metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: openai/whisper-base
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fleurs
type: fleurs
config: ps_af
split: test
args: ps_af
metrics:
- name: Wer
type: wer
value: 59.200968523002416
openai/whisper-base
This model is a fine-tuned version of openai/whisper-base on the fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.9339
- Wer: 59.2010
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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.9153 | 2.5 | 100 | 1.0240 | 68.9864 |
| 0.6865 | 5.0 | 200 | 0.8968 | 61.7660 |
| 0.5474 | 7.5 | 300 | 0.8744 | 60.5554 |
| 0.4646 | 10.0 | 400 | 0.8710 | 60.0560 |
| 0.4557 | 12.5 | 500 | 0.8732 | 59.4658 |
| 0.3882 | 15.0 | 600 | 0.8819 | 59.0648 |
| 0.3346 | 17.5 | 700 | 0.9032 | 59.4809 |
| 0.2947 | 20.0 | 800 | 0.9144 | 59.7685 |
| 0.2724 | 22.5 | 900 | 0.9289 | 58.9815 |
| 0.2785 | 25.0 | 1000 | 0.9339 | 59.2010 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2