|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- common_voice |
|
model-index: |
|
- name: wav2vec2-large-xls-r-300m-hi-d3 |
|
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-hi-d3 |
|
|
|
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: 0.7988 |
|
- Wer: 0.3713 |
|
|
|
## 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: 0.000388 |
|
- 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: 750 |
|
- num_epochs: 50 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:| |
|
| 8.2826 | 1.36 | 200 | 3.5253 | 1.0 | |
|
| 2.7019 | 2.72 | 400 | 1.1744 | 0.7360 | |
|
| 0.7358 | 4.08 | 600 | 0.7781 | 0.5501 | |
|
| 0.4942 | 5.44 | 800 | 0.7590 | 0.5345 | |
|
| 0.4056 | 6.8 | 1000 | 0.6885 | 0.4776 | |
|
| 0.3243 | 8.16 | 1200 | 0.7195 | 0.4861 | |
|
| 0.2785 | 9.52 | 1400 | 0.7473 | 0.4930 | |
|
| 0.2448 | 10.88 | 1600 | 0.7201 | 0.4574 | |
|
| 0.2155 | 12.24 | 1800 | 0.7686 | 0.4648 | |
|
| 0.2039 | 13.6 | 2000 | 0.7440 | 0.4624 | |
|
| 0.1792 | 14.96 | 2200 | 0.7815 | 0.4658 | |
|
| 0.1695 | 16.33 | 2400 | 0.7678 | 0.4557 | |
|
| 0.1598 | 17.68 | 2600 | 0.7468 | 0.4393 | |
|
| 0.1568 | 19.05 | 2800 | 0.7440 | 0.4422 | |
|
| 0.1391 | 20.41 | 3000 | 0.7656 | 0.4317 | |
|
| 0.1283 | 21.77 | 3200 | 0.7892 | 0.4299 | |
|
| 0.1194 | 23.13 | 3400 | 0.7646 | 0.4192 | |
|
| 0.1116 | 24.49 | 3600 | 0.8156 | 0.4330 | |
|
| 0.1111 | 25.85 | 3800 | 0.7661 | 0.4322 | |
|
| 0.1023 | 27.21 | 4000 | 0.7419 | 0.4276 | |
|
| 0.1007 | 28.57 | 4200 | 0.8488 | 0.4245 | |
|
| 0.0925 | 29.93 | 4400 | 0.8062 | 0.4070 | |
|
| 0.0918 | 31.29 | 4600 | 0.8412 | 0.4218 | |
|
| 0.0813 | 32.65 | 4800 | 0.8045 | 0.4087 | |
|
| 0.0805 | 34.01 | 5000 | 0.8411 | 0.4113 | |
|
| 0.0774 | 35.37 | 5200 | 0.7664 | 0.3943 | |
|
| 0.0666 | 36.73 | 5400 | 0.8082 | 0.3939 | |
|
| 0.0655 | 38.09 | 5600 | 0.7948 | 0.4000 | |
|
| 0.0617 | 39.45 | 5800 | 0.8084 | 0.3932 | |
|
| 0.0606 | 40.81 | 6000 | 0.8223 | 0.3841 | |
|
| 0.0569 | 42.18 | 6200 | 0.7892 | 0.3832 | |
|
| 0.0544 | 43.54 | 6400 | 0.8326 | 0.3834 | |
|
| 0.0508 | 44.89 | 6600 | 0.7952 | 0.3774 | |
|
| 0.0492 | 46.26 | 6800 | 0.7923 | 0.3756 | |
|
| 0.0459 | 47.62 | 7000 | 0.7925 | 0.3701 | |
|
| 0.0423 | 48.98 | 7200 | 0.7988 | 0.3713 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.16.2 |
|
- Pytorch 1.10.0+cu111 |
|
- Datasets 1.18.3 |
|
- Tokenizers 0.11.0 |
|
|
