metadata
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- generator
metrics:
- wer
model-index:
- name: wav2vec2-bert-swahili-noise
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: generator
type: generator
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 0.2959163543105149
wav2vec2-bert-swahili-noise
This model is a fine-tuned version of facebook/w2v-bert-2.0 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 0.5771
- Wer: 0.2959
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: 1e-05
- train_batch_size: 38
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 900
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.142 | 0.0817 | 100 | 3.0798 | 0.9999 |
| 1.9368 | 0.1634 | 200 | 1.4137 | 0.9886 |
| 0.9455 | 0.2451 | 300 | 0.7745 | 0.4115 |
| 0.8236 | 0.3268 | 400 | 0.6644 | 0.3485 |
| 0.7723 | 0.4085 | 500 | 0.6475 | 0.3313 |
| 0.7603 | 0.4902 | 600 | 0.6082 | 0.3097 |
| 0.6848 | 0.5719 | 700 | 0.5972 | 0.3072 |
| 0.683 | 0.6536 | 800 | 0.5762 | 0.2986 |
| 0.6967 | 0.7353 | 900 | 0.5771 | 0.2959 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.8.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.2