---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-timit-xls-r-53-wandb-colab
  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. -->

# wav2vec2-timit-xls-r-53-wandb-colab

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: 0.2451
- Wer: 0.2503
- Cer: 0.0799

## 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    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| No log        | 0.69  | 400   | 3.1514          | 1.0    | 0.9790 |
| 5.094         | 1.38  | 800   | 2.8674          | 1.0    | 0.9790 |
| 2.595         | 2.08  | 1200  | 0.5208          | 0.5344 | 0.1541 |
| 0.8139        | 2.77  | 1600  | 0.3568          | 0.4234 | 0.1275 |
| 0.4793        | 3.46  | 2000  | 0.2954          | 0.3645 | 0.1106 |
| 0.4793        | 4.15  | 2400  | 0.2649          | 0.3475 | 0.1037 |
| 0.3771        | 4.84  | 2800  | 0.2452          | 0.3186 | 0.0976 |
| 0.2912        | 5.54  | 3200  | 0.2385          | 0.3079 | 0.0960 |
| 0.2632        | 6.23  | 3600  | 0.2292          | 0.2954 | 0.0911 |
| 0.2176        | 6.92  | 4000  | 0.2248          | 0.2910 | 0.0908 |
| 0.2176        | 7.61  | 4400  | 0.2279          | 0.2816 | 0.0888 |
| 0.1958        | 8.3   | 4800  | 0.2227          | 0.2819 | 0.0878 |
| 0.1846        | 9.0   | 5200  | 0.2277          | 0.2779 | 0.0876 |
| 0.1573        | 9.69  | 5600  | 0.2280          | 0.2830 | 0.0877 |
| 0.1471        | 10.38 | 6000  | 0.2345          | 0.2770 | 0.0880 |
| 0.1471        | 11.07 | 6400  | 0.2389          | 0.2714 | 0.0852 |
| 0.133         | 11.76 | 6800  | 0.2253          | 0.2730 | 0.0869 |
| 0.1317        | 12.46 | 7200  | 0.2179          | 0.2662 | 0.0846 |
| 0.1268        | 13.15 | 7600  | 0.2315          | 0.2678 | 0.0851 |
| 0.1147        | 13.84 | 8000  | 0.2501          | 0.2679 | 0.0849 |
| 0.1147        | 14.53 | 8400  | 0.2463          | 0.2663 | 0.0839 |
| 0.1151        | 15.22 | 8800  | 0.2429          | 0.2662 | 0.0848 |
| 0.0968        | 15.92 | 9200  | 0.2502          | 0.2639 | 0.0839 |
| 0.0985        | 16.61 | 9600  | 0.2589          | 0.2616 | 0.0838 |
| 0.0938        | 17.3  | 10000 | 0.2414          | 0.2595 | 0.0835 |
| 0.0938        | 17.99 | 10400 | 0.2420          | 0.2617 | 0.0839 |
| 0.0878        | 18.69 | 10800 | 0.2257          | 0.2597 | 0.0829 |
| 0.0872        | 19.38 | 11200 | 0.2654          | 0.2586 | 0.0825 |
| 0.0774        | 20.07 | 11600 | 0.2558          | 0.2579 | 0.0829 |
| 0.0743        | 20.76 | 12000 | 0.2375          | 0.2564 | 0.0824 |
| 0.0743        | 21.45 | 12400 | 0.2522          | 0.2568 | 0.0813 |
| 0.0832        | 22.15 | 12800 | 0.2363          | 0.2569 | 0.0817 |
| 0.0698        | 22.84 | 13200 | 0.2510          | 0.2574 | 0.0816 |
| 0.0677        | 23.53 | 13600 | 0.2535          | 0.2570 | 0.0818 |
| 0.0648        | 24.22 | 14000 | 0.2595          | 0.2571 | 0.0819 |
| 0.0648        | 24.91 | 14400 | 0.2441          | 0.2542 | 0.0815 |
| 0.0685        | 25.61 | 14800 | 0.2503          | 0.2534 | 0.0803 |
| 0.066         | 26.3  | 15200 | 0.2489          | 0.2533 | 0.0804 |
| 0.0583        | 26.99 | 15600 | 0.2471          | 0.2512 | 0.0802 |
| 0.0624        | 27.68 | 16000 | 0.2487          | 0.2516 | 0.0804 |
| 0.0624        | 28.37 | 16400 | 0.2470          | 0.2518 | 0.0804 |
| 0.0663        | 29.07 | 16800 | 0.2478          | 0.2510 | 0.0800 |
| 0.0569        | 29.76 | 17200 | 0.2451          | 0.2503 | 0.0799 |


### Framework versions

- Transformers 4.32.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3