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---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-MCV15
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-xls-r-300m-MCV15
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: 0.9816
- Wer: 0.6048
- Cer: 0.2217
## 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: 6e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 11.1167 | 4.5 | 250 | 3.6649 | 1.0 | 1.0000 |
| 3.1388 | 9.01 | 500 | 2.9534 | 1.0 | 1.0000 |
| 2.1614 | 13.51 | 750 | 1.3123 | 0.8240 | 0.3193 |
| 1.0783 | 18.02 | 1000 | 1.0311 | 0.7298 | 0.2684 |
| 0.7555 | 22.52 | 1250 | 0.9512 | 0.6806 | 0.2486 |
| 0.6159 | 27.03 | 1500 | 0.9362 | 0.6561 | 0.2418 |
| 0.5212 | 31.53 | 1750 | 0.9738 | 0.6409 | 0.2344 |
| 0.4684 | 36.04 | 2000 | 0.9576 | 0.6223 | 0.2282 |
| 0.4275 | 40.54 | 2250 | 0.9829 | 0.6178 | 0.2267 |
| 0.3856 | 45.05 | 2500 | 0.9753 | 0.6102 | 0.2244 |
| 0.3665 | 49.55 | 2750 | 0.9797 | 0.6058 | 0.2223 |
| 0.3668 | 54.05 | 3000 | 0.9690 | 0.6046 | 0.2217 |
| 0.3294 | 58.56 | 3250 | 0.9816 | 0.6048 | 0.2217 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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