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_M01_word_only_2
  results: []
---

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# torgo_xlsr_finetune_M01_word_only_2

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5197
- Wer: 0.4523

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.28  | 250  | 5.5883          | 1.0    |
| 32.6551       | 0.57  | 500  | 3.4592          | 1.0    |
| 32.6551       | 0.85  | 750  | 3.4807          | 1.0    |
| 3.536         | 1.13  | 1000 | 3.4312          | 1.0    |
| 3.536         | 1.42  | 1250 | 3.4500          | 1.0    |
| 3.4905        | 1.7   | 1500 | 3.4075          | 1.0    |
| 3.4905        | 1.98  | 1750 | 3.1965          | 1.0    |
| 3.1337        | 2.27  | 2000 | 2.4374          | 1.0    |
| 3.1337        | 2.55  | 2250 | 1.9984          | 0.9512 |
| 1.796         | 2.83  | 2500 | 1.7787          | 0.8337 |
| 1.796         | 3.11  | 2750 | 1.7683          | 0.8248 |
| 1.0903        | 3.4   | 3000 | 1.4810          | 0.6452 |
| 1.0903        | 3.68  | 3250 | 1.5670          | 0.6408 |
| 0.8207        | 3.96  | 3500 | 1.4513          | 0.6585 |
| 0.8207        | 4.25  | 3750 | 1.2161          | 0.5632 |
| 0.6562        | 4.53  | 4000 | 1.5037          | 0.6208 |
| 0.6562        | 4.81  | 4250 | 1.2742          | 0.5698 |
| 0.5897        | 5.1   | 4500 | 1.4467          | 0.5543 |
| 0.5897        | 5.38  | 4750 | 1.5197          | 0.5477 |
| 0.5008        | 5.66  | 5000 | 1.6018          | 0.5654 |
| 0.5008        | 5.95  | 5250 | 1.4544          | 0.5299 |
| 0.4653        | 6.23  | 5500 | 1.4768          | 0.5122 |
| 0.4653        | 6.51  | 5750 | 1.4434          | 0.5277 |
| 0.4165        | 6.8   | 6000 | 1.4197          | 0.4834 |
| 0.4165        | 7.08  | 6250 | 1.5469          | 0.5299 |
| 0.3873        | 7.36  | 6500 | 1.5274          | 0.5233 |
| 0.3873        | 7.64  | 6750 | 1.4026          | 0.4812 |
| 0.355         | 7.93  | 7000 | 1.4866          | 0.4723 |
| 0.355         | 8.21  | 7250 | 1.5028          | 0.4989 |
| 0.3382        | 8.49  | 7500 | 1.5790          | 0.4834 |
| 0.3382        | 8.78  | 7750 | 1.4626          | 0.4590 |
| 0.2974        | 9.06  | 8000 | 1.5658          | 0.4856 |
| 0.2974        | 9.34  | 8250 | 1.5510          | 0.4701 |
| 0.2975        | 9.63  | 8500 | 1.5392          | 0.4501 |
| 0.2975        | 9.91  | 8750 | 1.5197          | 0.4523 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.13.3
