transformers pytorch tensorboard wav2vec2 automatic-speech-recognition generated_from_trainer license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: torgo_xlsr_finetune_M03
  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. -->

# torgo_xlsr_finetune_M03

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1905
- Wer: 0.2097

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.5007        | 0.85  | 1000  | 3.2973          | 1.0    |
| 2.2459        | 1.71  | 2000  | 1.9925          | 0.8829 |
| 0.9013        | 2.56  | 3000  | 1.1537          | 0.6138 |
| 0.6388        | 3.41  | 4000  | 1.2210          | 0.5017 |
| 0.5391        | 4.27  | 5000  | 1.2570          | 0.4032 |
| 0.4528        | 5.12  | 6000  | 1.1298          | 0.3718 |
| 0.3892        | 5.97  | 7000  | 1.1642          | 0.3090 |
| 0.3382        | 6.83  | 8000  | 1.0970          | 0.3149 |
| 0.3279        | 7.68  | 9000  | 1.1686          | 0.3107 |
| 0.2816        | 8.53  | 10000 | 1.3912          | 0.3107 |
| 0.2667        | 9.39  | 11000 | 1.2643          | 0.2776 |
| 0.2517        | 10.24 | 12000 | 1.2157          | 0.2504 |
| 0.2312        | 11.09 | 13000 | 1.2624          | 0.2640 |
| 0.2239        | 11.95 | 14000 | 1.2676          | 0.2640 |
| 0.1849        | 12.8  | 15000 | 1.1427          | 0.2623 |
| 0.1841        | 13.65 | 16000 | 1.2277          | 0.2547 |
| 0.1793        | 14.51 | 17000 | 1.3833          | 0.2572 |
| 0.1704        | 15.36 | 18000 | 1.3813          | 0.2691 |
| 0.1688        | 16.21 | 19000 | 1.3418          | 0.2589 |
| 0.1527        | 17.06 | 20000 | 1.2787          | 0.2343 |
| 0.1304        | 17.92 | 21000 | 1.2078          | 0.2190 |
| 0.1332        | 18.77 | 22000 | 1.2041          | 0.2105 |
| 0.1253        | 19.62 | 23000 | 1.1905          | 0.2097 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.13.3
