Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Arabic
whisper
hf-asr-leaderboard
Generated from Trainer
Instructions to use monaf3/whisper-small-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use monaf3/whisper-small-ar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="monaf3/whisper-small-ar")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("monaf3/whisper-small-ar") model = AutoModelForMultimodalLM.from_pretrained("monaf3/whisper-small-ar") - Notebooks
- Google Colab
- Kaggle
Whisper Small Ai - Monaf solieman
This model is a fine-tuned version of openai/whisper-small on the Common Voice 4.0 dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.3953
- eval_wer: 119.1335
- eval_runtime: 1677.2897
- eval_samples_per_second: 1.011
- eval_steps_per_second: 0.126
- epoch: 2.53
- step: 600
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: 16
- 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
- training_steps: 4000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Model tree for monaf3/whisper-small-ar
Base model
openai/whisper-small