Instructions to use ilyaslbern7347/whisper-darija-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ilyaslbern7347/whisper-darija-final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="ilyaslbern7347/whisper-darija-final")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("ilyaslbern7347/whisper-darija-final") model = AutoModelForSpeechSeq2Seq.from_pretrained("ilyaslbern7347/whisper-darija-final") - Notebooks
- Google Colab
- Kaggle
whisper-darija-final
This model is a fine-tuned version of ilyaslbern7347/whisper-darija-stage1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1049
- Wer: 10.8108
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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 25
- training_steps: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0626 | 4.5714 | 50 | 0.1494 | 15.3153 |
| 0.0011 | 9.0952 | 100 | 0.1049 | 10.8108 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.8.3
- Tokenizers 0.22.2
- Downloads last month
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Model tree for ilyaslbern7347/whisper-darija-final
Base model
openai/whisper-small Finetuned
ychafiqui/whisper-small-darija Finetuned
ilyaslbern7347/whisper-darija-stage1