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---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_15_0
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-br
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_15_0
type: common_voice_15_0
config: br
split: None
args: br
metrics:
- name: Wer
type: wer
value: 49.79811574697174
---
<!-- 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-br
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on the common_voice_15_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8887
- Wer: 49.7981
- Cer: 17.3877
## 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: 5e-05
- 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: 500
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 5.1153 | 2.18 | 1000 | 2.8854 | 100.0 | 100.0 |
| 1.4117 | 4.36 | 2000 | 0.9161 | 71.2786 | 25.3180 |
| 0.7888 | 6.54 | 3000 | 0.7753 | 62.7456 | 22.0767 |
| 0.6316 | 8.71 | 4000 | 0.7550 | 58.1786 | 20.5383 |
| 0.5434 | 10.89 | 5000 | 0.7508 | 56.5096 | 20.1168 |
| 0.4672 | 13.07 | 6000 | 0.7844 | 54.9125 | 19.3835 |
| 0.4237 | 15.25 | 7000 | 0.7786 | 53.2705 | 18.5765 |
| 0.3899 | 17.43 | 8000 | 0.8050 | 53.0552 | 18.6105 |
| 0.3607 | 19.61 | 9000 | 0.8280 | 51.9874 | 18.3024 |
| 0.3355 | 21.79 | 10000 | 0.7967 | 51.5388 | 17.9811 |
| 0.3098 | 23.97 | 11000 | 0.8296 | 51.2876 | 17.9547 |
| 0.2937 | 26.14 | 12000 | 0.8544 | 50.9915 | 17.7827 |
| 0.2793 | 28.32 | 13000 | 0.8909 | 51.5478 | 18.1286 |
| 0.2641 | 30.5 | 14000 | 0.8740 | 50.4800 | 17.6561 |
| 0.2552 | 32.68 | 15000 | 0.8832 | 49.9776 | 17.4463 |
| 0.2467 | 34.86 | 16000 | 0.8753 | 50.3096 | 17.4765 |
| 0.2378 | 37.04 | 17000 | 0.8895 | 49.8789 | 17.3952 |
| 0.2337 | 39.22 | 18000 | 0.8887 | 49.7981 | 17.3877 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
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