---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
base_model: facebook/wav2vec2-large-xlsr-53
model-index:
- name: torgo_xlsr_finetune-M01-2
  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-M01-2

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: 1.5763
- Wer: 0.9555

## 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

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 23.0716       | 0.9   | 500   | 3.3142          | 1.0    |
| 3.4092        | 1.8   | 1000  | 3.2440          | 1.0    |
| 2.9015        | 2.7   | 1500  | 2.8209          | 1.0    |
| 2.7211        | 3.6   | 2000  | 2.4913          | 1.2728 |
| 2.0884        | 4.5   | 2500  | 1.7817          | 1.4841 |
| 1.3426        | 5.41  | 3000  | 1.5117          | 1.4678 |
| 0.9866        | 6.31  | 3500  | 1.4760          | 1.3781 |
| 0.7874        | 7.21  | 4000  | 1.2179          | 1.2516 |
| 0.6424        | 8.11  | 4500  | 1.4501          | 1.2226 |
| 0.5505        | 9.01  | 5000  | 1.4132          | 1.3343 |
| 0.4709        | 9.91  | 5500  | 1.3289          | 1.1604 |
| 0.4358        | 10.81 | 6000  | 1.2615          | 1.1102 |
| 0.3892        | 11.71 | 6500  | 1.5597          | 1.1060 |
| 0.3602        | 12.61 | 7000  | 1.4205          | 1.1322 |
| 0.3298        | 13.51 | 7500  | 1.4411          | 1.1237 |
| 0.3184        | 14.41 | 8000  | 1.4017          | 1.1004 |
| 0.2954        | 15.32 | 8500  | 1.3428          | 1.0806 |
| 0.2745        | 16.22 | 9000  | 1.4793          | 1.0982 |
| 0.2533        | 17.12 | 9500  | 1.6004          | 1.1124 |
| 0.2378        | 18.02 | 10000 | 1.5802          | 1.0700 |
| 0.2234        | 18.92 | 10500 | 1.4462          | 1.0473 |
| 0.2147        | 19.82 | 11000 | 1.3814          | 1.0042 |
| 0.202         | 20.72 | 11500 | 1.5665          | 1.0226 |
| 0.1691        | 21.62 | 12000 | 1.4534          | 0.9958 |
| 0.1993        | 22.52 | 12500 | 1.4851          | 0.9894 |
| 0.1591        | 23.42 | 13000 | 1.3746          | 0.9746 |
| 0.1602        | 24.32 | 13500 | 1.4077          | 0.9710 |
| 0.1417        | 25.23 | 14000 | 1.5074          | 0.9668 |
| 0.1302        | 26.13 | 14500 | 1.5024          | 0.9456 |
| 0.1334        | 27.03 | 15000 | 1.4816          | 0.9541 |
| 0.1269        | 27.93 | 15500 | 1.5501          | 0.9541 |
| 0.1254        | 28.83 | 16000 | 1.5593          | 0.9527 |
| 0.12          | 29.73 | 16500 | 1.5763          | 0.9555 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 1.18.3
- Tokenizers 0.13.2