---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: only_head_const_lr_1-e4
  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: 0.9999698904010599
---

<!-- 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. -->

# only_head_const_lr_1-e4

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: 2.7371
- Wer: 1.0000
- Cer: 0.8540

## 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 9.1084        | 1.5385 | 500  | 9.3283          | 1.5175 | 0.7359 |
| 4.8706        | 3.0769 | 1000 | 5.0357          | 1.0044 | 0.8964 |
| 3.8282        | 4.6154 | 1500 | 3.8866          | 0.9999 | 0.9809 |
| 3.1519        | 6.1538 | 2000 | 3.1656          | 0.9998 | 0.9309 |
| 2.8747        | 7.6923 | 2500 | 2.8780          | 1.0002 | 0.8692 |
| 2.7465        | 9.2308 | 3000 | 2.7371          | 1.0000 | 0.8540 |


### Framework versions

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