metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- automatic-speech-recognition
- gttsehu/basque_parliament_1
- generated_from_trainer
metrics:
- wer
model-index:
- name: facebook/wav2vec2-xls-r-300m
results: []
datasets:
- gttsehu/basque_parliament_1
facebook/wav2vec2-xls-r-300m
This work was partially funded by the Spanish Ministry of Science and Innovation (OPENSPEECH project, PID2019-106424RB-I00).
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GTTSEHU/BASQUE_PARLIAMENT_1 - NA dataset. It achieves the following results on the evaluation set:
- Loss: 0.0846
- Wer: 0.0367
- Cer: 0.0132
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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|---|---|---|---|---|---|
| 0.7054 | 0.19 | 4000 | 0.1011 | 0.0871 | 0.0227 |
| 0.0856 | 0.39 | 8000 | 0.0995 | 0.0747 | 0.0207 |
| 0.075 | 0.58 | 12000 | 0.0868 | 0.0647 | 0.0185 |
| 0.0694 | 0.77 | 16000 | 0.0853 | 0.0619 | 0.0183 |
| 0.0658 | 0.97 | 20000 | 0.0778 | 0.0573 | 0.0171 |
| 0.0589 | 1.16 | 24000 | 0.0821 | 0.0546 | 0.0166 |
| 0.0572 | 1.35 | 28000 | 0.0827 | 0.0558 | 0.0170 |
| 0.0551 | 1.55 | 32000 | 0.0830 | 0.0533 | 0.0169 |
| 0.054 | 1.74 | 36000 | 0.0788 | 0.0512 | 0.0162 |
| 0.0524 | 1.93 | 40000 | 0.0783 | 0.0489 | 0.0156 |
| 0.048 | 2.13 | 44000 | 0.0861 | 0.0492 | 0.0160 |
| 0.046 | 2.32 | 48000 | 0.0763 | 0.0494 | 0.0154 |
| 0.0456 | 2.51 | 52000 | 0.0835 | 0.0471 | 0.0153 |
| 0.0439 | 2.71 | 56000 | 0.0790 | 0.0469 | 0.0152 |
| 0.0436 | 2.9 | 60000 | 0.0832 | 0.0472 | 0.0155 |
| 0.0406 | 3.09 | 64000 | 0.0810 | 0.0442 | 0.0148 |
| 0.0386 | 3.29 | 68000 | 0.0810 | 0.0436 | 0.0146 |
| 0.038 | 3.48 | 72000 | 0.0778 | 0.0430 | 0.0143 |
| 0.0373 | 3.67 | 76000 | 0.0785 | 0.0430 | 0.0144 |
| 0.0363 | 3.87 | 80000 | 0.0788 | 0.0421 | 0.0144 |
| 0.0348 | 4.06 | 84000 | 0.0823 | 0.0423 | 0.0144 |
| 0.0323 | 4.25 | 88000 | 0.0819 | 0.0407 | 0.0143 |
| 0.0319 | 4.45 | 92000 | 0.0809 | 0.0410 | 0.0142 |
| 0.0314 | 4.64 | 96000 | 0.0821 | 0.0400 | 0.0138 |
| 0.0306 | 4.83 | 100000 | 0.0813 | 0.0389 | 0.0137 |
| 0.0295 | 5.03 | 104000 | 0.0820 | 0.0377 | 0.0131 |
| 0.0275 | 5.22 | 108000 | 0.0866 | 0.0378 | 0.0137 |
| 0.0267 | 5.41 | 112000 | 0.0831 | 0.0376 | 0.0134 |
| 0.0264 | 5.61 | 116000 | 0.0845 | 0.0369 | 0.0132 |
| 0.0258 | 5.8 | 120000 | 0.0859 | 0.0370 | 0.0133 |
| 0.0254 | 6.0 | 124000 | 0.0846 | 0.0367 | 0.0132 |
Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.15.1.dev0
- Tokenizers 0.15.0