|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: wav2vec2-xls-r-timit-trainer |
|
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. --> |
|
|
|
# wav2vec2-xls-r-timit-trainer |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.1064 |
|
- Wer: 1.0 |
|
|
|
## 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: 0.0003 |
|
- train_batch_size: 16 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 2 |
|
- total_train_batch_size: 32 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 500 |
|
- num_epochs: 100 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| 3.5537 | 4.03 | 500 | 0.6078 | 1.0 | |
|
| 0.5444 | 8.06 | 1000 | 0.4990 | 0.9994 | |
|
| 0.3744 | 12.1 | 1500 | 0.5530 | 1.0 | |
|
| 0.2863 | 16.13 | 2000 | 0.6401 | 1.0 | |
|
| 0.2357 | 20.16 | 2500 | 0.6485 | 1.0 | |
|
| 0.1933 | 24.19 | 3000 | 0.7448 | 0.9994 | |
|
| 0.162 | 28.22 | 3500 | 0.7502 | 1.0 | |
|
| 0.1325 | 32.26 | 4000 | 0.7801 | 1.0 | |
|
| 0.1169 | 36.29 | 4500 | 0.8334 | 1.0 | |
|
| 0.1031 | 40.32 | 5000 | 0.8269 | 1.0 | |
|
| 0.0913 | 44.35 | 5500 | 0.8432 | 1.0 | |
|
| 0.0793 | 48.39 | 6000 | 0.8738 | 1.0 | |
|
| 0.0694 | 52.42 | 6500 | 0.8897 | 1.0 | |
|
| 0.0613 | 56.45 | 7000 | 0.8966 | 1.0 | |
|
| 0.0548 | 60.48 | 7500 | 0.9398 | 1.0 | |
|
| 0.0444 | 64.51 | 8000 | 0.9548 | 1.0 | |
|
| 0.0386 | 68.55 | 8500 | 0.9647 | 1.0 | |
|
| 0.0359 | 72.58 | 9000 | 0.9901 | 1.0 | |
|
| 0.0299 | 76.61 | 9500 | 1.0151 | 1.0 | |
|
| 0.0259 | 80.64 | 10000 | 1.0526 | 1.0 | |
|
| 0.022 | 84.67 | 10500 | 1.0754 | 1.0 | |
|
| 0.0189 | 88.71 | 11000 | 1.0688 | 1.0 | |
|
| 0.0161 | 92.74 | 11500 | 1.0914 | 1.0 | |
|
| 0.0138 | 96.77 | 12000 | 1.1064 | 1.0 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.11.3 |
|
- Pytorch 1.10.0+cu111 |
|
- Datasets 1.13.3 |
|
- Tokenizers 0.10.3 |
|
|
