metadata
base_model: openai/whisper-large-v3
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-large-v3-natbed-non-native-model
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: natbed
type: natbed
config: en
split: test
metrics:
- type: wer
value: 42.4
name: WER
whisper-large-v3-natbed-non-native-model
This model is a fine-tuned version of openai/whisper-large-v3 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8693
- Wer: 52.1349
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: 1.75e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 30000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.5641 | 0.5631 | 250 | 0.8395 | 70.9929 |
| 0.821 | 1.1261 | 500 | 0.7848 | 65.2830 |
| 0.6791 | 1.6892 | 750 | 0.7238 | 59.8611 |
| 0.5596 | 2.2523 | 1000 | 0.7156 | 55.1339 |
| 0.46 | 2.8153 | 1250 | 0.7180 | 54.4222 |
| 0.3263 | 3.3784 | 1500 | 0.7762 | 56.3707 |
| 0.3088 | 3.9414 | 1750 | 0.7282 | 51.8807 |
| 0.1838 | 4.5045 | 2000 | 0.7987 | 52.5246 |
| 0.1694 | 5.0676 | 2250 | 0.8901 | 53.5920 |
| 0.1054 | 5.6306 | 2500 | 0.8693 | 52.1349 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1