|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- common_voice |
|
model-index: |
|
- name: wav2vec2-large-xls-r-300m-tr |
|
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-large-xls-r-300m-tr |
|
|
|
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2891 |
|
- Wer: 0.4741 |
|
|
|
## 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: 10 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| 5.4933 | 0.39 | 400 | 1.0543 | 0.9316 | |
|
| 0.7039 | 0.78 | 800 | 0.6927 | 0.7702 | |
|
| 0.4768 | 1.17 | 1200 | 0.4779 | 0.6774 | |
|
| 0.4004 | 1.57 | 1600 | 0.4462 | 0.6450 | |
|
| 0.3739 | 1.96 | 2000 | 0.4287 | 0.6296 | |
|
| 0.317 | 2.35 | 2400 | 0.4395 | 0.6248 | |
|
| 0.3027 | 2.74 | 2800 | 0.4052 | 0.6027 | |
|
| 0.2633 | 3.13 | 3200 | 0.4026 | 0.5938 | |
|
| 0.245 | 3.52 | 3600 | 0.3814 | 0.5902 | |
|
| 0.2415 | 3.91 | 4000 | 0.3691 | 0.5708 | |
|
| 0.2193 | 4.31 | 4400 | 0.3626 | 0.5623 | |
|
| 0.2057 | 4.7 | 4800 | 0.3591 | 0.5551 | |
|
| 0.1874 | 5.09 | 5200 | 0.3670 | 0.5512 | |
|
| 0.1782 | 5.48 | 5600 | 0.3483 | 0.5406 | |
|
| 0.1706 | 5.87 | 6000 | 0.3392 | 0.5338 | |
|
| 0.153 | 6.26 | 6400 | 0.3189 | 0.5207 | |
|
| 0.1493 | 6.65 | 6800 | 0.3185 | 0.5164 | |
|
| 0.1381 | 7.05 | 7200 | 0.3199 | 0.5185 | |
|
| 0.1244 | 7.44 | 7600 | 0.3082 | 0.4993 | |
|
| 0.1182 | 7.83 | 8000 | 0.3122 | 0.4998 | |
|
| 0.1136 | 8.22 | 8400 | 0.3003 | 0.4936 | |
|
| 0.1047 | 8.61 | 8800 | 0.2945 | 0.4858 | |
|
| 0.0986 | 9.0 | 9200 | 0.2827 | 0.4809 | |
|
| 0.0925 | 9.39 | 9600 | 0.2894 | 0.4786 | |
|
| 0.0885 | 9.78 | 10000 | 0.2891 | 0.4741 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.18.0 |
|
- Pytorch 1.12.1+cu116 |
|
- Datasets 2.1.0 |
|
- Tokenizers 0.12.1 |
|
|
