metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: openai/whisper-base
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: cs
split: test
args: cs
metrics:
- name: Wer
type: wer
value: 26.914789782876923
openai/whisper-base
This model is a fine-tuned version of openai/whisper-base on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4828
- Wer: 26.9148
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: 0.0001
- 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: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0606 | 4.01 | 1000 | 0.4679 | 34.8189 |
| 0.0153 | 8.02 | 2000 | 0.5124 | 32.4451 |
| 0.0018 | 12.03 | 3000 | 0.4891 | 29.0068 |
| 0.0004 | 16.04 | 4000 | 0.4804 | 27.1210 |
| 0.0002 | 21.01 | 5000 | 0.4828 | 26.9148 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2