metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- nyagen
metrics:
- wer
model-index:
- name: whisper-medium-nyagen-baseline-model
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: nyagen
type: nyagen
metrics:
- name: Wer
type: wer
value: 0.31363034847816496
whisper-medium-nyagen-baseline-model
This model is a fine-tuned version of openai/whisper-medium on the nyagen dataset. It achieves the following results on the evaluation set:
- Loss: 0.4721
- Wer: 0.3136
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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|---|---|---|---|---|
| 1.6573 | 1.0257 | 200 | 0.7472 | 0.4663 |
| 0.8271 | 2.0514 | 400 | 0.4985 | 0.4389 |
| 0.4472 | 3.0771 | 600 | 0.4721 | 0.3136 |
| 0.1499 | 4.1028 | 800 | 0.4968 | 0.3092 |
| 0.064 | 5.1285 | 1000 | 0.4892 | 0.3335 |
| 0.0521 | 6.1542 | 1200 | 0.5093 | 0.3048 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0