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_F03
  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_F03

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: 0.2366
- Wer: 0.0535

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.4672        | 0.85  | 1000  | 3.4147          | 1.0    |
| 2.149         | 1.69  | 2000  | 0.9590          | 0.7064 |
| 0.9304        | 2.54  | 3000  | 0.3999          | 0.3896 |
| 0.6396        | 3.39  | 4000  | 0.2892          | 0.2332 |
| 0.5114        | 4.24  | 5000  | 0.2362          | 0.1701 |
| 0.454         | 5.08  | 6000  | 0.2241          | 0.1468 |
| 0.3899        | 5.93  | 7000  | 0.2356          | 0.0919 |
| 0.3466        | 6.78  | 8000  | 0.2607          | 0.0796 |
| 0.316         | 7.63  | 9000  | 0.2358          | 0.0919 |
| 0.2889        | 8.47  | 10000 | 0.2159          | 0.0617 |
| 0.2476        | 9.32  | 11000 | 0.2129          | 0.0713 |
| 0.2608        | 10.17 | 12000 | 0.1812          | 0.0878 |
| 0.2338        | 11.02 | 13000 | 0.2010          | 0.0631 |
| 0.2302        | 11.86 | 14000 | 0.1623          | 0.0576 |
| 0.2037        | 12.71 | 15000 | 0.2469          | 0.0604 |
| 0.1995        | 13.56 | 16000 | 0.1836          | 0.0562 |
| 0.1668        | 14.41 | 17000 | 0.2070          | 0.0741 |
| 0.1597        | 15.25 | 18000 | 0.2269          | 0.0658 |
| 0.1574        | 16.1  | 19000 | 0.2087          | 0.0713 |
| 0.1397        | 16.95 | 20000 | 0.1998          | 0.0576 |
| 0.1325        | 17.8  | 21000 | 0.1844          | 0.0549 |
| 0.1273        | 18.64 | 22000 | 0.2306          | 0.0535 |
| 0.1204        | 19.49 | 23000 | 0.2366          | 0.0535 |


### Framework versions

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