Instructions to use MoatazNLP/whisper-small-egy-ds-v0-t1111 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoatazNLP/whisper-small-egy-ds-v0-t1111 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MoatazNLP/whisper-small-egy-ds-v0-t1111")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("MoatazNLP/whisper-small-egy-ds-v0-t1111") model = AutoModelForSpeechSeq2Seq.from_pretrained("MoatazNLP/whisper-small-egy-ds-v0-t1111") - Notebooks
- Google Colab
- Kaggle
whisper-small-egy-ds-v0-t1111
This model is a fine-tuned version of openai/whisper-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0160
- Wer: 54.3210
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.1602 | 1.0 | 1 | 1.0160 | 54.3210 |
| 1.1602 | 2.0 | 2 | 1.0160 | 54.3210 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu126
- Datasets 3.3.2
- Tokenizers 0.21.0
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Model tree for MoatazNLP/whisper-small-egy-ds-v0-t1111
Base model
openai/whisper-small