metadata

language:
  - pt
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - robust-speech-event
  - pt
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

wav2vec2-large-xls-r-300m-pt-cv

This model is a fine-tuned version of 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