Instructions to use zainulhakim/240624-wav2vec2-ASR-Arab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zainulhakim/240624-wav2vec2-ASR-Arab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="zainulhakim/240624-wav2vec2-ASR-Arab")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("zainulhakim/240624-wav2vec2-ASR-Arab") model = AutoModelForCTC.from_pretrained("zainulhakim/240624-wav2vec2-ASR-Arab") - Notebooks
- Google Colab
- Kaggle
240624-wav2vec2-ASR-Arab
This model is a fine-tuned version of zainulhakim/240615-wav2vec2-ASR-Arabs on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.6706
- Wer: 0.9697
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 5
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| No log | 6.25 | 100 | 2.5459 | 1.0 |
| No log | 12.5 | 200 | 1.4102 | 1.0 |
| No log | 18.75 | 300 | 1.6706 | 0.9697 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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