metadata
base_model: openai/whisper-base
datasets:
- fleurs
language:
- zh
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Base Chinese - Chee Li
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: fleurs
config: cmn_hans_cn
split: None
args: 'config: zh split: test'
metrics:
- type: wer
value: 21.01513260055415
name: Wer
Whisper Base Chinese - Chee Li
This model is a fine-tuned version of openai/whisper-base on the Google Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.4109
- Wer: 21.0151
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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0705 | 4.3668 | 1000 | 0.3557 | 23.0095 |
| 0.0066 | 8.7336 | 2000 | 0.3853 | 21.0578 |
| 0.0026 | 13.1004 | 3000 | 0.4046 | 20.9695 |
| 0.002 | 17.4672 | 4000 | 0.4109 | 21.0151 |
Framework versions
- Transformers 4.43.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1