metadata
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Small en - Stan
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fleurs
type: google/fleurs
config: en_us
split: None
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 8.637757947573899
Whisper Small en - Stan
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: 0.3236
- Wer: 8.6378
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: 200
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.5755 | 0.61 | 100 | 0.5716 | 8.6029 |
| 0.1769 | 1.23 | 200 | 0.2722 | 8.3659 |
| 0.1153 | 1.84 | 300 | 0.2791 | 8.7842 |
| 0.0356 | 2.45 | 400 | 0.2852 | 8.7981 |
| 0.0208 | 3.07 | 500 | 0.2923 | 8.6866 |
| 0.0105 | 3.68 | 600 | 0.3050 | 8.6517 |
| 0.0032 | 4.29 | 700 | 0.3126 | 8.6238 |
| 0.0033 | 4.91 | 800 | 0.3174 | 8.6308 |
| 0.0028 | 5.52 | 900 | 0.3227 | 8.5611 |
| 0.0017 | 6.13 | 1000 | 0.3236 | 8.6378 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2