Hugging Face's logo

lgris
/
wav2vec2-xls-r-300m-gn-cv8-4

Automatic Speech Recognition Transformers PyTorch TensorBoard
Guaraní wav2vec2 generated_from_trainer robust-speech-event hf-asr-leaderboard Eval Results Inference Endpoints
Model card Files Metrics Community
wav2vec2-xls-r-300m-gn-cv8-4 / README.md
anton-l's picture
anton-l HF staff
Upload README.md
7c40351
preview code
|
raw history blame contribute delete
No virus
4.5 kB
metadata
language:
  - gn
license: apache-2.0
tags:
  - automatic-speech-recognition
  - generated_from_trainer
  - gn
  - robust-speech-event
  - hf-asr-leaderboard
datasets:
  - mozilla-foundation/common_voice_8_0
model-index:
  - name: wav2vec2-xls-r-300m-gn-cv8-4
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 8.0
          type: mozilla-foundation/common_voice_8_0
          args: gn
        metrics:
          - name: Test WER
            type: wer
            value: 68.45

wav2vec2-xls-r-300m-gn-cv8-4

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: 1.5805
  • Wer: 0.7545

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: 0.0001
  • 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: 100
  • training_steps: 13000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
9.2216 16.65 300 3.2771 1.0
3.1804 33.32 600 2.2869 1.0
1.5856 49.97 900 0.9573 0.8772
1.0299 66.65 1200 0.9044 0.8082
0.8916 83.32 1500 0.9478 0.8056
0.8451 99.97 1800 0.8814 0.8107
0.7649 116.65 2100 0.9897 0.7826
0.7185 133.32 2400 0.9988 0.7621
0.6595 149.97 2700 1.0607 0.7749
0.6211 166.65 3000 1.1826 0.7877
0.59 183.32 3300 1.1060 0.7826
0.5383 199.97 3600 1.1826 0.7852
0.5205 216.65 3900 1.2148 0.8261
0.4786 233.32 4200 1.2710 0.7928
0.4482 249.97 4500 1.1943 0.7980
0.4149 266.65 4800 1.2449 0.8031
0.3904 283.32 5100 1.3100 0.7928
0.3619 299.97 5400 1.3125 0.7596
0.3496 316.65 5700 1.3699 0.7877
0.3277 333.32 6000 1.4344 0.8031
0.2958 349.97 6300 1.4093 0.7980
0.2883 366.65 6600 1.3296 0.7570
0.2598 383.32 6900 1.4026 0.7980
0.2564 399.97 7200 1.4847 0.8031
0.2408 416.65 7500 1.4896 0.8107
0.2266 433.32 7800 1.4232 0.7698
0.224 449.97 8100 1.5560 0.7903
0.2038 466.65 8400 1.5355 0.7724
0.1948 483.32 8700 1.4624 0.7621
0.1995 499.97 9000 1.5808 0.7724
0.1864 516.65 9300 1.5653 0.7698
0.18 533.32 9600 1.4868 0.7494
0.1689 549.97 9900 1.5379 0.7749
0.1624 566.65 10200 1.5936 0.7749
0.1537 583.32 10500 1.6436 0.7801
0.1455 599.97 10800 1.6401 0.7673
0.1437 616.65 11100 1.6069 0.7673
0.1452 633.32 11400 1.6041 0.7519
0.139 649.97 11700 1.5758 0.7545
0.1299 666.65 12000 1.5559 0.7545
0.127 683.32 12300 1.5776 0.7596
0.1264 699.97 12600 1.5790 0.7519
0.1209 716.65 12900 1.5805 0.7545

Framework versions

  • Transformers 4.16.1
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.2
  • Tokenizers 0.11.0