---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
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-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3325
- Wer: 0.2897
- Cer: 0.0940

## 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   | 3.1507          | 1.0    | 0.9806 |
| 4.3857        | 1.38  | 800   | 3.0109          | 1.0    | 0.9806 |
| 2.6835        | 2.08  | 1200  | 0.6181          | 0.5756 | 0.1795 |
| 0.9327        | 2.77  | 1600  | 0.4239          | 0.4718 | 0.1456 |
| 0.5602        | 3.46  | 2000  | 0.3691          | 0.4141 | 0.1301 |
| 0.5602        | 4.15  | 2400  | 0.3386          | 0.3894 | 0.1231 |
| 0.4407        | 4.84  | 2800  | 0.3122          | 0.3676 | 0.1177 |
| 0.3437        | 5.54  | 3200  | 0.3149          | 0.3601 | 0.1152 |
| 0.3154        | 6.23  | 3600  | 0.3146          | 0.3495 | 0.1119 |
| 0.267         | 6.92  | 4000  | 0.3039          | 0.3427 | 0.1089 |
| 0.267         | 7.61  | 4400  | 0.3313          | 0.3409 | 0.1092 |
| 0.2354        | 8.3   | 4800  | 0.2986          | 0.3365 | 0.1064 |
| 0.2191        | 9.0   | 5200  | 0.3235          | 0.3353 | 0.1074 |
| 0.1937        | 9.69  | 5600  | 0.3117          | 0.3320 | 0.1071 |
| 0.1803        | 10.38 | 6000  | 0.3102          | 0.3233 | 0.1040 |
| 0.1803        | 11.07 | 6400  | 0.3176          | 0.3196 | 0.1030 |
| 0.1635        | 11.76 | 6800  | 0.3166          | 0.3220 | 0.1036 |
| 0.1551        | 12.46 | 7200  | 0.2836          | 0.3160 | 0.1021 |
| 0.1566        | 13.15 | 7600  | 0.3146          | 0.3186 | 0.1032 |
| 0.1424        | 13.84 | 8000  | 0.3392          | 0.3167 | 0.1036 |
| 0.1424        | 14.53 | 8400  | 0.3254          | 0.3109 | 0.1001 |
| 0.1379        | 15.22 | 8800  | 0.3249          | 0.3127 | 0.1009 |
| 0.1192        | 15.92 | 9200  | 0.3408          | 0.3119 | 0.1010 |
| 0.1178        | 16.61 | 9600  | 0.3551          | 0.3061 | 0.0997 |
| 0.1112        | 17.3  | 10000 | 0.3250          | 0.3059 | 0.0991 |
| 0.1112        | 17.99 | 10400 | 0.3127          | 0.3037 | 0.0983 |
| 0.1022        | 18.69 | 10800 | 0.3370          | 0.3067 | 0.0994 |
| 0.1031        | 19.38 | 11200 | 0.3351          | 0.3048 | 0.0991 |
| 0.0926        | 20.07 | 11600 | 0.3433          | 0.2994 | 0.0974 |
| 0.0861        | 20.76 | 12000 | 0.3145          | 0.3003 | 0.0971 |
| 0.0861        | 21.45 | 12400 | 0.3367          | 0.2980 | 0.0973 |
| 0.0935        | 22.15 | 12800 | 0.3139          | 0.3016 | 0.0986 |
| 0.0784        | 22.84 | 13200 | 0.3181          | 0.2990 | 0.0972 |
| 0.078         | 23.53 | 13600 | 0.3347          | 0.2938 | 0.0961 |
| 0.0761        | 24.22 | 14000 | 0.3371          | 0.2921 | 0.0949 |
| 0.0761        | 24.91 | 14400 | 0.3274          | 0.2916 | 0.0952 |
| 0.0784        | 25.61 | 14800 | 0.3152          | 0.2927 | 0.0942 |
| 0.0714        | 26.3  | 15200 | 0.3237          | 0.2924 | 0.0943 |
| 0.0671        | 26.99 | 15600 | 0.3183          | 0.2914 | 0.0945 |
| 0.0684        | 27.68 | 16000 | 0.3307          | 0.2931 | 0.0950 |
| 0.0684        | 28.37 | 16400 | 0.3383          | 0.2913 | 0.0940 |
| 0.07          | 29.07 | 16800 | 0.3318          | 0.2901 | 0.0940 |
| 0.0624        | 29.76 | 17200 | 0.3325          | 0.2897 | 0.0940 |


### Framework versions

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