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---
language:
- pt
license: apache-2.0
tags:
- automatic-speech-recognition
- generated_from_trainer
- pt
- robust-speech-event
- hf-asr-leaderboard
model-index:
- name: wav2vec2-xls-r-1b-portuguese-CORAA-3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8
type: mozilla-foundation/common_voice_8_0
args: pt
metrics:
- name: Test WER
type: wer
value: 71.67
- name: Test CER
type: cer
value: 30.64
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 7
type: mozilla-foundation/common_voice_7_0
args: pt
metrics:
- name: Test WER
type: wer
value: 68.18
- name: Test CER
type: cer
value: 28.34
- 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: 56.76
- name: Test CER
type: cer
value: 23.7
---
<!-- 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-1b-portuguese-CORAA-3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on [CORAA dataset](https://github.com/nilc-nlp/CORAA).
It achieves the following results on the evaluation set:
- Loss: 1.0029
- Wer: 0.6020
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 5000
- training_steps: 30000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.0169 | 0.21 | 5000 | 1.9582 | 0.9283 |
| 1.8561 | 0.42 | 10000 | 1.6144 | 0.8554 |
| 1.6823 | 0.63 | 15000 | 1.4165 | 0.7710 |
| 1.52 | 0.84 | 20000 | 1.2441 | 0.7289 |
| 1.3757 | 1.05 | 25000 | 1.1061 | 0.6491 |
| 1.2377 | 1.26 | 30000 | 1.0029 | 0.6020 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
|