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metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2_xls_r_300m_BIG-C_Bemba_20hr_v5
    results: []

wav2vec2_xls_r_300m_BIG-C_Bemba_20hr_v5

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5637
  • Model Preparation Time: 0.0067
  • Wer: 0.4975
  • Cer: 0.1239

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: 32
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Model Preparation Time Wer Cer
6.9607 1.0 154 inf 0.0067 1.0 1.0
2.4999 2.0 308 inf 0.0067 0.9981 0.2571
0.8598 3.0 462 inf 0.0067 0.6688 0.1653
0.6842 4.0 616 inf 0.0067 0.7770 0.1976
0.6052 5.0 770 inf 0.0067 0.5482 0.1443
0.557 6.0 924 inf 0.0067 0.5141 0.1334
0.5169 7.0 1078 inf 0.0067 0.6443 0.1941
0.4708 8.0 1232 inf 0.0067 0.5685 0.1573
0.4684 9.0 1386 inf 0.0067 0.5414 0.1489
0.5716 10.0 1540 inf 0.0067 0.6515 0.1929
1.1996 11.0 1694 inf 0.0067 0.9517 0.3363
1.4771 12.0 1848 inf 0.0067 0.9655 0.3621
1.3761 13.0 2002 inf 0.0067 0.8545 0.2684
1.6064 14.0 2156 inf 0.0067 1.0 0.3888
1.8647 15.0 2310 inf 0.0067 0.9999 0.5035
1.7455 16.0 2464 inf 0.0067 1.0 0.3958
1.6089 17.0 2618 inf 0.0067 1.0 0.3634
1.4697 18.0 2772 inf 0.0067 0.9999 0.3466
1.4016 19.0 2926 inf 0.0067 1.0 0.3309
1.3563 20.0 3080 inf 0.0067 1.0 0.3232
1.3217 21.0 3234 inf 0.0067 0.9999 0.3116

Framework versions

  • Transformers 4.43.4
  • Pytorch 2.2.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1