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---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec-bert-2.0-even-pakendorf-0406-1347
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 0.5968606805108706
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec-bert-2.0-even-pakendorf-0406-1347
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Cer: 0.2128
- Loss: inf
- Wer: 0.5969
## 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: 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Cer | Validation Loss | Wer |
|:-------------:|:------:|:----:|:------:|:---------------:|:------:|
| 4.5767 | 0.5051 | 200 | 0.4932 | inf | 0.9973 |
| 1.8775 | 1.0101 | 400 | 0.3211 | inf | 0.8494 |
| 1.6006 | 1.5152 | 600 | 0.3017 | inf | 0.8040 |
| 1.4476 | 2.0202 | 800 | 0.2896 | inf | 0.7534 |
| 1.2213 | 2.5253 | 1000 | 0.2610 | inf | 0.7080 |
| 1.1485 | 3.0303 | 1200 | 0.2684 | inf | 0.6800 |
| 0.9554 | 3.5354 | 1400 | 0.2459 | inf | 0.6732 |
| 0.9379 | 4.0404 | 1600 | 0.2275 | inf | 0.6251 |
| 0.7644 | 4.5455 | 1800 | 0.2235 | inf | 0.6224 |
| 0.7891 | 5.0505 | 2000 | 0.2180 | inf | 0.6053 |
| 0.633 | 5.5556 | 2200 | 0.2130 | inf | 0.5996 |
| 0.6197 | 6.0606 | 2400 | 0.2126 | inf | 0.6032 |
| 0.5212 | 6.5657 | 2600 | 0.2196 | inf | 0.6019 |
| 0.4881 | 7.0707 | 2800 | 0.2125 | inf | 0.5894 |
| 0.4 | 7.5758 | 3000 | 0.2066 | inf | 0.5852 |
| 0.4008 | 8.0808 | 3200 | 0.2076 | inf | 0.5790 |
| 0.3304 | 8.5859 | 3400 | 0.2096 | inf | 0.5884 |
| 0.3446 | 9.0909 | 3600 | 0.2124 | inf | 0.5983 |
| 0.3237 | 9.5960 | 3800 | 0.2128 | inf | 0.5969 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|