metadata
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: whisper_base_finetuned
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: validation
args: default
metrics:
- name: Wer
type: wer
value: 0.3192600084831479
whisper_base_finetuned
This model was trained from scratch on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3375
- Wer: 0.3193
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: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.4111 | 1.0 | 973 | 0.4590 | 0.4551 |
| 0.4068 | 2.0 | 1946 | 0.3847 | 0.4812 |
| 0.3617 | 3.0 | 2919 | 0.3585 | 0.4326 |
| 0.3144 | 4.0 | 3892 | 0.3436 | 0.3594 |
| 0.272 | 5.0 | 4865 | 0.3425 | 0.3639 |
| 0.2246 | 6.0 | 5838 | 0.3371 | 0.3341 |
| 0.1541 | 7.0 | 6811 | 0.3404 | 0.3377 |
| 0.1387 | 8.0 | 7784 | 0.3370 | 0.3196 |
| 0.1554 | 9.0 | 8757 | 0.3387 | 0.3113 |
| 0.1692 | 10.0 | 9730 | 0.3375 | 0.3193 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.2.1
- Datasets 2.19.0
- Tokenizers 0.19.1