metadata
library_name: transformers
language:
- wo
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper-WOLOF-5-hours-Google-Fleurs-dataset
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs
type: google/fleurs
config: wo_sn
split: None
args: 'config: wo, split: test'
metrics:
- name: Wer
type: wer
value: 49.03357070193286
Whisper-WOLOF-5-hours-Google-Fleurs-dataset
This model is a fine-tuned version of openai/whisper-small on the google/fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 1.5579
- Wer: 49.0336
- Cer: 18.1546
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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.7747 | 12.1951 | 500 | 1.3158 | 48.9318 | 18.0097 |
| 0.0052 | 24.3902 | 1000 | 1.4793 | 48.9431 | 18.1792 |
| 0.0012 | 36.5854 | 1500 | 1.5371 | 49.2144 | 18.0521 |
| 0.0008 | 48.7805 | 2000 | 1.5579 | 49.0336 | 18.1546 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.1.0+cu118
- Datasets 3.0.1
- Tokenizers 0.20.1