metadata
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: openai/whisper-small
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: sl
split: test
args: sl
metrics:
- name: Wer
type: wer
value: 28.965014577259474
openai/whisper-small
This model is a fine-tuned version of openai/whisper-small on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.0872
- Wer: 28.9650
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: 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.006 | 13.0 | 1000 | 0.5072 | 28.3382 |
| 0.0011 | 26.0 | 2000 | 0.7109 | 27.0262 |
| 0.0005 | 39.01 | 3000 | 0.8865 | 27.1866 |
| 0.0 | 52.01 | 4000 | 1.0261 | 29.0087 |
| 0.0 | 65.01 | 5000 | 1.0872 | 28.9650 |
Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.2.dev0
- Tokenizers 0.13.3