# Load model directly
from transformers import AutoProcessor, AutoModelForCTC
processor = AutoProcessor.from_pretrained("Elliotte/Hubert-base-superb")
model = AutoModelForCTC.from_pretrained("Elliotte/Hubert-base-superb")Quick Links
Hubert-base-superb
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: 0.6712
- Wer: 0.4781
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: 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: 250
- num_epochs: 7
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.7884 | 0.8 | 500 | 0.8900 | 0.6940 |
| 0.6603 | 1.6 | 1000 | 0.7378 | 0.6103 |
| 0.5401 | 2.4 | 1500 | 0.7107 | 0.5762 |
| 0.4604 | 3.2 | 2000 | 0.6563 | 0.5320 |
| 0.3936 | 4.0 | 2500 | 0.6315 | 0.5244 |
| 0.3186 | 4.8 | 3000 | 0.6525 | 0.5007 |
| 0.2727 | 5.6 | 3500 | 0.6553 | 0.4855 |
| 0.2296 | 6.4 | 4000 | 0.6712 | 0.4781 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Elliotte/Hubert-base-superb")