---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-timit-xls-r-300m-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-300m-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: 0.3293
- Wer: 0.2879
- Cer: 0.0927

## 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   | 3.1501          | 1.0    | 0.9865 |
| 4.3829        | 1.38  | 800   | 2.9694          | 1.0    | 0.9865 |
| 2.6389        | 2.08  | 1200  | 0.6558          | 0.5876 | 0.1878 |
| 0.9293        | 2.77  | 1600  | 0.4232          | 0.4722 | 0.1462 |
| 0.5686        | 3.46  | 2000  | 0.3513          | 0.4118 | 0.1279 |
| 0.5686        | 4.15  | 2400  | 0.3246          | 0.3994 | 0.1227 |
| 0.4388        | 4.84  | 2800  | 0.3037          | 0.3716 | 0.1158 |
| 0.3431        | 5.54  | 3200  | 0.3055          | 0.3674 | 0.1158 |
| 0.3164        | 6.23  | 3600  | 0.2973          | 0.3589 | 0.1128 |
| 0.2678        | 6.92  | 4000  | 0.3053          | 0.3421 | 0.1080 |
| 0.2678        | 7.61  | 4400  | 0.3058          | 0.3435 | 0.1083 |
| 0.2376        | 8.3   | 4800  | 0.3144          | 0.3408 | 0.1094 |
| 0.2199        | 9.0   | 5200  | 0.3177          | 0.3371 | 0.1052 |
| 0.1988        | 9.69  | 5600  | 0.3123          | 0.3299 | 0.1057 |
| 0.1816        | 10.38 | 6000  | 0.2918          | 0.3282 | 0.1049 |
| 0.1816        | 11.07 | 6400  | 0.3195          | 0.3270 | 0.1049 |
| 0.1652        | 11.76 | 6800  | 0.3080          | 0.3280 | 0.1056 |
| 0.1576        | 12.46 | 7200  | 0.2859          | 0.3218 | 0.1031 |
| 0.1558        | 13.15 | 7600  | 0.3143          | 0.3179 | 0.1018 |
| 0.1411        | 13.84 | 8000  | 0.3354          | 0.3171 | 0.1045 |
| 0.1411        | 14.53 | 8400  | 0.3285          | 0.3149 | 0.1018 |
| 0.1381        | 15.22 | 8800  | 0.3048          | 0.3138 | 0.1010 |
| 0.1178        | 15.92 | 9200  | 0.3421          | 0.3140 | 0.1012 |
| 0.1182        | 16.61 | 9600  | 0.3258          | 0.3109 | 0.1001 |
| 0.1131        | 17.3  | 10000 | 0.3220          | 0.3120 | 0.1002 |
| 0.1131        | 17.99 | 10400 | 0.3156          | 0.3098 | 0.0991 |
| 0.1031        | 18.69 | 10800 | 0.3198          | 0.3062 | 0.0980 |
| 0.1023        | 19.38 | 11200 | 0.3227          | 0.3021 | 0.0972 |
| 0.0959        | 20.07 | 11600 | 0.3187          | 0.3025 | 0.0973 |
| 0.0881        | 20.76 | 12000 | 0.3177          | 0.3004 | 0.0965 |
| 0.0881        | 21.45 | 12400 | 0.3435          | 0.2976 | 0.0960 |
| 0.0919        | 22.15 | 12800 | 0.3142          | 0.2958 | 0.0954 |
| 0.0787        | 22.84 | 13200 | 0.3010          | 0.3000 | 0.0970 |
| 0.0794        | 23.53 | 13600 | 0.3528          | 0.3008 | 0.0973 |
| 0.0751        | 24.22 | 14000 | 0.3352          | 0.2954 | 0.0961 |
| 0.0751        | 24.91 | 14400 | 0.3314          | 0.2977 | 0.0963 |
| 0.0778        | 25.61 | 14800 | 0.3214          | 0.2955 | 0.0953 |
| 0.0711        | 26.3  | 15200 | 0.3277          | 0.2936 | 0.0944 |
| 0.0681        | 26.99 | 15600 | 0.3237          | 0.2915 | 0.0940 |
| 0.0682        | 27.68 | 16000 | 0.3284          | 0.2918 | 0.0943 |
| 0.0682        | 28.37 | 16400 | 0.3304          | 0.2904 | 0.0933 |
| 0.0731        | 29.07 | 16800 | 0.3307          | 0.2881 | 0.0927 |
| 0.0619        | 29.76 | 17200 | 0.3293          | 0.2879 | 0.0927 |


### Framework versions

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