metadata
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- wer
model-index:
- name: whisper-tiny-en
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.2883917775090689
whisper-tiny-en
This model is a fine-tuned version of openai/whisper-tiny on the minds14 dataset. It achieves the following results on the evaluation set:
- Loss: 0.7626
- Wer Ortho: 0.2891
- Wer: 0.2884
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_steps: 50
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|---|---|---|---|---|---|
| 0.0005 | 35.71 | 500 | 0.6319 | 0.2684 | 0.2684 |
| 0.0002 | 71.43 | 1000 | 0.6820 | 0.2709 | 0.2709 |
| 0.0001 | 107.14 | 1500 | 0.7092 | 0.2740 | 0.2739 |
| 0.0001 | 142.86 | 2000 | 0.7275 | 0.2854 | 0.2848 |
| 0.0001 | 178.57 | 2500 | 0.7423 | 0.2885 | 0.2878 |
| 0.0 | 214.29 | 3000 | 0.7531 | 0.2898 | 0.2890 |
| 0.0 | 250.0 | 3500 | 0.7604 | 0.2898 | 0.2890 |
| 0.0 | 285.71 | 4000 | 0.7626 | 0.2891 | 0.2884 |
Framework versions
- Transformers 4.39.2
- Pytorch 1.13.0+cu117
- Datasets 2.16.1
- Tokenizers 0.15.1