metadata
datasets:
- arbml/mgb2
license: apache-2.0
metrics:
- wer
tags:
- whisper-event
- generated_from_trainer
- hf-asr-leaderboard
model-index:
- name: Whisper Medium ar - Zaid Alyafeai
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: ar
split: test
args: ar
metrics:
- type: wer
value: 34.28
name: Wer
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: google/fleurs
type: google/fleurs
config: ar_eg
split: test
args: ar
metrics:
- type: wer
value: 12.04
name: Wer
openai/whisper-medium
This model is a fine-tuned version of openai/whisper-medium on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.8488
- Wer: 16.5882
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: 32
- 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: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2963 | 0.1 | 1000 | 0.9115 | 27.3641 |
| 0.2676 | 0.2 | 2000 | 0.8796 | 24.1024 |
| 0.3166 | 0.3 | 3000 | 0.8467 | 20.1700 |
| 0.2797 | 0.4 | 4000 | 0.8756 | 29.4889 |
| 0.2302 | 0.5 | 5000 | 0.8523 | 19.6414 |
| 0.2803 | 0.6 | 6000 | 0.8715 | 19.7413 |
| 0.2794 | 0.7 | 7000 | 0.8548 | 18.6840 |
| 0.2173 | 0.8 | 8000 | 0.8543 | 17.9019 |
| 0.217 | 0.9 | 9000 | 0.8518 | 16.3840 |
| 0.1718 | 1.0 | 10000 | 0.8488 | 16.5882 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2