---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: adapter_freezed_base_const_lr
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: hy-AM
      split: test
      args: hy-AM
    metrics:
    - name: Wer
      type: wer
      value: 0.9281584969288209
---

<!-- 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. -->

# adapter_freezed_base_const_lr

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9200
- Wer: 0.9282
- Cer: 0.2562

## 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: 0.0001
- 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: constant
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 1.3224        | 0.6154 | 200  | 1.3171          | 0.9949 | 0.3890 |
| 1.02          | 1.2308 | 400  | 1.0780          | 0.9728 | 0.3233 |
| 0.9256        | 1.8462 | 600  | 0.9799          | 0.9738 | 0.2955 |
| 0.8377        | 2.4615 | 800  | 0.9756          | 0.9663 | 0.2919 |
| 0.7836        | 3.0769 | 1000 | 0.9143          | 0.9535 | 0.2730 |
| 0.7516        | 3.6923 | 1200 | 0.8908          | 0.9373 | 0.2671 |
| 0.6714        | 4.3077 | 1400 | 0.9088          | 0.9497 | 0.2692 |
| 0.6749        | 4.9231 | 1600 | 0.9006          | 0.9566 | 0.2681 |
| 0.6223        | 5.5385 | 1800 | 0.8686          | 0.9322 | 0.2587 |
| 0.5643        | 6.1538 | 2000 | 0.8846          | 0.9422 | 0.2580 |
| 0.5773        | 6.7692 | 2200 | 0.8960          | 0.9396 | 0.2644 |
| 0.5067        | 7.3846 | 2400 | 0.8778          | 0.9273 | 0.2545 |
| 0.5123        | 8.0    | 2600 | 0.8919          | 0.9379 | 0.2601 |
| 0.4729        | 8.6154 | 2800 | 0.9131          | 0.9597 | 0.2587 |
| 0.406         | 9.2308 | 3000 | 0.9032          | 0.9389 | 0.2564 |
| 0.4286        | 9.8462 | 3200 | 0.9200          | 0.9282 | 0.2562 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1