Whisper Small Chinese Base
This model is a fine-tuned version of openai/whisper-small on the google/fleurs cmn_hans_cn dataset. It achieves the following results on the evaluation set:
- Loss: 0.3573
- Wer: 16.6439
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: 64
- eval_batch_size: 32
- 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: 5000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
0.0005 | 76.0 | 1000 | 0.3573 | 16.6439 |
0.0002 | 153.0 | 2000 | 0.3897 | 16.9749 |
0.0001 | 230.0 | 3000 | 0.4125 | 17.2330 |
0.0001 | 307.0 | 4000 | 0.4256 | 17.2451 |
0.0001 | 384.0 | 5000 | 0.4330 | 17.2300 |
Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
- Downloads last month
- 334
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Dataset used to train Jingmiao/whisper-small-chinese_base
Evaluation results
- Wer on google/fleurs cmn_hans_cntest set self-reported16.644