metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: openai/whisper-small
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: 56.651029055690074
openai/whisper-small
This model is a fine-tuned version of openai/whisper-small on the fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 1.2309
- Wer: 56.6510
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: 600
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.1183 | 3.7 | 100 | 1.3170 | 76.9522 |
| 0.8565 | 7.41 | 200 | 0.9367 | 61.9930 |
| 0.2246 | 11.11 | 300 | 0.9642 | 58.8302 |
| 0.054 | 14.81 | 400 | 1.0876 | 57.9903 |
| 0.0159 | 18.52 | 500 | 1.1798 | 57.8768 |
| 0.0045 | 22.22 | 600 | 1.2309 | 56.6510 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2