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---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- robust-speech-event
- pt
- hf-asr-leaderboard
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-pt-cv
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 6
type: common_voice
args: pt
metrics:
- name: Test WER
type: wer
value: 24.29
- name: Test CER
type: cer
value: 7.51
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 55.72
- name: Test CER
type: cer
value: 21.82
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: pt
metrics:
- name: Test WER
type: wer
value: 47.88
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: pt
metrics:
- name: Test WER
type: wer
value: 50.78
---
<!-- 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-large-xls-r-300m-pt-cv
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 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3418
- Wer: 0.3581
## 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: 0.0003
- 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 10.9035 | 0.2 | 100 | 4.2750 | 1.0 |
| 3.3275 | 0.41 | 200 | 3.0334 | 1.0 |
| 3.0016 | 0.61 | 300 | 2.9494 | 1.0 |
| 2.1874 | 0.82 | 400 | 1.4355 | 0.8721 |
| 1.09 | 1.02 | 500 | 0.9987 | 0.7165 |
| 0.8251 | 1.22 | 600 | 0.7886 | 0.6406 |
| 0.6927 | 1.43 | 700 | 0.6753 | 0.5801 |
| 0.6143 | 1.63 | 800 | 0.6300 | 0.5509 |
| 0.5451 | 1.84 | 900 | 0.5586 | 0.5156 |
| 0.5003 | 2.04 | 1000 | 0.5493 | 0.5027 |
| 0.3712 | 2.24 | 1100 | 0.5271 | 0.4872 |
| 0.3486 | 2.45 | 1200 | 0.4953 | 0.4817 |
| 0.3498 | 2.65 | 1300 | 0.4619 | 0.4538 |
| 0.3112 | 2.86 | 1400 | 0.4570 | 0.4387 |
| 0.3013 | 3.06 | 1500 | 0.4437 | 0.4147 |
| 0.2136 | 3.27 | 1600 | 0.4176 | 0.4124 |
| 0.2131 | 3.47 | 1700 | 0.4281 | 0.4194 |
| 0.2099 | 3.67 | 1800 | 0.3864 | 0.3949 |
| 0.1925 | 3.88 | 1900 | 0.3926 | 0.3913 |
| 0.1709 | 4.08 | 2000 | 0.3764 | 0.3804 |
| 0.1406 | 4.29 | 2100 | 0.3787 | 0.3742 |
| 0.1342 | 4.49 | 2200 | 0.3645 | 0.3693 |
| 0.1305 | 4.69 | 2300 | 0.3463 | 0.3625 |
| 0.1298 | 4.9 | 2400 | 0.3418 | 0.3581 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
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