Instructions to use MohammedNasri/whisper-tiny-ar-common-fleurs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MohammedNasri/whisper-tiny-ar-common-fleurs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="MohammedNasri/whisper-tiny-ar-common-fleurs")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("MohammedNasri/whisper-tiny-ar-common-fleurs") model = AutoModelForSpeechSeq2Seq.from_pretrained("MohammedNasri/whisper-tiny-ar-common-fleurs") - Notebooks
- Google Colab
- Kaggle
whisper-tiny-ar-common-fleurs
This model is a fine-tuned version of MohammedNasri/whisper-tiny-ar-common-fleurs on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.5489
- eval_wer: 93.9628
- eval_runtime: 2687.9844
- eval_samples_per_second: 4.043
- eval_steps_per_second: 0.253
- epoch: 0.39
- step: 1000
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: 16
- 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: 3000
- mixed_precision_training: Native AMP
Framework versions
- Transformers 4.26.0
- Pytorch 1.12.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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