whisper-large-ar6 / README.md
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---
language:
- ar
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- whitefox123/tashkeel
metrics:
- wer
model-index:
- name: Whisper large - tuned
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: CLARtts
type: whitefox123/tashkeel
config: default
split: None
args: 'config: ar, split: test'
metrics:
- name: Wer
type: wer
value: 217.9099099099099
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper large - tuned
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the CLARtts dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1346
- Wer: 217.9099
## 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: 3125
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0843 | 1.6 | 1000 | 0.1141 | 248.9730 |
| 0.024 | 3.2 | 2000 | 0.1194 | 274.9189 |
| 0.0108 | 4.8 | 3000 | 0.1346 | 217.9099 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2