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whisper-small-aishell
This model is a fine-tuned version of openai/whisper-small on the aishell zh-cn dataset. It achieves the following results on the evaluation set:
- Loss: 0.1770
- Wer: 0.4068
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
train data:aishell train test data:aishell test
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
---|---|---|---|---|---|
0.042 | 4.26 | 1000 | 0.1227 | 0.3990 | |
0.0134 | 8.52 | 2000 | 0.1312 | 0.4004 | |
0.0042 | 12.78 | 3000 | 0.1402 | 0.4027 | 0.051 |
0.0022 | 17.04 | 4000 | 0.1479 | 0.4045 | |
0.001 | 21.3 | 5000 | 0.1568 | 0.4069 | |
0.0007 | 25.56 | 6000 | 0.1568 | 0.3990 | |
0.0004 | 29.82 | 7000 | 0.1644 | 0.4037 | |
0.0003 | 34.08 | 8000 | 0.1697 | 0.4045 | |
0.0002 | 38.34 | 9000 | 0.1751 | 0.4072 | |
0.0002 | 42.6 | 10000 | 0.1770 | 0.4068 |
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.0
- Datasets 2.10.1
- Tokenizers 0.13.2