metadata
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-yoruba-colab-CV17.0-v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: yo
split: test
args: yo
metrics:
- name: Wer
type: wer
value: 0.5771575538197752
w2v-bert-2.0-yoruba-colab-CV17.0-v2
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.8450
- Wer: 0.5772
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: 5e-05
- 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_ratio: 0.15
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.7641 | 3.0769 | 200 | 1.0220 | 0.7877 |
| 0.6684 | 6.1538 | 400 | 0.9003 | 0.6490 |
| 0.4959 | 9.2308 | 600 | 0.9080 | 0.7072 |
| 0.359 | 12.3077 | 800 | 0.9788 | 0.6147 |
| 0.2047 | 15.3846 | 1000 | 1.0914 | 0.6017 |
| 0.0858 | 18.4615 | 1200 | 1.4604 | 0.5973 |
| 0.0426 | 21.5385 | 1400 | 1.5740 | 0.5988 |
| 0.0088 | 24.6154 | 1600 | 1.7418 | 0.5753 |
| 0.0017 | 27.6923 | 1800 | 1.8206 | 0.5779 |
| 0.001 | 30.7692 | 2000 | 1.8450 | 0.5772 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1