wav2vec2-base-dataset_asr-demo-colab
This model is a fine-tuned version of ntu-spml/distilhubert on the superb dataset. It achieves the following results on the evaluation set:
- Loss: 295.0834
- Wer: 0.8282
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: 0.001
- train_batch_size: 32
- 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: 250
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 5638.536 | 1.6 | 500 | 409.4785 | 0.8556 |
| 2258.6455 | 3.19 | 1000 | 326.0520 | 0.8369 |
| 1389.4919 | 4.79 | 1500 | 295.0834 | 0.8282 |
Framework versions
- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
- Downloads last month
- 4
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.