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---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: xlsr-aiish-nose
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. -->
# xlsr-aiish-nose
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: 0.0000
- Wer: 0.3068
## 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: 0.0004
- 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: 132
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 4.497 | 1.9704 | 200 | 2.6013 | 1.0 |
| 1.5579 | 3.9409 | 400 | 0.1774 | 0.5513 |
| 0.2962 | 5.9113 | 600 | 0.0348 | 0.3826 |
| 0.1312 | 7.8818 | 800 | 0.0160 | 0.3325 |
| 0.1006 | 9.8522 | 1000 | 0.0058 | 0.3166 |
| 0.0806 | 11.8227 | 1200 | 0.0047 | 0.3117 |
| 0.0746 | 13.7931 | 1400 | 0.0014 | 0.3105 |
| 0.0548 | 15.7635 | 1600 | 0.0014 | 0.3093 |
| 0.0468 | 17.7340 | 1800 | 0.0009 | 0.3093 |
| 0.053 | 19.7044 | 2000 | 0.0299 | 0.3117 |
| 0.0516 | 21.6749 | 2200 | 0.0034 | 0.3105 |
| 0.0286 | 23.6453 | 2400 | 0.0003 | 0.3068 |
| 0.0376 | 25.6158 | 2600 | 0.0004 | 0.3068 |
| 0.0293 | 27.5862 | 2800 | 0.0003 | 0.3068 |
| 0.0257 | 29.5567 | 3000 | 0.0002 | 0.3068 |
| 0.0212 | 31.5271 | 3200 | 0.0003 | 0.3093 |
| 0.0249 | 33.4975 | 3400 | 0.0007 | 0.3068 |
| 0.0192 | 35.4680 | 3600 | 0.0002 | 0.3081 |
| 0.0221 | 37.4384 | 3800 | 0.0008 | 0.3142 |
| 0.0181 | 39.4089 | 4000 | 0.0003 | 0.3105 |
| 0.0192 | 41.3793 | 4200 | 0.0010 | 0.3093 |
| 0.0219 | 43.3498 | 4400 | 0.0010 | 0.3117 |
| 0.0139 | 45.3202 | 4600 | 0.0010 | 0.3105 |
| 0.0125 | 47.2906 | 4800 | 0.0001 | 0.3068 |
| 0.0107 | 49.2611 | 5000 | 0.0002 | 0.3068 |
| 0.0119 | 51.2315 | 5200 | 0.0128 | 0.3240 |
| 0.0104 | 53.2020 | 5400 | 0.0001 | 0.3068 |
| 0.0093 | 55.1724 | 5600 | 0.0001 | 0.3142 |
| 0.0085 | 57.1429 | 5800 | 0.0001 | 0.3068 |
| 0.0087 | 59.1133 | 6000 | 0.0001 | 0.3068 |
| 0.0082 | 61.0837 | 6200 | 0.0001 | 0.3068 |
| 0.0079 | 63.0542 | 6400 | 0.0001 | 0.3068 |
| 0.0083 | 65.0246 | 6600 | 0.0001 | 0.3068 |
| 0.0056 | 66.9951 | 6800 | 0.0001 | 0.3068 |
| 0.0099 | 68.9655 | 7000 | 0.0001 | 0.3068 |
| 0.0052 | 70.9360 | 7200 | 0.0000 | 0.3068 |
| 0.0051 | 72.9064 | 7400 | 0.0000 | 0.3068 |
| 0.0061 | 74.8768 | 7600 | 0.0001 | 0.3068 |
| 0.0035 | 76.8473 | 7800 | 0.0000 | 0.3081 |
| 0.0051 | 78.8177 | 8000 | 0.0000 | 0.3068 |
| 0.0037 | 80.7882 | 8200 | 0.0000 | 0.3068 |
| 0.0036 | 82.7586 | 8400 | 0.0000 | 0.3068 |
| 0.0041 | 84.7291 | 8600 | 0.0002 | 0.3068 |
| 0.0031 | 86.6995 | 8800 | 0.0006 | 0.3068 |
| 0.0026 | 88.6700 | 9000 | 0.0000 | 0.3068 |
| 0.0017 | 90.6404 | 9200 | 0.0000 | 0.3068 |
| 0.0023 | 92.6108 | 9400 | 0.0000 | 0.3068 |
| 0.0018 | 94.5813 | 9600 | 0.0000 | 0.3068 |
| 0.002 | 96.5517 | 9800 | 0.0000 | 0.3068 |
| 0.0018 | 98.5222 | 10000 | 0.0000 | 0.3068 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
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