Edit model card

tachiwin_totonac

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the audiofolder dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7535
  • Wer: 0.6465

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: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_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: 90

Training results

Training Loss Epoch Step Validation Loss Wer
5.1063 5.19 200 2.9834 1.0
2.9016 10.39 400 2.4405 0.9959
1.7606 15.58 600 1.1942 0.8532
1.0549 20.78 800 1.1132 0.7788
0.7553 25.97 1000 1.1224 0.6899
0.6639 31.51 1200 1.2641 0.7082
0.5344 36.7 1400 1.3247 0.6835
0.4527 41.9 1600 1.3915 0.7022
0.3839 47.09 1800 1.4051 0.6791
0.3065 52.29 2000 1.3899 0.6706
0.2714 57.48 2200 1.5455 0.6573
0.2437 62.68 2400 1.6798 0.6601
0.2103 67.87 2600 1.7406 0.6674
0.1899 73.06 2800 1.7625 0.6522
0.1841 78.26 3000 1.7443 0.6535
0.1544 83.45 3200 1.7405 0.6465
0.1461 88.65 3400 1.7535 0.6465

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.1
  • Tokenizers 0.13.3
Downloads last month
75
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.

Model tree for ljcamargo/tachiwin_tutunaku

Finetuned
(196)
this model
Quantizations
1 model

Evaluation results