---
base_model: microsoft/wavlm-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wavlm_torgo_0H
  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. -->

# wavlm_torgo_0H

This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2230
- Wer: 1.0

## 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: 1
- 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: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:---:|
| 36.5759       | 0.1882 | 500   | 5.7798          | 1.0 |
| 4.1355        | 0.3764 | 1000  | 4.3661          | 1.0 |
| 3.9484        | 0.5645 | 1500  | 4.2577          | 1.0 |
| 3.6159        | 0.7527 | 2000  | 4.1272          | 1.0 |
| 3.6944        | 0.9409 | 2500  | 3.9745          | 1.0 |
| 3.8285        | 1.1291 | 3000  | 4.0134          | 1.0 |
| 3.6116        | 1.3173 | 3500  | 4.1692          | 1.0 |
| 3.5828        | 1.5055 | 4000  | 4.0013          | 1.0 |
| 3.5703        | 1.6936 | 4500  | 4.1055          | 1.0 |
| 3.5841        | 1.8818 | 5000  | 4.1041          | 1.0 |
| 3.8079        | 2.0700 | 5500  | 4.1574          | 1.0 |
| 3.5977        | 2.2582 | 6000  | 4.3217          | 1.0 |
| 3.5523        | 2.4464 | 6500  | 4.1800          | 1.0 |
| 3.5661        | 2.6346 | 7000  | 4.2053          | 1.0 |
| 3.5676        | 2.8227 | 7500  | 4.3885          | 1.0 |
| 3.794         | 3.0109 | 8000  | 4.2958          | 1.0 |
| 3.5647        | 3.1991 | 8500  | 4.2959          | 1.0 |
| 3.5805        | 3.3873 | 9000  | 4.3383          | 1.0 |
| 3.5475        | 3.5755 | 9500  | 4.1639          | 1.0 |
| 3.5523        | 3.7636 | 10000 | 4.2241          | 1.0 |
| 3.5982        | 3.9518 | 10500 | 4.3270          | 1.0 |
| 3.7088        | 4.1400 | 11000 | 4.2886          | 1.0 |
| 3.561         | 4.3282 | 11500 | 4.2801          | 1.0 |
| 3.5367        | 4.5164 | 12000 | 4.6914          | 1.0 |
| 3.5573        | 4.7046 | 12500 | 4.2071          | 1.0 |
| 3.5613        | 4.8927 | 13000 | 4.4513          | 1.0 |
| 3.719         | 5.0809 | 13500 | 4.3972          | 1.0 |
| 3.5376        | 5.2691 | 14000 | 4.3590          | 1.0 |
| 3.5313        | 5.4573 | 14500 | 4.3130          | 1.0 |
| 3.5384        | 5.6455 | 15000 | 4.4599          | 1.0 |
| 3.5755        | 5.8336 | 15500 | 4.3602          | 1.0 |
| 3.6912        | 6.0218 | 16000 | 4.2520          | 1.0 |
| 3.532         | 6.2100 | 16500 | 4.2731          | 1.0 |
| 3.565         | 6.3982 | 17000 | 4.2608          | 1.0 |
| 3.5328        | 6.5864 | 17500 | 4.2221          | 1.0 |
| 3.5361        | 6.7746 | 18000 | 4.2500          | 1.0 |
| 3.4975        | 6.9627 | 18500 | 4.2042          | 1.0 |
| 3.6749        | 7.1509 | 19000 | 4.2319          | 1.0 |
| 3.5316        | 7.3391 | 19500 | 4.2101          | 1.0 |
| 3.5262        | 7.5273 | 20000 | 4.2657          | 1.0 |
| 3.6605        | 7.7155 | 20500 | 4.2559          | 1.0 |
| 3.528         | 7.9037 | 21000 | 4.2230          | 1.0 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1