|
--- |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: wavlm-large-timit-punctuation |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# wavlm-large-timit-punctuation |
|
|
|
This model is a fine-tuned version of [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3360 |
|
- Wer: 0.2580 |
|
|
|
## 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: 0.0001 |
|
- train_batch_size: 8 |
|
- 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: 1000 |
|
- num_epochs: 30 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| 5.2206 | 1.0 | 500 | 3.1111 | 1.0 | |
|
| 2.4555 | 2.01 | 1000 | 1.0331 | 0.7992 | |
|
| 0.9277 | 3.01 | 1500 | 0.5219 | 0.4888 | |
|
| 0.5215 | 4.02 | 2000 | 0.3833 | 0.3981 | |
|
| 0.3557 | 5.02 | 2500 | 0.3330 | 0.3570 | |
|
| 0.2715 | 6.02 | 3000 | 0.3084 | 0.3255 | |
|
| 0.2139 | 7.03 | 3500 | 0.2969 | 0.3129 | |
|
| 0.1858 | 8.03 | 4000 | 0.2884 | 0.3029 | |
|
| 0.1563 | 9.04 | 4500 | 0.2860 | 0.2960 | |
|
| 0.149 | 10.04 | 5000 | 0.2972 | 0.2918 | |
|
| 0.1343 | 11.04 | 5500 | 0.3161 | 0.2927 | |
|
| 0.11 | 12.05 | 6000 | 0.3061 | 0.2788 | |
|
| 0.0982 | 13.05 | 6500 | 0.2983 | 0.2802 | |
|
| 0.0967 | 14.06 | 7000 | 0.3280 | 0.2768 | |
|
| 0.0873 | 15.06 | 7500 | 0.3185 | 0.2721 | |
|
| 0.0809 | 16.06 | 8000 | 0.3121 | 0.2694 | |
|
| 0.0787 | 17.07 | 8500 | 0.3177 | 0.2643 | |
|
| 0.0709 | 18.07 | 9000 | 0.3189 | 0.2657 | |
|
| 0.0712 | 19.08 | 9500 | 0.3213 | 0.2628 | |
|
| 0.0621 | 20.08 | 10000 | 0.3206 | 0.2600 | |
|
| 0.0601 | 21.08 | 10500 | 0.3191 | 0.2600 | |
|
| 0.0605 | 22.09 | 11000 | 0.3241 | 0.2591 | |
|
| 0.058 | 23.09 | 11500 | 0.3230 | 0.2584 | |
|
| 0.0503 | 24.1 | 12000 | 0.3346 | 0.2602 | |
|
| 0.0498 | 25.1 | 12500 | 0.3359 | 0.2593 | |
|
| 0.0506 | 26.1 | 13000 | 0.3339 | 0.2592 | |
|
| 0.0468 | 27.11 | 13500 | 0.3357 | 0.2563 | |
|
| 0.0422 | 28.11 | 14000 | 0.3368 | 0.2568 | |
|
| 0.0512 | 29.12 | 14500 | 0.3360 | 0.2580 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.19.2 |
|
- Pytorch 1.8.2+cu111 |
|
- Datasets 1.17.0 |
|
- Tokenizers 0.11.6 |
|
|
