metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-breton-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: br
split: test
args: br
metrics:
- name: Wer
type: wer
value: 0.4936988936988937
wav2vec2-large-xls-r-300m-breton-colab
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.2211
- Wer: 0.4937
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 5.3288 | 1.34 | 400 | 1.7076 | 0.9809 |
| 1.2014 | 2.69 | 800 | 1.0803 | 0.7733 |
| 0.7687 | 4.03 | 1200 | 0.9806 | 0.6642 |
| 0.5539 | 5.38 | 1600 | 0.9914 | 0.6301 |
| 0.4456 | 6.72 | 2000 | 0.9797 | 0.6265 |
| 0.3586 | 8.07 | 2400 | 1.0354 | 0.5803 |
| 0.2922 | 9.41 | 2800 | 0.9996 | 0.5821 |
| 0.2628 | 10.76 | 3200 | 1.0250 | 0.5708 |
| 0.2284 | 12.1 | 3600 | 1.0865 | 0.5722 |
| 0.1908 | 13.45 | 4000 | 1.0674 | 0.5450 |
| 0.1732 | 14.79 | 4400 | 1.1775 | 0.5614 |
| 0.153 | 16.13 | 4800 | 1.1542 | 0.5435 |
| 0.14 | 17.48 | 5200 | 1.1807 | 0.5449 |
| 0.1302 | 18.82 | 5600 | 1.1679 | 0.5376 |
| 0.1142 | 20.17 | 6000 | 1.1441 | 0.5276 |
| 0.104 | 21.51 | 6400 | 1.2243 | 0.5355 |
| 0.0882 | 22.86 | 6800 | 1.1837 | 0.5316 |
| 0.0807 | 24.2 | 7200 | 1.1986 | 0.5132 |
| 0.0744 | 25.55 | 7600 | 1.2182 | 0.5108 |
| 0.0646 | 26.89 | 8000 | 1.2116 | 0.5047 |
| 0.0551 | 28.24 | 8400 | 1.2009 | 0.4948 |
| 0.0503 | 29.58 | 8800 | 1.2211 | 0.4937 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3