtitling, voice assistants, and transcription services. |
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- Enhancing accessibility for Kurdish speakers by providing speech-to-text functionality in their native language. |
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### Limitations |
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- The model's performance may degrade with speakers who have strong accents or dialects not well-represented in the training data. |
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- It may not perform well in noisy environments or with overlapping speech. |
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- The Wer (Word Error Rate) of 44.8788 indicates room for improvement in accuracy. |
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## Training and evaluation data |
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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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 15000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:-----:|:---------------:|:-------:| |
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| 0.253 | 0.1927 | 1000 | 0.3988 | 76.9180 | |
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| 0.2675 | 0.3854 | 2000 | 0.3212 | 65.9103 | |
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| 0.231 | 0.5780 | 3000 | 0.2816 | 61.2462 | |
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| 0.1703 | 0.7707 | 4000 | 0.2539 | 59.0665 | |
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| 0.1399 | 0.9634 | 5000 | 0.2321 | 55.0053 | |
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| 0.1671 | 1.1561 | 6000 | 0.2174 | 57.1154 | |
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| 0.1732 | 1.3487 | 7000 | 0.2026 | 54.5581 | |
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| 0.1258 | 1.5414 | 8000 | 0.1900 | 52.7660 | |
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| 0.1692 | 1.7341 | 9000 | 0.1817 | 57.1055 | |
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| 0.1854 | 1.9268 | 10000 | 0.1691 | 49.9702 | |
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| 0.2143 | 2.1195 | 11000 | 0.1588 | 48.6816 | |
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| 0.1562 | 2.3121 | 12000 | 0.1515 | 49.2083 | |
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| 0.0966 | 2.5048 | 13000 | 0.1445 | 47.7408 | |
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| 0.1071 | 2.6975 | 14000 | 0.1372 | 48.3868 | |
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| 0.0794 | 2.8902 | 15000 | 0.1310 | 44.8788 | |
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## Framework versions |
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- Transformers 4.42.0.dev0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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