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---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- timit_asr
metrics:
- wer
model-index:
- name: timit-xls-r-300m
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: timit_asr
type: timit_asr
config: clean
split: None
args: clean
metrics:
- name: Wer
type: wer
value: 0.2466404796361381
---
<!-- 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. -->
# timit-xls-r-300m
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the timit_asr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4457
- Wer: 0.2466
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.0049 | 1.72 | 500 | 1.9735 | 1.0655 |
| 1.033 | 3.45 | 1000 | 0.6172 | 0.5115 |
| 0.4499 | 5.17 | 1500 | 0.5231 | 0.4395 |
| 0.2551 | 6.9 | 2000 | 0.4768 | 0.3772 |
| 0.1724 | 8.62 | 2500 | 0.4699 | 0.3626 |
| 0.133 | 10.34 | 3000 | 0.4346 | 0.3329 |
| 0.1082 | 12.07 | 3500 | 0.4479 | 0.3163 |
| 0.0886 | 13.79 | 4000 | 0.4393 | 0.3167 |
| 0.0766 | 15.52 | 4500 | 0.4920 | 0.3100 |
| 0.0637 | 17.24 | 5000 | 0.4510 | 0.3013 |
| 0.0607 | 18.97 | 5500 | 0.4284 | 0.2808 |
| 0.0495 | 20.69 | 6000 | 0.4270 | 0.2820 |
| 0.0479 | 22.41 | 6500 | 0.4294 | 0.2852 |
| 0.0444 | 24.14 | 7000 | 0.4456 | 0.2816 |
| 0.0378 | 25.86 | 7500 | 0.4236 | 0.2763 |
| 0.0325 | 27.59 | 8000 | 0.4365 | 0.2849 |
| 0.031 | 29.31 | 8500 | 0.4482 | 0.2862 |
| 0.0285 | 31.03 | 9000 | 0.4388 | 0.2691 |
| 0.0252 | 32.76 | 9500 | 0.4253 | 0.2692 |
| 0.0229 | 34.48 | 10000 | 0.4598 | 0.2641 |
| 0.0223 | 36.21 | 10500 | 0.4462 | 0.2533 |
| 0.0188 | 37.93 | 11000 | 0.4350 | 0.2673 |
| 0.0163 | 39.66 | 11500 | 0.4460 | 0.2608 |
| 0.0167 | 41.38 | 12000 | 0.4441 | 0.2683 |
| 0.0138 | 43.1 | 12500 | 0.4290 | 0.2528 |
| 0.0127 | 44.83 | 13000 | 0.4360 | 0.2508 |
| 0.0124 | 46.55 | 13500 | 0.4406 | 0.2511 |
| 0.0107 | 48.28 | 14000 | 0.4482 | 0.2477 |
| 0.0108 | 50.0 | 14500 | 0.4457 | 0.2466 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
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