metadata
library_name: transformers
language:
- nan
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: Hokkien-to-Tai Lo Whisper ver 2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 16.0
type: mozilla-foundation/common_voice_16_1
config: nan-tw
split: test
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 137.93654416799816
Hokkien-to-Tai Lo Whisper ver 2
This model is a fine-tuned version of openai/whisper-small on the Common Voice 16.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.6832
- Wer: 137.9365
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-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.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: 1000
- training_steps: 8000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 5.7216 | 0.2581 | 800 | 1.5008 | 134.0105 |
| 1.1329 | 0.5161 | 1600 | 1.0312 | 125.0399 |
| 0.9625 | 0.7742 | 2400 | 0.8906 | 127.3682 |
| 0.8124 | 1.0323 | 3200 | 0.7961 | 132.3214 |
| 0.7005 | 1.2903 | 4000 | 0.7438 | 127.8703 |
| 0.6735 | 1.5484 | 4800 | 0.7058 | 132.8920 |
| 0.632 | 1.8065 | 5600 | 0.6832 | 137.9365 |
Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0