|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- common_voice |
|
model-index: |
|
- name: wav2vec2-large-xls-r-300m-kika5_my-colab |
|
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-kika5_my-colab |
|
|
|
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: 0.3860 |
|
- Wer: 0.3505 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| 4.0007 | 4.82 | 400 | 0.6696 | 0.8283 | |
|
| 0.2774 | 9.64 | 800 | 0.4231 | 0.5476 | |
|
| 0.1182 | 14.46 | 1200 | 0.4253 | 0.5102 | |
|
| 0.0859 | 19.28 | 1600 | 0.4600 | 0.4866 | |
|
| 0.0693 | 24.1 | 2000 | 0.4030 | 0.4533 | |
|
| 0.0611 | 28.92 | 2400 | 0.4189 | 0.4412 | |
|
| 0.0541 | 33.73 | 2800 | 0.4272 | 0.4380 | |
|
| 0.0478 | 38.55 | 3200 | 0.4537 | 0.4505 | |
|
| 0.0428 | 43.37 | 3600 | 0.4349 | 0.4181 | |
|
| 0.038 | 48.19 | 4000 | 0.4562 | 0.4199 | |
|
| 0.0345 | 53.01 | 4400 | 0.4209 | 0.4310 | |
|
| 0.0316 | 57.83 | 4800 | 0.4336 | 0.4058 | |
|
| 0.0288 | 62.65 | 5200 | 0.4004 | 0.3920 | |
|
| 0.025 | 67.47 | 5600 | 0.4115 | 0.3857 | |
|
| 0.0225 | 72.29 | 6000 | 0.4296 | 0.3948 | |
|
| 0.0182 | 77.11 | 6400 | 0.3963 | 0.3772 | |
|
| 0.0165 | 81.93 | 6800 | 0.3921 | 0.3687 | |
|
| 0.0152 | 86.75 | 7200 | 0.3969 | 0.3592 | |
|
| 0.0133 | 91.57 | 7600 | 0.3803 | 0.3527 | |
|
| 0.0118 | 96.39 | 8000 | 0.3860 | 0.3505 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.15.0 |
|
- Pytorch 1.10.0+cu111 |
|
- Datasets 1.17.0 |
|
- Tokenizers 0.10.3 |
|
|
