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-tigre-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: tig
split: test
args: tig
metrics:
- name: Wer
type: wer
value: 0.43169398907103823
w2v-bert-2.0-tigre-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: 1.6193
- Wer: 0.4317
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 |
|---|---|---|---|---|
| 6.7881 | 13.7931 | 200 | 0.9737 | 0.6175 |
| 0.1599 | 27.5862 | 400 | 1.2407 | 0.5310 |
| 0.0256 | 41.3793 | 600 | 1.3566 | 0.4781 |
| 0.0036 | 55.1724 | 800 | 1.5251 | 0.4554 |
| 0.0058 | 68.9655 | 1000 | 1.4813 | 0.4699 |
| 0.0023 | 82.7586 | 1200 | 1.5533 | 0.4435 |
| 0.0001 | 96.5517 | 1400 | 1.5861 | 0.4372 |
| 0.0001 | 110.3448 | 1600 | 1.6056 | 0.4362 |
| 0.0001 | 124.1379 | 1800 | 1.6159 | 0.4326 |
| 0.0001 | 137.9310 | 2000 | 1.6193 | 0.4317 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1