metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: openai/whisper-medium
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_16_0
type: common_voice_16_0
config: eu
split: test
args: eu
metrics:
- name: Wer
type: wer
value: 9.188591686749389
openai/whisper-medium
This model is a fine-tuned version of openai/whisper-medium on the common_voice_16_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1503
- Wer: 9.1886
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: 4
- 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: 8000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4647 | 0.06 | 500 | 0.4529 | 34.2140 |
| 0.3163 | 0.12 | 1000 | 0.3516 | 26.0232 |
| 0.3232 | 0.19 | 1500 | 0.2996 | 21.1825 |
| 0.266 | 0.25 | 2000 | 0.2686 | 18.5126 |
| 0.2383 | 0.31 | 2500 | 0.2489 | 16.9412 |
| 0.1916 | 0.38 | 3000 | 0.2233 | 15.2831 |
| 0.2009 | 0.44 | 3500 | 0.2134 | 14.1419 |
| 0.2014 | 0.5 | 4000 | 0.2015 | 13.6579 |
| 0.1964 | 0.56 | 4500 | 0.1853 | 12.0198 |
| 0.1758 | 0.62 | 5000 | 0.1796 | 11.4651 |
| 0.2067 | 0.69 | 5500 | 0.1679 | 10.7989 |
| 0.213 | 0.75 | 6000 | 0.1618 | 10.3139 |
| 0.1272 | 1.03 | 6500 | 0.1551 | 9.8687 |
| 0.0744 | 1.09 | 7000 | 0.1534 | 9.5172 |
| 0.0726 | 1.16 | 7500 | 0.1518 | 9.3240 |
| 0.0627 | 1.22 | 8000 | 0.1503 | 9.1886 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2