---
license: apache-2.0
base_model: jadasdn/wav2vec2-2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-3
  results: []
---

<!-- 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. -->

# wav2vec2-3

This model is a fine-tuned version of [jadasdn/wav2vec2-2](https://huggingface.co/jadasdn/wav2vec2-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9689
- Wer: 0.3821

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.5494        | 0.5   | 500   | 0.4696          | 0.3879 |
| 0.4979        | 1.0   | 1000  | 0.5284          | 0.4156 |
| 0.438         | 1.5   | 1500  | 0.5127          | 0.4121 |
| 0.4577        | 2.0   | 2000  | 0.5421          | 0.4162 |
| 0.3528        | 2.5   | 2500  | 0.5667          | 0.4117 |
| 0.3685        | 3.0   | 3000  | 0.5354          | 0.4109 |
| 0.3047        | 3.5   | 3500  | 0.5899          | 0.4244 |
| 0.3103        | 4.0   | 4000  | 0.5587          | 0.4107 |
| 0.2626        | 4.5   | 4500  | 0.6265          | 0.4255 |
| 0.2735        | 5.0   | 5000  | 0.6153          | 0.4104 |
| 0.2244        | 5.5   | 5500  | 0.5916          | 0.4058 |
| 0.2418        | 6.0   | 6000  | 0.5921          | 0.4134 |
| 0.2013        | 6.5   | 6500  | 0.6632          | 0.4079 |
| 0.211         | 7.0   | 7000  | 0.6563          | 0.4206 |
| 0.182         | 7.5   | 7500  | 0.6546          | 0.4096 |
| 0.1912        | 8.0   | 8000  | 0.6429          | 0.4043 |
| 0.1621        | 8.5   | 8500  | 0.7105          | 0.4060 |
| 0.1695        | 9.0   | 9000  | 0.6985          | 0.4027 |
| 0.1512        | 9.5   | 9500  | 0.7729          | 0.4060 |
| 0.1521        | 10.0  | 10000 | 0.7261          | 0.4047 |
| 0.1355        | 10.5  | 10500 | 0.7665          | 0.4097 |
| 0.1385        | 11.0  | 11000 | 0.7366          | 0.4044 |
| 0.1275        | 11.5  | 11500 | 0.7494          | 0.4046 |
| 0.1278        | 12.0  | 12000 | 0.7756          | 0.4028 |
| 0.1165        | 12.5  | 12500 | 0.8292          | 0.4064 |
| 0.1176        | 13.0  | 13000 | 0.7808          | 0.4053 |
| 0.1091        | 13.5  | 13500 | 0.8157          | 0.4052 |
| 0.1092        | 14.0  | 14000 | 0.8295          | 0.4071 |
| 0.1024        | 14.5  | 14500 | 0.8617          | 0.4032 |
| 0.1005        | 15.0  | 15000 | 0.8066          | 0.4042 |
| 0.0932        | 15.5  | 15500 | 0.8588          | 0.4022 |
| 0.0932        | 16.0  | 16000 | 0.8429          | 0.4036 |
| 0.087         | 16.5  | 16500 | 0.8975          | 0.4037 |
| 0.0866        | 17.0  | 17000 | 0.8916          | 0.3976 |
| 0.0835        | 17.5  | 17500 | 0.8566          | 0.3955 |
| 0.0828        | 18.0  | 18000 | 0.8581          | 0.3993 |
| 0.0756        | 18.5  | 18500 | 0.8827          | 0.3962 |
| 0.0789        | 19.0  | 19000 | 0.9072          | 0.3957 |
| 0.071         | 19.5  | 19500 | 0.9349          | 0.3920 |
| 0.0719        | 20.0  | 20000 | 0.9064          | 0.3931 |
| 0.0698        | 20.5  | 20500 | 0.9208          | 0.3932 |
| 0.0671        | 21.0  | 21000 | 0.8935          | 0.3927 |
| 0.0656        | 21.5  | 21500 | 0.9271          | 0.3936 |
| 0.0612        | 22.0  | 22000 | 0.9792          | 0.3979 |
| 0.0576        | 22.5  | 22500 | 0.9530          | 0.3917 |
| 0.0588        | 23.0  | 23000 | 0.9617          | 0.3928 |
| 0.0532        | 23.5  | 23500 | 0.9754          | 0.3854 |
| 0.0563        | 24.0  | 24000 | 0.9559          | 0.3915 |
| 0.053         | 24.5  | 24500 | 0.9845          | 0.3894 |
| 0.0512        | 25.0  | 25000 | 0.9629          | 0.3876 |
| 0.0516        | 25.5  | 25500 | 0.9565          | 0.3873 |
| 0.0491        | 26.0  | 26000 | 0.9726          | 0.3877 |
| 0.0473        | 26.5  | 26500 | 0.9831          | 0.3877 |
| 0.0441        | 27.0  | 27000 | 0.9645          | 0.3839 |
| 0.042         | 27.5  | 27500 | 1.0040          | 0.3851 |
| 0.0458        | 28.0  | 28000 | 0.9685          | 0.3839 |
| 0.043         | 28.5  | 28500 | 0.9687          | 0.3841 |
| 0.0399        | 29.0  | 29000 | 0.9764          | 0.3831 |
| 0.0421        | 29.5  | 29500 | 0.9691          | 0.3828 |
| 0.041         | 30.0  | 30000 | 0.9689          | 0.3821 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0