metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- whisper-event
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: Whisper medium nan-tw common voice
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder nan-tw
type: audiofolder
config: nan-tw
split: test
args: nan-tw
metrics:
- name: Wer
type: wer
value: 0.9615384615384616
Whisper medium nan-tw common voice
This model is a fine-tuned version of openai/whisper-medium on the audiofolder nan-tw dataset. It achieves the following results on the evaluation set:
- Loss: 0.0141
- Model Preparation Time: 0.0121
- Wer: 0.9615
- Cer: 0.9524
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
- optimizer: Use adamw_bnb_8bit 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 | Model Preparation Time | Wer | Cer |
|---|---|---|---|---|---|---|
| 0.97 | 0.2 | 1000 | 0.7356 | 0.0121 | 38.1731 | 38.4762 |
| 0.3044 | 1.0388 | 2000 | 0.3099 | 0.0121 | 23.4615 | 23.9048 |
| 0.3108 | 1.2388 | 3000 | 0.1153 | 0.0121 | 7.5 | 7.7143 |
| 0.0544 | 2.0776 | 4000 | 0.0295 | 0.0121 | 2.3077 | 2.2857 |
| 0.0678 | 2.2776 | 5000 | 0.0141 | 0.0121 | 0.9615 | 0.9524 |
Framework versions
- Transformers 4.47.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3