---
base_model: facebook/w2v-bert-2.0
language: eve
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
- cer
model-index:
- name: wav2vec-bert-2.0-even-pakendorf
  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

How to use:

```python
from transformers import AutoModelForCTC, Wav2Vec2BertProcessor

model = AutoModelForCTC.from_pretrained("tbkazakova/wav2vec-bert-2.0-even-pakendorf")
processor = Wav2Vec2BertProcessor.from_pretrained("tbkazakova/wav2vec-bert-2.0-even-pakendorf")

data, sampling_rate = librosa.load('audio.wav')
librosa.resample(data, orig_sr=sampling_rate, target_sr=16000)
logits = model(torch.tensor(processor(data,
                                      sampling_rate=16000).input_features[0]).unsqueeze(0)).logits

pred_ids = torch.argmax(logits, dim=-1)[0]
print(processor.decode(pred_ids))
```

## 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