metadata
base_model: facebook/w2v-bert-2.0
datasets:
- common_voice_17_0
library_name: transformers
license: mit
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: w2v-bert-2.0-slovak-colab-CV17.0
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: sk
split: test
args: sk
metrics:
- type: wer
value: 0.1358878674797488
name: Wer
w2v-bert-2.0-slovak-colab-CV17.0
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: 0.3823
- Wer: 0.1359
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_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 3.1601 | 1.6393 | 300 | 0.4869 | 0.2985 |
| 0.2141 | 3.2787 | 600 | 0.3886 | 0.2144 |
| 0.1323 | 4.9180 | 900 | 0.3180 | 0.1840 |
| 0.0754 | 6.5574 | 1200 | 0.3019 | 0.1750 |
| 0.0401 | 8.1967 | 1500 | 0.3717 | 0.1525 |
| 0.022 | 9.8361 | 1800 | 0.3408 | 0.1503 |
| 0.0083 | 11.4754 | 2100 | 0.3489 | 0.1413 |
| 0.0027 | 13.1148 | 2400 | 0.3681 | 0.1358 |
| 0.0011 | 14.7541 | 2700 | 0.3823 | 0.1359 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1
- Datasets 2.19.1
- Tokenizers 0.20.1