Edit model card

wav2vec2_common_voice_accents_5

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: 0.0027

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: 48
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 384
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss
4.4163 1.34 400 0.5520
0.3305 2.68 800 0.1698
0.2138 4.03 1200 0.1104
0.1714 5.37 1600 0.0944
0.1546 6.71 2000 0.0700
0.1434 8.05 2400 0.0610
0.1272 9.4 2800 0.0493
0.1183 10.74 3200 0.0371
0.1113 12.08 3600 0.0468
0.1013 13.42 4000 0.0336
0.0923 14.77 4400 0.0282
0.0854 16.11 4800 0.0410
0.0791 17.45 5200 0.0252
0.0713 18.79 5600 0.0128
0.0662 20.13 6000 0.0252
0.0635 21.48 6400 0.0080
0.0607 22.82 6800 0.0098
0.0557 24.16 7200 0.0069
0.0511 25.5 7600 0.0057
0.0474 26.85 8000 0.0046
0.045 28.19 8400 0.0037
0.0426 29.53 8800 0.0027

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.4
  • Tokenizers 0.11.6
Downloads last month
2
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train willcai/wav2vec2_common_voice_accents_5