README.md

metadata

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

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