metadata
language:
- ar
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: Whisper Medium ar
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: ar
split: test
args: ar
metrics:
- name: Wer
type: wer
value: 52.53145797968367
Whisper Medium ar
This model is a fine-tuned version of openai/whisper-medium on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3309
- Wer: 52.5315
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: 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: 5000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2054 | 0.8237 | 1000 | 0.2990 | 54.4903 |
| 0.1135 | 1.6474 | 2000 | 0.2926 | 63.7189 |
| 0.0618 | 2.4712 | 3000 | 0.3019 | 59.1518 |
| 0.0255 | 3.2949 | 4000 | 0.3154 | 53.1552 |
| 0.0141 | 4.1186 | 5000 | 0.3309 | 52.5315 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1