Translation
Transformers
TensorBoard
Safetensors
Arabic
English
whisper
automatic-speech-recognition
egyptian-arabic
code-switching
Generated from Trainer
Eval Results (legacy)
# Load model directly
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
processor = AutoProcessor.from_pretrained("AssemGamal955/OUTPUT_DIR1")
model = AutoModelForSpeechSeq2Seq.from_pretrained("AssemGamal955/OUTPUT_DIR1")Quick Links
Whisper Medium Egyptian Arabic --> english Translation
This model is a fine-tuned version of openai/whisper-medium on the Assemgamal955/egyptian-english-translation dataset. It achieves the following results on the evaluation set:
- Loss: 0.9538
- Model Preparation Time: 0.0096
- Bleu: 9.4839
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: 7e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Bleu |
|---|---|---|---|---|---|
| 0.9989 | 1.0 | 1563 | 0.9642 | 0.0096 | 7.6351 |
Framework versions
- Transformers 4.53.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.2
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Model tree for AssemGamal955/OUTPUT_DIR1
Base model
openai/whisper-mediumEvaluation results
- Bleu on Assemgamal955/egyptian-english-translationself-reported9.484
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="AssemGamal955/OUTPUT_DIR1")